%0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e56877 %T Assessing COVID-19 Mortality in Serbia’s Capital: Model-Based Analysis of Excess Deaths %A Cvijanovic,Dane %A Grubor,Nikola %A Rajovic,Nina %A Vucevic,Mira %A Miltenovic,Svetlana %A Laban,Marija %A Mostic,Tatjana %A Tasic,Radica %A Matejic,Bojana %A Milic,Natasa %K COVID-19 %K COVID-19 impact %K SARS-Cov-2 %K coronavirus %K respiratory %K infectious disease %K pulmonary %K pandemic %K excess mortality %K death rate %K death toll %K centralized health care %K urban %K Serbia %K dense population %K public health %K surveillance %D 2025 %7 17.4.2025 %9 %J JMIR Public Health Surveill %G English %X Background: Concerns have been raised about discrepancies in COVID-19 mortality data, particularly between preliminary and final datasets of vital statistics in Serbia. In the original preliminary dataset, released daily during the ongoing pandemic, there was an underestimation of deaths in contrast to those reported in the subsequently released yearly dataset of vital statistics. Objective: This study aimed to assess the accuracy of the final mortality dataset and justify its use in further analyses. In addition, we quantified the relative impact of COVID-19 on the death rate in the Serbian capital’s population. In the process, we aimed to explore whether any evidence of cause-of-death misattribution existed in the final published datasets. Methods: Data were sourced from the electronic databases of the Statistical Office of the Republic of Serbia. The dataset included yearly recorded deaths and the causes of death of all citizens currently living in the territory of Belgrade, the capital of the Republic of Serbia, from 2015 to 2021. Standardization and modeling techniques were utilized to quantify the direct impact of COVID-19 and to estimate excess deaths. To account for year-to-year trends, we used a mixed-effects hierarchical Poisson generalized linear regression model to predict mortality for 2020 and 2021. The model was fitted to the mortality data observed from 2015 to 2019 and used to generate mortality predictions for 2020 and 2021. Actual death rates were then compared to the obtained predictions and used to generate excess mortality estimates. Results: The total number of excess deaths, calculated from model estimates, was 3175 deaths (99% CI 1715-4094) for 2020 and 8321 deaths (99% CI 6975-9197) for 2021. The ratio of estimated excess deaths to reported COVID-19 deaths was 1.07. The estimated increase in mortality during 2020 and 2021 was 12.93% (99% CI 15.74%-17.33%) and 39.32% (99% CI 35.91%-39.32%) from the expected values, respectively. Those aged 0‐19 years experienced an average decrease in mortality of 22.43% and 23.71% during 2020 and 2021, respectively. For those aged up to 39 years, there was a slight increase in mortality (4.72%) during 2020. However, in 2021, even those aged 20‐39 years had an estimated increase in mortality of 32.95%. For people aged 60‐79 years, there was an estimated increase in mortality of 16.95% and 38.50% in 2020 and 2021, respectively. For those aged >80 years, the increase was estimated at 11.50% and 34.14% in 2020 and 2021, respectively. The model-predicted deaths matched the non-COVID-19 deaths recorded in the territory of Belgrade. This concordance between the predicted and recorded non-COVID-19 deaths provides evidence that the cause-of-death misattribution did not occur in the territory of Belgrade. Conclusions: The finalized mortality dataset for Belgrade can be safely used in COVID-19 impact analysis. Belgrade experienced a significant increase in mortality during 2020 and 2021, with most of the excess mortality attributable to SARS-CoV-2. Concerns about increased mortality from causes other than COVID-19 in Belgrade seem misplaced as their impact appears negligible. %R 10.2196/56877 %U https://publichealth.jmir.org/2025/1/e56877 %U https://doi.org/10.2196/56877 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e66223 %T Leveraging Electronic Health Records in International Humanitarian Clinics for Population Health Research: Cross-Sectional Study %A Draugelis,Sarah %A Hunnewell,Jessica %A Bishop,Sam %A Goswami,Reena %A Smith,Sean G %A Sutherland,Philip %A Hickman,Justin %A Donahue,Donald A %A Yendewa,George A %A Mohareb,Amir M %K refugee %K population health %K disaster medicine %K humanitarian clinic %K electronic health record %K Fast Electronic Medical Record %K fEMR %D 2025 %7 17.4.2025 %9 %J JMIR Public Health Surveill %G English %X Background: As more humanitarian relief organizations are beginning to use electronic medical records in their operations, data from clinical encounters can be leveraged for public health planning. Currently, medical data from humanitarian medical workers are infrequently available in a format that can be analyzed, interpreted, and used for public health. Objectives: This study aims to develop and test a methodology by which diagnosis and procedure codes can be derived from free-text medical encounters by medical relief practitioners for the purposes of data analysis. Methods: We conducted a cross-sectional study of clinical encounters from humanitarian clinics for displaced persons in Mexico between August 3, 2021, and December 5, 2022. We developed and tested a method by which free-text encounters were reviewed by medical billing coders and assigned codes from the International Classification of Diseases, Tenth Revision (ICD-10) and the Current Procedural Terminology (CPT). Each encounter was independently reviewed in duplicate and assigned ICD-10 and CPT codes in a blinded manner. Encounters with discordant codes were reviewed and arbitrated by a more experienced medical coder, whose decision was used to determine the final ICD-10 and CPT codes. We used chi-square tests of independence to compare the ICD-10 codes for concordance across single-diagnosis and multidiagnosis encounters and across patient characteristics, such as age, sex, and country of origin. Results: We analyzed 8460 encounters representing 5623 unique patients and 2774 unique diagnosis codes. These free-text encounters had a mean of 20.5 words per encounter in the clinical documentation. There were 58.78% (4973/8460) encounters where both coders assigned 1 diagnosis code, 18.56% (1570/8460) encounters where both coders assigned multiple diagnosis codes, and 22.66% (1917/8460) encounters with a mixed number of codes assigned. Of the 4973 encounters with a single code, only 11.82% (n=588) had a unique diagnosis assigned by the arbitrator that was not assigned by either of the initial 2 coders. Of the 1570 encounters with multiple diagnosis codes, only 3.38% (n=53) had unique diagnosis codes assigned by the arbitrator that were not initially assigned by either coder. The frequency of complete concordance across diagnosis codes was similar across sex categories and ranged from 30.43% to 46.05% across age groups and countries of origin. Conclusions: Free-text electronic medical records from humanitarian relief clinics can be used to develop a database of diagnosis and procedure codes. The method developed in this study, which used multiple independent reviews of clinical encounters, appears to reliably assign diagnosis codes across a diverse patient population in a resource-limited setting. %R 10.2196/66223 %U https://publichealth.jmir.org/2025/1/e66223 %U https://doi.org/10.2196/66223 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e68936 %T Digital Health Intervention on Awareness of Vaccination Against Influenza Among Adults With Diabetes: Pragmatic Randomized Follow-Up Study %A Fundoiano-Hershcovitz,Yifat %A Lee,Felix %A Stanger,Catherine %A Breuer Asher,Inbar %A Horwitz,David L %A Manejwala,Omar %A Liska,Jan %A Kerr,David %+ Dario Health, Ofek 8, 5 Tarshish St, Caesarea, 3079821, Israel, 972 0525296979, yifat@dariohealth.com %K digital health %K diabetes management %K influenza vaccination %K flu vaccination awareness %K mobile health %D 2025 %7 10.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Diabetes mellitus significantly increases the risk of severe complications from influenza, necessitating targeted vaccination efforts. Despite vaccination being the most effective preventive measure, coverage remains below the World Health Organization’s targets, partly due to limited awareness among patients. This study evaluated a digital health intervention aimed at improving influenza vaccination rates among adults with diabetes. Objective: This study aimed to demonstrate the effectiveness of digital health platforms in increasing vaccination rates among people with diabetes and to emphasize the impact of tailored messaging frequency on patient engagement and health behavior change. We hypothesized that digital tools providing empirical evidence of increased health risk awareness can effectively drive preventive actions. Methods: The study leveraged the Dario (Dario Health Corp) digital health platform to retrospectively analyze data from 64,904 users with diabetes assigned by the platform into three groups: (1) Group A received previously studied monthly flu nudge messages; (2) Group B received an adapted intervention with 2-3 monthly messages; (3) Group C served as the control with no intervention. Surveys were conducted at baseline, 3 months, and 6 months to assess vaccination status, awareness of influenza risks, and recollection of educational content. Statistical analyses, including logistic regression, chi-square tests, and t tests, were used to evaluate differences between groups. Results: Out of 64,904 users, 8431 completed the surveys. Vaccination rates were 71.0% in group A, 71.9% in group B, and 70.5% in group C. Group B showed significantly higher awareness of influenza risks compared with the control group odds ratio (OR; OR 1.35, 95% CI 1.12-1.63; P=.001), while group A did not (OR 1.10, 95% CI 0.92-1.32; P=.27). Recollection of educational content was also higher in groups A (OR 1.29, 95% CI 1.07-1.56; P=.008) and B (OR 1.92, 95% CI 1.59-2.33; P<.001) compared with the control. In addition, a significant correlation between awareness and vaccination rates was found only in group B (χ2(df=1)=6.12, P=.01). Conclusions: The adapted digital intervention (group B) effectively increased awareness of influenza risks and recollection of educational content, which correlated with the higher trend in vaccination rates. This study demonstrates the potential of digital health tools to enhance influenza vaccination among people with diabetes by improving risk awareness and education. Further research should focus on optimizing these interventions to achieve significant improvements in vaccination uptake and overall public health outcomes. Trial Registration: ClinicalTrials.gov NCT06840236; https://clinicaltrials.gov/study/NCT06840236 %M 40209214 %R 10.2196/68936 %U https://www.jmir.org/2025/1/e68936 %U https://doi.org/10.2196/68936 %U http://www.ncbi.nlm.nih.gov/pubmed/40209214 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e55356 %T Data Parameters From Participatory Surveillance Systems in Human, Animal, and Environmental Health From Around the Globe: Descriptive Analysis %A McNeil,Carrie %A Divi,Nomita %A Bargeron IV,Charles Thomas %A Capobianco Dondona,Andrea %A Ernst,Kacey C %A Gupta,Angela S %A Fasominu,Olukayode %A Keatts,Lucy %A Kelly,Terra %A Leal Neto,Onicio B %A Lwin,May O %A Makhasi,Mvuyo %A Mutagahywa,Eric Beda %A Montecino-Latorre,Diego %A Olson,Sarah %A Pandit,Pranav S %A Paolotti,Daniela %A Parker,Matt C %A Samad,Muhammad Haiman %A Sewalk,Kara %A Sheldenkar,Anita %A Srikitjakarn,Lertrak %A Suy Lan,Channé %A Wilkes,Michael %A Yano,Terdsak %A Smolinski,Mark %+ Ending Pandemics, 870 Market Street, Suite 528, San Francisco, CA, 94102, United States, 1 9169522185, carrie.s.mcneil@gmail.com %K participatory surveillance %K One Health %K citizen science %K community-based surveillance %K digital disease detection %K environmental health %K wildlife health %K livestock health %K human health %K data standards %D 2025 %7 26.3.2025 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Emerging pathogens and zoonotic spillover highlight the need for One Health surveillance to detect outbreaks as early as possible. Participatory surveillance empowers communities to collect data at the source on the health of animals, people, and the environment. Technological advances increase the use and scope of these systems. This initiative sought to collate information from active participatory surveillance systems to better understand parameters collected across the One Health spectrum. Objective: This study aims to develop a compendium of One Health data parameters by examining participatory surveillance systems active in 2023. The expected outcomes of the compendium were to pinpoint specific parameters related to human, animal, and environmental health collected globally by participatory surveillance systems and to detail how each parameter is collected. The compendium was designed to help understand which parameters are currently collected and serve as a reference for future systems and for data standardization initiatives. Methods: Contacts associated with the 60 systems identified through the One Health Participatory Surveillance System Map were invited by email to provide specific data parameters, methodologies used for data collection, and parameter-specific considerations. Information was received from 38 (63%) active systems. Data were compiled into a searchable spreadsheet-based compendium organized into 5 sections: general, livestock, wildlife, environmental, and human parameters. An advisory group comprising experts in One Health participatory surveillance reviewed the collected parameters, refined the compendium structure, and contributed to the descriptive analysis. Results: A comprehensive compendium of data parameters from a diverse array of single-sector and multisector participatory surveillance systems was collated and reviewed. The compendium includes parameters from 38 systems used in Africa (n=3, 8%), Asia (n=9, 24%), Europe (n=12, 32%), Australia (n=3, 8%), and the Americas (n=12, 32%). Almost one-third of the systems (n=11, 29%) collect data across multiple sectors. Many (n=17, 45%) focus solely on human health. Variations in data collection techniques were observed for commonly used parameters, such as demographics and clinical signs or symptoms. Most human health systems collected parameters from a cohort of users tracking their own health over time, whereas many wildlife and environmental systems incorporated event-based parameters. Conclusions: Several participatory surveillance systems have already adopted a One Health approach, enhancing traditional surveillance by identifying shared health threats among animals, people, and the environment. The compendium reveals substantial variation in how parameters are collected, underscoring the need for further work in system interoperability and data standards to allow for timely data sharing across systems during outbreaks. Parameters collated from across the One Health spectrum represent a valuable resource for informing the development of future systems and identifying opportunities to expand existing systems for multisector surveillance. %M 40138683 %R 10.2196/55356 %U https://publichealth.jmir.org/2025/1/e55356 %U https://doi.org/10.2196/55356 %U http://www.ncbi.nlm.nih.gov/pubmed/40138683 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e58767 %T Monitoring Public Health Through a Comprehensive Primary Care Database in the Netherlands: Overview of the Nivel Syndromic Surveillance System %A Baliatsas,Christos %A van Summeren,Jojanneke %A van Beusekom,Sander %A Matser,Amy %A Hooiveld,Mariette %K surveillance %K monitoring %K general practice %K public health %D 2025 %7 12.3.2025 %9 %J JMIR Public Health Surveill %G English %X Background: Syndromic surveillance systems are crucial for the monitoring of population health and the early detection of emerging health problems. Internationally, there are numerous established systems reporting on different types of data. In the Netherlands, the Nivel syndromic surveillance system provides real-time monitoring on all diseases and symptoms presented in general practice. Objective: The present article introduces the national syndromic surveillance system in primary care, emphasizing its role in providing real-time information on infectious diseases and various health problems at the population level, in the Netherlands. In addition, we report on the central role of the participating general practices in data provision, and discuss the applicability of the syndromic surveillance data in different contexts of public health research. Methods: The Nivel syndromic surveillance system is part of the Nivel Primary Care Database (Nivel-PCD) that collects routinely recorded data from electronic health records of about 10% of the Dutch population, on the basis of approximately 500 practices. This translates to approximately 1.9 million citizens. Since 2010, the surveillance system relies on representative, pseudonymized data collected on a weekly basis from a subset of about 400 practices in the Nivel-PCD, for the entire practice population. Health problems are registered according to the International Classification of Primary Care, applied in all general practices in the Netherlands. Prevalence rates are recalculated and reported every week in the form of figures, also stratified by age, sex, and region. Weekly rates are defined as the number of people that consulted the general practitioner in a certain week for a specific health problem, divided by the total number of registered individuals in the practice. Results: While utilizing data from general practitioners’ electronic health records, the system allows for the timely monitoring and identification of symptom and disease patterns and trends, not only among individuals who seek primary health care, but the entire registered population. Besides their use in disease monitoring, syndromic surveillance data are useful in various public health research contexts, such as environmental health and disaster research. Conclusions: The Nivel syndromic surveillance system serves as a valuable tool for health monitoring and research, offering valuable insights into public health. %R 10.2196/58767 %U https://publichealth.jmir.org/2025/1/e58767 %U https://doi.org/10.2196/58767 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e58882 %T Living With and Managing Uncomplicated Urinary Tract Infection: Mixed Methods Analysis of Patient Insights From Social Media %A Kramer,Melissa L %A Polo,Jose Medina %A Kumar,Nishant %A Mulgirigama,Aruni %A Benkiran,Amina %+ Live UTI Free Ltd, Suite 7, The Courtyard, Carmanhall Road, Sandyford, Dublin, D18 NW62, Ireland, 386 64 157 997, melissa@liveutifree.com %K acute cystitis %K bladder infection %K HCP interactions %K urology %K patient experience %K patient insights %K social media %K uncomplicated urinary tract infection %K urinary tract infection %K urinary %K women %K quality of life %K disease management %K cystitis %K healthcare professional %K self-management %K patient behavior %K UTI %D 2025 %7 11.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Uncomplicated urinary tract infections (uUTIs) affect more than half of women in their lifetime and can impact on quality of life. We analyzed social media posts discussing uUTIs to gather insights into the patient experience, including aspects of their disease management journey and associated opinions and concerns. Objective: This study aims to gather patient experience insights by analyzing social media posts that discussed uUTI. Methods: A search string (“urinary tract infection” [UTI] or “bladder infection” or “cystitis” or “UTI” not “interstitial cystitis”) was used to identify posts from public blogs and patient forums (June 2021 to June 2023). Posts were excluded if they were not written in English or discussed complicated UTI (posts that mentioned “pregnancy” or “pregnant” or “trimester” or “catheter” or “interstitial”). Posts were limited to publicly available sources and anonymized. The primary objective was to gather patient perspectives on key elements of the uUTI experience, including health care professional (HCP) interactions, diagnosis, treatment, and recurrence. Results: In total, more than 42,000 unique posts were identified (mostly from reddit.com; 29,506/42,265, 70%) and >3600 posts were analyzed. Posts were most commonly from users in the United States (6707/11,180, 60%), the United Kingdom (2261/11,180, 20%), Canada (509/11,180, 5%), Germany (356/11,180, 3%), or India (320/11,180, 3%). Six main themes were identified: symptom awareness and information seeking, HCP interactions, diagnosis and management challenges, management with antibiotics, self-management, and challenges with recurrent UTI. Most posts highlighted the importance of seeking professional medical advice, while some patients raised concerns regarding their HCP interactions and lack of shared decision-making. Patients searched for advice and guidance on the web prior to consulting an HCP, described their symptoms, and discussed lifestyle adjustments. Most patients tried self-management and shared their experiences with nonprescribed treatment options. There was general agreement among posts that antibiotics are necessary to cure UTIs and prevent associated complications. Conclusions: Social media posts provide valuable insight into the experiences and opinions of patients with uUTIs in Canada, Germany, India, the United Kingdom, and the United States. The insights from this study provide a more complete picture of patient behaviors and highlight the potential for HCP and patient education, as well as better communication through shared decision-making to improve care. %M 40067345 %R 10.2196/58882 %U https://www.jmir.org/2025/1/e58882 %U https://doi.org/10.2196/58882 %U http://www.ncbi.nlm.nih.gov/pubmed/40067345 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66032 %T Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era %A Uddin,Jamal %A Feng,Cheng %A Xu,Junfang %+ Department of Pharmacy, Second Affiliated Hospital, School of Public health, Zhejiang University School of Medicine, 866 Yuhangtang road, Xihu district, Hangzhou, 310058, China, 86 18801230482 ext 000, junfangxuhappy1987@163.com %K internet %K generative AI %K artificial intelligence %K ChatGPT %K health communication %K health promotion %K health disparity %K health %K communication %K internet %K AI %K generative %K tool %K genAI %K gratification theory %K gratification %K public health %K inequity %K disparity %D 2025 %7 6.3.2025 %9 Viewpoint %J J Med Internet Res %G English %X Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer new opportunities for both the public and professionals. However, these advancements also pose risks of exacerbating health disparities. Limited research has focused on combining these health communication mediums, particularly those enabled by new technologies like GenAI, and their applications for health promotion and health disparities. Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview of health communication mediums and their role in understanding health promotion and disparities in the GenAI era. Additionally, health promotion and health disparities associated with GenAI are briefly discussed through the lens of the Technology Acceptance Model 2, the uses and gratifications theory, and the knowledge gap hypothesis. This viewpoint discusses the limitations and barriers of previous internet-based communication mediums regarding real-time responses, personalized advice, and follow-up inquiries, highlighting the potential of new technology for public health promotion. It also discusses the health disparities caused by the limitations of GenAI, such as individuals’ inability to evaluate information, restricted access to services, and the lack of skill development. Overall, this study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities. It underscores the need for more empirical studies, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations. %M 40053755 %R 10.2196/66032 %U https://www.jmir.org/2025/1/e66032 %U https://doi.org/10.2196/66032 %U http://www.ncbi.nlm.nih.gov/pubmed/40053755 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e53214 %T Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in East Asia and the Pacific Region: Longitudinal Trend Analysis %A Lundberg,Alexander L %A Soetikno,Alan G %A Wu,Scott A %A Ozer,Egon %A Welch,Sarah B %A Liu,Yingxuan %A Hawkins,Claudia %A Mason,Maryann %A Murphy,Robert %A Havey,Robert J %A Moss,Charles B %A Achenbach,Chad J %A Post,Lori Ann %+ Buehler Center for Health Policy and Economics, Robert J Havey, MD Institute for Global Health, Northwestern University, 420 E Superior St, Chicago, IL, 60611, United States, 1 3125031706, lori.post@northwestern.edu %K SARS-CoV-2 %K COVID-19 %K East Asia %K Pacific %K American Samoa %K Australia %K Brunei Darussalam %K Cambodia %K China %K Fiji %K French Polynesia %K Guam %K Hong Kong %K Indonesia %K Japan %K Kiribati %K People’s Democratic Republic of Korea %K Republic of Korea %K Lao People’s Democratic Republic %K Macao %K Malaysia %K Marshall Islands %K Federated States of Micronesia %K Mongolia %K Myanmar %K Nauru, New Caledonia %K New Zealand %K Northern Mariana Islands %K Palau %K Papua New Guinea %K Philippines %K Samoa %K Singapore %K Solomon Islands %K Thailand %K Timor-Leste %K Tonga %K Tuvalu %K Vanuatu %K Vietnam %K pandemic %K surveillance %K public health %K speed %K acceleration %K deceleration %K jerk %K dynamic panel %K generalized method of moments %K Arellano-Bond %K 7-day lag %D 2025 %7 21.2.2025 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: This study updates the COVID-19 pandemic surveillance in East Asia and the Pacific region that we first conducted in 2020 with 2 additional years of data for the region. Objective: First, we aimed to measure whether there was an expansion or contraction of the pandemic in East Asia and the Pacific region when the World Health Organization (WHO) declared the end of the COVID-19 public health emergency of international concern on May 5, 2023. Second, we used dynamic and genomic surveillance methods to describe the dynamic history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Finally, we aimed to provide historical context for the course of the pandemic in East Asia and the Pacific region. Methods: In addition to updates of traditional surveillance data and dynamic panel estimates from the original study, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether the regional weekly speed was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period. Results: Several countries in East Asia and the Pacific region had COVID-19 transmission rates above an outbreak threshold at the point of the WHO declaration (Brunei, New Zealand, Australia, and South Korea). However, the regional transmission rate had remained below the outbreak threshold for 4 months. In the rolling 6-month window t test for regional outbreak status, the final P value ≤.10 implies a rejection of the null hypothesis (at the α=.10 level) that the region as a whole was not in an outbreak for the period from November 5, 2022, to May 5, 2023. From January 2022 onward, nearly every sequenced SARS-CoV-2 specimen in the region was identified as the Omicron variant. Conclusions: While COVID-19 continued to circulate in East Asia and the Pacific region, transmission rates had fallen below outbreak status by the time of the WHO declaration. Compared to other global regions, East Asia and the Pacific region had the latest outbreaks driven by the Omicron variant. COVID-19 appears to be endemic in the region, no longer reaching the threshold for a pandemic definition. However, the late outbreaks raise uncertainty about whether the pandemic was truly over in the region at the time of the WHO declaration. %M 39804185 %R 10.2196/53214 %U https://publichealth.jmir.org/2025/1/e53214 %U https://doi.org/10.2196/53214 %U http://www.ncbi.nlm.nih.gov/pubmed/39804185 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e63881 %T InfectA-Chat, an Arabic Large Language Model for Infectious Diseases: Comparative Analysis %A Selcuk,Yesim %A Kim,Eunhui %A Ahn,Insung %+ , Department of Data-Centric Problem Solving Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea, 82 42 869 1053, isahn@kisti.re.kr %K large language model %K Arabic large language models %K AceGPT %K multilingual large language model %K infectious disease monitoring %K public health %D 2025 %7 10.2.2025 %9 Original Paper %J JMIR Med Inform %G English %X Background: Infectious diseases have consistently been a significant concern in public health, requiring proactive measures to safeguard societal well-being. In this regard, regular monitoring activities play a crucial role in mitigating the adverse effects of diseases on society. To monitor disease trends, various organizations, such as the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC), collect diverse surveillance data and make them publicly accessible. However, these platforms primarily present surveillance data in English, which creates language barriers for non–English-speaking individuals and global public health efforts to accurately observe disease trends. This challenge is particularly noticeable in regions such as the Middle East, where specific infectious diseases, such as Middle East respiratory syndrome coronavirus (MERS-CoV), have seen a dramatic increase. For such regions, it is essential to develop tools that can overcome language barriers and reach more individuals to alleviate the negative impacts of these diseases. Objective: This study aims to address these issues; therefore, we propose InfectA-Chat, a cutting-edge large language model (LLM) specifically designed for the Arabic language but also incorporating English for question and answer (Q&A) tasks. InfectA-Chat leverages its deep understanding of the language to provide users with information on the latest trends in infectious diseases based on their queries. Methods: This comprehensive study was achieved by instruction tuning the AceGPT-7B and AceGPT-7B-Chat models on a Q&A task, using a dataset of 55,400 Arabic and English domain–specific instruction–following data. The performance of these fine-tuned models was evaluated using 2770 domain-specific Arabic and English instruction–following data, using the GPT-4 evaluation method. A comparative analysis was then performed against Arabic LLMs and state-of-the-art models, including AceGPT-13B-Chat, Jais-13B-Chat, Gemini, GPT-3.5, and GPT-4. Furthermore, to ensure the model had access to the latest information on infectious diseases by regularly updating the data without additional fine-tuning, we used the retrieval-augmented generation (RAG) method. Results: InfectA-Chat demonstrated good performance in answering questions about infectious diseases by the GPT-4 evaluation method. Our comparative analysis revealed that it outperforms the AceGPT-7B-Chat and InfectA-Chat (based on AceGPT-7B) models by a margin of 43.52%. It also surpassed other Arabic LLMs such as AceGPT-13B-Chat and Jais-13B-Chat by 48.61%. Among the state-of-the-art models, InfectA-Chat achieved a leading performance of 23.78%, competing closely with the GPT-4 model. Furthermore, the RAG method in InfectA-Chat significantly improved document retrieval accuracy. Notably, RAG retrieved more accurate documents based on queries when the top-k parameter value was increased. Conclusions: Our findings highlight the shortcomings of general Arabic LLMs in providing up-to-date information about infectious diseases. With this study, we aim to empower individuals and public health efforts by offering a bilingual Q&A system for infectious disease monitoring. %M 39928922 %R 10.2196/63881 %U https://medinform.jmir.org/2025/1/e63881 %U https://doi.org/10.2196/63881 %U http://www.ncbi.nlm.nih.gov/pubmed/39928922 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e67145 %T Ethical Considerations for Wastewater Surveillance Conducted by the US Department of Defense %A Smith,Hunter Jackson %A Agans,Richard T %A Kowallis,William J %K wastewater %K surveillance %K ethics %K military %K Department of Defense %D 2025 %7 6.2.2025 %9 %J JMIR Public Health Surveill %G English %X The US Department of Defense (DoD) is establishing its wastewater surveillance capacities to support national security objectives and promote the public health and medical readiness of US service members. Wastewater surveillance is an emerging technology that has traditionally been leveraged for detecting infectious diseases. However, its potential future applications could yield a vast and unpredictable amount of information that could be used for a wide variety of both health- and nonhealth-related purposes. The US military also serves an inimitable role for the country and its citizens, and exercises significant levels of control over its service members compared to civilian organizations. Further, its present and potential wastewater surveillance activities may reach far beyond just military installations. These factors raise unique ethical considerations that must be accounted for by leaders and policymakers to ensure the DoD implements a wastewater surveillance network in a manner that is both impactful in supporting public health and appropriate to the scope and population under surveillance. This paper explores important ethical features in conducting wastewater surveillance that are both specific to the DoD experience and applicable to wider public health initiatives. %R 10.2196/67145 %U https://publichealth.jmir.org/2025/1/e67145 %U https://doi.org/10.2196/67145 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e65439 %T Consistency of Daily Number of Reported COVID-19 Cases in 191 Countries From 2020 to 2022: Comparative Analysis of 2 Major Data Sources %A Liu,Han %A Zong,Huiying %A Yang,Yang %A Schwebel,David C %A Xie,Bin %A Ning,Peishan %A Rao,Zhenzhen %A Li,Li %A Hu,Guoqing %K COVID-19 %K pandemic %K data consistency %K World Health Organization %K data quality %D 2025 %7 6.2.2025 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic represents one of the most challenging public health emergencies in recent world history, causing about 7.07 million deaths globally by September 24, 2024. Accurate, timely, and consistent data are critical for early response to situations like the COVID-19 pandemic. Objective: This study aimed to evaluate consistency of daily reported COVID-19 cases in 191 countries from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and the World Health Organization (WHO) dashboards during 2020‐2022. Methods: We retrieved data concerning new daily COVID-19 cases in 191 countries covered by both data sources from January 22, 2020, to December 31, 2022. The ratios of numbers of daily reported cases from the 2 sources were calculated to measure data consistency. We performed simple linear regression to examine significant changes in the ratio of numbers of daily reported cases during the study period. Results: Of 191 WHO member countries, only 60 displayed excellent data consistency in the number of daily reported COVID-19 cases between the WHO and JHU CSSE dashboards (mean ratio 0.9-1.1). Data consistency changed greatly across the 191 countries from 2020 to 2022 and differed across 4 types of countries, categorized by income. Data inconsistency between the 2 data sources generally decreased slightly over time, both for the 191 countries combined and within the 4 types of income-defined countries. The absolute relative difference between the 2 data sources increased in 84 countries, particularly for Malta (R2=0.25), Montenegro (R2=0.30), and the United States (R2=0.29), but it decreased significantly in 40 countries. Conclusions: The inconsistency between the 2 data sources warrants further research. Construction of public health surveillance and data collection systems for public health emergencies like the COVID-19 pandemic should be strengthened in the future. %R 10.2196/65439 %U https://publichealth.jmir.org/2025/1/e65439 %U https://doi.org/10.2196/65439 %0 Journal Article %@ 2563-6316 %I JMIR Publications %V 6 %N %P e50458 %T Converting Organic Municipal Solid Waste Into Volatile Fatty Acids and Biogas: Experimental Pilot and Batch Studies With Statistical Analysis %A Borhany,Hojjat %K multistep fermentation %K specific methane production %K anaerobic digestion %K kinetics study %K biochar %K first-order %K modified Gompertz %K mass balance %K waste management %K environment sustainability %D 2025 %7 4.2.2025 %9 %J JMIRx Med %G English %X Background: Italy can augment its profit from biorefinery products by altering the operation of digesters or different designs to obtain more precious bioproducts like volatile fatty acids (VFAs) than biogas from organic municipal solid waste. In this context, recognizing the process stability and outputs through operational interventions and its technical and economic feasibility is a critical issue. Hence, this study involves an anaerobic digester in Treviso in northern Italy. Objective: This research compares a novel line, consisting of pretreatment, acidogenic fermentation, and anaerobic digestion, with single-step anaerobic digestion regarding financial profit and surplus energy. Therefore, a mass flow model was created and refined based on the outputs from the experimental and numerical studies. These studies examine the influence of hydraulic retention time (HRT), pretreatment, biochar addition, and fine-tuned feedstock/inoculum (FS/IN) ratio on bioproducts and operational parameters. Methods: VFA concentration, VFA weight ratio distribution, and biogas yield were quantified by gas chromatography. A t test was then conducted to analyze the significance of dissimilar HRTs in changing the VFA content. Further, a feasible biochar dosage was identified for an assumed FS/IN ratio with an adequately long HRT using the first-order rate model. Accordingly, the parameters for a mass flow model were adopted for 70,000 population equivalents to determine the payback period and surplus energy for two scenarios. We also explored the effectiveness of amendments in improving the process kinetics. Results: Both HRTs were identical concerning the ratio of VFA/soluble chemical oxygen demand (0.88 kg/kg) and VFA weight ratio distribution: mainly, acetic acid (40%), butyric acid (24%), and caproic acid (17%). However, a significantly higher mean VFA content was confirmed for an HRT of 4.5 days than the quantity for an HRT of 3 days (30.77, SD 2.82 vs 27.66, SD 2.45 g–soluble chemical oxygen demand/L), using a t test (t8=−2.68; P=.03; CI=95%). In this research, 83% of the fermented volatile solids were converted into biogas to obtain a specific methane (CH4) production of 0.133 CH4-Nm3/kg–volatile solids. While biochar addition improved only the maximum methane content by 20% (86% volumetric basis [v/v]), the FS/IN ratio of 0.3 volatile solid basis with thermal plus fermentative pretreatment improved the hydrolysis rate substantially (0.57 vs 0.07, 1/d). Furthermore, the biochar dosage of 0.12 g-biochar/g–volatile solids with an HRT of 20 days was identified as a feasible solution. Principally, the payback period for our novel line would be almost 2 years with surplus energy of 2251 megajoules [MJ] per day compared to 45 years and 21,567 MJ per day for single-step anaerobic digestion. Conclusions: This research elaborates on the advantage of the refined novel line over the single-step anaerobic digestion and confirms its financial and technical feasibility. Further, changing the HRT and other amendments significantly raised the VFA concentration and the process kinetics and stability. %R 10.2196/50458 %U https://xmed.jmir.org/2025/1/e50458 %U https://doi.org/10.2196/50458 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 5 %N %P e58539 %T Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational Study %A Arifi,Dorian %A Resch,Bernd %A Santillana,Mauricio %A Guan,Weihe Wendy %A Knoblauch,Steffen %A Lautenbach,Sven %A Jaenisch,Thomas %A Morales,Ivonne %A Havas,Clemens %+ Department of Geoinformatics, University of Salzburg, Kapitelgasse 4/6, Salzburg, 5020, Austria, 43 662 80440, dorian.arifi@plus.ac.at %K spatiotemporal epidemiology %K geo-social media data %K digital disease surveillance %K political polarization %K epidemiological early warning %K digital early warning %D 2025 %7 30.1.2025 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities. Objective: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs. Methods: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series. Results: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals. Conclusions: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research. %M 39883923 %R 10.2196/58539 %U https://infodemiology.jmir.org/2025/1/e58539 %U https://doi.org/10.2196/58539 %U http://www.ncbi.nlm.nih.gov/pubmed/39883923 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e57457 %T Availability and Key Characteristics of National Early Warning Systems for Emerging Profiles of Antimicrobial Resistance in High-Income Countries: Systematic Review %A Iera,Jessica %A Isonne,Claudia %A Seghieri,Chiara %A Tavoschi,Lara %A Ceparano,Mariateresa %A Sciurti,Antonio %A D'Alisera,Alessia %A Sane Schepisi,Monica %A Migliara,Giuseppe %A Marzuillo,Carolina %A Villari,Paolo %A D'Ancona,Fortunato %A Baccolini,Valentina %K early warning system %K surveillance %K emerging AMR %K high-income countries %K antimicrobial resistance %D 2025 %7 15.1.2025 %9 %J JMIR Public Health Surveill %G English %X Background: The World Health Organization (WHO) recently advocated an urgent need for implementing national surveillance systems for the timely detection and reporting of emerging antimicrobial resistance (AMR). However, public information on the existing national early warning systems (EWSs) is often incomplete, and a comprehensive overview on this topic is currently lacking. Objective: This review aimed to map the availability of EWSs for emerging AMR in high-income countries and describe their main characteristics. Methods: A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report, or web page describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR-reporting WHO framework. Results: We identified 7 national EWSs from 72 high-income countries: 2 in the East Asia and Pacific Region (Australia and Japan), 3 in Europe and Central Asia (France, Sweden, and the United Kingdom), and 2 in North America (the United States and Canada). The systems were established quite recently; in most cases, they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of the organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions: A broader implementation and investment of national surveillance systems for the early detection of emerging AMR are still needed to establish EWSs in countries and regions lacking such capabilities. More standardized data collection and reporting are also advisable to improve cooperation on a global scale. Further research is required to provide an in-depth analysis of EWSs, as this study is limited to publicly available data in high-income countries. %R 10.2196/57457 %U https://publichealth.jmir.org/2025/1/e57457 %U https://doi.org/10.2196/57457 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e58862 %T Wastewater Monitoring During the COVID-19 Pandemic in the Veneto Region, Italy: Longitudinal Observational Study %A Ocagli,Honoria %A Zambito,Marco %A Da Re,Filippo %A Groppi,Vanessa %A Zampini,Marco %A Terrini,Alessia %A Rigoli,Franco %A Amoruso,Irene %A Baldovin,Tatjana %A Baldo,Vincenzo %A Russo,Francesca %A Gregori,Dario %K wastewater-based epidemiology %K SARS-CoV-2 %K COVID-19 %K CUSUM %K WBE %K cumulative sum chart %D 2025 %7 14.1.2025 %9 %J JMIR Public Health Surveill %G English %X Background: As the COVID-19 pandemic has affected populations around the world, there has been substantial interest in wastewater-based epidemiology (WBE) as a tool to monitor the spread of SARS-CoV-2. This study investigates the use of WBE to anticipate COVID-19 trends by analyzing the correlation between viral RNA concentrations in wastewater and reported COVID-19 cases in the Veneto region of Italy. Objective: We aimed to evaluate the effectiveness of the cumulative sum (CUSUM) control chart method in detecting changes in SARS-CoV-2 concentrations in wastewater and its potential as an early warning system for COVID-19 outbreaks. Additionally, we aimed to validate these findings over different time periods to ensure robustness. Methods: This study analyzed the temporal correlation between SARS-CoV-2 RNA concentrations in wastewater and COVID-19 clinical outcomes, including confirmed cases, hospitalizations, and intensive care unit (ICU) admissions, from October 2021 to August 2022 in the Veneto region, Italy. Wastewater samples were collected weekly from 10 wastewater treatment plants and analyzed using a reverse transcription–quantitative polymerase chain reaction. The CUSUM method was used to detect significant shifts in the data, with an initial analysis conducted from October 2021 to February 2022, followed by validation in a second period from February 2022 to August 2022. Results: The study found that peaks in SARS-CoV-2 RNA concentrations in wastewater consistently preceded peaks in reported COVID-19 cases by 5.2 days. Hospitalizations followed with a delay of 4.25 days, while ICU admissions exhibited a lead time of approximately 6 days. Notably, certain health care districts exhibited stronger correlations, with notable values in wastewater anticipating ICU admissions by an average of 13.5 and 9.5 days in 2 specific districts. The CUSUM charts effectively identified early changes in viral load, indicating potential outbreaks before clinical cases increased. Validation during the second period confirmed the consistency of these findings, reinforcing the robustness of the CUSUM method in this context. Conclusions: WBE, combined with the CUSUM method, offers valuable insight into the level of COVID-19 outbreaks in a community, including asymptomatic cases, thus acting as a precious early warning tool for infectious disease outbreaks with pandemic potential. %R 10.2196/58862 %U https://publichealth.jmir.org/2025/1/e58862 %U https://doi.org/10.2196/58862 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 17 %N %P e56495 %T Nowcasting to Monitor Real-Time Mpox Trends During the 2022 Outbreak in New York City: Evaluation Using Reportable Disease Data Stratified by Race or Ethnicity %A Rohrer,Rebecca %A Wilson,Allegra %A Baumgartner,Jennifer %A Burton,Nicole %A Ortiz,Ray R %A Dorsinville,Alan %A Jones,Lucretia E %A Greene,Sharon K %K data quality %K epidemiology %K forecasting %K infectious disease %K morbidity and mortality trends %K mpox %K nowcasting %K public health practice %K surveillance %D 2025 %7 14.1.2025 %9 %J Online J Public Health Inform %G English %X Background: Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends despite data lags and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City, we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity (Black or African American, Hispanic or Latino, and White). However, in real time, it was unclear if the estimates were accurate. Objective: We evaluated the accuracy of estimated mpox case counts across a range of NobBS implementation options. Methods: We evaluated NobBS performance for New York City residents with a confirmed or probable mpox diagnosis or illness onset from July 8 through September 30, 2022, as compared with fully accrued cases. We used the exponentiated average log score (average score) to compare moving window lengths, stratifying or not by race or ethnicity, diagnosis and onset dates, and daily and weekly aggregation. Results: During the study period, 3305 New York City residents were diagnosed with mpox (median 4, IQR 3-5 days from diagnosis to diagnosis report). Of these, 812 (25%) had missing onset dates, and of these, 230 (28%) had unknown race or ethnicity. The median lag in days from onset to onset report was 10 (IQR 7-14). For daily hindcasts by diagnosis date, the average score was 0.27 for the 14-day moving window used in real time. Average scores improved (increased) with longer moving windows (maximum: 0.47 for 49-day window). Stratifying by race or ethnicity improved performance, with an overall average score of 0.38 for the 14-day moving window (maximum: 0.57 for 49 day-window). Hindcasts for White patients performed best, with average scores of 0.45 for the 14-day window and 0.75 for the 49-day window. For unstratified, daily hindcasts by onset date, the average score ranged from 0.16 for the 42-day window to 0.30 for the 14-day window. Performance was not improved by weekly aggregation. Hindcasts underestimated diagnoses in early August after the epidemic peaked, then overestimated diagnoses in late August as the epidemic waned. Estimates were most accurate during September when cases were low and stable. Conclusions: Performance was better when hindcasting by diagnosis date than by onset date, consistent with shorter lags and higher completeness for diagnoses. For daily hindcasts by diagnosis date, longer moving windows performed better, but direct comparisons are limited because longer windows could only be assessed after case counts in this outbreak had stabilized. Stratification by race or ethnicity improved performance and identified differences in epidemic trends across patient groups. Contributors to differences in performance across strata might include differences in case volume, epidemic trends, delay distributions, and interview success rates. Health departments need reliable nowcasting and rapid evaluation tools, particularly to promote health equity by ensuring accurate estimates within all strata. %R 10.2196/56495 %U https://ojphi.jmir.org/2025/1/e56495 %U https://doi.org/10.2196/56495 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59937 %T Unified Mobile App for Streamlining Verbal Autopsy and Cause of Death Assignment in India: Design and Development Study %A Kaur,Harleen %A Tripathi,Stuti %A Chalga,Manjeet Singh %A Benara,Sudhir K %A Dhiman,Amit %A Gupta,Shefali %A Nair,Saritha %A Menon,Geetha %A Gulati,B K %A Sharma,Sandeep %A Sharma,Saurabh %K verbal autopsy %K cause of death %K mortality %K mHealth %K public health %K India %K mobile health %D 2025 %7 10.1.2025 %9 %J JMIR Form Res %G English %X Background: Verbal autopsy (VA) has been a crucial tool in ascertaining population-level cause of death (COD) estimates, specifically in countries where medical certification of COD is relatively limited. The World Health Organization has released an updated instrument (Verbal Autopsy Instrument 2022) that supports electronic data collection methods along with analytical software for assigning COD. This questionnaire encompasses the primary signs and symptoms associated with prevalent diseases across all age groups. Traditional methods have primarily involved paper-based questionnaires and physician-coded approaches for COD assignment, which is time-consuming and resource-intensive. Although computer-coded algorithms have advanced the COD assignment process, data collection in densely populated countries like India remains a logistical challenge. Objective: This study aimed to develop an Android-based mobile app specifically tailored for streamlining VA data collection by leveraging the existing Indian public health workforce. The app has been designed to integrate real-time data collection by frontline health workers and seamless data transmission and digital reporting of COD by physicians. This process aimed to enhance the efficiency and accuracy of COD assignment through VA. Methods: The app was developed using Android Studio, the primary integrated development environment for developing Android apps using Java. The front-end interface was developed using XML, while SQLite and MySQL were employed to streamline complete data storage on the local and server databases, respectively. The communication between the app and the server was facilitated through a PHP application programming interface to synchronize data from the local to the server database. The complete prototype was specifically built to reduce manual intervention and automate VA data collection. Results: The app was developed to align with the current Indian public health system for district-level COD estimation. By leveraging this mobile app, the average duration required for VA data collection to ascertainment of COD, which typically ranges from 6 to 8 months, is expected to decrease by approximately 80%, reducing it to about 1‐2 months. Based on annual caseload projections, the smallest administrative public health unit, health and wellness centers, is anticipated to handle 35‐40 VA cases annually, while medical officers at primary health centers are projected to manage 150‐200 physician-certified VAs each year. The app’s data collection and transmission efficiency were further improved based on feedback from user and subject area experts. Conclusions: The development of a unified mobile app could streamline the VA process, enabling the generation of accurate national and subnational COD estimates. This mobile app can be further piloted and scaled to different regions to integrate the automated VA model into the existing public health system for generating comprehensive mortality statistics in India. %R 10.2196/59937 %U https://formative.jmir.org/2025/1/e59937 %U https://doi.org/10.2196/59937 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e62939 %T Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024) %A Petit,Pascal %A Vuillerme,Nicolas %+ Laboratoire AGEIS, Université Grenoble Alpes, Bâtiment Jean Roget, UFR de Médecine, Domaine de La Merci, La Tronche Cedex, 38706, France, 33 4 76 63 71 04, pascal.petit@univ-grenoble-alpes.fr %K farming population %K digital public health %K digital epidemiology %K administrative health database %K farming exposome %K review %K bibliometric analysis %K data reuse %D 2025 %7 9.1.2025 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Although agricultural health has gained importance, to date, much of the existing research relies on traditional epidemiological approaches that often face limitations related to sample size, geographic scope, temporal coverage, and the range of health events examined. To address these challenges, a complementary approach involves leveraging and reusing data beyond its original purpose. Administrative health databases (AHDs) are increasingly reused in population-based research and digital public health, especially for populations such as farmers, who face distinct environmental risks. Objective: We aimed to explore the reuse of AHDs in addressing health issues within farming populations by summarizing the current landscape of AHD-based research and identifying key areas of interest, research gaps, and unmet needs. Methods: We conducted a scoping review and bibliometric analysis using PubMed and Web of Science. Building upon previous reviews of AHD-based public health research, we conducted a comprehensive literature search using 72 terms related to the farming population and AHDs. To identify research hot spots, directions, and gaps, we used keyword frequency, co-occurrence, and thematic mapping. We also explored the bibliometric profile of the farming exposome by mapping keyword co-occurrences between environmental factors and health outcomes. Results: Between 1975 and April 2024, 296 publications across 118 journals, predominantly from high-income countries, were identified. Nearly one-third of these publications were associated with well-established cohorts, such as Agriculture and Cancer and Agricultural Health Study. The most frequently used AHDs included disease registers (158/296, 53.4%), electronic health records (124/296, 41.9%), insurance claims (106/296, 35.8%), population registers (95/296, 32.1%), and hospital discharge databases (41/296, 13.9%). Fifty (16.9%) of 296 studies involved >1 million participants. Although a broad range of exposure proxies were used, most studies (254/296, 85.8%) relied on broad proxies, which failed to capture the specifics of farming tasks. Research on the farming exposome remains underexplored, with a predominant focus on the specific external exposome, particularly pesticide exposure. A limited range of health events have been examined, primarily cancer, mortality, and injuries. Conclusions: The increasing use of AHDs holds major potential to advance public health research within farming populations. However, substantial research gaps persist, particularly in low-income regions and among underrepresented farming subgroups, such as women, children, and contingent workers. Emerging issues, including exposure to per- and polyfluoroalkyl substances, biological agents, microbiome, microplastics, and climate change, warrant further research. Major gaps also persist in understanding various health conditions, including cardiovascular, reproductive, ocular, sleep-related, age-related, and autoimmune diseases. Addressing these overlooked areas is essential for comprehending the health risks faced by farming communities and guiding public health policies. Within this context, promoting AHD-based research, in conjunction with other digital data sources (eg, mobile health, social health data, and wearables) and artificial intelligence approaches, represents a promising avenue for future exploration. %M 39787587 %R 10.2196/62939 %U https://publichealth.jmir.org/2025/1/e62939 %U https://doi.org/10.2196/62939 %U http://www.ncbi.nlm.nih.gov/pubmed/39787587 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e60022 %T Strategies to Increase Response Rate and Reduce Nonresponse Bias in Population Health Research: Analysis of a Series of Randomized Controlled Experiments during a Large COVID-19 Study %A Atchison,Christina J %A Gilby,Nicholas %A Pantelidou,Galini %A Clemens,Sam %A Pickering,Kevin %A Chadeau-Hyam,Marc %A Ashby,Deborah %A Barclay,Wendy S %A Cooke,Graham S %A Darzi,Ara %A Riley,Steven %A Donnelly,Christl A %A Ward,Helen %A Elliott,Paul %K study recruitment %K response rate %K population-based research %K COVID-19 %K SARS-CoV-2 %K web-based questionnaires %D 2025 %7 9.1.2025 %9 %J JMIR Public Health Surveill %G English %X Background: High response rates are needed in population-based studies, as nonresponse reduces effective sample size and bias affects accuracy and decreases the generalizability of the study findings. Objective: We tested different strategies to improve response rate and reduce nonresponse bias in a national population–based COVID-19 surveillance program in England, United Kingdom. Methods: Over 19 rounds, a random sample of individuals aged 5 years and older from the general population in England were invited by mail to complete a web-based questionnaire and return a swab for SARS-CoV-2 testing. We carried out several nested randomized controlled experiments to measure the impact on response rates of different interventions, including (1) variations in invitation and reminder letters and SMS text messages and (2) the offer of a conditional monetary incentive to return a swab, reporting absolute changes in response and relative response rate (95% CIs). Results: Monetary incentives increased the response rate (completed swabs returned as a proportion of the number of individuals invited) across all age groups, sex at birth, and area deprivation with the biggest increase among the lowest responders, namely teenagers and young adults and those living in more deprived areas. With no monetary incentive, the response rate was 3.4% in participants aged 18‐22 years, increasing to 8.1% with a £10 (US $12.5) incentive, 11.9% with £20 (US $25.0), and 18.2% with £30 (US $37.5) (relative response rate 2.4 [95% CI 2.0-2.9], 3.5 [95% CI 3.0-4.2], and 5.4 [95% CI 4.4-6.7], respectively). Nonmonetary strategies had a modest, if any, impact on response rate. The largest effect was observed for sending an additional swab reminder (SMS text message or email). For example, those receiving an additional SMS text message were more likely to return a completed swab compared to those receiving the standard email-SMS approach, 73.3% versus 70.2%: percentage difference 3.1% (95% CI 2.2%-4.0%). Conclusions: Conditional monetary incentives improved response rates to a web-based survey, which required the return of a swab test, particularly for younger age groups. Used in a selective way, incentives may be an effective strategy for improving sample response and representativeness in population-based studies. %R 10.2196/60022 %U https://publichealth.jmir.org/2025/1/e60022 %U https://doi.org/10.2196/60022 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e49567 %T An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach %A Luo,Waylon %A Jin,Ruoming %A Kenne,Deric %A Phan,NhatHai %A Tang,Tang %+ Department of Computer Science, Kent State University, 1300 Lester A Lefton Esplanade, Kent, OH, 44241, United States, 1 330 672 9063, rjin1@kent.edu %K Twitter (X) %K popular music %K big data analysis %K music %K lyrics %K big data %K substance abuse %K tweet %K social media %K drug %K alcohol %D 2024 %7 30.12.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: “tweets that reference established drug lyrics.” Objective: We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists’ gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X. Methods: X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics. Results: We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98% of drug-related lyrics from these genres and 84.21% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time. Conclusions: Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use. %M 39753225 %R 10.2196/49567 %U https://formative.jmir.org/2024/1/e49567 %U https://doi.org/10.2196/49567 %U http://www.ncbi.nlm.nih.gov/pubmed/39753225 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e55376 %T Using Laboratory Test Results for Surveillance During a New Outbreak of Acute Hepatitis in 3-Week- to 5-Year-Old Children in the United Kingdom, the Netherlands, Ireland, and Curaçao: Observational Cohort Study %A Swets,Maaike C %A Kerr,Steven R %A MacKenna,Brian %A Fisher,Louis %A van Wijnen,Merel %A Brandwagt,Diederik %A Schenk,Paul W %A Fraaij,Pieter %A Visser,Leonardus G %A Bacon,Sebastian %A Mehrkar,Amir %A Nichol,Alistair %A Twomey,Patrick %A Matthews,Philippa C %A , %A Semple,Malcolm G %A Groeneveld,Geert H %A Goldacre,Ben %A Jones,Iain %A Baillie,J Kenneth %K pediatric hepatitis %K disease surveillance %K outbreak detection %K pandemic preparedness %K acute hepatitis %K children %K data analytics %K hospital %K laboratory %K all age groups %K pre-pandemic %K United Kingdom %K Netherlands %K Ireland Curacao %K single center %K federated analytics %K pandemic surveillance %K outbreaks %K public health %D 2024 %7 23.12.2024 %9 %J JMIR Public Health Surveill %G English %X Background: In March 2022, a concerning rise in cases of unexplained pediatric hepatitis was reported in multiple countries. Cases were defined as acute hepatitis with serum transaminases >500 U/L (aspartate transaminase [AST] or alanine transaminase [ALT]) in children aged 16 years or younger. We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. We conducted a pragmatic survey to analyze changes in the proportion of hospitalized children with elevated AST or ALT over time. In addition, we studied the feasibility of using routinely collected clinical laboratory results to detect or follow-up the outbreak of an infectious disease. Objective: We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. Methods: We provided hospitals with a simple computational tool to enable laboratories to share nondisclosive summary-level data. Summary statistics for AST and ALT measurements were collected from the last 10 years across all age groups. Measurements were considered elevated if ALT or AST was >200 U/L. The rate of elevated AST or ALT test for 3-week- to 5-year-olds was compared between a period of interest in which cases of hepatitis were reported (December 1, 2021, to August 31, 2022) and a prepandemic baseline period (January 1, 2012, to December 31, 2019). We calculated a z score, which measures the extent to which the rate for elevated ALT or AST was higher or lower in the period of interest compared to a baseline period, for the 3-week- to 5-year-olds. Results: Our approach of sharing a simple software tool for local use enabled rapid, federated data analysis. A total of 34 hospitals in the United Kingdom, the Netherlands, Ireland, and Curaçao were asked to contribute summary data, and 30 (88%) submitted their data. For all locations combined, the rate of elevated AST or ALT measurements in the period of interest was not elevated (z score=−0.46; P=.64). Results from individual regions were discordant, with a higher rate of elevated AST or ALT values in the Netherlands (z score=4.48; P<.001), driven by results from a single center in Utrecht. We did not observe any clear indication of changes in primary care activity or test results in the same period. Conclusions: Hospital laboratories collect large amounts of data on a daily basis that can potentially be of use for disease surveillance, but these are currently not optimally used. Federated analytics using nondisclosive, summary-level laboratory data sharing was successful, safe, and efficient. The approach holds potential as a tool for pandemic surveillance in future outbreaks. Our findings do not indicate the presence of a broader outbreak of mild hepatitis cases among young children, although there was an increase in elevated AST or ALT values locally in the Netherlands. %R 10.2196/55376 %U https://publichealth.jmir.org/2024/1/e55376 %U https://doi.org/10.2196/55376 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e66052 %T Evaluating the Effectiveness and Scalability of the World Health Organization MyopiaEd Digital Intervention: Mixed Methods Study %A Lee,Yeonsu %A Keel,Stuart %A Yoon,Sangchul %+ Department of Medical Humanities and Social Sciences, College of Medicine, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 2 2228 2467, littleluke@yuhs.ac %K World Health Organization %K digital intervention %K MyopiaEd %K behavior change %K risk factor %K myopia %K refractive error %K mobile phone %D 2024 %7 16.12.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The rapid rise of myopia worldwide, particularly in East and Southeast Asia, has implied environmental influences beyond genetics. To address this growing public health concern, the World Health Organization and International Telecommunication Union launched the MyopiaEd program. South Korea, with its high rates of myopia and smartphone use, presented a suitable context for implementing and evaluating the MyopiaEd program. Objective: This is the first study to date to evaluate the effectiveness and scalability of the MyopiaEd program in promoting eye health behavior change among parents of children in South Korea. Methods: Parents of children aged 7 and 8 years were recruited through an open-access website with a recruitment notice distributed to public elementary schools in Gwangju Metropolitan City. Beginning in September 2022, parents received 42 SMS text messages from the MyopiaEd program over 6 months. This digital trial used a mixed methods approach combining both quantitative and qualitative data collection. Pre- and postintervention surveys were used to assess changes in parental knowledge and behavior regarding myopia prevention. Additionally, semistructured interviews were conducted to explore participants’ experiences in depth and receive feedback on program design. Prior to the intervention, the MyopiaEd program design and message libraries were adapted for the Korean context following World Health Organization and International Telecommunication Union guidelines. Results: A total of 133 parents participated in this study, including 60 parents whose children had myopia and 73 parents whose children did not. Both groups reported high engagement and satisfaction with the program. Significant increases in knowledge about myopia were observed in both groups (P<.001). While time spent on near-work activities did not change significantly, parents of children with myopia reported increased outdoor time for their children (P=.048). A substantial increase in eye checkups was observed, with 52 (86.7%) out of 60 children with myopia and 50 (68.5%) out of 73 children without myopia receiving eye examinations following the intervention. Qualitative analysis indicated a shift in parents’ attitudes toward outdoor activities, as increased recognition of their benefits prompted positive changes in behavior. However, reducing near-work activities posed challenges due to children’s preference for smartphone use during leisure periods and the demands of after-school academies. The credibility of the institution delivering the program enhanced parental engagement and children’s adoption of healthy behaviors. Messages that corrected common misconceptions about eye health and provided specific behavioral guidance were regarded as impactful elements of the program. Conclusions: This study demonstrates the MyopiaEd program’s potential as a scalable and innovative digital intervention to reduce myopia risk in children. The program’s effectiveness provides support for broader adoption and offers valuable insights to inform future myopia prevention policies. %M 39680893 %R 10.2196/66052 %U https://publichealth.jmir.org/2024/1/e66052 %U https://doi.org/10.2196/66052 %U http://www.ncbi.nlm.nih.gov/pubmed/39680893 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e66049 %T Using Video Cameras to Assess Physical Activity and Other Well-Being Behaviors in Urban Environments: Feasibility, Reliability, and Participant Reactivity Studies %A Benton,Jack S %A Evans,James %A Anderson,Jamie %A French,David P %+ Manchester Centre for Health Psychology, Division of Psychology and Mental Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 01613066000, jack.benton@manchester.ac.uk %K unobtrusive observation %K video cameras %K measurement %K physical activity %K well-being %K urban environments %D 2024 %7 16.12.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Unobtrusive observation is a promising method for assessing physical activity and other well-being behaviors (eg, social interactions) in urban environments, without participant burden and biases associated with self-report. However, current methods require multiple in-person observers. Using video cameras instead could allow for more accurate observations at lower cost and with greater flexibility in scheduling. Objective: This research aimed to test the feasibility of using stationary wireless video cameras to observe physical activity and other well-being behaviors, and to assess its reliability and potential participant reactivity. Methods: Across 3 cross-sectional studies, 148 hours of video recordings were collected from 6 outdoor public spaces in Manchester, United Kingdom. The videos were coded by 3 researchers using MOHAWk (Method for Observing Physical Activity and Wellbeing)—a validated in-person observation tool for assessing physical activity, social interactions, and people taking notice of the environment. Inter- and intrarater reliabilities were assessed using intraclass correlation coefficients (ICCs). Intercept surveys were conducted to assess public awareness of the cameras and whether they altered their behavior due to the presence of cameras. Results: The 148 hours of video recordings were coded in 85 hours. Interrater reliability between independent coders was mostly “excellent” (ICCs>0.90; n=36), with a small number of “good” (ICCs>0.75; n=2), “moderate” (ICCs=0.5-0.75; n=3), or “poor” (ICCs<0.5; n=1) ICC values. Reliability decreased at night, particularly for coding ethnic group and social interactions, but remained mostly “excellent” or “good.” Intrarater reliability within a single coder after a 2-week interval was “excellent” for all but 1 code, with 1 “good” ICC value for assessing vigorous physical activity, indicating that the coder could reproduce similar results over time. Intrarater reliability was generally similar during the day and night, apart from ICC values for coding ethnic group, which reduced from “excellent” to “good” at night. Intercept surveys with 86 public space users found that only 5 (5.8%) participants noticed the cameras used for this study. Importantly, all 5 said that they did not alter their behavior as a result of noticing these cameras, therefore, indicating no evidence of reactivity. Conclusions: Camera-based observation methods are more reliable than in-person observations and do not produce participant reactivity often associated with self-report methods. This method requires less time for data collection and coding, while allowing for safe nighttime observation without the risk to research staff. This research is a significant first step in demonstrating the potential for camera-based methods to improve natural experimental studies of real-world environmental interventions. It also provides a rigorous foundation for developing more scalable automated computer vision algorithms for assessing human behaviors. %M 39680427 %R 10.2196/66049 %U https://publichealth.jmir.org/2024/1/e66049 %U https://doi.org/10.2196/66049 %U http://www.ncbi.nlm.nih.gov/pubmed/39680427 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e64995 %T The Prevalence of Sickle Cell Disease in Colorado and Methodologies of the Colorado Sickle Cell Data Collection Program: Public Health Surveillance Study %A Miller,Joshua I %A Hassell,Kathryn L %A Kellar-Guenther,Yvonne %A Quesada,Stacey %A West,Rhonda %A Sontag,Marci %K sickle cell disease %K public health surveillance %K prevalence %K birth prevalence %K Colorado %K sickle cell %K surveillance %K SCD %K USA %K data collection %K blood disorder %K policy development %K hematology %K United States %D 2024 %7 9.12.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Sickle cell disease (SCD) is a genetic blood disorder that affects approximately 100,000 individuals in the United States, with the highest prevalence among Black or African American populations. While advances in care have improved survival, comprehensive state-level data on the prevalence of SCD remain limited, which hampers efforts to optimize health care services. To address this gap, the Colorado Sickle Cell Data Collection (CO-SCDC) program was established in 2021 as part of the Centers for Disease Control and Prevention’s initiative to enhance surveillance and public health efforts for SCD. Objective: The objectives of this study were to describe the establishment of the CO-SCDC program and to provide updated estimates of the prevalence and birth prevalence of SCD in Colorado, including geographic dispersion. Additional objectives include evaluating the accuracy of case identification methods and leveraging surveillance activities to inform public health initiatives. Methods: Data were collected from Health Data Compass (a multi-institutional data warehouse) containing electronic health records from the University of Colorado Health and Children’s Hospital Colorado for the years 2012‐2020. Colorado newborn screening program data were included for confirmed SCD diagnoses from 2001 to 2020. Records were linked using the Colorado University Record Linkage tool and deidentified for analysis. Case definitions, adapted from the Centers for Disease Control and Prevention’s Registry and Surveillance System for Hemoglobinopathies project, classified cases as possible, probable, or definite SCD. Clinical validation by hematologists was performed to ensure accuracy, and prevalence rates were calculated using 2020 US Census population estimates. Results: In 2019, 435 individuals were identified as living with SCD in Colorado, an increase of 16%‐40% over previous estimates, with the majority (n=349, 80.2%) identifying as Black or African American. The median age of individuals was 19 years. The prevalence of SCD was highest in urban counties, with concentrations in Arapahoe, Denver, and El Paso counties. Birth prevalence of SCD increased from 11.9 per 100,000 live births between 2010 and 2014 to 20.1 per 100,000 live births between 2015 and 2019 with 58.5% (n=38) of cases being hemoglobin (Hb) SS or HbSβ0 thalassemia subtypes. The study highlighted a 67% (n=26) increase in SCD births over the decade, correlating with the growth of the Black or African American population in the state. Conclusions: The CO-SCDC program successfully established the capacity to perform SCD surveillance and, in doing so, identified baseline prevalence estimates for SCD in Colorado. The findings highlight geographic dispersion across Colorado counties, highlighting the need for equitable access to specialty care, particularly for rural populations. The combination of automated data linkage and clinical validation improved case identification accuracy. Future efforts will expand surveillance to include claims data to better capture health care use and address potential underreporting. These results will guide public health interventions aimed at improving care for individuals with SCD in Colorado. %R 10.2196/64995 %U https://publichealth.jmir.org/2024/1/e64995 %U https://doi.org/10.2196/64995 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e59844 %T The University of California Study of Outcomes in Mothers and Infants (a Population-Based Research Resource): Retrospective Cohort Study %A Baer,Rebecca J %A Bandoli,Gretchen %A Jelliffe-Pawlowski,Laura %A Chambers,Christina D %+ Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, United States, 1 2063510850, rjbaer@ucsd.edu %K birth certificate %K vital statistics %K hospital discharge %K administrative data %K linkage %K pregnancy outcome %K birth outcome %K infant outcome %K adverse outcome %K preterm birth %K birth defects %K pregnancy %K prenatal %K California %K policy %K disparities %K children %K data collection %D 2024 %7 3.12.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Population-based databases are valuable for perinatal research. The California Department of Health Care Access and Information (HCAI) created a linked birth file covering the years 1991 through 2012. This file includes birth and fetal death certificate records linked to the hospital discharge records of the birthing person and infant. In 2019, the University of California Study of Outcomes in Mothers and Infants received approval to create similar linked birth files for births from 2011 onward, with 2 years of overlapping birth files to allow for linkage comparison. Objective: This paper aims to describe the University of California Study of Outcomes in Mothers and Infants linkage methodology, examine the linkage quality, and discuss the benefits and limitations of the approach. Methods: Live birth and fetal death certificates were linked to hospital discharge records for California infants between 2005 and 2020. The linkage algorithm includes variables such as birth hospital and date of birth, and linked record selection is made based on a “link score.” The complete file includes California Vital Statistics and HCAI hospital discharge records for the birthing person (1 y before delivery and 1 y after delivery) and infant (1 y after delivery). Linkage quality was assessed through a comparison of linked files and California Vital Statistics only. Comparisons were made to previous linked birth files created by the HCAI for 2011 and 2012. Results: Of the 8,040,000 live births, 7,427,738 (92.38%) California Vital Statistics live birth records were linked to HCAI records for birthing people, 7,680,597 (95.53%) birth records were linked to HCAI records for the infant, and 7,285,346 (90.61%) California Vital Statistics birth records were linked to HCAI records for both the birthing person and the infant. The linkage rates were 92.44% (976,526/1,056,358) for Asian and 86.27% (28,601/33,151) for Hawaiian or Pacific Islander birthing people. Of the 44,212 fetal deaths, 33,355 (75.44%) had HCAI records linked to the birthing person. When assessing variables in both California Vital Statistics and hospital records, the percentage was greatest when using both sources: the rates of gestational diabetes were 4.52% (329,128/7,285,345) in the California Vital Statistics records, 8.2% (597,534/7,285,345) in the HCAI records, and 9.34% (680,757/7,285,345) when using both data sources. Conclusions: We demonstrate that the linkage strategy used for this data platform is similar in linkage rate and linkage quality to the previous linked birth files created by the HCAI. The linkage provides higher rates of crucial variables, such as diabetes, compared to birth certificate records alone, although selection bias from the linkage must be considered. This platform has been used independently to examine health outcomes, has been linked to environmental datasets and residential data, and has been used to obtain and examine maternal serum and newborn blood spots. %M 39625748 %R 10.2196/59844 %U https://publichealth.jmir.org/2024/1/e59844 %U https://doi.org/10.2196/59844 %U http://www.ncbi.nlm.nih.gov/pubmed/39625748 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 16 %N %P e60261 %T Population Digital Health: Continuous Health Monitoring and Profiling at Scale %A Hossein Motlagh,Naser %A Zuniga,Agustin %A Thi Nguyen,Ngoc %A Flores,Huber %A Wang,Jiangtao %A Tarkoma,Sasu %A Prosperi,Mattia %A Helal,Sumi %A Nurmi,Petteri %K digital health %K population health %K modeling, health data %K health monitoring %K monitoring %K wearable devices %K wearables %K machine learning %K networking infrastructure %K cost-effectiveness %K device %K sensor %K PDH %K equity %D 2024 %7 20.11.2024 %9 %J Online J Public Health Inform %G English %X This paper introduces population digital health (PDH)—the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling—as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices’ sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions. %R 10.2196/60261 %U https://ojphi.jmir.org/2024/1/e60261 %U https://doi.org/10.2196/60261 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e65199 %T Measuring Technology-Facilitated Sexual Violence and Abuse in the Chinese Context: Development Study and Content Validity Analysis %A Pak,Sharon Hoi Lam %A Wu,Chanchan %A Choi,Kitty Wai Ying %A Choi,Edmond Pui Hang %+ School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Academic Building, 3 Sassoon Road, Pokfulam, Hong Kong, China (Hong Kong), 852 39176972, h0714919@connect.hku.hk %K technology-facilitated sexual violence and abuse %K TFSVA %K image-based sexual abuse %K sexual abuse %K content validity %K measurement %K questionnaire %K China %D 2024 %7 19.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Technology-facilitated sexual violence and abuse (TFSVA) encompasses a range of behaviors where digital technologies are used to enable both virtual and in-person sexual violence. Given that TFSVA is an emerging and continually evolving form of sexual abuse, it has been challenging to establish a universally accepted definition or to develop standardized measures for its assessment. Objective: This study aimed to address the significant gap in research on TFSVA within the Chinese context. Specifically, it sought to develop a TFSVA measurement tool with robust content validity, tailored for use in subsequent epidemiological studies within the Chinese context. Methods: The first step in developing the measurement approach for TFSVA victimization and perpetration was to conduct a thorough literature review of existing empirical research on TFSVA and relevant measurement tools. After the initial generation of items, all the items were reviewed by an expert panel to assess the face validity. The measurement items were further reviewed by potential research participants, who were recruited through snowball sampling via online platforms. The assessment results were quantified by computing the content validity index (CVI). The participants were asked to rate each scale item in terms of its relevance, appropriateness, and clarity regarding the topic. Results: The questionnaire was reviewed by 24 lay experts, with a mean age of 27.96 years. They represented different genders and sexual orientations. The final questionnaire contained a total of 89 items. Three key domains were identified to construct the questionnaire, which included image-based sexual abuse, nonimage-based TFSVA, and online-initiated physical sexual violence. The overall scale CVI values of relevance, appropriateness, and clarity for the scale were 0.90, 0.96, and 0.97, respectively, which indicated high content validity for all the instrument items. To ensure the measurement accurately reflects the experiences of diverse demographic groups, the content validity was further analyzed by gender and sexual orientation. This analysis revealed variations in item validity among participants from different genders and sexual orientations. For instance, heterosexual male respondents showed a particularly low CVI for relevance of 0.20 in the items related to nudity, including “male’s chest/nipples are visible” and “the person is sexually suggestive.” This underscored the importance of an inclusive approach when developing a measurement for TFSVA. Conclusions: This study greatly advances the assessment of TFSVA by examining the content validity of our newly developed measurement. The findings revealed that our measurement tool demonstrated adequate content validity, thereby providing a strong foundation for assessing TFSVA within the Chinese context. Implementing this tool is anticipated to enhance our understanding of TFSVA and aid in the development of effective interventions to combat this form of abuse. %M 39561365 %R 10.2196/65199 %U https://formative.jmir.org/2024/1/e65199 %U https://doi.org/10.2196/65199 %U http://www.ncbi.nlm.nih.gov/pubmed/39561365 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e57476 %T Use of Digital Tools in Arbovirus Surveillance: Scoping Review %A Melo,Carolina Lopes %A Mageste,Larissa Rangel %A Guaraldo,Lusiele %A Paula,Daniela Polessa %A Wakimoto,Mayumi Duarte %+ Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Avenida Brasil, 4365, Rio de Janeiro, 21040-900, Brazil, 55 2138659110, mayumi.wakimoto@ini.fiocruz.br %K arbovirus infections %K dengue %K zika virus %K chikungunya fever %K public health surveillance %K digital tool %K technology %D 2024 %7 18.11.2024 %9 Review %J J Med Internet Res %G English %X Background: The development of technology and information systems has led to important changes in public health surveillance. Objective: This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance. Methods: The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society). Results: Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance. Conclusions: In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools’ data, considering ethical aspects. %M 39556803 %R 10.2196/57476 %U https://www.jmir.org/2024/1/e57476 %U https://doi.org/10.2196/57476 %U http://www.ncbi.nlm.nih.gov/pubmed/39556803 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e58140 %T Progress of Implementation of World Health Organization Global Antimicrobial Resistance Surveillance System Recommendations on Priority Pathogen-Antibiotic Sensitivity Testing in Africa: Protocol for a Scoping Review %A Hope,Mackline %A Kiggundu,Reuben %A Byonanebye,Dathan M %A Mayito,Jonathan %A Tabajjwa,Dickson %A Lwigale,Fahad %A Tumwine,Conrad %A Mwanja,Herman %A Kambugu,Andrew %A Kakooza,Francis %+ Infectious Diseases Institute, P.O. Box 22418, Kampala, 22418, Uganda, 256 772659941, hmackline@idi.co.ug %K antimicrobial resistance %K antibiotic sensitivity testing %K global antimicrobial resistance surveillance system %K GLASS implementation %K AMR Surveillance %K Africa %D 2024 %7 15.11.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Antimicrobial resistance (AMR) is a major global public health concern, particularly in low- and middle-income countries where resources and infrastructure for an adequate response are limited. The World Health Organization (WHO) Global Antimicrobial Resistance Surveillance System (GLASS) was introduced in 2016 to address these challenges, outlining recommendations for priority pathogen-antibiotic combinations. Despite this initiative, implementation in Africa remains understudied. This scoping review aims to assess the current state of implementing WHO GLASS recommendations on antimicrobial sensitivity testing (AST) in Africa. Objective: The primary objective of this study is to determine the current state of implementing the WHO GLASS recommendations on AST for priority pathogen-antimicrobial combinations. The review will further document if the reporting of AST results is according to “susceptible,” “intermediate,” and “resistant” recommendations according to GLASS. Methods: Following the methodological framework by Arksey and O’Malley, studies published between January 2016 and November 2023 will be included. Search strategies will target electronic databases, including MEDLINE, Scopus, CINAHL, and Embase. Eligible studies will document isolates tested for antimicrobial sensitivity, focusing on WHO-priority specimens and pathogens. Data extraction will focus on key study characteristics, study context, population, and adherence to WHO GLASS recommendations on AST. Descriptive statistics involving summarizing the quantitative data extracted through measures of central tendency and variation will be used. Covidence and Microsoft Excel software will be used. This study will systematically identify, collate, and analyze relevant studies and data sources based on clear inclusion criteria to provide a clear picture of the progress achieved in the implementation of the WHO GLASS recommendations. Areas for further improvement will be documented to inform future efforts to strengthen GLASS implementation for enhanced AMR surveillance in Africa. Results: The study results are expected in August 2024. Conclusions: To our knowledge, this scoping review will be the first to comprehensively examine the implementation of WHO GLASS recommendations in Africa, shedding light on the challenges and successes of AMR surveillance in the region. Addressing these issues aims to contribute to global efforts to combat AMR. International Registered Report Identifier (IRRID): PRR1-10.2196/58140 %M 39546786 %R 10.2196/58140 %U https://www.researchprotocols.org/2024/1/e58140 %U https://doi.org/10.2196/58140 %U http://www.ncbi.nlm.nih.gov/pubmed/39546786 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53340 %T Survey of Citizens’ Preferences for Combined Contact Tracing App Features During a Pandemic: Conjoint Analysis %A Bito,Seiji %A Hayashi,Yachie %A Fujita,Takanori %A Takahashi,Ikuo %A Arai,Hiromi %A Yonemura,Shigeto %+ National Hospital Organization Tokyo Medical Center, 2-5-1 Higashigaoka, Meguro-ku, Tokyo, 1528602, Japan, 81 334113130, bitoseiji@gmail.com %K digital contact tracing apps %K infectious disease %K conjoint analysis %K user attitudes %K public preferences %K citizen values %K attitude to health %K COVID-19 %K contact tracing %K privacy %K questionnaires %D 2024 %7 14.11.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: During the COVID-19 pandemic, an increased need for novel solutions such as digital contact tracing apps to mitigate virus spread became apparent. These apps have the potential to enhance public health initiatives through timely contact tracing and infection rate reduction. However, public and academic scrutiny has emerged around the adoption and use of these apps due to privacy concerns. Objective: This study aims to investigate public attitudes and preferences for contact tracing apps, specifically in Japan, using conjoint analysis to examine what specifications the public values most in such apps. By offering a nuanced understanding of the values that citizens prioritize, this study can help balance public health benefits and data privacy standards when designing contact tracing apps and serve as reference data for discussions on legal development and social consensus formation in the future. Methods: A cross-sectional, web-based questionnaire survey was conducted to determine how various factors related to the development and integration of infectious disease apps affect the public’s intention to use such apps. Individuals were recruited anonymously by a survey company. All respondents were asked to indicate their preferences for a combination of basic attributes and infectious disease app features for conjoint analysis. The respondents were randomly divided into 2 groups: one responded to a scenario where the government was assumed to be the entity dealing with infectious disease apps (ie, the government cluster), and the other responded to a scenario where a commercial company was assumed to be this entity (ie, the business cluster). Samples of 500 respondents from each randomly selected group were used as target data. Results: For the government cluster, the most important attribute in scenario A was distributor rights (42.557), followed by public benefits (29.458), personal health benefits (22.725), and profit sharing (5.260). For the business cluster, the most important attribute was distributor rights (45.870), followed by public benefits (32.896), personal health benefits (13.994), and profit sharing (7.240). Hence, personal health benefits tend to be more important in encouraging active app use than personal financial benefits. However, the factor that increased motivation for app use the most was the public health benefits of cutting infections by half. Further, concern about the use of personal data collected by the app for any secondary purpose was a negative incentive, which was more significant toward app use compared to the other 3 factors. Conclusions: The findings suggest that potential app users are positively motivated not only by personal health benefits but also by contributing to public health. Thus, a combined approach can be taken to increase app use. %M 39541579 %R 10.2196/53340 %U https://publichealth.jmir.org/2024/1/e53340 %U https://doi.org/10.2196/53340 %U http://www.ncbi.nlm.nih.gov/pubmed/39541579 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e53622 %T Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics %A Camirand Lemyre,Félix %A Lévesque,Simon %A Domingue,Marie-Pier %A Herrmann,Klaus %A Ethier,Jean-François %K distributed algorithms %K generalized linear models %K horizontally partitioned data %K GLMs %K learning health systems %K distributed analysis %K federated analysis %K data science %K data custodians %K algorithms %K statistics %K synthesis %K review methods %K searches %K scoping %D 2024 %7 14.11.2024 %9 %J JMIR Med Inform %G English %X Background: Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks. Objective: This study aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data, (2) describing the methods applicable to generalized linear models (GLMs) and assessing their underlying distributional assumptions, and (3) adapting existing methods to make them fully usable in health settings. Methods: A scoping review methodology was used for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and derive the properties of the resulting estimators. Results: From the review, 41 articles were selected and 6 approaches were extracted to conduct standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information sharing requirements and operational complexity. Conclusions: This study contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data by adapting these methods to the context of heterogeneous health data and clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics. %R 10.2196/53622 %U https://medinform.jmir.org/2024/1/e53622 %U https://doi.org/10.2196/53622 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e59772 %T Methods Used in Co-Creation Within the Health CASCADE Co-Creation Database and Gray Literature: Systematic Methods Overview %A Agnello,Danielle Marie %A Balaskas,George %A Steiner,Artur %A Chastin,Sebastien %+ School of Health and Life Sciences, Glasgow Caledonian University, Cowcaddens Rd, Glasgow, G4 0BA, United Kingdom, 44 7871788785, danielle.agnello@gcu.ac.uk %K co-creation %K coproduction %K co-design %K methods %K participatory %K inventory %K text mining %K methodology %K research methods %K CASCADE %K research methods %D 2024 %7 11.11.2024 %9 Review %J Interact J Med Res %G English %X Background: Co-creation is increasingly recognized for its potential to generate innovative solutions, particularly in addressing complex and wicked problems in public health. Despite this growing recognition, there are no standards or recommendations for method use in co-creation, leading to confusion and inconsistency. While some studies have examined specific methods, a comprehensive overview is lacking, limiting the collective understanding and ability to make informed decisions about the most appropriate methods for different contexts and research objectives. Objective: This study aimed to systematically compile and analyze methods used in co-creation to enhance transparency and deepen understanding of how co-creation is practiced. Methods: To enhance transparency and deepen understanding of how co-creation is practiced, this study systematically inventoried and analyzed methods used in co-creation. We conducted a systematic methods overview, applying 2 parallel processes: one within the peer-reviewed Health CASCADE Co-Creation Database and another within gray literature. An artificial intelligence–assisted recursive search strategy, coupled with a 2-step screening process, ensured that we captured relevant methods. We then extracted method names and conducted textual, comparative, and bibliometric analyses to assess the content, relationship between methods, fields of research, and the methodological underpinnings of the included sources. Results: We examined a total of 2627 academic papers and gray literature sources, with the literature primarily drawn from health sciences, medical research, and health services research. The dominant methodologies identified were co-creation, co-design, coproduction, participatory research methodologies, and public and patient involvement. From these sources, we extracted and analyzed 956 co-creation methods, noting that only 10% (n=97) of the methods overlap between academic and gray literature. Notably, 91.3% (230/252) of the methods in academic literature co-occurred, often involving combinations of multiple qualitative methods. The most frequently used methods in academic literature included surveys, focus groups, photo voice, and group discussion, whereas gray literature highlighted methods such as world café, focus groups, role-playing, and persona. Conclusions: This study presents the first systematic overview of co-creation methods, providing a clear understanding of the diverse methods currently in use. Our findings reveal a significant methodological gap between researchers and practitioners, offering insights into the relative prevalence and combinations of methods. By shedding light on these methods, this study helps bridge the gap and supports researchers in making informed decisions about which methods to apply in their work. Additionally, it offers a foundation for further investigation into method use in co-creation. This systematic investigation is a valuable resource for anyone engaging in co-creation or similar participatory methodologies, helping to navigate the diverse landscape of methods. %M 39527793 %R 10.2196/59772 %U https://www.i-jmr.org/2024/1/e59772 %U https://doi.org/10.2196/59772 %U http://www.ncbi.nlm.nih.gov/pubmed/39527793 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55311 %T The Validity of Impressions as a Media Dose Metric in a Tobacco Public Education Campaign Evaluation: Observational Study %A Davis,Kevin %A Curry,Laurel %A Bradfield,Brian %A Stupplebeen,David A %A Williams,Rebecca J %A Soria,Sandra %A Lautsch,Julie %+ RTI International, 701 13th St. NW, Ste 750, Washington, DC, 20005, United States, 1 202 728 2086, lcurry@rti.org %K communication %K public education %K tobacco %K media %K public health %D 2024 %7 5.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Evaluation research increasingly needs alternatives to target or gross rating points to comprehensively measure total exposure to modern multichannel public education campaigns that use multiple channels, including TV, radio, digital video, and paid social media, among others. Ratings data typically only capture delivery of broadcast media (TV and radio) and excludes other channels. Studies are needed to validate objective cross-channel metrics such as impressions against self-reported exposure to campaign messages. Objective: This study aimed to examine whether higher a volume of total media campaign impressions is predictive of individual-level self-reported campaign exposure in California. Methods: We analyzed over 3 years of advertisement impressions from the California Tobacco Prevention Program’s statewide tobacco education campaigns from August 2019 through December 2022. Impressions data varied across designated market areas (DMAs) and across time. These data were merged to individual respondents from 45 waves of panel survey data of Californians aged 18-55 years (N=151,649). Impressions were merged to respondents based on respondents’ DMAs and time of survey completion. We used logistic regression to estimate the odds of respondents’ campaign recall as a function of cumulative and past 3-month impressions delivered to each respondent’s DMA. Results: Cumulative impressions were positively and significantly associated with recall of each of the Flavors Hook Kids (odds ratio [OR] 1.15, P<.001), Dark Balloons and Apartment (OR 1.20, P<.001), We Are Not Profit (OR 1.36, P<.001), Tell Your Story (E-cigarette, or Vaping, product use Associated Lung Injury; OR 1.06, P<.05), and Thrown Away and Little Big Lies (OR 1.05, P<.01) campaigns. Impressions delivered in the past 3 months were associated with recall of the Flavors Hook Kids (OR 1.13, P<.001), Dark Balloons and Apartment (OR 1.08, P<.001), We Are Not Profit (OR 1.14, P<.001), and Thrown Away and Little Big Lies (OR 1.04, P<.001) campaigns. Past 3-month impressions were not significantly associated with Tell Your Story campaign recall. Overall, magnitudes of these associations were greater for cumulative impressions. We visualize recall based on postestimation predicted values from our multivariate logistic regression models. Conclusions: Variation in cumulative impressions for California Tobacco Prevention Program’s long-term multichannel tobacco education campaign is predictive of increased self-reported campaign recall, suggesting that impressions may be a valid proxy for potential campaign exposure. The use of impressions for purposes of evaluating public education campaigns may help address current methodological limitations arising from the fragmented nature of modern multichannel media campaigns. %M 39499542 %R 10.2196/55311 %U https://www.jmir.org/2024/1/e55311 %U https://doi.org/10.2196/55311 %U http://www.ncbi.nlm.nih.gov/pubmed/39499542 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53409 %T Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Sub-Saharan Africa: Longitudinal Trend Analysis %A Lundberg,Alexander L %A Soetikno,Alan G %A Wu,Scott A %A Ozer,Egon A %A Welch,Sarah B %A Mason,Maryann %A Murphy,Robert L %A Hawkins,Claudia %A Liu,Yingxuan %A Moss,Charles B %A Havey,Robert J %A Achenbach,Chad J %A Post,Lori A %+ Buehler Center for Health Policy and Economics, Robert J Havey, MD Institute for Global Health, Northwestern University, 420 E. Superior, Chicago, IL, 60611, United States, 1 312 503 1706, lori.post@northwestern.edu %K SARS-CoV-2 %K COVID-19 %K sub-Saharan Africa %K pandemic %K surveillance %K public health %K COVID-19 transmission %K speed %K acceleration %K deceleration %K jerk %K dynamic panel %K generalized method of moments %K Arellano-Bond %D 2024 %7 23.10.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: This study updates the initial COVID-19 pandemic surveillance in sub-Saharan Africa (SSA) from 2020 by providing 2 additional years of data for the region. Objective: First, we aimed to measure whether there was an expansion or contraction in the pandemic in SSA when the World Health Organization (WHO) declared an end to the public health emergency for the COVID-19 pandemic on May 5, 2023. Second, we used dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Third, we aimed to provide historical context for the course of the pandemic in SSA. Methods: In addition to updates of traditional surveillance data and dynamic panel estimates from the original study by Post et al (2021), this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and used Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t-test to assess whether regional weekly speed was greater than an outbreak threshold of 10. We ran the test iteratively with a rolling 6-month window of data across the sample period. Results: Speed for the region remained well below the outbreak threshold before and after the WHO declaration. Acceleration and jerk were also low and stable. The 7-day persistence coefficient remained somewhat large (1.11) and statistically significant. However, both shift parameters for the weeks around the WHO declaration were negative, meaning the clustering effect of new COVID-19 cases had become recently smaller. From November 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t-test of speed equal to 10 was insignificant for the entire sample period. Conclusions: While COVID-19 continues to circulate in SSA, the region never reached outbreak status, and the weekly transmission rate remained below 1 case per 100,000 population for well over 1 year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold for its classification as a pandemic. Both standard and enhanced surveillance metrics confirm that the pandemic ended in SSA by the time the WHO made its declaration. %M 39013111 %R 10.2196/53409 %U https://publichealth.jmir.org/2024/1/e53409 %U https://doi.org/10.2196/53409 %U http://www.ncbi.nlm.nih.gov/pubmed/39013111 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 3 %N %P e58463 %T Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation %A Tan,Joshua Kuan %A Quan,Le %A Salim,Nur Nasyitah Mohamed %A Tan,Jen Hong %A Goh,Su-Yen %A Thumboo,Julian %A Bee,Yong Mong %+ Health Services Research Unit, Singapore General Hospital, 10 Hospital Blvd, Singapore, 168582, Singapore, 65 6222 3322, joshua.tank@mohh.com.sg %K diabetes mellitus %K type 2 diabetes %K health care utilization %K population health management %K population health %K machine learning %K artificial intelligence %K predictive model %K predictive system %K practical model %D 2024 %7 17.10.2024 %9 Original Paper %J JMIR AI %G English %X Background: The cost of health care in many countries is increasing rapidly. There is a growing interest in using machine learning for predicting high health care utilizers for population health initiatives. Previous studies have focused on individuals who contribute to the highest financial burden. However, this group is small and represents a limited opportunity for long-term cost reduction. Objective: We developed a collection of models that predict future health care utilization at various thresholds. Methods: We utilized data from a multi-institutional diabetes database from the year 2019 to develop binary classification models. These models predict health care utilization in the subsequent year across 6 different outcomes: patients having a length of stay of ≥7, ≥14, and ≥30 days and emergency department attendance of ≥3, ≥5, and ≥10 visits. To address class imbalance, random and synthetic minority oversampling techniques were employed. The models were then applied to unseen data from 2020 and 2021 to predict health care utilization in the following year. A portfolio of performance metrics, with priority on area under the receiver operating characteristic curve, sensitivity, and positive predictive value, was used for comparison. Explainability analyses were conducted on the best performing models. Results: When trained with random oversampling, 4 models, that is, logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron consistently achieved high area under the receiver operating characteristic curve (>0.80) and sensitivity (>0.60) across training-validation and test data sets. Correcting for class imbalance proved critical for model performance. Important predictors for all outcomes included age, number of emergency department visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean hemoglobin A1c levels. Explainability analyses using partial dependence plots demonstrated that for the best performing models, the learned patterns were consistent with real-world knowledge, thereby supporting the validity of the models. Conclusions: We successfully developed machine learning models capable of predicting high service level utilization with strong performance and valid explainability. These models can be integrated into wider diabetes-related population health initiatives. %M 39418089 %R 10.2196/58463 %U https://ai.jmir.org/2024/1/e58463 %U https://doi.org/10.2196/58463 %U http://www.ncbi.nlm.nih.gov/pubmed/39418089 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e55170 %T Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults %A Yi,Li %A Hart,Jaime E %A Straczkiewicz,Marcin %A Karas,Marta %A Wilt,Grete E %A Hu,Cindy R %A Librett,Rachel %A Laden,Francine %A Chavarro,Jorge E %A Onnela,Jukka-Pekka %A James,Peter %+ Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02215, United States, 1 6174321333, n3lyi@channing.harvard.edu %K big data %K daily mobility %K digital phenotyping %K ecological momentary assessment %K epidemiological monitoring %K health behavior %K smartphone apps and sensors %K mobile phone %D 2024 %7 11.10.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. Objective: The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Methods: Participants (N=2394) of the Beiwe Substudy of the Nurses’ Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants’ key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Results: Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. Conclusions: In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease. %M 39392682 %R 10.2196/55170 %U https://publichealth.jmir.org/2024/1/e55170 %U https://doi.org/10.2196/55170 %U http://www.ncbi.nlm.nih.gov/pubmed/39392682 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e59274 %T Association Between Ursodeoxycholic Acid and Clinical Outcomes in Patients With COVID-19 Infection: Population-Based Cohort Study %A Lee,Hyunjun %A Kim,Min Gul %A Yeom,Sang Woo %A Noh,Sang Jae %A Jeong,Cho Yun %A Kim,Min Ji %A Kang,Min Gu %A Ko,Ji Hoon %A Park,Su Cheol %A Kweon,Hyeok Tae %A Sim,Sang Il %A Lee,Hyun %A You,Yeon Seok %A Kim,Jong Seung %+ Department of Medical Informatics, Department of Otorhinolaryngology, Jeonbuk National University School of Medicine and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju-si, Jeonbuk-do, Jeonju, 54907, Republic of Korea, 82 +82 63 250 2792, kjsjdk@gmail.com %K Covid 19 %K COVID-19 %K ursodeoxycholic acid %K population-based cohort study %K SARS-CoV-2 %K Coronavirus %K pandemic %K population-based %K retrospective cohort study %K propensity score %K UDCA %K public health %K common data model %K clinical %K severity %D 2024 %7 7.10.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Several studies have investigated the relationship between ursodeoxycholic acid (UDCA) and COVID-19 infection. However, complex and conflicting results have generated confusion in the application of these results. Objective: We aimed to investigate whether the association between UDCA and COVID-19 infection can also be demonstrated through the analysis of a large-scale cohort. Methods: This retrospective study used local and nationwide cohorts, namely, the Jeonbuk National University Hospital into the Observational Medical Outcomes Partnership common data model cohort (JBUH CDM) and the Korean National Health Insurance Service claim–based database (NHIS). We investigated UDCA intake and its relationship with COVID-19 susceptibility and severity using validated propensity score matching. Results: Regarding COVID-19 susceptibility, the adjusted hazard ratio (aHR) value of the UDCA intake was significantly lowered to 0.71 in the case of the JBUH CDM (95% CI 0.52-0.98) and was significantly lowered to 0.93 (95% CI 0.90-0.96) in the case of the NHIS. Regarding COVID-19 severity, the UDCA intake was found to be significantly lowered to 0.21 (95% CI 0.09-0.46) in the case of JBUH CDM. Furthermore, the aHR value was significantly lowered to 0.77 in the case of NHIS (95% CI 0.62-0.95). Conclusions: Using a large-scale local and nationwide cohort, we confirmed that UDCA intake was significantly associated with reductions in COVID-19 susceptibility and severity. These trends remained consistent regardless of the UDCA dosage. This suggests the potential of UDCA as a preventive and therapeutic agent for COVID-19 infection. %M 39139026 %R 10.2196/59274 %U https://publichealth.jmir.org/2024/1/e59274 %U https://doi.org/10.2196/59274 %U http://www.ncbi.nlm.nih.gov/pubmed/39139026 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e62863 %T Association Between Climatic Factors and Varicella Incidence in Wuxi, East China, 2010-2019: Surveillance Study %A Zhang,Kehong %A Shen,Ganglei %A Yuan,Yue %A Shi,Chao %+ President Office, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Room 707, Building 5, Number 8 Zhongnan West Road, Wuxi, 214071, China, 86 051088859999 ext 17003, 1358599249@qq.com %K varicella %K meteorological factors %K Generalized Additive Model %K Segmented Linear Regression Model %K China %K meteorology %K regression %K statistics %K surveillance %D 2024 %7 2.10.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Varicella is a common infectious disease and a growing public health concern in China, with increasing outbreaks in Wuxi. Analyzing the correlation between climate factors and varicella incidence in Wuxi is crucial for guiding public health prevention efforts. Objective: This study examines the impact of meteorological variables on varicella incidence in Wuxi, eastern China, from 2010 to 2019, offering insights for public health interventions. Methods: We collected daily meteorological data and varicella case records from January 1, 2010, to December 31, 2019, in Wuxi, China. Generalized cross-validation identified optimal lag days by selecting those with the lowest score. The relationship between meteorological factors and varicella incidence was analyzed using Poisson generalized additive models and segmented linear regression. Subgroup analyses were conducted by gender and age. Results: The study encompassed 64,086 varicella cases. Varicella incidence in Wuxi city displayed a bimodal annual pattern, with peak occurrences from November to January of the following year and lower peaks from May to June. Several meteorological factors influencing varicella risk were identified. A decrease of 1°C when temperatures were ≤20°C corresponded to a 1.99% increase in varicella risk (95% CI 1.57-2.42, P<.001). Additionally, a decrease of 1°C below 22.38°C in ground temperature was associated with a 1.36% increase in varicella risk (95% CI 0.96-1.75, P<.001). Each 1 mm increase in precipitation above 4.88 mm was associated with a 1.62% increase in varicella incidence (95% CI 0.93-2.30, P<.001). A 1% rise in relative humidity above 57.18% increased varicella risk by 2.05% (95% CI 1.26-2.84, P<.001). An increase in air pressure of 1 hPa below 1011.277 hPa was associated with a 1.75% rise in varicella risk (95% CI 0.75-2.77, P<.001). As wind speed and evaporation increased, varicella risk decreased linearly with a 16-day lag. Varicella risk was higher with sunshine durations exceeding 1.825 hours, with a 14-day lag, increasing by 1.30% for each additional hour of sunshine (95% CI 0.62-2.00, P=.006). Subgroup analyses revealed that teenagers and children under 17 years of age faced higher varicella risks associated with temperature, average ground temperature, precipitation, relative humidity, and air pressure. Adults aged 18-64 years experienced increased risk with longer sunshine durations. Additionally, males showed higher varicella risks related to ground temperature and air pressure compared with females. However, no significant gender differences were observed regarding varicella risks associated with temperature (male: P<.001; female P<.001), precipitation (male: P=.001; female: P=.06), and sunshine duration (male: P=.53; female: P=.04). Conclusions: Our preliminary findings highlight the interplay between varicella outbreaks in Wuxi city and meteorological factors. These insights provide valuable support for developing policies aimed at reducing varicella risks through informed public health measures. %M 39228304 %R 10.2196/62863 %U https://publichealth.jmir.org/2024/1/e62863 %U https://doi.org/10.2196/62863 %U http://www.ncbi.nlm.nih.gov/pubmed/39228304 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53368 %T COVID-19 Health Impact: A Use Case for Syndromic Surveillance System Monitoring Based on Primary Care Patient Registries in the Netherlands %A Rahmon,Imme %A Bosmans,Mark %A Baliatsas,Christos %A Hooiveld,Mariette %A Marra,Elske %A Dückers,Michel %K SARS-CoV-2 %K epidemic surveillance %K public health %K general practice %K disaster health research %D 2024 %7 26.9.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic challenged societies worldwide. The implementation of mitigation measures to limit the number of SARS-CoV-2 infections resulted in unintended health effects. Objective: The objective of this study is to demonstrate the use of an existing syndromic surveillance system in primary care during a first series of quarterly cross-sectional monitoring cycles, targeting health problems presented in primary care among Dutch youth since August 2021. Methods: Aggregated data from the surveillance system of Nivel Primary Care Database were analyzed quarterly to monitor 20 health problems often reported in the aftermath of disasters and environmental incidents. Results were stratified by age (ie, 0‐4, 5‐14, and 15‐24 years), sex, and region (province). Weekly prevalence rates were calculated as the number of persons consulting their general practitioner in a particular week, using the number of enlisted persons as the denominator. Findings were compared to quarterly survey panel data, collected in the context of the Integrated Health Monitor COVID-19, and the Dutch stringency index values, indicative of the intensity of COVID-19 mitigation measures. Results: Over time, weekly rates pointed to an increased number of consultations for depressive feelings and suicide (attempts) among youth, during and after periods with intensified domestic restrictions. Conclusions: The results illustrate how, from a disaster health research perspective based on the COVID-19 pandemic, health consequences of pandemics could be successfully followed over time using an existing infrastructure for syndromic surveillance and monitoring. Particular areas of health concern can be defined beforehand, and may be modified or expanded during the monitoring activities to track relevant developments. Although an association between patterns and changes in the strictness of mitigation measures might seem probable, claims about causality should be made with caution. %R 10.2196/53368 %U https://publichealth.jmir.org/2024/1/e53368 %U https://doi.org/10.2196/53368 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53711 %T An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study %A Lim,Sachiko %A Johannesson,Paul %+ Department of Computer and Systems Sciences, Stockholm University, Nodhuset, Borgarfjordsgatan 12, Kista, SE-164 07, Sweden, 46 0760968462, sachiko@dsv.su.se %K infectious disease %K ontology %K IoT %K infectious disease surveillance %K patient monitoring %K infectious disease management %K risk analysis %K early warning %K data integration %K semantic interoperability %K public health %D 2024 %7 26.9.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. Objective: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. Methods: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. Results: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. Conclusions: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner. %M 39325530 %R 10.2196/53711 %U https://formative.jmir.org/2024/1/e53711 %U https://doi.org/10.2196/53711 %U http://www.ncbi.nlm.nih.gov/pubmed/39325530 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e54861 %T Kinetics of Viral Shedding for Outbreak Surveillance of Emerging Infectious Diseases: Modeling Approach to SARS-CoV-2 Alpha and Omicron Infection %A Lin,Ting-Yu %A Yen,Amy Ming-Fang %A Chen,Sam Li-Sheng %A Hsu,Chen-Yang %A Lai,Chao-Chih %A Luh,Dih-Ling %A Yeh,Yen-Po %A Chen,Tony Hsiu-Hsi %+ Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, No 17, Xu-Zhou Road, Taipei, 100, Taiwan, 886 233668033, chenlin@ntu.edu.tw %K COVID-19 %K PCR testing %K Ct values %K viral load %K kinetics of viral shedding %K emerging infectious disease %K SARS-CoV-2 variants %K infection surveillance %D 2024 %7 19.9.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values. Objective: This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery. Methods: This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the “zero-COVID-19” policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the “zero-COVID-19” policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model. Results: The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25. Conclusions: The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks. %M 39298261 %R 10.2196/54861 %U https://publichealth.jmir.org/2024/1/e54861 %U https://doi.org/10.2196/54861 %U http://www.ncbi.nlm.nih.gov/pubmed/39298261 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58704 %T From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England %A Elliot,Alex J %A Hughes,Helen E %A Harcourt,Sally E %A Smith,Sue %A Loveridge,Paul %A Morbey,Roger A %A Bains,Amardeep %A Edeghere,Obaghe %A Jones,Natalia R %A Todkill,Daniel %A Smith,Gillian E %+ Real-time Syndromic Surveillance Team, UK Health Security Agency, 23 Stephenson Street, Birmingham, B2 4BH, United Kingdom, 44 1212329211, alex.elliot@ukhsa.gov.uk %K epidemiology %K population surveillance %K sentinel surveillance %K public health surveillance %K bioterrorism %K mass gathering %K pandemics %D 2024 %7 17.9.2024 %9 Viewpoint %J J Med Internet Res %G English %X The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on “cold or flu” calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization. %M 39288377 %R 10.2196/58704 %U https://www.jmir.org/2024/1/e58704 %U https://doi.org/10.2196/58704 %U http://www.ncbi.nlm.nih.gov/pubmed/39288377 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e54750 %T Wastewater Surveillance Pilot at US Military Installations: Cost Model Analysis %A Sanjak,Jaleal S %A McAuley,Erin M %A Raybern,Justin %A Pinkham,Richard %A Tarnowski,Jacob %A Miko,Nicole %A Rasmussen,Bridgette %A Manalo,Christian J %A Goodson,Michael %A Stamps,Blake %A Necciai,Bryan %A Sozhamannan,Shanmuga %A Maier,Ezekiel J %K wastewater surveillance %K cost analysis %K military health %K public health %K sanitation %K sanitary %K water %K wastewater %K surveillance %K environment %K environmental %K cost %K costs %K economic %K economics %K finance %K financial %K pathogen %K pathogens %K biosurveillance %D 2024 %7 6.9.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic highlighted the need for pathogen surveillance systems to augment both early warning and outbreak monitoring/control efforts. Community wastewater samples provide a rapid and accurate source of environmental surveillance data to complement direct patient sampling. Due to its global presence and critical missions, the US military is a leader in global pandemic preparedness efforts. Clinical testing for COVID-19 on US Air Force (USAF) bases (AFBs) was effective but costly with respect to direct monetary costs and indirect costs due to lost time. To remain operating at peak capacity, such bases sought a more passive surveillance option and piloted wastewater surveillance (WWS) at 17 AFBs to demonstrate feasibility, safety, utility, and cost-effectiveness from May 2021 to January 2022. Objective: We model the costs of a wastewater program for pathogens of public health concern within the specific context of US military installations using assumptions based on the results of the USAF and Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense pilot program. The objective was to determine the cost of deploying WWS to all AFBs relative to clinical swab testing surveillance regimes. Methods: A WWS cost projection model was built based on subject matter expert input and actual costs incurred during the WWS pilot program at USAF AFBs. Several SARS-CoV-2 circulation scenarios were considered, and the costs of both WWS and clinical swab testing were projected. Analysis was conducted to determine the break-even point and how a reduction in swab testing could unlock funds to enable WWS to occur in parallel. Results: Our model confirmed that WWS is complementary and highly cost-effective when compared to existing alternative forms of biosurveillance. We found that the cost of WWS was between US $10.5-$18.5 million less expensive annually in direct costs as compared to clinical swab testing surveillance. When the indirect cost of lost work was incorporated, including lost work associated with required clinical swab testing, we estimated that over two-thirds of clinical swab testing could be maintained with no additional costs upon implementation of WWS. Conclusions: Our results support the adoption of WWS across US military installations as part of a more comprehensive and early warning system that will enable adaptive monitoring during disease outbreaks in a more cost-effective manner than swab testing alone. %R 10.2196/54750 %U https://publichealth.jmir.org/2024/1/e54750 %U https://doi.org/10.2196/54750 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e47465 %T Patient-Reported Experiences of Persistent Post–COVID-19 Conditions After Hospital Discharge During the Second and Third Waves of the Pandemic in Switzerland: Cross-Sectional Questionnaire Study %A Tacchini-Jacquier,Nadine %A Monnay,Sévrine %A Coquoz,Nicolas %A Bonvin,Eric %A Verloo,Henk %+ Department of Nursing, School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), 5, Chemin de l’Agasse, Sion, 1950, Switzerland, 41 78 769 89 90, henk.verloo@hevs.ch %K patient-reported experience measures %K PREMs %K long COVID %K fatigue %K post-traumatic stress disorder %K depression %K anxiety %K SARS-CoV-2 infection %K post-COVID %K COVID-19 %K pandemic %K hospital discharge %K pandemic %D 2024 %7 28.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Hospitalized patients infected with SARS-CoV-2 should recover within a few weeks. However, even those with mild versions can experience symptoms lasting 4 weeks or longer. These post–COVID-19 condition (PCC) comprise various new, returning, or ongoing symptoms that can last for months or years and cause disability. Few studies have investigated PCC using self-reports from discharged patients infected with SARS-CoV-2 to complement clinical and biomarker studies. Objective: This study aimed to investigate self-reported, persistent PCC among patients infected with SARS-CoV-2 who were discharged during the second and third waves of the COVID-19 pandemic. Methods: We designed, pretested, and posted an ad hoc paper questionnaire to all eligible inpatients discharged between October 2020 and April 2021. At 4 months post discharge, we collected data on PCC and scores for the Multidimensional Fatigue Inventory (MFI), the Patient Health Questionnaire-4 (PHQ-4), a Brief Memory Screening Scale (Q3PC), and a posttraumatic stress disorder scale (PCL-5). Descriptive, inferential, and multivariate linear regression statistics assessed PCC symptomatology, associations, and differences regarding sociodemographic characteristics and hospital length of stay (LOS). We examined whether our variables of interest significantly predicted MFI scores. Results: Of the 1993 valid questionnaires returned, 245 were from discharged patients with SARS-CoV-2 (median age 71, IQR 62.7-77 years). Only 28.2% (69/245) of respondents were symptom-free after 4 months. Women had significantly more persistent PCC symptoms than men (P≤.001). Patients with a hospital LOS ≥11 days had more PCC symptoms as well (P<.001)—women had more symptoms and longer LOS. No significant differences were found between age groups (18-64, 65-74, and ≥75 years old; P=.50) or between intensive care units and other hospitalization units (P=.09). Patients self-reported significantly higher PHQ-4 scores during their hospitalization than at 4 months later (P<.001). Three-fourth (187/245, 76.4%) of the respondents reported memory loss and concentration disorders (Q3PC). No significant differences in the median MFI score (56, IQR 1-3, range 50-60]) were associated with sociodemographic variables. Patients with a hospital LOS of ≥11 days had a significantly higher median PCL-5 score (P<.001). Multivariate linear regression allowed us to calculate that the combination of PHQ-4, Q3PC, and PCL-5 scores, adjusted for age, sex, and LOS (of either ≥11 days [median 2 symptoms, IQR 1-5] or <11 days), did not significantly predict MFI scores (R2=0.09; F4,7 =1.5; P=.22; adjusted R2=0.06). Conclusions: The majority of inpatients infected with SARS-CoV-2 presented with PCC 4 months after discharge, with complex clinical pictures. Only one-third of them were symptom-free during that time. Based on our findings, MFI scores were not directly related to self-reported depression, anxiety, or posttraumatic scores adjusted for age, sex, or LOS. Further research is needed to explore PCC and fatigue based on self-reported health experiences of discharged inpatients infected with SARS-CoV-2. %M 39197160 %R 10.2196/47465 %U https://publichealth.jmir.org/2024/1/e47465 %U https://doi.org/10.2196/47465 %U http://www.ncbi.nlm.nih.gov/pubmed/39197160 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53331 %T South Asia’s COVID-19 History and Surveillance: Updated Epidemiological Assessment %A Post,Lori A %A Soetikno,Alan G %A Wu,Scott A %A Hawkins,Claudia %A Mason,Maryann %A Ozer,Egon A %A Murphy,Robert L %A Welch,Sarah B %A Liu,Yingxuan %A Havey,Robert J %A Moss,Charles B %A Achenbach,Chad J %A Lundberg,Alexander L %+ Buehler Center for Health Policy and Economics, Robert J Havey, MD Institute for Global Health, Northwestern University, 420 E Superior St, Chicago, IL, 60611, United States, 1 312 503 1706, lori.post@northwestern.edu %K SARS-CoV-2 %K COVID-19 %K South Asia %K pandemic history %K Bangladesh %K Bhutan %K India %K Maldives %K Nepal %K Pakistan %K Sri Lanka %K surveillance %K speed %K acceleration %K jerk %K dynamic panel data %K generalized method of moments %K GMM %K Arellano-Bond %K 7-day lag %D 2024 %7 26.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: This study updates our findings from the COVID-19 pandemic surveillance we first conducted in South Asia in 2020 with 2 additional years of data for the region. We assess whether COVID-19 had transitioned from pandemic to endemic at the point the World Health Organization (WHO) ended the public health emergency status for COVID-19 on May 5, 2023. Objective: First, we aim to measure whether there was an expansion or contraction in the pandemic in South Asia around the WHO declaration. Second, we use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Third, we aim to provide historical context for the course of the pandemic in South Asia. Methods: In addition to updating the traditional surveillance data and dynamic panel estimates from our original study, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional weekly speed or transmission rate per 100,000 population was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period. Results: Speed for the region had remained below the outbreak threshold for over a year by the time of the WHO declaration. Acceleration and jerk were also low and stable. While the 1-day persistence coefficients remained statistically significant and positive (1.168), the 7-day persistence coefficient was negative (–0.185), suggesting limited cluster effects in which cases on a given day predict cases 7 days forward. Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, did not indicate any overall change in the persistence measure around the time of the WHO declaration. From December of 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of speed equal to 10 was statistically insignificant across the entire pandemic. Conclusions: While COVID-19 continued to circulate in South Asia, the rate of transmission had remained below the outbreak threshold for well over a year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. Both standard and enhanced surveillance metrics confirm that the pandemic had ended by the time of the WHO declaration. Prevention policies should be a focus ahead of future pandemics. On that point, policy should emphasize an epidemiological task force with widespread testing and a contact-tracing system. %M 39013116 %R 10.2196/53331 %U https://publichealth.jmir.org/2024/1/e53331 %U https://doi.org/10.2196/53331 %U http://www.ncbi.nlm.nih.gov/pubmed/39013116 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e57742 %T Risk Index of Regional Infection Expansion of COVID-19: Moving Direction Entropy Study Using Mobility Data and Its Application to Tokyo %A Ohsawa,Yukio %A Sun,Yi %A Sekiguchi,Kaira %A Kondo,Sae %A Maekawa,Tomohide %A Takita,Morihito %A Tanimoto,Tetsuya %A Kami,Masahiro %+ School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan, 81 358417012, ohsawa@sys.t.u-tokyo.ac.jp %K suppressing the spread of infection %K index for risk assessment %K local regions %K diversity of mobility %K mobility data %K moving direction entropy %K MDE %K social network model %K COVID-19 %K influenza %K sexually transmitted diseases %D 2024 %7 21.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Policies, such as stay home, bubbling, and stay with your community, recommending that individuals reduce contact with diverse communities, including families and schools, have been introduced to mitigate the spread of the COVID-19 pandemic. However, these policies are violated if individuals from various communities gather, which is a latent risk in a real society where people move among various unreported communities. Objective: We aimed to create a physical index to assess the possibility of contact between individuals from diverse communities, which serves as an indicator of the potential risk of SARS-CoV-2 spread when considered and combined with existing indices. Methods: Moving direction entropy (MDE), which quantifies the diversity of moving directions of individuals in each local region, is proposed as an index to evaluate a region’s risk of contact of individuals from diverse communities. MDE was computed for each inland municipality in Tokyo using mobility data collected from smartphones before and during the COVID-19 pandemic. To validate the hypothesis that the impact of intercommunity contact on infection expansion becomes larger for a virus with larger infectivity, we compared the correlations of the expansion of infectious diseases with indices, including MDE and the densities of supermarkets, restaurants, etc. In addition, we analyzed the temporal changes in MDE in municipalities. Results: This study had 4 important findings. First, the MDE values for local regions showed significant invariance between different periods according to the Spearman rank correlation coefficient (>0.9). Second, MDE was found to correlate with the rate of infection cases of COVID-19 among local populations in 53 inland regions (average of 0.76 during the period of expansion). The density of restaurants had a similar correlation with COVID-19. The correlation between MDE and the rate of infection was smaller for influenza than for COVID-19, and tended to be even smaller for sexually transmitted diseases (order of infectivity). These findings support the hypothesis. Third, the spread of COVID-19 was accelerated in regions with high-rank MDE values compared to those with high-rank restaurant densities during and after the period of the governmental declaration of emergency (P<.001). Fourth, the MDE values tended to be high and increased during the pandemic period in regions where influx or daytime movement was present. A possible explanation for the third and fourth findings is that policymakers and living people have been overlooking MDE. Conclusions: We recommend monitoring the regional values of MDE to reduce the risk of infection spread. To aid in this monitoring, we present a method to create a heatmap of MDE values, thereby drawing public attention to behaviors that facilitate contact between communities during a highly infectious disease pandemic. %M 39037745 %R 10.2196/57742 %U https://publichealth.jmir.org/2024/1/e57742 %U https://doi.org/10.2196/57742 %U http://www.ncbi.nlm.nih.gov/pubmed/39037745 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53719 %T Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis %A Pham,Hai-Thanh %A Do,Toan %A Baek,Jonggyu %A Nguyen,Cong-Khanh %A Pham,Quang-Thai %A Nguyen,Hoa L %A Goldberg,Robert %A Pham,Quang Loc %A Giang,Le Minh %K imputation method %K COVID-19 incidence rate %K crude bias %K crude RMSE %K root mean square error %K percentage change %K pandemic %K Vietnam %K surveillance %K population health %K analytical method %D 2024 %7 20.8.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts. Objective: We aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations. Methods: This study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method. Results: Our study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC. Conclusions: Our study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR. %R 10.2196/53719 %U https://publichealth.jmir.org/2024/1/e53719 %U https://doi.org/10.2196/53719 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e48825 %T Using Social Vulnerability Indices to Predict Priority Areas for Prevention of Sudden Unexpected Infant Death in Cook County, IL: Cross-Sectional Study %A Riggins,Daniel P %A Zhang,Huiyuan %A Trick,William E %K infant %K socioeconomic disparities in health %K sudden unexpected infant death %K SUID %K sudden infant death %K SID %K geographic information systems %K structural racism %K predict %K social vulnerability %K racial disparity %K socioeconomic %K disparity %K child %K infancy %K pediatric %K sudden infant death syndrome %K SIDS %D 2024 %7 20.8.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly the same level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racial and socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods. Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities. Objective: We sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred in Cook County, IL, from 2015 to 2019 and predicting where it would occur in 2021‐2025 using a semiautomated, reproducible workflow based on open-source software and data. Methods: This cross-sectional retrospective study queried geocoded medical examiner data from 2015‐2019 to identify SUID cases in Cook County, IL, and aggregated them to “communities” as the unit of analysis. We compared demographic factors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We used social vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each given community for 2015‐2019. We applied indicators from 2020 to the trained model to make predictions for 2021‐2025. Results: Validation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95% (95% CI 93%‐97%) and a specificity of 98% (95% CI 94%‐100%). Case counts at the community level ranged from a minimum of 0 to a maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. All communities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibited lower median proportions of non-Hispanic White residents at 17% versus 60% (P<.001) and higher median proportions of non-Hispanic Black residents at 32% versus 3% (P<.001). Our predictive model showed moderate accuracy when assessed on the training data (Nagelkerke R2=70.2% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), Auburn Gresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021 and 2025. Conclusions: Sharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to 2019. Our predictive model and maps identify precise regions within the county for local health departments to target for intervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communities will be most affected by SUID. %R 10.2196/48825 %U https://publichealth.jmir.org/2024/1/e48825 %U https://doi.org/10.2196/48825 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53175 %T Comparison of Different Reverse Transcriptase–Polymerase Chain Reaction–Based Methods for Wastewater Surveillance of SARS-CoV-2: Exploratory Study %A Länsivaara,Annika %A Lehto,Kirsi-Maarit %A Hyder,Rafiqul %A Janhonen,Erja Sinikka %A Lipponen,Anssi %A Heikinheimo,Annamari %A Pitkänen,Tarja %A Oikarinen,Sami %A , %+ Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland, 358 503241937, annika.lansivaara@tuni.fi %K wastewater surveillance %K surveillance systems %K SARS-CoV-2 %K COVID-19 %K wastewater %K surveillance %K Finland %K monitoring %K detection %K low-resource settings %K RNA %K spatial %K temporal changes %K reverse transcription droplet digital polymerase chain reaction %K quantitative reverse transcription polymerase chain reaction %K reverse transcription strand invasion based amplification %D 2024 %7 19.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Many countries have applied the wastewater surveillance of the COVID-19 pandemic to their national public health monitoring measures. The most used methods for detecting SARS-CoV-2 in wastewater are quantitative reverse transcriptase–polymerase chain reaction (RT-qPCR) and reverse transcriptase–droplet digital polymerase chain reaction (RT-ddPCR). Previous comparison studies have produced conflicting results, thus more research on the subject is required. Objective: This study aims to compare RT-qPCR and RT-ddPCR for detecting SARS-CoV-2 in wastewater. It also aimed to investigate the effect of changes in the analytical pipeline, including the RNA extraction kit, RT-PCR kit, and target gene assay, on the results. Another aim was to find a detection method for low-resource settings. Methods: We compared 2 RT-qPCR kits, TaqMan RT-qPCR and QuantiTect RT-qPCR, and RT-ddPCR based on sensitivity, positivity rates, variability, and correlation of SARS-CoV-2 gene copy numbers in wastewater to the incidence of COVID-19. Furthermore, we compared 2 RNA extraction methods, column- and magnetic-bead–based. In addition, we assessed 2 target gene assays for RT-qPCR, N1 and N2, and 2 target gene assays for ddPCR N1 and E. Reverse transcription strand invasion-based amplification (RT-SIBA) was used to detect SARS-CoV-2 from wastewater qualitatively. Results: Our results indicated that the most sensitive method to detect SARS-CoV-2 in wastewater was RT-ddPCR. It had the highest positivity rate (26/30), and its limit of detection was the lowest (0.06 gene copies/µL). However, we obtained the best correlation between COVID-19 incidence and SARS-CoV-2 gene copy number in wastewater using TaqMan RT-qPCR (correlation coefficient [CC]=0.697, P<.001). We found a significant difference in sensitivity between the TaqMan RT-qPCR kit and the QuantiTect RT-qPCR kit, the first having a significantly lower limit of detection and a higher positivity rate than the latter. Furthermore, the N1 target gene assay was the most sensitive for both RT-qPCR kits, while no significant difference was found between the gene targets using RT-ddPCR. In addition, the use of different RNA extraction kits affected the result when the TaqMan RT-qPCR kit was used. RT-SIBA was able to detect SARS-CoV-2 RNA in wastewater. Conclusions: As our study, as well as most of the previous studies, has shown RT-ddPCR to be more sensitive than RT-qPCR, its use in the wastewater surveillance of SARS-CoV-2 should be considered, especially if the amount of SARS-CoV-2 circulating in the population was low. All the analysis steps must be optimized for wastewater surveillance as our study showed that all the analysis steps including the compatibility of the RNA extraction, the RT-PCR kit, and the target gene assay influence the results. In addition, our study showed that RT-SIBA could be used to detect SARS-CoV-2 in wastewater if a qualitative result is sufficient. %M 39158943 %R 10.2196/53175 %U https://publichealth.jmir.org/2024/1/e53175 %U https://doi.org/10.2196/53175 %U http://www.ncbi.nlm.nih.gov/pubmed/39158943 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e59924 %T Developing the DIGIFOOD Dashboard to Monitor the Digitalization of Local Food Environments: Interdisciplinary Approach %A Jia,Si Si %A Luo,Xinwei %A Gibson,Alice Anne %A Partridge,Stephanie Ruth %+ Susan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, University of Sydney, Level 8, Susan Wakil Health Building, Camperdown, Sydney, 2006, Australia, 61 2 8627 1697, sisi.jia@sydney.edu.au %K online food delivery %K food environment %K dashboard %K web scraping %K big data %K surveillance %K monitoring %K prevention %K food %K food delivery %K development study %K development %K accessibility %K Australia %K monitoring tool %K tool %K tools %D 2024 %7 13.8.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Online food delivery services (OFDS) enable individuals to conveniently access foods from any deliverable location. The increased accessibility to foods may have implications on the consumption of healthful or unhealthful foods. Concerningly, previous research suggests that OFDS offer an abundance of energy-dense and nutrient-poor foods, which are heavily promoted through deals or discounts. Objective: In this paper, we describe the development of the DIGIFOOD dashboard to monitor the digitalization of local food environments in New South Wales, Australia, resulting from the proliferation of OFDS. Methods: Together with a team of data scientists, we designed a purpose-built dashboard using Microsoft Power BI. The development process involved three main stages: (1) data acquisition of food outlets via web scraping, (2) data cleaning and processing, and (3) visualization of food outlets on the dashboard. We also describe the categorization process of food outlets to characterize the healthfulness of local, online, and hybrid food environments. These categories included takeaway franchises, independent takeaways, independent restaurants and cafes, supermarkets or groceries, bakeries, alcohol retailers, convenience stores, and sandwich or salad shops. Results: To date, the DIGIFOOD dashboard has mapped 36,967 unique local food outlets (locally accessible and scraped from Google Maps) and 16,158 unique online food outlets (accessible online and scraped from Uber Eats) across New South Wales, Australia. In 2023, the market-leading OFDS operated in 1061 unique suburbs or localities in New South Wales. The Sydney-Parramatta region, a major urban area in New South Wales accounting for 28 postcodes, recorded the highest number of online food outlets (n=4221). In contrast, the Far West and Orana region, a rural area in New South Wales with only 2 postcodes, recorded the lowest number of food outlets accessible online (n=7). Urban areas appeared to have the greatest increase in total food outlets accessible via online food delivery. In both local and online food environments, it was evident that independent restaurants and cafes comprised the largest proportion of food outlets at 47.2% (17,437/36,967) and 51.8% (8369/16,158), respectively. However, compared to local food environments, the online food environment has relatively more takeaway franchises (2734/16,158, 16.9% compared to 3273/36,967, 8.9%) and independent takeaway outlets (2416/16,158, 14.9% compared to 4026/36,967, 10.9%). Conclusions: The DIGIFOOD dashboard leverages the current rich data landscape to display and contrast the availability and healthfulness of food outlets that are locally accessible versus accessible online. The DIGIFOOD dashboard can be a useful monitoring tool for the evolving digital food environment at a regional scale and has the potential to be scaled up at a national level. Future iterations of the dashboard, including data from additional prominent OFDS, can be used by policy makers to identify high-priority areas with limited access to healthful foods both online and locally. %M 39137032 %R 10.2196/59924 %U https://publichealth.jmir.org/2024/1/e59924 %U https://doi.org/10.2196/59924 %U http://www.ncbi.nlm.nih.gov/pubmed/39137032 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e59604 %T Molecular Evolutionary Dynamics of Coxsackievirus A6 Causing Hand, Foot, and Mouth Disease From 2021 to 2023 in China: Genomic Epidemiology Study %A Chen,Yu %A Chen,Shouhang %A Shen,Yuanfang %A Li,Zhi %A Li,Xiaolong %A Zhang,Yaodong %A Zhang,Xiaolong %A Wang,Fang %A Jin,Yuefei %K coxsackievirus A6 %K hand, foot, and mouth disease %K evolution %K molecular epidemiology %K China %K CV-A6 %K HFMD %D 2024 %7 31.7.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Hand, foot, and mouth disease (HFMD) is a global public health concern, notably within the Asia-Pacific region. Recently, the primary pathogen causing HFMD outbreaks across numerous countries, including China, is coxsackievirus (CV) A6, one of the most prevalent enteroviruses in the world. It is a new variant that has undergone genetic recombination and evolution, which might not only induce modifications in the clinical manifestations of HFMD but also heighten its pathogenicity because of nucleotide mutation accumulation. Objective: The study assessed the epidemiological characteristics of HFMD in China and characterized the molecular epidemiology of the major pathogen (CV-A6) causing HFMD. We attempted to establish the association between disease progression and viral genetic evolution through a molecular epidemiological study. Methods: Surveillance data from the Chinese Center for Disease Control and Prevention from 2021 to 2023 were used to analyze the epidemiological seasons and peaks of HFMD in Henan, China, and capture the results of HFMD pathogen typing. We analyzed the evolutionary characteristics of all full-length CV-A6 sequences in the NCBI database and the isolated sequences in Henan. To characterize the molecular evolution of CV-A6, time-scaled tree and historical population dynamics regarding CV-A6 sequences were estimated. Additionally, we analyzed the isolated strains for mutated or missing amino acid sites compared to the prototype CV-A6 strain. Results: The 2021-2023 epidemic seasons for HFMD in Henan usually lasted from June to August, with peaks around June and July. The monthly case reporting rate during the peak period ranged from 20.7% (4854/23,440) to 35% (12,135/34,706) of the total annual number of cases. Analysis of the pathogen composition of 2850 laboratory-confirmed cases identified 8 enterovirus serotypes, among which CV-A6 accounted for the highest proportion (652/2850, 22.88%). CV-A6 emerged as the major pathogen for HFMD in 2022 (203/732, 27.73%) and 2023 (262/708, 37.01%). We analyzed all CV-A6 full-length sequences in the NCBI database and the evolutionary features of viruses isolated in Henan. In China, the D3 subtype gradually appeared from 2011, and by 2019, all CV-A6 virus strains belonged to the D3 subtype. The VP1 sequences analyzed in Henan showed that its subtypes were consistent with the national subtypes. Furthermore, we analyzed the molecular evolutionary features of CV-A6 using Bayesian phylogeny and found that the most recent common ancestor of CV-A6 D3 dates back to 2006 in China, earlier than the 2011 HFMD outbreak. Moreover, the strains isolated in 2023 had mutations at several amino acid sites compared to the original strain. Conclusions: The CV-A6 virus may have been introduced and circulating covertly within China prior to the large-scale HFMD outbreak. Our laboratory testing data confirmed the fluctuation and periodic patterns of CV-A6 prevalence. Our study provides valuable insights into understanding the evolutionary dynamics of CV-A6. %R 10.2196/59604 %U https://publichealth.jmir.org/2024/1/e59604 %U https://doi.org/10.2196/59604 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e45030 %T Contraceptive Use Measured in a National Population–Based Approach: Cross-Sectional Study of Administrative Versus Survey Data %A Congy,Juliette %A Rahib,Delphine %A Leroy,Céline %A Bouyer,Jean %A de La Rochebrochard,Elise %+ Sexual and Reproductive Health and Rights Unit, Institut National d'Etudes Démographiques, 9 Cours des Humanités, Aubervilliers, 93300, France, 33 616448773, congyjuliette@gmail.com %K contraception %K administrative data %K health data %K implant %K oral contraceptives %K intrauterine device %K IUD %K contraceptive prevalence %K contraceptive %K birth control %K monitoring %K public health issue %K population-based survey %K prevalence %D 2024 %7 22.7.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Prescribed contraception is used worldwide by over 400 million women of reproductive age. Monitoring contraceptive use is a major public health issue that usually relies on population-based surveys. However, these surveys are conducted on average every 6 years and do not allow close follow-up of contraceptive use. Moreover, their sample size is often too limited for the study of specific population subgroups such as people with low income. Health administrative data could be an innovative and less costly source to study contraceptive use. Objective: We aimed to explore the potential of health administrative data to study prescribed contraceptive use and compare these data with observations based on survey data. Methods: We selected all women aged 15-49 years, covered by French health insurance and living in France, in the health administrative database, which covers 98% of the resident population (n=14,788,124), and in the last French population–based representative survey, the Health Barometer Survey, conducted in 2016 (n=4285). In health administrative data, contraceptive use was recorded with detailed information on the product delivered, whereas in the survey, it was self-declared by the women. In both sources, the prevalence of contraceptive use was estimated globally for all prescribed contraceptives and by type of contraceptive: oral contraceptives, intrauterine devices (IUDs), and implants. Prevalences were analyzed by age. Results: There were more low-income women in health administrative data than in the population-based survey (1,576,066/14,770,256, 11% vs 188/4285, 7%, respectively; P<.001). In health administrative data, 47.6% (7034,710/14,770,256; 95% CI 47.6%-47.7%) of women aged 15-49 years used a prescribed contraceptive versus 50.5% (2297/4285; 95% CI 49.1%-52.0%) in the population-based survey. Considering prevalences by the type of contraceptive in health administrative data versus survey data, they were 26.9% (95% CI 26.9%-26.9%) versus 27.7% (95% CI 26.4%-29.0%) for oral contraceptives, 17.7% (95% CI 17.7%-17.8%) versus 19.6% (95% CI 18.5%-20.8%) for IUDs, and 3% (95% CI 3.0%-3.0%) versus 3.2% (95% CI 2.7%-3.7%) for implants. In both sources, the same overall tendency in prevalence was observed for these 3 contraceptives. Implants remained little used at all ages, oral contraceptives were highly used among young women, whereas IUD use was low among young women. Conclusions: Compared with survey data, health administrative data exhibited the same overall tendencies for oral contraceptives, IUDs, and implants. One of the main strengths of health administrative data is the high quality of information on contraceptive use and the large number of observations, allowing studies of subgroups of population. Health administrative data therefore appear as a promising new source to monitor contraception in a population-based approach. They could open new perspectives for research and be a valuable new asset to guide public policies on reproductive and sexual health. %M 39037774 %R 10.2196/45030 %U https://publichealth.jmir.org/2024/1/e45030 %U https://doi.org/10.2196/45030 %U http://www.ncbi.nlm.nih.gov/pubmed/39037774 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e53927 %T Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review %A Maaß,Laura %A Angoumis,Konstantinos %A Freye,Merle %A Pan,Chen-Chia %+ University of Bremen, SOCIUM Research Center on Inequality and Social Policy, Mary-Somerville-Straße 3, Bremen, 28359, Germany, 49 421 218 58610, laura.maass@uni-bremen.de %K digital public health %K digital health %K public health %K telemedicine %K electronic health records %K e-prescription %K e-referral %K e-consultation %K e-surveillance %K e-vaccination registries %K scoping review %D 2024 %7 17.7.2024 %9 Review %J J Med Internet Res %G English %X Background: The rapid progression and integration of digital technologies into public health have reshaped the global landscape of health care delivery and disease prevention. In pursuit of better population health and health care accessibility, many countries have integrated digital interventions into their health care systems, such as web-based consultations, electronic health records, and telemedicine. Despite the increasing prevalence and relevance of digital technologies in public health and their varying definitions, there has been a shortage of studies examining whether these technologies align with the established definition and core characteristics of digital public health (DiPH) interventions. Hence, the imperative need for a scoping review emerges to explore the breadth of literature dedicated to this subject. Objective: This scoping review aims to outline DiPH interventions from different implementation stages for health promotion, primary to tertiary prevention, including health care and disease surveillance and monitoring. In addition, we aim to map the reported intervention characteristics, including their technical features and nontechnical elements. Methods: Original studies or reports of DiPH intervention focused on population health were eligible for this review. PubMed, Web of Science, CENTRAL, IEEE Xplore, and the ACM Full-Text Collection were searched for relevant literature (last updated on October 5, 2022). Intervention characteristics of each identified DiPH intervention, such as target groups, level of prevention or health care, digital health functions, intervention types, and public health functions, were extracted and used to map DiPH interventions. MAXQDA 2022.7 (VERBI GmbH) was used for qualitative data analysis of such interventions’ technical functions and nontechnical characteristics. Results: In total, we identified and screened 15,701 records, of which 1562 (9.94%) full texts were considered relevant and were assessed for eligibility. Finally, we included 185 (11.84%) publications, which reported 179 different DiPH interventions. Our analysis revealed a diverse landscape of interventions, with telemedical services, health apps, and electronic health records as dominant types. These interventions targeted a wide range of populations and settings, demonstrating their adaptability. The analysis highlighted the multifaceted nature of digital interventions, necessitating precise definitions and standardized terminologies for effective collaboration and evaluation. Conclusions: Although this scoping review was able to map characteristics and technical functions among 13 intervention types in DiPH, emerging technologies such as artificial intelligence might have been underrepresented in our study. This review underscores the diversity of DiPH interventions among and within intervention groups. Moreover, it highlights the importance of precise terminology for effective planning and evaluation. This review promotes cross-disciplinary collaboration by emphasizing the need for clear definitions, distinct technological functions, and well-defined use cases. It lays the foundation for international benchmarks and comparability within DiPH systems. Further research is needed to map intervention characteristics in this still-evolving field continuously. Trial Registration: PROSPERO CRD42021265562; https://tinyurl.com/43jksb3k International Registered Report Identifier (IRRID): RR2-10.2196/33404 %M 39018096 %R 10.2196/53927 %U https://www.jmir.org/2024/1/e53927 %U https://doi.org/10.2196/53927 %U http://www.ncbi.nlm.nih.gov/pubmed/39018096 %0 Journal Article %@ 2369-2960 %I %V 10 %N %P e54551 %T Syndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study %A Wass,Lily Minh %A O'Keeffe Hoare,Derek %A Smits,Georgia Elena %A Osman,Marwan %A Zhang,Ning %A Klepack,William %A Parrilla,Lara %A Busche,Jefferson M %A Clarkberg,Marin E %A Basu,Sumanta %A Cazer,Casey L %K COVID-19 %K epidemiology %K epidemiological %K SARS-CoV-2 %K syndromic surveillance %K surveillance system %K syndromic %K surveillance %K coronavirus %K pandemic %K epidemic %K respiratory %K infectious %K predict %K predictive %K prediction %K predictions %D 2024 %7 27.6.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19–like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing. Objective: This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider–reported CLI in university and county settings, respectively. Methods: We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020‐2021) and Tompkins County Health Department (2020‐2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests. Results: In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider–reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005). Conclusions: The real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries. %R 10.2196/54551 %U https://publichealth.jmir.org/2024/1/e54551 %U https://doi.org/10.2196/54551 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 16 %N %P e52686 %T The Association of Broadband Internet Use With Drug Overdose Mortality Rates in the United States: Cross-Sectional Analysis %A Karakis,Ioannis %A Kostandini,Genti %A Tsamakis,Konstantinos %A Zahirovic-Herbert,Velma %+ Department of Neurology, Emory University School of Medicine, 49 Jesse Hill Jr Dr SE, Atlanta, GA, 30303, United States, 1 404 727 6123, ioannis.karakis@emory.edu %K opioids %K broadband internet %K mortality %K public health %K digital divide %K access %K availability %K causal %K association %K correlation %K overdose %K drug abuse %K addiction %K substance abuse %K demographic %K United States %K population %D 2024 %7 26.6.2024 %9 Original Paper %J Online J Public Health Inform %G English %X Background: The availability and use of broadband internet play an increasingly important role in health care and public health. Objective: This study examined the associations between broadband internet availability and use with drug overdose deaths in the United States. Methods: We linked 2019 county-level drug overdose death data in restricted-access multiple causes of death files from the National Vital Statistics System at the US Centers for Disease Control and Prevention with the 2019 county-level broadband internet rollout data from the Federal Communications Commission and the 2019 county-level broadband usage data available from Microsoft’s Airband Initiative. Cross-sectional analysis was performed with the fixed-effects regression method to assess the association of broadband internet availability and usage with opioid overdose deaths. Our model also controlled for county-level socioeconomic characteristics and county-level health policy variables. Results: Overall, a 1% increase in broadband internet use was linked with a 1.2% increase in overall drug overdose deaths. No significant association was observed for broadband internet availability. Although similar positive associations were found for both male and female populations, the association varied across different age subgroups. The positive association on overall drug overdose deaths was the greatest among Hispanic and Non-Hispanic White populations. Conclusions: Broadband internet use was positively associated with increased drug overdose deaths among the overall US population and some subpopulations, even after controlling for broadband availability, sociodemographic characteristics, unemployment, and median household income. %M 38922664 %R 10.2196/52686 %U https://ojphi.jmir.org/2024/1/e52686 %U https://doi.org/10.2196/52686 %U http://www.ncbi.nlm.nih.gov/pubmed/38922664 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e54250 %T Development of a Consolidated Health Facility Masterlist Using Data From Polio Electronic Surveillance in the World Health Organization African Region %A Babona Nshuti,Marie Aimee %A Touray,Kebba %A Muluh,Ticha Johnson %A Ubong,Godwin Akpan %A Ngofa,Reuben Opara %A Mohammed,Bello Isa %A Roselyne,Ishimwe %A Oviaesu,David %A Bakata,Evans Mawa Oliver %A Lau,Fiona %A Kipterer,John %A Green,Hugh Henry W %A Seaman,Vincent %A Ahmed,Jamal A %A Ndoutabe,Modjirom %+ World Health Organization Regional Office for Africa, Cite du Djoué, Bacongo, PO Box 06, Brazzaville, Congo, 242 069248040, shutaime03@gmail.com %K African region %K electronic surveillance %K geographic information systems %K Global Polio Eradication Initiative %K integrated supportive supervision %K polio %D 2024 %7 21.6.2024 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Geospatial data reporting from surveillance and immunization efforts is a key aspect of the World Health Organization (WHO) Global Polio Eradication Initiative in Africa. These activities are coordinated through the WHO Regional Office for Africa Geographic Information Systems Centre. To ensure the accuracy of field-collected data, the WHO Regional Office for Africa Geographic Information Systems Centre has developed mobile phone apps such as electronic surveillance (eSURV) and integrated supportive supervision (ISS) geospatial data collection programs. While eSURV and ISS have played a vital role in efforts to eradicate polio and control other communicable diseases in Africa, disease surveillance efforts have been hampered by incomplete and inaccurate listings of health care sites throughout the continent. To address this shortcoming, data compiled from eSURV and ISS are being used to develop, update, and validate a Health Facility master list for the WHO African region that contains comprehensive listings of the names, locations, and types of health facilities in each member state. The WHO and Ministry of Health field officers are responsible for documenting and transmitting the relevant geospatial location information regarding health facilities and traditional medicine sites using the eSURV and ISS form; this information is then used to update the Health Facility master list and is also made available to national ministries of health to update their respective health facility lists. This consolidation of health facility information into a single registry is expected to improve disease surveillance and facilitate epidemiologic research for the Global Polio Eradication Initiative, as well as aid public health efforts directed at other diseases across the African continent. This review examines active surveillance using eSURV at the district, country, and regional levels, highlighting its role in supporting polio surveillance and immunization efforts, as well as its potential to serve as a fundamental basis for broader public health initiatives and research throughout Africa. %M 38904997 %R 10.2196/54250 %U https://publichealth.jmir.org/2024/1/e54250 %U https://doi.org/10.2196/54250 %U http://www.ncbi.nlm.nih.gov/pubmed/38904997 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53219 %T Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in the Middle East and North Africa: Longitudinal Trend Analysis %A Soetikno,Alan G %A Lundberg,Alexander L %A Ozer,Egon A %A Wu,Scott A %A Welch,Sarah B %A Mason,Maryann %A Liu,Yingxuan %A Havey,Robert J %A Murphy,Robert L %A Hawkins,Claudia %A Moss,Charles B %A Post,Lori Ann %+ Buehler Center for Health Policy and Economics, Robert J. Havey, MD Institute for Global Health, Northwestern University, 420 E. Superior, Chicago, IL, 60611, United States, 1 3125031706, lori.post@northwestern.edu %K SARS-CoV-2 %K COVID-19 %K Middle East %K North Africa %K Bahrain %K Iran %K Iraq %K Israel %K Jordan %K Kuwait %K Lebanon %K Oman %K Qatar %K Saudi Arabia %K Syria %K the United Arab Emirates %K Yemen %K Algeria %K Djibouti %K Egypt %K Libya %K Morocco %K Tunisia %K pandemic history %K COVID-19 transmission %K speed %K acceleration %K deceleration %K jerk %K dynamic panel %K generalized method of moments %K Arellano-Bond %K 7-day lag %D 2024 %7 12.6.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: This study updates the COVID-19 pandemic surveillance in the Middle East and North Africa (MENA) we first conducted in 2020 with 2 additional years of data for the region. Objective: The objective of this study is to determine whether the MENA region meets the criteria for moving from a pandemic to endemic. In doing so, this study considers pandemic trends, dynamic and genomic surveillance methods, and region-specific historical context for the pandemic. These considerations continue through the World Health Organization (WHO) declaration of the end of the public health emergency for the COVID-19 pandemic on May 5, 2023. Methods: In addition to updates to traditional surveillance data and dynamic panel estimates from the original study by Post et al, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional weekly speed of COVID-19 spread was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data from September 4, 2020, to May 12, 2023. Results: The speed of COVID-19 spread for the region had remained below the outbreak threshold for 7 continuous months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant and positive, the weekly shift parameters suggested the coefficients had most recently turned negative, meaning the clustering effect of new COVID-19 cases became even smaller in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of the speed of spread equal to 10 became entirely insignificant from October 2022 onward. Conclusions: The COVID-19 pandemic had far-reaching effects on MENA, impacting health care systems, economies, and social well-being. Although COVID-19 continues to circulate in the MENA region, the rate of transmission remained well below the threshold of an outbreak for over 1 year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. Both standard and enhanced surveillance metrics confirm that the pandemic had transitioned to endemic by the time of the WHO declaration. %M 38568184 %R 10.2196/53219 %U https://publichealth.jmir.org/2024/1/e53219 %U https://doi.org/10.2196/53219 %U http://www.ncbi.nlm.nih.gov/pubmed/38568184 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e49562 %T Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach %A Azizi,Mehrnoosh %A Jamali,Ali Akbar %A Spiteri,Raymond J %+ Department of Computer Science, University of Saskatchewan, S425 Thorvaldson Building, 110 Science Place, Saskatoon, SK, S7N5C9, Canada, 1 306 966 2909, spiteri@cs.usask.ca %K machine learning %K dementia %K Alzheimer disease %K COVID-19 %K X (Twitter) %K natural language processing %D 2024 %7 4.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: During the pandemic, patients with dementia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, therefore, identifying posts (formerly tweets) relevant to dementia can be an important support for patients with dementia and their caregivers. However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. Objective: The objective of this study was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. Methods: We used a combination of natural language processing and ML algorithms with manually annotated posts to identify posts relevant to dementia and COVID-19. We used 3 data sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. Results: Our results showed that (pretrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms tested, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an area under the curve of 83.53%. ALBERT substantially outperformed the other algorithms tested, further emphasizing the superior performance of transfer learning algorithms in the classification of posts. Conclusions: Transfer learning algorithms such as ALBERT are highly effective in identifying topic-specific posts, even when trained with limited or adjacent data, highlighting their superiority over other ML algorithms and applicability to other studies involving analysis of social media posts. Such an automated approach reduces the workload of manual coding of posts and facilitates their analysis for researchers and policy makers to support patients with dementia and their caregivers and other vulnerable populations. %M 38833288 %R 10.2196/49562 %U https://formative.jmir.org/2024/1/e49562 %U https://doi.org/10.2196/49562 %U http://www.ncbi.nlm.nih.gov/pubmed/38833288 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e51191 %T Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study %A García-García,David %A Fernández-Martínez,Beatriz %A Bartumeus,Frederic %A Gómez-Barroso,Diana %+ Department of Communicable Diseases, National Centre of Epidemiology, Instituto de Salud Carlos III, Monforte de Lemos 5, Madrid, 28029, Spain, 34 91 822 26 46, dawex14@gmail.com %K epidemiology %K imported infections %K modeling %K surveillance system %K vector-borne diseases %D 2024 %7 27.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Understanding the patterns of disease importation through international travel is paramount for effective public health interventions and global disease surveillance. While global airline network data have been used to assist in outbreak prevention and effective preparedness, accurately estimating how these imported cases disseminate locally in receiving countries remains a challenge. Objective: This study aimed to describe and understand the regional distribution of imported cases of dengue and malaria upon arrival in Spain via air travel. Methods: We have proposed a method to describe the regional distribution of imported cases of dengue and malaria based on the computation of the “travelers’ index” from readily available socioeconomic data. We combined indicators representing the main drivers for international travel, including tourism, economy, and visits to friends and relatives, to measure the relative appeal of each region in the importing country for travelers. We validated the resulting estimates by comparing them with the reported cases of malaria and dengue in Spain from 2015 to 2019. We also assessed which motivation provided more accurate estimates for imported cases of both diseases. Results: The estimates provided by the best fitted model showed high correlation with notified cases of malaria (0.94) and dengue (0.87), with economic motivation being the most relevant for imported cases of malaria and visits to friends and relatives being the most relevant for imported cases of dengue. Conclusions: Factual descriptions of the local movement of international travelers may substantially enhance the design of cost-effective prevention policies and control strategies, and essentially contribute to decision-support systems. Our approach contributes in this direction by providing a reliable estimate of the number of imported cases of nonendemic diseases, which could be generalized to other applications. Realistic risk assessments will be obtained by combining this regional predictor with the observed local distribution of vectors. %M 38801767 %R 10.2196/51191 %U https://publichealth.jmir.org/2024/1/e51191 %U https://doi.org/10.2196/51191 %U http://www.ncbi.nlm.nih.gov/pubmed/38801767 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e39297 %T A Profile of Influenza Vaccine Coverage for 2019-2020: Database Study of the English Primary Care Sentinel Cohort %A Hoang,Uy %A Delanerolle,Gayathri %A Fan,Xuejuan %A Aspden,Carole %A Byford,Rachel %A Ashraf,Mansoor %A Haag,Mendel %A Elson,William %A Leston,Meredith %A Anand,Sneha %A Ferreira,Filipa %A Joy,Mark %A Hobbs,Richard %A de Lusignan,Simon %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Eagle House, 7 Walton Well Road, Oxford, OX2 6ED, United Kingdom, 44 01865 289 344, simon.delusignan@phc.ox.ac.uk %K medical records systems %K computerize %K influenza %K influenza vaccines %K sentinel surveillance %K vocabulary controlled %K general practitioners %K general practice %K primary health care %K vaccine %K public health %K surveillance %K uptake %D 2024 %7 24.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks. Objective: This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC). Methods: We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency’s national data. We used paired t tests to compare populations, reported with 95% CI. Results: The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5%; 95% CI 42.5%-42.6%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3%; 95% CI 4.12%-41.3%; P<.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7% (1,731,062/7,010,627; 95% CI 24.7%-24.7%) of people of all ages received an influenza vaccine compared with 24.2% (14,468,665/59,764,928; 95% CI 24.2%-24.2%; P<.001) in national data. The highest coverage was in people aged ≥65 years (913,695/1,264,700, 72.3%; 95% CI 72.2%-72.3%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8% (284,280/407,228; 95% CI 69.7%-70%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2% (983,727/1,607,996; 95% CI 61.1%-61.3%; P<.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8% (358,365/498,923; 95% CI 71.7%-72%) of people aged 16-64 years and 81.9% (748,312/913,695; 95% CI 81.8%-82%) of people aged ≥65 years, compared with 23.6% (696,880/2,900,000) and 17.8% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays. Conclusions: The PCSC’s sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage. %M 38787605 %R 10.2196/39297 %U https://publichealth.jmir.org/2024/1/e39297 %U https://doi.org/10.2196/39297 %U http://www.ncbi.nlm.nih.gov/pubmed/38787605 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e44398 %T Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Latin America and the Caribbean: Longitudinal Trend Analysis %A Post,Lori Ann %A Wu,Scott A %A Soetikno,Alan G %A Ozer,Egon A %A Liu,Yingxuan %A Welch,Sarah B %A Hawkins,Claudia %A Moss,Charles B %A Murphy,Robert L %A Mason,Maryann %A Havey,Robert J %A Lundberg,Alexander L %+ Buehler Center for Health Policy and Economics, Robert J Havey, MD Institute for Global Health, Northwestern University, 420 E Superior, Chicago, IL, 60611, United States, 1 312 503 1706, lori.post@northwestern.edu %K SARS-CoV-2 %K COVID-19 %K Latin America %K Caribbean %K pandemic %K surveillance %K COVID-19 transmission %K speed %K acceleration %K deceleration %K jerk %K dynamic panel %K generalized method of moments %K GMM %K Arellano-Bond %K 7-day lag %K epidemiological %K pandemic %K genomic %K transmission %D 2024 %7 17.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In May 2020, the World Health Organization (WHO) declared Latin America and the Caribbean (LAC) the epicenter of the COVID-19 pandemic, with over 40% of worldwide COVID-19–related deaths at the time. This high disease burden was a result of the unique circumstances in LAC. Objective: This study aimed to (1) measure whether the pandemic was expanding or contracting in LAC when the WHO declared the end of COVID-19 as a public health emergency of international concern on May 5, 2023; (2) use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history; and (3) provide, with a focus on prevention policies, a historical context for the course of the pandemic in the region. Methods: In addition to updates of traditional surveillance data and dynamic panel estimates from the original study, we used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern (VOCs). We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Additionally, we conducted a 1-sided t test for whether the regional weekly speed (rate of novel COVID-19 transmission) was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the period from August 2020 to May 2023. Results: The speed of pandemic spread for the region had remained below the outbreak threshold for 6 months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant for the 120-day period ending on the week of May 5, 2023, the coefficients were relatively modest in magnitude (0.457 and 0.491, respectively). Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, did not indicate any change in this clustering effect of cases on future cases. From December 2021 onward, Omicron was the predominant VOC in sequenced viral samples. The rolling t test of speed=10 became entirely insignificant from January 2023 onward. Conclusions: Although COVID-19 continues to circulate in LAC, surveillance data suggest COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. However, the region experienced a high COVID-19 burden in the early stages of the pandemic, and prevention policies should be an immediate focus in future pandemics. Ahead of vaccination development, these policies can include widespread testing of individuals and an epidemiological task force with a contact-tracing system. %M 38568194 %R 10.2196/44398 %U https://publichealth.jmir.org/2024/1/e44398 %U https://doi.org/10.2196/44398 %U http://www.ncbi.nlm.nih.gov/pubmed/38568194 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e52843 %T National Public Health Dashboards: Protocol for a Scoping Review %A Yanovitzky,Itzhak %A Stahlman,Gretchen %A Quow,Justine %A Ackerman,Matthew %A Perry,Yehuda %A Kim,Miriam %+ School of Communication & Information, Rutgers University, 4 Huntington St, New Brunswick, NJ, 08901, United States, 1 848 932 8852, itzhak@rutgers.edu %K dashboard %K scoping review %K public health %K design %K development %K implementation %K evaluation %K user need %K protocol %K data dashboards %K audiences %K audience %K systematic treatment %K public health data dashboards %K PRISMA-ScR %K snowballing techniques %K gray literature sources %K evidence-informed framework %K framework %K COVID-19 %K pandemic %D 2024 %7 16.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The COVID-19 pandemic highlighted the importance of robust public health data systems and the potential utility of data dashboards for ensuring access to critical public health data for diverse groups of stakeholders and decision makers. As dashboards are becoming ubiquitous, it is imperative to consider how they may be best integrated with public health data systems and the decision-making routines of diverse audiences. However, additional progress on the continued development, improvement, and sustainability of these tools requires the integration and synthesis of a largely fragmented scholarship regarding the purpose, design principles and features, successful implementation, and decision-making supports provided by effective public health data dashboards across diverse users and applications. Objective: This scoping review aims to provide a descriptive and thematic overview of national public health data dashboards including their purpose, intended audiences, health topics, design elements, impact, and underlying mechanisms of use and usefulness of these tools in decision-making processes. It seeks to identify gaps in the current literature on the topic and provide the first-of-its-kind systematic treatment of actionability as a critical design element of public health data dashboards. Methods: The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The review considers English-language, peer-reviewed journal papers, conference proceedings, book chapters, and reports that describe the design, implementation, and evaluation of a public health dashboard published between 2000 and 2023. The search strategy covers scholarly databases (CINAHL, PubMed, Medline, and Web of Science) and gray literature sources and uses snowballing techniques. An iterative process of testing for and improving intercoder reliability was implemented to ensure that coders are properly trained to screen documents according to the inclusion criteria prior to beginning the full review of relevant papers. Results: The search process initially identified 2544 documents, including papers located via databases, gray literature searching, and snowballing. Following the removal of duplicate documents (n=1416), nonrelevant items (n=839), and items classified as literature reviews and background information (n=73), 216 documents met the inclusion criteria: US case studies (n=90) and non-US case studies (n=126). Data extraction will focus on key variables, including public health data characteristics; dashboard design elements and functionalities; intended users, usability, logistics, and operation; and indicators of usefulness and impact reported. Conclusions: The scoping review will analyze the goals, design, use, usefulness, and impact of public health data dashboards. The review will also inform the continued development and improvement of these tools by analyzing and synthesizing current practices and lessons emerging from the literature on the topic and proposing a theory-grounded and evidence-informed framework for designing, implementing, and evaluating public health data dashboards. International Registered Report Identifier (IRRID): DERR1-10.2196/52843 %M 38753428 %R 10.2196/52843 %U https://www.researchprotocols.org/2024/1/e52843 %U https://doi.org/10.2196/52843 %U http://www.ncbi.nlm.nih.gov/pubmed/38753428 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e46029 %T Ambulance Services Attendance for Mental Health and Overdose Before and During COVID-19 in Canada and the United Kingdom: Interrupted Time Series Study %A Law,Graham %A Cooper,Rhiannon %A Pirrie,Melissa %A Ferron,Richard %A McLeod,Brent %A Spaight,Robert %A Siriwardena,A Niroshan %A Agarwal,Gina %A , %+ Department of Family Medicine, McMaster University, 100 Main Street West, Hamilton, ON, , Canada, 1 905 525 9140, gina.agarwal@gmail.com %K COVID-19 %K mental health %K overdose %K emergency medical services %K administrative data %K Canada %K the United Kingdom %K ambulance %K sex %K age %K lockdown %K pandemic planning %K emergency service %D 2024 %7 10.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic impacted mental health and health care systems worldwide. Objective: This study examined the COVID-19 pandemic’s impact on ambulance attendances for mental health and overdose, comparing similar regions in the United Kingdom and Canada that implemented different public health measures. Methods: An interrupted time series study of ambulance attendances was conducted for mental health and overdose in the United Kingdom (East Midlands region) and Canada (Hamilton and Niagara regions). Data were obtained from 182,497 ambulance attendance records for the study period of December 29, 2019, to August 1, 2020. Negative binomial regressions modeled the count of attendances per week per 100,000 population in the weeks leading up to the lockdown, the week the lockdown was initiated, and the weeks following the lockdown. Stratified analyses were conducted by sex and age. Results: Ambulance attendances for mental health and overdose had very small week-over-week increases prior to lockdown (United Kingdom: incidence rate ratio [IRR] 1.002, 95% CI 1.002-1.003 for mental health). However, substantial changes were observed at the time of lockdown; while there was a statistically significant drop in the rate of overdose attendances in the study regions of both countries (United Kingdom: IRR 0.573, 95% CI 0.518-0.635 and Canada: IRR 0.743, 95% CI 0.602-0.917), the rate of mental health attendances increased in the UK region only (United Kingdom: IRR 1.125, 95% CI 1.031-1.227 and Canada: IRR 0.922, 95% CI 0.794-1.071). Different trends were observed based on sex and age categories within and between study regions. Conclusions: The observed changes in ambulance attendances for mental health and overdose at the time of lockdown differed between the UK and Canada study regions. These results may inform future pandemic planning and further research on the public health measures that may explain observed regional differences. %M 38728683 %R 10.2196/46029 %U https://publichealth.jmir.org/2024/1/e46029 %U https://doi.org/10.2196/46029 %U http://www.ncbi.nlm.nih.gov/pubmed/38728683 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e49769 %T Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis %A Iqbal,Fahad Mujtaba %A Aggarwal,Ravi %A Joshi,Meera %A King,Dominic %A Martin,Guy %A Khan,Sadia %A Wright,Mike %A Ashrafian,Hutan %A Darzi,Ara %+ Division of Surgery, Imperial College London, 10th Floor QEQM Building, Praed Street, London, W2 1NY, United Kingdom, 44 020 3312 6666, fahad.iqbal@doctors.org.uk %K implementation science %K health plan implementation %K mobile health %K health care industry %K stakeholder %K COVID-19 %K remote monitoring %K digital tools %K digital health %K pandemic %K virtual wards %K virtual ward %K health care delivery %K telemedicine %K telehealth %K wearables %K wearable %K technology %K United Kingdom %K UK %K digital services %D 2024 %7 6.5.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Implementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems–based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken. Objective: We aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations. Methods: A mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed. Results: A total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology). Conclusions: The recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of “virtual wards.” Trial Registration: ClinicalTrials.gov NCT05321004; https://www.clinicaltrials.gov/study/NCT05321004 %M 37338929 %R 10.2196/49769 %U https://humanfactors.jmir.org/2024/1/e49769 %U https://doi.org/10.2196/49769 %U http://www.ncbi.nlm.nih.gov/pubmed/37338929 %0 Journal Article %@ 2563-6316 %I %V 5 %N %P e55976 %T Thyroid Hyperplasia and Neoplasm Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists in the Food and Drug Administration Adverse Event Reporting System: Retrospective Analysis %A Makunts,Tigran %A Joulfayan,Haroutyun %A Abagyan,Ruben %K GLP-1 %K FDA %K averse event reporting %K cancer %K oncology %K neoplasm %K drugs %K pharmacy %K pharmacology %K pharmaceutics %K medication %K medications %K glucagon-like peptide-1 %K Food and Drug Administration %K weight loss %K diabetes %K obesity %K thyroid hyperplasia %K FAERS %K FDA Adverse Event Reporting System %D 2024 %7 1.5.2024 %9 %J JMIRx Med %G English %X Background: Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are one of the most commonly used drugs for type 2 diabetes mellitus. Clinical guidelines recommend GLP-1 RAs as an adjunct to diabetes therapy in patients with chronic kidney disease, presence or risk of atherosclerotic cardiovascular disease, and obesity. The weight loss observed in clinical trials has been explored further in healthy individuals, putting GLP-1 RAs on track to be the next weight loss treatment. Objective: Although the adverse event profile is relatively safe, most GLP-1 RAs come with a labeled boxed warning for the risk of thyroid cancers, based on animal models and some postmarketing case reports in humans. Considering the increasing popularity of this drug class and its expansion into a new popular indication, a further review of the most recent postmarketing safety data was warranted to quantify thyroid hyperplasia and neoplasm instances. Methods: GLP-1 RA patient reports from the US Food and Drug Administration (FDA) Adverse Event Reporting System database were analyzed using reporting odds ratios and 95% CIs. Results: In this study, we analyzed over 18 million reports from the US FDA Adverse Event Reporting System and provided evidence of significantly increased propensity for thyroid hyperplasias and neoplasms in patients taking GLP-1 RA monotherapy when compared to patients taking sodium-glucose cotransporter-2 (SGLT-2) inhibitor monotherapy. Conclusions: GLP-1 RAs, regardless of indication, are associated with an over 10-fold increase in thyroid neoplasm and hyperplasia adverse event reporting when compared to SGLT-2 inhibitors. %R 10.2196/55976 %U https://xmed.jmir.org/2024/1/e55976 %U https://doi.org/10.2196/55976 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e49841 %T Defining the Subtypes of Long COVID and Risk Factors for Prolonged Disease: Population-Based Case-Crossover Study %A Resendez,Skyler %A Brown,Steven H %A Ruiz Ayala,Hugo Sebastian %A Rangan,Prahalad %A Nebeker,Jonathan %A Montella,Diane %A Elkin,Peter L %+ Department of Biomedical Informatics, University at Buffalo, State University of New York, 77 Goodell Street, Suite 540, Buffalo, NY, 14203, United States, 1 5073581341, elkinp@buffalo.edu %K long COVID %K PASC %K postacute sequelae of COVID-19 %K public health %K policy initiatives %K pandemic %K diagnosis %K COVID-19 treatment %K long COVID cause %K health care support %K public safety %K COVID-19 %K Veterans Affairs %K United States %K COVID-19 testing %K clinician %K mobile phone %D 2024 %7 30.4.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: There have been over 772 million confirmed cases of COVID-19 worldwide. A significant portion of these infections will lead to long COVID (post–COVID-19 condition) and its attendant morbidities and costs. Numerous life-altering complications have already been associated with the development of long COVID, including chronic fatigue, brain fog, and dangerous heart rhythms. Objective: We aim to derive an actionable long COVID case definition consisting of significantly increased signs, symptoms, and diagnoses to support pandemic-related clinical, public health, research, and policy initiatives. Methods: This research employs a case-crossover population-based study using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) data generated at Veterans Affairs medical centers nationwide between January 1, 2020, and August 18, 2022. In total, 367,148 individuals with ICD-10-CM data both before and after a positive COVID-19 test were selected for analysis. We compared ICD-10-CM codes assigned 1 to 7 months following each patient’s positive test with those assigned up to 6 months prior. Further, 350,315 patients had novel codes assigned during this window of time. We defined signs, symptoms, and diagnoses as being associated with long COVID if they had a novel case frequency of ≥1:1000, and they significantly increased in our entire cohort after a positive test. We present odds ratios with CIs for long COVID signs, symptoms, and diagnoses, organized by ICD-10-CM functional groups and medical specialty. We used our definition to assess long COVID risk based on a patient’s demographics, Elixhauser score, vaccination status, and COVID-19 disease severity. Results: We developed a long COVID definition consisting of 323 ICD-10-CM diagnosis codes grouped into 143 ICD-10-CM functional groups that were significantly increased in our 367,148 patient post–COVID-19 population. We defined 17 medical-specialty long COVID subtypes such as cardiology long COVID. Patients who were COVID-19–positive developed signs, symptoms, or diagnoses included in our long COVID definition at a proportion of at least 59.7% (268,320/449,450, based on a denominator of all patients who were COVID-19–positive). The long COVID cohort was 8 years older with more comorbidities (2-year Elixhauser score 7.97 in the patients with long COVID vs 4.21 in the patients with non–long COVID). Patients who had a more severe bout of COVID-19, as judged by their minimum oxygen saturation level, were also more likely to develop long COVID. Conclusions: An actionable, data-driven definition of long COVID can help clinicians screen for and diagnose long COVID, allowing identified patients to be admitted into appropriate monitoring and treatment programs. This long COVID definition can also support public health, research, and policy initiatives. Patients with COVID-19 who are older or have low oxygen saturation levels during their bout of COVID-19, or those who have multiple comorbidities should be preferentially watched for the development of long COVID. %M 38687984 %R 10.2196/49841 %U https://publichealth.jmir.org/2024/1/e49841 %U https://doi.org/10.2196/49841 %U http://www.ncbi.nlm.nih.gov/pubmed/38687984 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e52191 %T The Role of Trust as a Driver of Private-Provider Participation in Disease Surveillance: Cross-Sectional Survey From Nigeria %A Mitchell,Ellen MH %A Adejumo,Olusola Adedeji %A Abdur-Razzaq,Hussein %A Ogbudebe,Chidubem %A Gidado,Mustapha %+ Mycobacterial Diseases and Neglected Tropical Diseases Unit, Department of Public Health, Institute for Tropical Medicine, 155 Nationaalestraat, Roche, Antwerp, 2000, Belgium, 31 634790509, emitchell@itg.be %K surveillance %K trust %K Integrated Disease Surveillance and Response %K IDSR %K tuberculosis %K notification %K public-private mix %K infectious disease %K disease surveillance %K surveillance behavior %K health care worker %K health professional %K public health %K Nigeria %K survey %K behavior %K self-reported %D 2024 %7 25.4.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Recognition of the importance of valid, real-time knowledge of infectious disease risk has renewed scrutiny into private providers’ intentions, motives, and obstacles to comply with an Integrated Disease Surveillance Response (IDSR) framework. Appreciation of how private providers’ attitudes shape their tuberculosis (TB) notification behaviors can yield lessons for the surveillance of emerging pathogens, antibiotic stewardship, and other crucial public health functions. Reciprocal trust among actors and institutions is an understudied part of the “software” of surveillance. Objective: We aimed to assess the self-reported knowledge, motivation, barriers, and TB case notification behavior of private health care providers to public health authorities in Lagos, Nigeria. We measured the concordance between self-reported notification, TB cases found in facility records, and actual notifications received. Methods: A representative, stratified sample of 278 private health care workers was surveyed on TB notification attitudes, behavior, and perceptions of public health authorities using validated scales. Record reviews were conducted to identify the TB treatment provided and facility case counts were abstracted from the records. Self-reports were triangulated against actual notification behavior for 2016. The complex health system framework was used to identify potential predictors of notification behavior. Results: Noncompliance with the legal obligations to notify infectious diseases was not attributable to a lack of knowledge. Private providers who were uncomfortable notifying TB cases via the IDSR system scored lower on the perceived benevolence subscale of trust. Health care workers who affirmed “always” notifying via IDSR monthly reported higher median trust in the state’s public disease control capacity. Although self-reported notification behavior was predicted by age, gender, and positive interaction with public health bodies, the self-report numbers did not tally with actual TB notifications. Conclusions: Providers perceived both risks and benefits to recording and reporting TB cases. To improve private providers’ public health behaviors, policy makers need to transcend instrumental and transactional approaches to surveillance to include building trust in public health, simplifying the task, and enhancing the link to improved health. Renewed attention to the “software” of health systems (eg, norms, values, and relationships) is vital to address pandemic threats. Surveys with private providers may overestimate their actual participation in public health surveillance. %M 38506095 %R 10.2196/52191 %U https://publichealth.jmir.org/2024/1/e52191 %U https://doi.org/10.2196/52191 %U http://www.ncbi.nlm.nih.gov/pubmed/38506095 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e48784 %T Assessing SARS-CoV-2 Testing Adherence in a University Town: Recurrent Event Modeling Analysis %A García,Yury E %A Schmidt,Alec J %A Solis,Leslie %A Daza-Torres,María L %A Montesinos-López,J Cricelio %A Pollock,Brad H %A Nuño,Miriam %+ Department of Public Health Sciences, University of California, 1 Shields Avenue, Davis, CA, 95616, United States, 1 5056999560, mnuno@ucdavis.edu %K Healthy Davis Together %K COVID-19 %K COVID-19 surveillance program %K community surveillance %K HDT: HYT %K Healthy Yolo Together %K SARS-CoV-2 %K severe acute respiratory syndrome coronavirus 2 %K coronavirus %K demographic %K demographics %K testing %K adherence %K compliance %K USA %K United States %K response program %K response programs %K engagement %K participation %K infectious %K trend %K trends %K community based %K surveillance %K public health %K infection control %K PCR %K polymerase chain reaction %K RT-qPCR %K reverse transcription quantitative polymerase chain reaction %K viral %K virus %K viruses %D 2024 %7 17.4.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Healthy Davis Together was a program launched in September 2020 in the city of Davis, California, to mitigate the spread of COVID-19 and facilitate the return to normalcy. The program involved multiple interventions, including free saliva-based asymptomatic testing, targeted communication campaigns, education efforts, and distribution of personal protective equipment, community partnerships, and investments in the local economy. Objective: This study identified demographic characteristics of individuals that underwent testing and assessed adherence to testing over time in a community pandemic-response program launched in a college town in California, United States. Methods: This study outlines overall testing engagement, identifies demographic characteristics of participants, and evaluates testing participation changes over 4 periods of the COVID-19 pandemic, distinguished by the dominant variants Delta and Omicron. Additionally, a recurrent model is employed to explore testing patterns based on the participants’ frequency, timing, and demographic characteristics. Results: A total of 770,165 tests were performed between November 18, 2020, and June 30, 2022, among 89,924 (41.1% of total population) residents of Yolo County, with significant participation from racially or ethnically diverse participants and across age groups. Most positive cases (6351 of total) and highest daily participation (895 per 100,000 population) were during the Omicron period. There were some gender and age-related differences in the pattern of recurrent COVID-19 testing. Men were slightly less likely (hazard ratio [HR] 0.969, 95% CI 0.943-0.996) to be retested and more likely (HR 1.104, 95% CI 1.075-1.134) to stop testing altogether than women. People aged between 20 and 34 years were less likely to be retested (HR 0.861, 95% CI 0.828-0.895) and more likely to stop testing altogether (HR 2.617, 95% CI 2.538-2.699). However, older age groups were less likely to stop testing, especially those aged between 65-74 years and 75-84 years, than those aged between 0 and 19 years. The likelihood of stopping testing was lower (HR 0.93, 95% CI 0.889-0.976) for the Asian group and higher for the Hispanic or Latino (HR 1.185, 95% CI 1.148-1.223) and Black or African American (HR 1.198, 95% CI 1.054-1.350) groups than the White group. Conclusions: The unique features of a pandemic response program that supported community-wide access to free asymptomatic testing provide a unique opportunity to evaluate adherence to testing recommendations and testing trends over time. Identification of individual and group-level factors associated with testing behaviors can provide insights for identifying potential areas of improvement in future testing initiatives. %M 38631033 %R 10.2196/48784 %U https://publichealth.jmir.org/2024/1/e48784 %U https://doi.org/10.2196/48784 %U http://www.ncbi.nlm.nih.gov/pubmed/38631033 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e48060 %T Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study %A Rieckmann,Andreas %A Nielsen,Sebastian %A Dworzynski,Piotr %A Amini,Heresh %A Mogensen,Søren Wengel %A Silva,Isaquel Bartolomeu %A Chang,Angela Y %A Arah,Onyebuchi A %A Samek,Wojciech %A Rod,Naja Hulvej %A Ekstrøm,Claus Thorn %A Benn,Christine Stabell %A Aaby,Peter %A Fisker,Ane Bærent %+ Section of Epidemiology, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Copenhagen, 1353, Denmark, 45 35326765, aric@sund.ku.dk %K child mortality %K causal discovery %K Guinea-Bissau %K inductive-deductive %K machine learning %K targeted preventive and risk-mitigating interventions %D 2024 %7 9.4.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. Methods: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. Results: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. Conclusions: The study’s results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups. %M 38592761 %R 10.2196/48060 %U https://publichealth.jmir.org/2024/1/e48060 %U https://doi.org/10.2196/48060 %U http://www.ncbi.nlm.nih.gov/pubmed/38592761 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e48963 %T Leveraging Routinely Collected Program Data to Inform Extrapolated Size Estimates for Key Populations in Namibia: Small Area Estimation Study %A Loeb,Talia %A Willis,Kalai %A Velishavo,Frans %A Lee,Daniel %A Rao,Amrita %A Baral,Stefan %A Rucinski,Katherine %+ Data for Implementation (Data.FI), Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, United States, 1 410 955 3227, tloeb2@jh.edu %K female sex workers %K HIV %K key populations %K men who have sex with men %K Namibia %K population size estimation %K small area estimation %D 2024 %7 4.4.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Estimating the size of key populations, including female sex workers (FSW) and men who have sex with men (MSM), can inform planning and resource allocation for HIV programs at local and national levels. In geographic areas where direct population size estimates (PSEs) for key populations have not been collected, small area estimation (SAE) can help fill in gaps using supplemental data sources known as auxiliary data. However, routinely collected program data have not historically been used as auxiliary data to generate subnational estimates for key populations, including in Namibia. Objective: To systematically generate regional size estimates for FSW and MSM in Namibia, we used a consensus-informed estimation approach with local stakeholders that included the integration of routinely collected HIV program data provided by key populations’ HIV service providers. Methods: We used quarterly program data reported by key population implementing partners, including counts of the number of individuals accessing HIV services over time, to weight existing PSEs collected through bio-behavioral surveys using a Bayesian triangulation approach. SAEs were generated through simple imputation, stratified imputation, and multivariable Poisson regression models. We selected final estimates using an iterative qualitative ranking process with local key population implementing partners. Results: Extrapolated national estimates for FSW ranged from 4777 to 13,148 across Namibia, comprising 1.5% to 3.6% of female individuals aged between 15 and 49 years. For MSM, estimates ranged from 4611 to 10,171, comprising 0.7% to 1.5% of male individuals aged between 15 and 49 years. After the inclusion of program data as priors, the estimated proportion of FSW derived from simple imputation increased from 1.9% to 2.8%, and the proportion of MSM decreased from 1.5% to 0.75%. When stratified imputation was implemented using HIV prevalence to inform strata, the inclusion of program data increased the proportion of FSW from 2.6% to 4.0% in regions with high prevalence and decreased the proportion from 1.4% to 1.2% in regions with low prevalence. When population density was used to inform strata, the inclusion of program data also increased the proportion of FSW in high-density regions (from 1.1% to 3.4%) and decreased the proportion of MSM in all regions. Conclusions: Using SAE approaches, we combined epidemiologic and program data to generate subnational size estimates for key populations in Namibia. Overall, estimates were highly sensitive to the inclusion of program data. Program data represent a supplemental source of information that can be used to align PSEs with real-world HIV programs, particularly in regions where population-based data collection methods are challenging to implement. Future work is needed to determine how best to include and validate program data in target settings and in key population size estimation studies, ultimately bridging research with practice to support a more comprehensive HIV response. %M 38573760 %R 10.2196/48963 %U https://publichealth.jmir.org/2024/1/e48963 %U https://doi.org/10.2196/48963 %U http://www.ncbi.nlm.nih.gov/pubmed/38573760 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e52047 %T Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024 %A Gu,Xinchun %A Watson,Conall %A Agrawal,Utkarsh %A Whitaker,Heather %A Elson,William H. %A Anand,Sneha %A Borrow,Ray %A Buckingham,Anna %A Button,Elizabeth %A Curtis,Lottie %A Dunn,Dominic %A Elliot,Alex J. %A Ferreira,Filipa %A Goudie,Rosalind %A Hoang,Uy %A Hoschler,Katja %A Jamie,Gavin %A Kar,Debasish %A Kele,Beatrix %A Leston,Meredith %A Linley,Ezra %A Macartney,Jack %A Marsden,Gemma L %A Okusi,Cecilia %A Parvizi,Omid %A Quinot,Catherine %A Sebastianpillai,Praveen %A Sexton,Vanashree %A Smith,Gillian %A Suli,Timea %A Thomas,Nicholas P B %A Thompson,Catherine %A Todkill,Daniel %A Wimalaratna,Rashmi %A Inada-Kim,Matthew %A Andrews,Nick %A Tzortziou-Brown,Victoria %A Byford,Rachel %A Zambon,Maria %A Lopez-Bernal,Jamie %A de Lusignan,Simon %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 01865 617 283 ext 17 283, simon.delusignan@phc.ox.ac.uk %K sentinel surveillance %K pandemic %K COVID-19 %K human influenza %K influenza vaccines %K respiratory tract infections %K vaccination %K World Health Organization %K respiratory syncytial virus %K phenotype %K computerized medical record system %D 2024 %7 3.4.2024 %9 Protocol %J JMIR Public Health Surveill %G English %X Background: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO’s mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. Objective: We aim to describe the RSC’s plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. Methods: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA’s sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA’s reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. Results: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC’s pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. Conclusions: The RSC extended its surveillance activities to meet more but not all of the mosaic framework’s objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies. %M 38569175 %R 10.2196/52047 %U https://publichealth.jmir.org/2024/1/e52047 %U https://doi.org/10.2196/52047 %U http://www.ncbi.nlm.nih.gov/pubmed/38569175 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e43585 %T Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study %A Deji,Zhuoga %A Tong,Yuantao %A Huang,Honglian %A Zhang,Zeyu %A Fang,Meng %A Crabbe,M James C %A Zhang,Xiaoyan %A Wang,Ying %+ Department of Clinical Laboratory Medicine Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Rd, Shanghai, 200437, China, 86 021 65161782, nadger_wang@139.com %K COVID-19 %K environmental factors %K altitude %K population density %K virus mutation %D 2024 %7 25.3.2024 %9 Original Paper %J Interact J Med Res %G English %X Background: The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date. Objective: The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms. Methods: We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above. Results: Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours. Conclusions: By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention. %M 38526532 %R 10.2196/43585 %U https://www.i-jmr.org/2024/1/e43585 %U https://doi.org/10.2196/43585 %U http://www.ncbi.nlm.nih.gov/pubmed/38526532 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e53086 %T Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online %A Ashraf,Amir Reza %A Mackey,Tim Ken %A Fittler,András %+ Global Health Program, Department of Anthropology, University of California, 9500 Gilman Dr., MC 0505, La Jolla, CA, 92093, United States, 1 9514914161, tmackey@ucsd.edu %K generative artificial intelligence %K artificial intelligence %K comparative assessment %K search engines %K online pharmacies %K patient safety %K generative %K safety %K search engine %K search %K searches %K searching %K website %K websites %K Google %K Bing %K retrieval %K information seeking %K illegal %K pharmacy %K pharmacies %K risk %K risks %K consumer %K consumers %K customer %K customers %K recommendation %K recommendations %K vendor %K vendors %K substance use %K substance abuse %K controlled substances %K controlled substance %K drug %K drugs %K pharmaceutic %K pharmaceutics %K pharmaceuticals %K pharmaceutical %K medication %K medications %D 2024 %7 21.3.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. Objective: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. Methods: We conducted a comparative assessment of AI-generated recommendations from Google’s Search Generative Experience (SGE) and Microsoft Bing’s Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Results: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat’s and 13.23% (18/136) of Google SGE’s recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). Conclusions: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations. %M 38512343 %R 10.2196/53086 %U https://publichealth.jmir.org/2024/1/e53086 %U https://doi.org/10.2196/53086 %U http://www.ncbi.nlm.nih.gov/pubmed/38512343 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e46903 %T Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights %A Sahu,Kirti Sundar %A Dubin,Joel A %A Majowicz,Shannon E %A Liu,Sam %A Morita,Plinio P %+ School of Public Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 31372, plinio.morita@uwaterloo.ca %K population-level health indicators %K internet of things %K public health surveillance %K mobility %K risk factors %K chronic diseases %K chronic %K risk %K surveillance %K mobility %K movement %K sensor %K population %D 2024 %7 20.3.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google’s GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. Objective: This study investigates in-home mobility data from ecobee’s smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google’s residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. Methods: Motion sensor data were acquired from the ecobee “Donate Your Data” initiative via Google’s BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces—Ontario, Quebec, Alberta, and British Columbia—during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. Results: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google’s data set. Examination of Google’s daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. Conclusions: This study’s findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google’s out-of-house residential mobility data and ecobee’s in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts. %M 38506901 %R 10.2196/46903 %U https://publichealth.jmir.org/2024/1/e46903 %U https://doi.org/10.2196/46903 %U http://www.ncbi.nlm.nih.gov/pubmed/38506901 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e52093 %T Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics %A Divi,Nomita %A Mantero,Jaś %A Libel,Marlo %A Leal Neto,Onicio %A Schultheiss,Marinanicole %A Sewalk,Kara %A Brownstein,John %A Smolinski,Mark %+ Ending Pandemics, 870 Market Street, San Francisco, CA, 94201, United States, 1 6173591733, nomita@endingpandemics.org %K disease surveillance %K surveillance %K verification %K early detection %K epidemic intelligence, risk assessment %K threat %K threats %K crisis %K crises %K outbreak %K outbreaks %K warning %K warnings %K crowdsource %K crowdsourcing %K surveillance %K digital health %K detect %K detection %K risk %K risks %D 2024 %7 15.3.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore. Objective: This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats. Methods: This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities. Results: Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore. Conclusions: As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events. %M 38488832 %R 10.2196/52093 %U https://publichealth.jmir.org/2024/1/e52093 %U https://doi.org/10.2196/52093 %U http://www.ncbi.nlm.nih.gov/pubmed/38488832 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e47570 %T How to Identify e-Cigarette Brands Available in the United States During 2020-2022: Development and Usability Study %A Ma,Shaoying %A Kaareen,Aadeeba %A Park,Hojin %A He,Yanyun %A Jiang,Shuning %A Qiu,Zefeng %A Xie,Zidian %A Li,Dongmei %A Chen,Jian %A O’Connor,Richard J %A Fong,Geoffrey T %A Shang,Ce %+ Center for Tobacco Research, The Ohio State University Wexner Medical Center, 3650 Olentangy River Road, 1st Floor, Suite 110, Columbus, OH, 43214, United States, 1 6148976063, shaoying.ma@osumc.edu %K tobacco %K electronic cigarette %K e-cigarette %K electronic nicotine delivery systems %K electronic nicotine delivery system %K vaping %K market surveillance %K tobacco marketing %D 2024 %7 28.2.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Prior studies have demonstrated that the e-cigarette market contains a large number of brands. Identifying these existing e-cigarette brands is a key element of market surveillance, which will further assist in policy making and compliance checks. Objective: To facilitate the surveillance of the diverse product landscape in the e-cigarette market, we constructed a semantic database of e-cigarette brands that have appeared in the US market as of 2020-2022. Methods: In order to build the brand database, we searched and compiled e-cigarette brands from a comprehensive list of retail channels and sources, including (1) e-liquid and disposable brands sold in web-based stores, (2) e-cigarette brands sold in brick-and-mortar stores and collected by the Nielsen Retail Scanner Data, (3) e-cigarette brands compiled by Wikipedia, (4) self-reported e-cigarette brands from the 2020 International Tobacco Control Four-Country Smoking and Vaping (ITC 4CV) US survey, and (5) e-cigarette brands on Twitter. We also estimated the top 5 e-cigarette brands by sales volume in brick-and-mortar stores, by the frequency and variety of offerings in web-based shops, and by the frequency of self-reported brands from the 2020 ITC 4CV US survey. Results: As of 2020-2022, a total of 912 e-cigarette brands have been sold by various retail channels. During 2020-2022, the top 5 brands are JUUL, vuse, njoy, blu, and logic in brick-and-mortar stores; blu, king, monster, twist, and air factory for e-liquids in web-based stores; hyde, pod mesh, suorin, vaporlax, and xtra for disposables sold in web-based stores; and smok, aspire, vaporesso, innokin, and eleaf based on self-reported survey data. Conclusions: As the US Food and Drug Administration enforces the premarket tobacco market authorization, many e-cigarette brands may become illegal in the US market. In this context, how e-cigarette brands evolve and consolidate in different retail channels will be critical for understanding the regulatory impacts on product availability. Our semantic database of e-cigarette brands can serve as a useful tool to monitor product and marketplace development, conduct compliance checks, assess manufacturers’ marketing behaviors, and identify regulatory impacts. %M 38416562 %R 10.2196/47570 %U https://formative.jmir.org/2024/1/e47570 %U https://doi.org/10.2196/47570 %U http://www.ncbi.nlm.nih.gov/pubmed/38416562 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e47703 %T Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar %A Benda,Natalie %A Dougherty,Kylie %A Gebremariam Gobezayehu,Abebe %A Cranmer,John N %A Zawtha,Sakie %A Andreadis,Katerina %A Biza,Heran %A Masterson Creber,Ruth %+ School of Nursing, Columbia University, 560 W 168th St, New York, NY, 10032, United States, 1 212 305 5756, nb3115@cumc.columbia.edu %K low and middle income countries %K LMIC %K electronic data capture %K population health surveillance, sociotechnical system %K data infrastructure %K electronic data system %K health care system %K technology %K information system %K health program development %K intervention %D 2024 %7 12.2.2024 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms. %M 38345833 %R 10.2196/47703 %U https://publichealth.jmir.org/2024/1/e47703 %U https://doi.org/10.2196/47703 %U http://www.ncbi.nlm.nih.gov/pubmed/38345833 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e47444 %T Early Results of an Initiative to Assess Exposure to Firearm Violence in Ambulatory Care: Descriptive Analysis of Electronic Health Record Data %A Cook,Nicole %A Hoopes,Megan %A Biel,Frances M %A Cartwright,Natalie %A Gordon,Michelle %A Sills,Marion %+ OCHIN, PO Box 5426, Portland, OR, 97228, United States, 1 503 943 2500, cookn@ochin.org %K gun violence %K firearm injury %K surveillance %K primary care %K public health %K ambulatory care %K electronic health record %K violence %K burden %K emergency department %K data %K risk factor %D 2024 %7 5.2.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background:  Current research on firearm violence is largely limited to patients who received care in emergency departments or inpatient acute care settings or who died. This is because standardized disease classification codes for firearm injury only represent bodily trauma. As a result, research on pathways and health impacts of firearm violence is largely limited to people who experienced acute bodily trauma and does not include the estimated millions of individuals who were exposed to firearm violence but did not sustain acute injury. Assessing and collecting data on exposure to firearm violence in ambulatory care settings can expand research and more fully frame the public health issue. Objective: The aim of the study is to evaluate the demographic and clinical characteristics of patients who self-reported exposure to firearm violence during a behavioral health visit. Methods: This study assessed early data from an initiative implemented in 2022 across a national network of ambulatory behavioral health centers to support trauma-informed care by integrating structured data fields on trauma exposure into an electronic health record behavioral health patient assessment form (SmartForm), as such variables are generally not included in standard outpatient medical records. We calculated descriptive statistics on clinic characteristics, patient demographics, and select clinical conditions among clinics that chose to implement the SmartForm and among patients who reported an exposure to firearm violence. Data on patient counts are limited to positive reports of exposure to firearm violence, and the representativeness of firearm exposure among all patients could not be calculated due to unknown variability in the implementation of the SmartForm. Results: There were 323 of 629 (51%) clinics that implemented the SmartForm and reported at least 1 patient exposed to firearm violence. In the first 11 months of implementation, 3165 patients reported a recent or past exposure to firearm violence across the 323 clinics. Among patients reporting exposure, 52.7% (n=1669) were male, 38.8% (n=1229) were Black, 45.7% (n=1445) had posttraumatic stress disorder, 37.5% (n=1186) had a substance abuse disorder (other than nicotine), and 11.7% (n=371) had hypertension. Conclusions: Current research on firearm violence using standardized data is limited to acute care settings and death data. Early results from an initiative across a large network of behavioral health clinics demonstrate that a high number of clinics chose to implement the SmartForm, resulting in thousands of patients reporting exposure to firearm violence. This study demonstrates that collecting standardized data on firearm violence exposure in ambulatory care settings is feasible. This study further demonstrates that resultant data from ambulatory settings can be used for meaningful analysis in describing populations affected by firearm violence. The results of this study hold promise for further collection of structured data on exposure to firearm violence in ambulatory settings. %M 38315521 %R 10.2196/47444 %U https://publichealth.jmir.org/2024/1/e47444 %U https://doi.org/10.2196/47444 %U http://www.ncbi.nlm.nih.gov/pubmed/38315521 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e44648 %T Association Between Nitrogen Dioxide Pollution and Cause-Specific Mortality in China: Cross-Sectional Time Series Study %A Zeng,Jie %A Lin,Guozhen %A Dong,Hang %A Li,Mengmeng %A Ruan,Honglian %A Yang,Jun %+ School of Public Health, Guangzhou Medical University, No. 1 Xinzao Road, Xinzao Town, Panyu District, Guangzhou, 511436, China, 86 020 37103532, yangjun_eci@jnu.edu.cn %K nitrogen dioxide %K cause-specific mortality %K stratification effect %K vulnerable subpopulations %K China %D 2024 %7 5.2.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Nitrogen dioxide (NO2) has been frequently linked to a range of diseases and associated with high rates of mortality and morbidity worldwide. However, there is limited evidence regarding the risk of NO2 on a spectrum of causes of mortality. Moreover, adjustment for potential confounders in NO2 analysis has been insufficient, and the spatial resolution of exposure assessment has been limited. Objective: This study aimed to quantitatively assess the relationship between short-term NO2 exposure and death from a range of causes by adjusting for potential confounders in Guangzhou, China, and determine the modifying effect of gender and age. Methods: A time series study was conducted on 413,703 deaths that occurred in Guangzhou during the period of 2010 to 2018. The causes of death were classified into 10 categories and 26 subcategories. We utilized a generalized additive model with quasi-Poisson regression analysis using a natural cubic splines function with lag structure of 0 to 4 days to estimate the potential lag effect of NO2 on cause-specific mortality. We estimated the percentage change in cause-specific mortality rates per 10 μg/m3 increase in NO2 levels. We stratified meteorological factors such as temperature, humidity, wind speed, and air pressure into high and low levels with the median as the critical value and analyzed the effects of NO2 on various death-causing diseases at those high and low levels. To further identify potentially vulnerable subpopulations, we analyzed groups stratified by gender and age. Results: A significant association existed between NO2 exposure and deaths from multiple causes. Each 10 μg/m3 increment in NO2 density at a lag of 0 to 4 days increased the risks of all-cause mortality by 1.73% (95% CI 1.36%-2.09%) and mortality due to nonaccidental causes, cardiovascular disease, respiratory disease, endocrine disease, and neoplasms by 1.75% (95% CI 1.38%-2.12%), 2.06% (95% CI 1.54%-2.59%), 2.32% (95% CI 1.51%-3.13%), 2.40% (95% CI 0.84%-3.98%), and 1.18% (95% CI 0.59%-1.78%), respectively. Among the 26 subcategories, mortality risk was associated with 16, including intentional self-harm, hypertensive disease, and ischemic stroke disease. Relatively higher effect estimates of NO2 on mortality existed for low levels of temperature, relative humidity, wind speed, and air pressure than with high levels, except a relatively higher effect estimate was present for endocrine disease at a high air pressure level. Most of the differences between subgroups were not statistically significant. The effect estimates for NO2 were similar by gender. There were significant differences between the age groups for mortality due to all causes, nonaccidental causes, and cardiovascular disease. Conclusions: Short-term NO2 exposure may increase the risk of mortality due to a spectrum of causes, especially in potentially vulnerable populations. These findings may be important for predicting and modifying guidelines for NO2 exposure in China. %M 38315528 %R 10.2196/44648 %U https://publichealth.jmir.org/2024/1/e44648 %U https://doi.org/10.2196/44648 %U http://www.ncbi.nlm.nih.gov/pubmed/38315528 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e48159 %T Impact of the COVID-19 Pandemic on Health Care Utilization in the Vaccine Safety Datalink: Retrospective Cohort Study %A Qian,Lei %A Sy,Lina S %A Hong,Vennis %A Glenn,Sungching C %A Ryan,Denison S %A Nelson,Jennifer C %A Hambidge,Simon J %A Crane,Bradley %A Zerbo,Ousseny %A DeSilva,Malini B %A Glanz,Jason M %A Donahue,James G %A Liles,Elizabeth %A Duffy,Jonathan %A Xu,Stanley %+ Research and Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, 5th Floor, Pasadena, CA, 91101, United States, 1 626 564 3538, lei.x.qian@kp.org %K COVID-19 pandemic %K health care utilization %K telehealth %K inpatient %K emergency department %K outpatient %K vaccine safety %K electronic health record %K resource allocation %K difference-in-difference %K interrupted time series analysis %D 2024 %7 23.1.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Understanding the long-term impact of the COVID-19 pandemic on health care utilization is important to health care organizations and policy makers for strategic planning, as well as to researchers when designing studies that use observational electronic health record data during the pandemic period. Objective: This study aimed to evaluate the changes in health care utilization across all care settings among a large, diverse, and insured population in the United States during the COVID-19 pandemic. Methods: We conducted a retrospective cohort study within 8 health care organizations participating in the Vaccine Safety Datalink Project using electronic health record data from members of all ages from January 1, 2017, to December 31, 2021. The visit rates per person-year were calculated monthly during the study period for 4 health care settings combined as well as by inpatient, emergency department (ED), outpatient, and telehealth settings, both among all members and members without COVID-19. Difference-in-difference analysis and interrupted time series analysis were performed to assess the changes in visit rates from the prepandemic period (January 2017 to February 2020) to the early pandemic period (April-December 2020) and the later pandemic period (July-December 2021), respectively. An exploratory analysis was also conducted to assess trends through June 2023 at one of the largest sites, Kaiser Permanente Southern California. Results: The study included more than 11 million members from 2017 to 2021. Compared with the prepandemic period, we found reductions in visit rates during the early pandemic period for all in-person care settings. During the later pandemic period, overall use reached 8.36 visits per person-year, exceeding the prepandemic level of 7.49 visits per person-year in 2019 (adjusted percent change 5.1%, 95% CI 0.6%-9.9%); inpatient and ED visits returned to prepandemic levels among all members, although they remained low at 0.095 and 0.241 visits per person-year, indicating a 7.5% and 8% decrease compared to pre-pandemic levels among members without COVID-19, respectively. Telehealth visits, which were approximately 42% of the volume of outpatient visits during the later pandemic period, were increased by 97.5% (95% CI 86.0%-109.7%) from 0.865 visits per person-year in 2019 to 2.35 visits per person-year in the later pandemic period. The trends in Kaiser Permanente Southern California were similar to those of the entire study population. Visit rates from January 2022 to June 2023 were stable and appeared to be a continuation of the use levels observed at the end of 2021. Conclusions: Telehealth services became a mainstay of the health care system during the late COVID-19 pandemic period. Inpatient and ED visits returned to prepandemic levels, although they remained low among members without evidence of COVID-19. Our findings provide valuable information for strategic resource allocation for postpandemic patient care and for designing observational studies involving the pandemic period. %M 38091476 %R 10.2196/48159 %U https://publichealth.jmir.org/2024/1/e48159 %U https://doi.org/10.2196/48159 %U http://www.ncbi.nlm.nih.gov/pubmed/38091476 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e49185 %T Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review %A Clark,Emily C %A Neumann,Sophie %A Hopkins,Stephanie %A Kostopoulos,Alyssa %A Hagerman,Leah %A Dobbins,Maureen %+ National Collaborating Centre for Methods and Tools, 175 Longwood Rd S, Suite 210A, Hamilton, ON, L8P 0A1, Canada, 1 9055259140 ext 20450, dobbinsm@mcmaster.ca %K public health %K surveillance %K digital surveillance %K COVID-19 %K screening %K infodemiology %K big data %K mobility tracking %K wastewater %K ethics %K decision making %K public health surveillance %D 2024 %7 19.1.2024 %9 Review %J JMIR Public Health Surveill %G English %X Background: Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. Objective: This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. Methods: A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. Results: Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. Conclusions: The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm. %M 38241067 %R 10.2196/49185 %U https://publichealth.jmir.org/2024/1/e49185 %U https://doi.org/10.2196/49185 %U http://www.ncbi.nlm.nih.gov/pubmed/38241067 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e46635 %T Global, Regional, and National Burden of Myocarditis in 204 Countries and Territories From 1990 to 2019: Updated Systematic Analysis %A Kong,Qingyu %A Xu,Xue %A Li,Meng %A Meng,Xiao %A Zhao,Cuifen %A Yang,Xiaorong %+ Clinical Epidemiology Unit, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, China, 86 53182166672, yangxiaorong@sdu.edu.cn %K myocarditis %K global burden %K temporal trend %K systematic analysis %K incidence %K mortality %K disability-adjusted life years %D 2024 %7 11.1.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Myocarditis is characterized by high disability and mortality, and imposes a severe burden on population health globally. However, the latest global magnitude and secular trend of myocarditis burden have not been reported. Objective: This study aimed to delineate the epidemiological characteristics of myocarditis burden globally for optimizing targeted prevention and research. Methods: Based on the Global Burden of Disease Study 2019, the myocarditis burden from 1990 to 2019 was modeled using the Cause of Death Ensemble tool, DisMod-MR, and spatiotemporal Gaussian regression. We depicted the epidemiology and trends of myocarditis by sex, age, year, region, and sociodemographic index (SDI). R program version 4.2.1 (R Project for Statistical Computing) was applied for all statistical analyses, and a 2-sided P-value of <.05 was considered statistically significant. Results: The number of incident cases (1,268,000) and deaths (32,450) associated with myocarditis in 2019 increased by over 1.6 times compared with the values in 1990 globally. On the other hand, the age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) decreased slightly from 1990 to 2019. The disability-adjusted life years (DALYs) decreased slightly in the past 3 decades, while the age-standardized DALY rate (ASDR) decreased greatly from 18.29 per 100,000 person-years in 1990 to 12.81 per 100,000 person-years in 2019. High SDI regions always showed a more significant ASIR. The ASIR slightly decreased in all SDI regions between 1990 and 2019. Middle SDI regions had the highest ASMR and ASDR in 2019. Low SDI regions had the lowest ASMR and ASDR in 2019. The age-standardized rates (ASRs) of myocarditis were higher among males than among females from 1990 to 2019 globally. All ASRs among both sexes had a downward trend, except for the ASMR among males, which showed a stable trend, and females had a more significant decrease in the ASDR than males. Senior citizens had high incident cases and deaths among both sexes in 2019. The peak numbers of DALYs for both sexes were noted in the under 1 age group in 2019. At the national level, the estimated annual percentage changes in the ASRs had significant negative correlations with the baseline ASRs in 1990. Conclusions: Globally, the number of incident cases and deaths associated with myocarditis have increased significantly. On the other hand, the ASRs of myocarditis showed decreasing trends from 1990 to 2019. Males consistently showed higher ASRs of myocarditis than females from 1990 to 2019 globally. Senior citizens gradually predominated in terms of myocarditis burden. Policymakers should establish targeted control strategies based on gender, region, age, and SDI; strengthen aging-related health research; and take notice of the changes in the epidemic characteristics of myocarditis. %M 38206659 %R 10.2196/46635 %U https://publichealth.jmir.org/2024/1/e46635 %U https://doi.org/10.2196/46635 %U http://www.ncbi.nlm.nih.gov/pubmed/38206659 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e50379 %T Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis %A Galvez-Hernandez,Pablo %A Gonzalez-Viana,Angelina %A Gonzalez-de Paz,Luis %A Shankardass,Ketan %A Muntaner,Carles %+ Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Health Sciences Building, 4th Fl., 155 College St, Toronto, ON, M5T 3M6, Canada, 1 6475752195, pau.galvez@utoronto.ca %K web scraping %K text mining %K spatial overlay analysis %K program evaluation %K social environment %K contextual variables %K health assets %K social connection %K multilevel analysis %K health services research %D 2024 %7 8.1.2024 %9 Tutorial %J JMIR Public Health Surveill %G English %X Background: Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data. Objective: We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas. Methods: This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults’ social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas. Results: We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources. Conclusions: The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables. %M 38190245 %R 10.2196/50379 %U https://publichealth.jmir.org/2024/1/e50379 %U https://doi.org/10.2196/50379 %U http://www.ncbi.nlm.nih.gov/pubmed/38190245 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e44728 %T Evaluating the Added Value of Digital Contact Tracing Support Tools for Citizens: Framework Development %A Baron,Ruth %A Hamdiui,Nora %A Helms,Yannick B %A Crutzen,Rik %A Götz,Hannelore M %A Stein,Mart L %+ Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Postbus 1, Bilthoven, 3720 BA, Netherlands, 31 088 689 89 89, baronruth6@gmail.com %K contact tracing %K digital tools %K citizen involvement %K COVID-19 %K infectious disease outbreak %K framework %K mobile phone %D 2023 %7 29.11.2023 %9 Original Paper %J JMIR Res Protoc %G English %X Background: The COVID-19 pandemic revealed that with high infection rates, health services conducting contact tracing (CT) could become overburdened, leading to limited or incomplete CT. Digital CT support (DCTS) tools are designed to mimic traditional CT, by transferring a part of or all the tasks of CT into the hands of citizens. Besides saving time for health services, these tools may help to increase the number of contacts retrieved during the contact identification process, quantity and quality of contact details, and speed of the contact notification process. The added value of DCTS tools for CT is currently unknown. Objective: To help determine whether DCTS tools could improve the effectiveness of CT, this study aims to develop a framework for the comprehensive assessment of these tools. Methods: A framework containing evaluation topics, research questions, accompanying study designs, and methods was developed based on consultations with CT experts from municipal public health services and national public health authorities, complemented with scientific literature. Results: These efforts resulted in a framework aiming to assist with the assessment of the following aspects of CT: speed; comprehensiveness; effectiveness with regard to contact notification; positive case detection; potential workload reduction of public health professionals; demographics related to adoption and reach; and user experiences of public health professionals, index cases, and contacts. Conclusions: This framework provides guidance for researchers and policy makers in designing their own evaluation studies, the findings of which can help determine how and the extent to which DCTS tools should be implemented as a CT strategy for future infectious disease outbreaks. %M 38019583 %R 10.2196/44728 %U https://www.researchprotocols.org/2023/1/e44728 %U https://doi.org/10.2196/44728 %U http://www.ncbi.nlm.nih.gov/pubmed/38019583 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e32848 %T Effectiveness of Self-Collected, Ambient Temperature–Preserved Nasal Swabs Compared to Samples Collected by Trained Staff for Genotyping of Respiratory Viruses by Shotgun RNA Sequencing: Comparative Study %A Soto,Raymond %A Paul,Litty %A Porucznik,Christina A %A Xie,Heng %A Stinnett,Rita Czako %A Briggs,Benjamin %A Biggerstaff,Matthew %A Stanford,Joseph %A Schlaberg,Robert %+ Department of Family and Preventive Medicine, University of Utah, 375 Chipeta Way #A, Salt Lake City, UT, 84108, United States, 1 801 581 7498, joseph.stanford@utah.edu %K genotyping %K self-collected nasal swabs %K RNA sequencing %K respiratory virus surveillance %K surveillance %K respiratory virus %K influenza virus %K pandemic %K preparedness %K testing capacity %K self-test %K viral genome analysis %K swabs %K barriers %K early detection %K nasal swab %K temperature %K public health %K specimen %K collection %K diagnosis %K laboratory %K respiratory %K virus %K COVID-19 %D 2023 %7 24.11.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The SARS-CoV-2 pandemic has underscored the need for field specimen collection and transport to diagnostic and public health laboratories. Self-collected nasal swabs transported without dependency on a cold chain have the potential to remove critical barriers to testing, expand testing capacity, and reduce opportunities for exposure of health professionals in the context of a pandemic. Objective: We compared nasal swab collection by study participants from themselves and their children at home to collection by trained research staff. Methods: Each adult participant collected 1 nasal swab, sampling both nares with the single swab, after which they collected 1 nasal swab from 1 child. After all the participant samples were collected for the household, the research staff member collected a separate single duplicate sample from each individual. Immediately after the sample collection, the adult participants completed a questionnaire about the acceptability of the sampling procedures. Swabs were placed in temperature-stable preservative and respiratory viruses were detected by shotgun RNA sequencing, enabling viral genome analysis. Results: In total, 21 households participated in the study, each with 1 adult and 1 child, yielding 42 individuals with paired samples. Study participants reported that self-collection was acceptable. Agreement between identified respiratory viruses in both swabs by RNA sequencing demonstrated that adequate collection technique was achieved by brief instructions. Conclusions: Our results support the feasibility of a scalable and convenient means for the identification of respiratory viruses and implementation in pandemic preparedness for novel respiratory pathogens. %M 37999952 %R 10.2196/32848 %U https://formative.jmir.org/2023/1/e32848 %U https://doi.org/10.2196/32848 %U http://www.ncbi.nlm.nih.gov/pubmed/37999952 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e41207 %T Association Among Household Water, Sanitation, and Hygiene (WASH) Status and Typhoid Risk in Urban Slums: Prospective Cohort Study in Bangladesh %A Tadesse,Birkneh Tilahun %A Khanam,Farhana %A Ahmmed,Faisal %A Liu,Xinxue %A Islam,Md Taufiqul %A Kim,Deok Ryun %A Kang,Sophie SY %A Im,Justin %A Chowdhury,Fahima %A Ahmed,Tasnuva %A Aziz,Asma Binte %A Hoque,Masuma %A Park,Juyeon %A Pak,Gideok %A Jeon,Hyon Jin %A Zaman,Khalequ %A Khan,Ashraful Islam %A Kim,Jerome H %A Marks,Florian %A Qadri,Firdausi %A Clemens,John D %+ Epidemiology, Public Health, Impact Unit, International Vaccine Institute, SNU Research Park, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea, 82 1098041348, birkneh.tadesse@ivi.int %K water %K sanitation %K sanitary %K contaminated %K contamination %K hygiene %K hygienic %K WASH %K water, sanitation and hygiene %K typhoid fever %K enteric fever %K typhus %K typhoid %K enteric %K salmonella %K protection %K recursive partitioning %K Bangladesh %K low- and middle-income countries %K LMIC %K bacteria %K bacterial %K bacterial infection %K machine learning %K algorithm %K algorithms %K model %K low income %K slum %K slums %K risk %K infection control %K incidence %K prevalence %K epidemiology %K epidemiological %K poverty %D 2023 %7 20.11.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Typhoid fever, or enteric fever, is a highly fatal infectious disease that affects over 9 million people worldwide each year, resulting in more than 110,000 deaths. Reduction in the burden of typhoid in low-income countries is crucial for public health and requires the implementation of feasible water, sanitation, and hygiene (WASH) interventions, especially in densely populated urban slums. Objective: In this study, conducted in Mirpur, Bangladesh, we aimed to assess the association between household WASH status and typhoid risk in a training subpopulation of a large prospective cohort (n=98,087), and to evaluate the performance of a machine learning algorithm in creating a composite WASH variable. Further, we investigated the protection associated with living in households with improved WASH facilities and in clusters with increasing prevalence of such facilities during a 2-year follow-up period. Methods: We used a machine learning algorithm to create a dichotomous composite variable (“Better” and “Not Better”) based on 3 WASH variables: private toilet facility, safe drinking water source, and presence of water filter. The algorithm was trained using data from the training subpopulation and then validated in a distinct subpopulation (n=65,286) to assess its sensitivity and specificity. Cox regression models were used to evaluate the protective effect of living in “Better” WASH households and in clusters with increasing levels of “Better” WASH prevalence. Results: We found that residence in households with improved WASH facilities was associated with a 38% reduction in typhoid risk (adjusted hazard ratio=0.62, 95% CI 0.49-0.78; P<.001). This reduction was particularly pronounced in individuals younger than 10 years at the first census participation, with an adjusted hazard ratio of 0.49 (95% CI 0.36-0.66; P<.001). Furthermore, we observed an inverse relationship between the prevalence of “Better” WASH facilities in clusters and the incidence of typhoid, although this association was not statistically significant in the multivariable model. Specifically, the adjusted hazard of typhoid decreased by 0.996 (95% CI 0.986-1.006) for each percent increase in the prevalence of “Better” WASH in the cluster (P=.39). Conclusions: Our findings demonstrate that existing variations in household WASH are associated with differences in the risk of typhoid in densely populated urban slums. This suggests that attainable improvements in WASH facilities can contribute to enhanced typhoid control, especially in settings where major infrastructural improvements are challenging. These findings underscore the importance of implementing and promoting comprehensive WASH interventions in low-income countries as a means to reduce the burden of typhoid and improve public health outcomes in vulnerable populations. %M 37983081 %R 10.2196/41207 %U https://publichealth.jmir.org/2023/1/e41207 %U https://doi.org/10.2196/41207 %U http://www.ncbi.nlm.nih.gov/pubmed/37983081 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e41568 %T The Relationship Between Soil-Transmitted Helminth Infections and Environmental Factors in Puerto Iguazú, Argentina: Cross-Sectional Study %A Candela,Ernesto %A Goizueta,Carolina %A Sandon,Leonardo %A Muñoz-Antoli,Carla %A Periago,Maria Victoria %+ Department of Pharmacy Pharmaceutical Technology and Parasitology, Universitat de València, C/ Vicent Andres Estelles s/n, Burjassot, 46100, Spain, 34 653313156, ercansen@alumni.uv.es %K soil-transmitted helminths %K hookworm %K prevalence %K intensity: distribution: Iguazú %K Argentina %D 2023 %7 7.11.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Soil-transmitted helminths (STHs) are widely distributed throughout the world. Various factors, including the environment, socioeconomic characteristics, and access to water and sanitation, play an important role in the spread and persistence of these parasites within communities. They, in turn, affect the growth and development of members of the community, especially children. Studies in the northern provinces of Argentina have shown variable prevalence of STHs, but the factors associated with their presence have not been completely elucidated. Objective: This cross-sectional study aimed to identify the socioeconomic and environmental factors related to STH infection in indigenous villages located in Puerto Iguazú (Misiones), Argentina. Methods: Between 2018 and 2019, stool samples were collected from individuals ≥1 year residing in 3 villages: Mini-Marangatú, Yriapú, and Fortín Mbororé. Standard parasitological methods were used to determine STH prevalence. Standardized questionnaires were used to assess participants’ habits, customs, and household characteristics, and environmental data were obtained through satellite imagery. Multilinear regression with Akaike information criterion stepwise variables was used to explore relevant associations. Results: A total of 342 individuals from the 3 villages participated in this study. The prevalence of STHs varied across villages: 89.6% (43/48), in Mini-Marangatú, 80.8% (101/125) in Yriapú, and 68.5% (115/169) in Fortín Mbororé. Notably, there was a significant difference in hookworm infection among the villages (P=.02). The analysis highlighted the significant influence of specific environmental factors on STH presence and spatial distribution, particularly in relation to hookworm infection. Vegetation patterns represented by the Vegetation Heterogeneity Index, created ad hoc for this study, emerged as a critical factor, with 2 significant predictors related to it (P=.002 and P=.004) alongside impervious surface density with a significant predictor (P<.001). The multilinear regression model yielded a high F test score (F108=4.75, P<.001), indicating a strong fit (R2=0.5465). Furthermore, socioeconomic factors, including walking barefoot in houses with dirt floors and overcrowding, were significantly correlated with hookworm infection intensity (P<.001 and P=.001, respectively). We also used the multilinear regression model to calculate hookworm infection intensity (F110=21.15, P<.001; R2=0.4971). Conclusions: Our study underscores the complexity of STH transmission, as villages with similar living conditions and environmental characteristics displayed varied STH prevalence and spatial distribution. Specific environmental factors, such as vegetation pattern and impervious surface density, played major roles in STH presence, demonstrating the crucial relationship between environmental factors and hookworm infection distribution. Moreover, our findings emphasize the significant influence of socioeconomic factors on hookworm infection intensity. By gaining insights into this complex interplay, our research contributes to a better understanding of STH transmission characteristics, thereby informing targeted public health interventions for effective control. %M 37934580 %R 10.2196/41568 %U https://publichealth.jmir.org/2023/1/e41568 %U https://doi.org/10.2196/41568 %U http://www.ncbi.nlm.nih.gov/pubmed/37934580 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e49859 %T Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis %A Zhou,Ying %A Luo,Dan %A Liu,Kui %A Chen,Bin %A Chen,Songhua %A Pan,Junhang %A Liu,Zhengwei %A Jiang,Jianmin %+ Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, BinJiang District, Hangzhou, 310000, China, 86 057187115009, jmjiang@cdc.zj.cn %K tuberculous pleurisy %K spatio-temporal %K epidemiology %K prediction %K time series %D 2023 %7 30.10.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by Mycobacterium tuberculosis; however, few studies have described its spatial epidemiological characteristics in eastern China. Objective: This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province. Methods: Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation). Results: The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence. Conclusions: The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors. %M 37902822 %R 10.2196/49859 %U https://publichealth.jmir.org/2023/1/e49859 %U https://doi.org/10.2196/49859 %U http://www.ncbi.nlm.nih.gov/pubmed/37902822 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e50114 %T Burden of and Trends in Urticaria Globally, Regionally, and Nationally from 1990 to 2019: Systematic Analysis %A Liu,Xiaoli %A Cao,Yuan %A Wang,Wenhui %+ Department of Dermatology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China, 86 15611908751, wwh0608@126.com %K urticaria %K burden of disease %K prevalence %K incidence %K disability-adjusted life years %D 2023 %7 26.10.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Urticaria presents a significant global health challenge due to its sudden onset and potential for severe allergic reactions. Past data on worldwide prevalence and incidence is inconsistent due to differing study methodologies, regional differences, and evolving diagnostic criteria. Past studies have often provided broad ranges instead of specific figures, underscoring the necessity for a cohesive global perspective to inform public health strategies. Objective: We aimed to assess the global burden of urticaria using the 2019 Global Burden of Disease (GBD) study data and systematically analyze urticaria prevalence, incidence, and disability-adjusted life years (DALYs) at global, regional, and national levels, thereby informing more effective prevention and treatment strategies. Methods: We analyzed the global, regional, and national burden of urticaria from 1990 to 2019 using the 2019 GBD study coordinated by the Institute for Health Metrics and Evaluation. Estimations of urticaria prevalence, incidence, and DALYs were derived using DisMod-MR 2.1, a Bayesian meta-regression tool. The Socio-demographic Index (SDI) was used to correlate development status with health outcomes. The GBD’s division of the world into 21 regions and 204 countries and territories facilitated a comprehensive assessment. Age-standardized estimated annual percentage changes were generated for urticaria metrics to quantify temporal trends, with age standardization adjusting for potential confounding from age structure. Results: From 1990 to 2019, the global age-standardized prevalence, incidence, and DALY rates of urticaria showed marginal changes. In 2019, 65.14 million individuals were affected, with a prevalence rate of 841.88 per 100,000 population. The DALY rate was 50.39 per 100,000 population. Compared to 1990, the global age-standardized prevalence, incidence, and DALY rates saw increases of 2.92, 4.84, and 0.31 per 100,000 population, respectively. Women persistently had higher rates than men. At a regional level in 2019, low-middle SDI regions exhibited the highest age-standardized metrics, whereas high SDI regions reported the lowest. Central Europe showed the highest rates, contrasting with Western Europe’s lowest rates. Nationally, urticaria prevalence in 2019 varied dramatically, from a low of 27.1 per 100,000 population in Portugal to a high of 92.0 per 100,000 population in Nepal. India reported the most DALYs at 749,495.9, followed by China, Pakistan, and the United States. Agewise data showed higher rates in younger age groups, which diminished with age and then experienced a slight resurgence in the oldest populations. This pattern was pronounced in women and younger populations, with the largest rises seen in those aged less than 40 years and the smallest in those aged more than 70 years. Conclusions: Urticaria remains a significant global health issue, with considerable variation across regions, countries, and territories. The increased burden among women, the rising burden in younger populations, and the regional differences in disease burden call for tailored interventions and policies to tackle this emerging public health issue. %M 37883176 %R 10.2196/50114 %U https://publichealth.jmir.org/2023/1/e50114 %U https://doi.org/10.2196/50114 %U http://www.ncbi.nlm.nih.gov/pubmed/37883176 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e45353 %T Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study %A Severson,Marie A %A Onanong,Sathaporn %A Dolezal,Alexandra %A Bartelt-Hunt,Shannon L %A Snow,Daniel D %A McFadden,Lisa M %+ Division of Basic Biomedical Sciences, University of South Dakota, 414 E. Clark St., Vermillion, SD, 57069, United States, 1 605 658 6436, lisa.mcfadden@usd.edu %K methamphetamine %K opioids %K substance use disorder %K wastewater-based surveillance %K drug detection %K pilot study %K substance use %K detecting %K monitoring %K drugs %K surveillance %K community %D 2023 %7 26.10.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use using rapid and unbiased techniques are lacking. Wastewater-based surveillance is a cost-effective method for monitoring community drug use. However, the examination of the results often focuses on descriptive analysis. Objective: The objective of this study was to explore community substance use in the United States by analyzing wastewater samples. Geographic differences and commonalities of substance use were explored. Methods: Wastewater was sampled across the United States (n=12). Selected drugs with misuse potential, prescriptions, and over-the-counter drugs and their metabolites were tested across geographic locations for 7 days. Methods used included wastewater assessment of substances and metabolites paired with machine learning, specifically discriminant analysis and cluster analysis, to explore similarities and differences in wastewater measures. Results: Geographic variations in the wastewater drug or metabolite levels were found. Results revealed a higher use of methamphetamine (z=–2.27, P=.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=–1.95, P=.05; hydrocodone-to-methadone: z=–1.95, P=.05) in states west of the Mississippi River compared to the east. Discriminant analysis suggested temazepam and methadone were significant predictors of geographical locations. Precision, sensitivity, specificity, and F1-scores were 0.88, 1, 0.80, and 0.93, respectively. Finally, cluster analysis revealed similarities in substance use among communities. Conclusions: These findings suggest that wastewater-based surveillance has the potential to become an effective form of surveillance for substance use. Further, advanced analytical techniques may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Using automated analytics, these advanced surveillance techniques may help communities develop timely, tailored treatment and prevention efforts. %M 37883150 %R 10.2196/45353 %U https://formative.jmir.org/2023/1/e45353 %U https://doi.org/10.2196/45353 %U http://www.ncbi.nlm.nih.gov/pubmed/37883150 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e41862 %T Mortality Risk and Burden From a Spectrum of Causes in Relation to Size-Fractionated Particulate Matters: Time Series Analysis %A Yang,Jun %A Dong,Hang %A Yu,Chao %A Li,Bixia %A Lin,Guozhen %A Chen,Sujuan %A Cai,Dongjie %A Huang,Lin %A Wang,Boguang %A Li,Mengmeng %+ State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, No 651 Dongfeng East Road, Guangzhou 510060, Guangzhou, 510060, China, 86 020 87345679, limm@sysucc.org.cn %K size-fractionated particulate matter %K cause-specific mortality %K cardiovascular disease %K respiratory disease %K neoplasm %K attributable burden %D 2023 %7 9.10.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: There is limited evidence regarding the adverse impact of particulate matters (PMs) on multiple body systems from both epidemiological and mechanistic studies. The association between size-fractionated PMs and mortality risk, as well as the burden of a whole spectrum of causes of death, remains poorly characterized. Objective: We aimed to examine the wide range of susceptible diseases affected by different sizes of PMs. We also assessed the association between PMs with an aerodynamic diameter less than 1 µm (PM1), 2.5 µm (PM2.5), and 10 µm (PM10) and deaths from 36 causes in Guangzhou, China. Methods: Daily data were obtained on cause-specific mortality, PMs, and meteorology from 2014 to 2016. A time-stratified case-crossover approach was applied to estimate the risk and burden of cause-specific mortality attributable to PMs after adjusting for potential confounding variables, such as long-term trend and seasonality, relative humidity, temperature, air pressure, and public holidays. Stratification analyses were further conducted to explore the potential modification effects of season and demographic characteristics (eg, gender and age). We also assessed the reduction in mortality achieved by meeting the new air quality guidelines set by the World Health Organization (WHO). Results: Positive and monotonic associations were generally observed between PMs and mortality. For every 10 μg/m3 increase in 4-day moving average concentrations of PM1, PM2.5, and PM10, the risk of all-cause mortality increased by 2.00% (95% CI 1.08%-2.92%), 1.54% (95% CI 0.93%-2.16%), and 1.38% (95% CI 0.95%-1.82%), respectively. Significant effects of size-fractionated PMs were observed for deaths attributed to nonaccidental causes, cardiovascular disease, respiratory disease, neoplasms, chronic rheumatic heart diseases, hypertensive diseases, cerebrovascular diseases, stroke, influenza, and pneumonia. If daily concentrations of PM1, PM2.5, and PM10 reached the WHO target levels of 10, 15, and 45 μg/m3, 7921 (95% empirical CI [eCI] 4454-11,206), 8303 (95% eCI 5063-11,248), and 8326 (95% eCI 5980-10690) deaths could be prevented, respectively. The effect estimates of PMs were relatively higher during hot months, among female individuals, and among those aged 85 years and older, although the differences between subgroups were not statistically significant. Conclusions: We observed positive and monotonical exposure-response curves between PMs and deaths from several diseases. The effect of PM1 was stronger on mortality than that of PM2.5 and PM10. A substantial number of premature deaths could be preventable by adhering to the WHO’s new guidelines for PMs. Our findings highlight the importance of a size-based strategy in controlling PMs and managing their health impact. %M 37812487 %R 10.2196/41862 %U https://publichealth.jmir.org/2023/1/e41862 %U https://doi.org/10.2196/41862 %U http://www.ncbi.nlm.nih.gov/pubmed/37812487 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44211 %T Firearm Possession Rates in Home Countries and Firearm Suicide Rates Among US- and Foreign-Born Suicide Decedents in the United States: Analysis of Combined Data from the National Violent Death Reporting System and the Small Arms Survey %A Song,In Han %A Lee,Jin Hyuk %A Shin,Jee Soo %+ ICONS Convergence Academy, Yonsei University, Appenzeller Hall #205, Seoul, 03722, Republic of Korea, 82 221236217, isong@yonsei.ac.kr %K firearm suicide %K US born %K foreign born %K means of suicide %K firearm possession rate %K suicide decedents %D 2023 %7 29.9.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Suicide by firearms is a serious public health issue in the United States. However, little research has been conducted on the relationship between cultural backgrounds and suicide by firearms, specifically in those born and raised in the United States compared to those who have immigrated to the United States. Objective: To better understand the relationship between cultural backgrounds and suicide, this study aimed to examine firearm suicide rates among US- and foreign-born suicide decedents based on the firearm possession rate in the decedent’s home country. Methods: Multivariate logistic regression was performed to analyze data of 28,895 suicide decedents from 37 states obtained from the 2017 National Violent Death Reporting System data set. The firearm possession rate in the home countries of foreign-born suicide decedents was obtained from the 2017 Small Arms Survey. Results: The firearm suicide rate was about twice as high among US-born suicide decedents compared to their foreign-born counterparts. Meanwhile, suicide by hanging was about 75% higher among foreign-born compared to US-born suicide decedents. Those from countries with a low-to-medium firearm possession rate were significantly less likely to use firearms compared to US-born suicide decedents (adjusted odds ratio [AOR]=0.45, 95% CI 0.31-0.65, and AOR=0.46, 95% CI 0.39-0.53, respectively). Meanwhile, firearm suicide rates were not different between US- and foreign-born suicide decedents from countries with a similarly high firearm possession rate. Conclusions: The results suggest that there is an association between using firearms as a means of suicide and the firearm possession rate in the decedent’s home country. Suicide by firearms in the United States needs to be understood in the sociocultural context related to firearm possession. %M 37773604 %R 10.2196/44211 %U https://publichealth.jmir.org/2023/1/e44211 %U https://doi.org/10.2196/44211 %U http://www.ncbi.nlm.nih.gov/pubmed/37773604 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 15 %N %P e50936 %T Toxicology Test Results for Public Health Surveillance of the Opioid Epidemic: Retrospective Analysis %A Schleyer,Titus %A Robinson,Bill %A Parmar,Samir %A Janowiak,Diane %A Gibson,P Joseph %A Spangler,Val %+ Center for Biomedical Informatics, Regenstrief Institute, Inc, 1101 W 10th St, Indianapolis, IN, 46202, United States, 1 3172749204, schleyer@regenstrief.org %K opioid epidemic %K clinical laboratory techniques %K public health %K epidemiology %K toxicology %D 2023 %7 28.9.2023 %9 Original Paper %J Online J Public Health Inform %G English %X Background: Addressing the opioid epidemic requires timely insights into population-level factors, such as trends in prevalence of legal and illegal substances, overdoses, and deaths. Objective: This study aimed to examine whether toxicology test results of living individuals from a variety of sources could be useful in surveilling the opioid epidemic. Methods: A retrospective analysis standardized, merged, and linked toxicology results from 24 laboratories in Marion County, Indiana, United States, from September 1, 2018, to August 31, 2019. The data set consisted of 33,787 Marion County residents and their 746,681 results. We related the data to general Marion County demographics and compared alerts generated by toxicology results to opioid overdose–related emergency department visits. Nineteen domain experts helped prototype analytical visualizations. Main outcome measures included test positivity in the county and by ZIP code; selected demographics of individuals with toxicology results; and correlation of toxicology results with opioid overdose–related emergency department visits. Results: Four percent of Marion County residents had at least 1 toxicology result. Test positivity rates ranged from 3% to 19% across ZIP codes. Males were underrepresented in the data set. Age distribution resembled that of Marion County. Alerts for opioid toxicology results were not correlated with opioid overdose–related emergency department visits. Conclusions: Analyzing toxicology results at scale was impeded by varying data formats, completeness, and representativeness; changes in data feeds; and patient matching difficulties. In this study, toxicology results did not predict spikes in opioid overdoses. Larger, more rigorous and well-controlled studies are needed to assess the utility of toxicology tests in predicting opioid overdose spikes. %M 38046561 %R 10.2196/50936 %U https://ojphi.jmir.org/2023/1/e50936 %U https://doi.org/10.2196/50936 %U http://www.ncbi.nlm.nih.gov/pubmed/38046561 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45767 %T Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach %A Dolatabadi,Elham %A Moyano,Diana %A Bales,Michael %A Spasojevic,Sofija %A Bhambhoria,Rohan %A Bhatti,Junaid %A Debnath,Shyamolima %A Hoell,Nicholas %A Li,Xin %A Leng,Celine %A Nanda,Sasha %A Saab,Jad %A Sahak,Esmat %A Sie,Fanny %A Uppal,Sara %A Vadlamudi,Nirma Khatri %A Vladimirova,Antoaneta %A Yakimovich,Artur %A Yang,Xiaoxue %A Kocak,Sedef Akinli %A Cheung,Angela M %+ Faculty of Health, School of Health Policy and Management, York University, 4700 Keele Street, North York, Toronto, ON, M3J 1P3, Canada, 1 6477069756, edolatab@yorku.ca %K long COVID %K post–COVID-19 condition %K PCC %K social media %K natural language processing %K transformer models %K bidirectional encoder representations from transformers %K machine learning %K Twitter %K Reddit %K PRO %K patient-reported outcome %K patient-reported symptom %K health outcome %K symptom %K entity extraction %K entity normalization %D 2023 %7 19.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: While scientific knowledge of post–COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. Objective: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline’s potential as a surveillance tool. Methods: We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. Results: UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. Conclusions: The outcome of our social media–derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient’s journey that can help health care providers anticipate future needs. International Registered Report Identifier (IRRID): RR2-10.1101/2022.12.14.22283419 %M 37725432 %R 10.2196/45767 %U https://www.jmir.org/2023/1/e45767 %U https://doi.org/10.2196/45767 %U http://www.ncbi.nlm.nih.gov/pubmed/37725432 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e42446 %T Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19 %A Chu,Amanda MY %A Chong,Andy C Y %A Lai,Nick H T %A Tiwari,Agnes %A So,Mike K P %+ Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong, 852 23587726, immkpso@ust.hk %K internet search volumes %K network analysis %K pandemic risk %K health care analytics %K network connectedness %K infodemiology %K infoveillance %K mobile phone %K COVID-19 %D 2023 %7 7.9.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT’s normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks. Objective: This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk. Methods: We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated. Results: Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores. Conclusions: The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet. %M 37676701 %R 10.2196/42446 %U https://publichealth.jmir.org/2023/1/e42446 %U https://doi.org/10.2196/42446 %U http://www.ncbi.nlm.nih.gov/pubmed/37676701 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44950 %T Checkpoint Travel Numbers as a Proxy Variable in Population-Based Studies During the COVID-19 Pandemic: Validation Study %A Kreslake,Jennifer M %A Aarvig,Kathleen %A Muller-Tabanera,Hope %A Vallone,Donna M %A Hair,Elizabeth C %+ Schroeder Institute, Truth Initiative, 900 G Street Northwest, Floor 4, Washington, DC, 20001, United States, 1 2024545782, jkreslake@truthinitiative.org %K research methods %K public health %K data quality %K psychosocial factors %K history %K COVID-19 %K social %K behavioral %K validation %K social distancing %K tracking survey %K survey %K pandemic %D 2023 %7 29.8.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic had wide-ranging systemic impacts, with implications for social and behavioral factors in human health. The pandemic may introduce history bias in population-level research studies of other health topics during the COVID-19 period. Objective: We sought to identify and validate an accessible, flexible measure to serve as a covariate in research spanning the COVID-19 pandemic period. Methods: Transportation Security Administration checkpoint travel numbers were used to calculate a weekly sum of daily passengers and validated against two measures with strong face validity: (1) a self-reported item on social distancing practices drawn from a continuous tracking survey among a national sample of youths and young adults (15-24 years) in the United States (N=45,080, approximately 280 unique respondents each week); and (2) Google’s Community Mobility Reports, which calculate daily values at the national level to represent rates of change in visits and length of stays to public spaces. For the self-reported survey data, an aggregated week-level variable was calculated as the proportion of respondents who did not practice social distancing that week (January 1, 2019, to May 31, 2022). For the community mobility data, a weekly estimate of change was calculated using daily values compared to a 5-week prepandemic baseline period (January 3, 2020, to February 6, 2020). Spearman rank correlation coefficients were calculated for each comparison. Results: Checkpoint travel data ranged from 668,719 travelers in the week of April 8, 2020, to nearly 15.5 million travelers in the week of May 18, 2022. The weekly proportion of survey respondents who did not practice social distancing ranged from 18.1% (n=42; week of April 15, 2020) to 70.9% (n=213; week of May 25, 2022). The measures were strongly correlated from January 2019 to May 2022 (ρ=0.90, P<.001) and March 2020 to May 2022 (ρ=0.87, P<.001). Strong correlations were observed when analyses were restricted to age groups (15-17 years: ρ=0.90; P<.001; 18-20 years: ρ=0.87; P<.001; 21-24 years: ρ=0.88; P<.001), racial or ethnic minorities (ρ=0.86, P<.001), and respondents with lower socioeconomic status (ρ=0.88, P<.001). There were also strong correlations between the weekly change from the baseline period for checkpoint travel data and community mobility data for transit stations (ρ=0.92, P<.001) and retail and recreation (ρ=0.89, P<.001), and moderate significant correlations for grocery and pharmacy (ρ=0.68, P<.001) and parks (ρ=0.62, P<.001). A strong negative correlation was observed for places of residence (ρ=−0.78, P<.001), and a weak but significant positive correlation was found for workplaces (ρ=0.24, P<.001). Conclusions: The Transportation Security Administration’s travel checkpoint data provide a publicly available flexible time-varying metric to control for history bias introduced by the pandemic in research studies spanning the COVID-19 period in the United States. %M 37191643 %R 10.2196/44950 %U https://publichealth.jmir.org/2023/1/e44950 %U https://doi.org/10.2196/44950 %U http://www.ncbi.nlm.nih.gov/pubmed/37191643 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 12 %N %P e43492 %T Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study %A Bisrat,Haileleul %A Manyazewal,Tsegahun %A Fekadu,Abebaw %+ Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, BL_03_420 St., Addis Abeba, 9086, Ethiopia, 251 911940459, haylishb2@gmail.com %K mobile health %K mHealth %K digital health %K COVID-19 %K syndrome assessment %K surveillance %K Ethiopia %K public health %K syndrome surveillance %K self-care %K telemedicine %K telecom, SARS-CoV-2 %D 2023 %7 28.8.2023 %9 Original Paper %J Interact J Med Res %G English %X Background: Since most people in low-income countries do not have access to reliable laboratory services, early diagnosis of life-threatening diseases like COVID-19 remains challenging. Facilitating real-time assessment of the health status in a given population, mobile health (mHealth)–supported syndrome surveillance might help identify disease conditions earlier and save lives cost-effectively. Objective: This study aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia. Methods: A comparative cross-sectional study was conducted among adults randomly selected from the Ethio telecom list of mobile phone numbers. Participants underwent a comprehensive phone interview for COVID-19 syndromic assessments, and their symptoms were scored and interpreted based on national guidelines. Participants who exhibited COVID-19 syndromes were advised to have COVID-19 diagnostic testing at nearby health care facilities and seek treatment accordingly. Participants were asked about their test results, and these were cross-checked against the actual facility-based data. Estimates of COVID-19 detection by mHealth-supported syndromic assessments and facility-based tests were compared using Cohen Kappa (κ), the receiver operating characteristic curve, sensitivity, and specificity analysis. Results: A total of 2741 adults (n=1476, 53.8% men and n=1265, 46.2% women) were interviewed through the mHealth platform during the period from December 2021 to February 2022. Among them, 1371 (50%) had COVID-19 symptoms at least once and underwent facility-based COVID-19 diagnostic testing as self-reported, with 884 (64.5%) confirmed cases recorded in facility-based registries. The syndrome assessment model had an optimal likelihood cut-off point sensitivity of 46% (95% CI 38.4-54.6) and specificity of 98% (95% CI 96.7-98.9). The area under the receiver operating characteristic curve was 0.87 (95% CI 0.83-0.91). The level of agreement between the mHealth-supported syndrome assessment and the COVID-19 test results was moderate (κ=0.54, 95% CI 0.46-0.60). Conclusions: In this study, the level of agreement between the mHealth-supported syndromic assessment and the actual laboratory-confirmed results for COVID-19 was found to be reasonable, at 89%. The mHealth-supported syndromic assessment of COVID-19 represents a potential alternative method to the standard laboratory-based confirmatory diagnosis, enabling the early detection of COVID-19 cases in hard-to-reach communities, and informing patients about self-care and disease management in a cost-effective manner. These findings can guide future research efforts in developing and integrating digital health into continuous active surveillance of emerging infectious diseases. %M 37556182 %R 10.2196/43492 %U https://www.i-jmr.org/2023/1/e43492 %U https://doi.org/10.2196/43492 %U http://www.ncbi.nlm.nih.gov/pubmed/37556182 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e43790 %T Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases %A Maxwell,Sarah P %A Brooks,Chris %A Kim,Dohyeong %A McNeely,Connie L %A Cho,Seonga %A Thomas,Kevin C %+ School of Economic, Political and Policy Sciences, University of Texas at Dallas, 800 West Campbell Avenue, Richardson, TX, 75080, United States, 1 972 883 6469, sarah.maxwell@utdallas.edu %K tick-borne disease surveillance %K Lyme disease %K tick bite encounter %K One Health model %K triangulation %K entomology %K entomological %K tick %K thematic mapping %K spatial %K risk %K surveillance %K vector %D 2023 %7 23.8.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The extent of tick-borne disease (TBD) risk in the United States is generally unknown. Active surveillance using entomological measures, such as presence and density of infected nymphal Ixodes scapularis ticks, have served as indicators for assessing human risk, but results have been inconsistent and passive surveillance via public health systems suggests TBDs are underreported. Objective: Research using various data sources and collection methods (eg, Google Trends, apps, and tick bite encounters [TBEs] reports) has shown promise for assessing human TBD risk. In that vein, and engaging a One Health perspective, this study used multimodal databases, geographically overlaying patient survey data on TBEs and concomitant reports of TBDs with data drawn from other sources, such as canine serological reports, to glean insights and to determine and assess the use of various indicators as proxies for human TBD risk. Methods: This study used a mixed methods research strategy, relying on triangulation techniques and drawing on multiple data sources to provide insights into various aspects of human disease risk from TBEs and TBDs in the United States. A web-based survey was conducted over a 15-month period beginning in December 2020 to collect data on TBEs. To maximize the value of the covariate data, related analyses included TBE reports that occurred in the United States between January 1, 2000, and March 31, 2021. TBEs among patients diagnosed with Lyme disease were analyzed at the county level and compared to I scapularis and I pacificus tick presence, human cases identified by the Centers for Disease Control and Prevention (CDC), and canine serological data. Spatial analyses employed multilayer thematic mapping and other techniques. Results: After cleaning, survey results showed a total of 249 (75.7%) TBEs spread across 148 respondents (61.9% of all respondents, 81.7% of TBE-positive respondents); 144 (4.7%) counties in 30 states (60%) remained eligible for analysis, with an average of 1.68 (SD 1.00) and median of 1 (IQR 1) TBEs per respondent. Analysis revealed significant spatial matching at the county level among patient survey reports of TBEs and disease risk indicators from the CDC and other official sources. Thematic mapping results included one-for-one county-level matching of reported TBEs with at least 1 designated source of human disease risk (ie, positive canine serological tests, CDC-reported Lyme disease, or known tick presence). Conclusions: Use of triangulation methods to integrate patient data on TBE recall with established canine serological reports, tick presence, and official human TBD information offers more granular, county-level information regarding TBD risk to inform clinicians and public health officials. Such data may supplement public health sources to offer improved surveillance and provide bases for developing robust proxies for TBD risk among humans. %M 37610812 %R 10.2196/43790 %U https://publichealth.jmir.org/2023/1/e43790 %U https://doi.org/10.2196/43790 %U http://www.ncbi.nlm.nih.gov/pubmed/37610812 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e47315 %T A Developmental Surveillance Score for Quantitative Monitoring of Early Childhood Milestone Attainment: Algorithm Development and Validation %A Bilu,Yonatan %A Amit,Guy %A Sudry,Tamar %A Akiva,Pinchas %A Avgil Tsadok,Meytal %A Zimmerman,Deena R %A Baruch,Ravit %A Sadaka,Yair %+ KI Research Institute, Hazayit 11, Kfar Malal, 4592000, Israel, 972 99797788, yonatan@kinstitute.org.il %K child development %K risk scores %K scoring methods %K language delay %K motor skills delay %K developmental %K surveillance %K developmental delays %K developmental milestones %K young children %K intervention %K child %D 2023 %7 18.8.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Developmental surveillance, conducted routinely worldwide, is fundamental for timely identification of children at risk of developmental delays. It is typically executed by assessing age-appropriate milestone attainment and applying clinical judgment during health supervision visits. Unlike developmental screening and evaluation tools, surveillance typically lacks standardized quantitative measures, and consequently, its interpretation is often qualitative and subjective. Objective: Herein, we suggested a novel method for aggregating developmental surveillance assessments into a single score that coherently depicts and monitors child development. We described the procedure for calculating the score and demonstrated its ability to effectively capture known population-level associations. Additionally, we showed that the score can be used to describe longitudinal patterns of development that may facilitate tracking and classifying developmental trajectories of children. Methods: We described the Developmental Surveillance Score (DSS), a simple-to-use tool that quantifies the age-dependent severity level of a failure at attaining developmental milestones based on the recently introduced Israeli developmental surveillance program. We evaluated the DSS using a nationwide cohort of >1 million Israeli children from birth to 36 months of age, assessed between July 1, 2014, and September 1, 2021. We measured the score’s ability to capture known associations between developmental delays and characteristics of the mother and child. Additionally, we computed series of the DSS in consecutive visits to describe a child’s longitudinal development and applied cluster analysis to identify distinct patterns of these developmental trajectories. Results: The analyzed cohort included 1,130,005 children. The evaluation of the DSS on subpopulations of the cohort, stratified by known risk factors of developmental delays, revealed expected relations between developmental delay and characteristics of the child and mother, including demographics and obstetrics-related variables. On average, the score was worse for preterm children compared to full-term children and for male children compared to female children, and it was correspondingly worse for lower levels of maternal education. The trajectories of scores in 6 consecutive visits were available for 294,000 children. The clustering of these trajectories revealed 3 main types of developmental patterns that are consistent with clinical experience: children who successfully attain milestones, children who initially tend to fail but improve over time, and children whose failures tend to increase over time. Conclusions: The suggested score is straightforward to compute in its basic form and can be easily implemented as a web-based tool in its more elaborate form. It highlights known and novel relations between developmental delay and characteristics of the mother and child, demonstrating its potential usefulness for surveillance and research. Additionally, it can monitor the developmental trajectory of a child and characterize it. Future work is needed to calibrate the score vis-a-vis other screening tools, validate it worldwide, and integrate it into the clinical workflow of developmental surveillance. %M 37489583 %R 10.2196/47315 %U https://publichealth.jmir.org/2023/1/e47315 %U https://doi.org/10.2196/47315 %U http://www.ncbi.nlm.nih.gov/pubmed/37489583 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e43011 %T News Coverage of Face Masks in Australia During the Early COVID-19 Pandemic: Topic Modeling Study %A Dasgupta,Pritam %A Amin,Janaki %A Paris,Cecile %A MacIntyre,C Raina %+ Department of Health Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Level 3,, 75 Talavera Rd, Sydney, New South Wales, 2109, Australia, 61 480148227, pritam.dasgupta@students.mq.edu.au %K face masks %K mask %K COVID-19 %K web-based news %K community sentiment %K topic modeling %K latent Dirichlet allocation %D 2023 %7 16.8.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: During the COVID-19 pandemic, web-based media coverage of preventative strategies proliferated substantially. News media was constantly informing people about changes in public health policy and practices such as mask-wearing. Hence, exploring news media content on face mask use is useful to analyze dominant topics and their trends. Objective: The aim of the study was to examine news related to face masks as well as to identify related topics and temporal trends in Australian web-based news media during the early COVID-19 pandemic period. Methods: Following data collection from the Google News platform, a trend analysis on the mask-related news titles from Australian news publishers was conducted. Then, a latent Dirichlet allocation topic modeling algorithm was applied along with evaluation matrices (quantitative and qualitative measures). Afterward, topic trends were developed and analyzed in the context of mask use during the pandemic. Results: A total of 2345 face mask–related eligible news titles were collected from January 25, 2020, to January 25, 2021. Mask-related news showed an increasing trend corresponding to increasing COVID-19 cases in Australia. The best-fitted latent Dirichlet allocation model discovered 8 different topics with a coherence score of 0.66 and a perplexity measure of –11.29. The major topics were T1 (mask-related international affairs), T2 (introducing mask mandate in places such as Melbourne and Sydney), and T4 (antimask sentiment). Topic trends revealed that T2 was the most frequent topic in January 2021 (77 news titles), corresponding to the mandatory mask-wearing policy in Sydney. Conclusions: This study demonstrated that Australian news media reflected a wide range of community concerns about face masks, peaking as COVID-19 incidence increased. Harnessing the news media platforms for understanding the media agenda and community concerns may assist in effective health communication during a pandemic response. %M 37379362 %R 10.2196/43011 %U https://infodemiology.jmir.org/2023/1/e43011 %U https://doi.org/10.2196/43011 %U http://www.ncbi.nlm.nih.gov/pubmed/37379362 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e39754 %T Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study %A Rayo,Michael F %A Faulkner,Daria %A Kline,David %A Thornhill IV,Thomas %A Malloy,Samuel %A Della Vella,Dante %A Morey,Dane A %A Zhang,Net %A Gonsalves,Gregg %+ Public Health Modeling Unit, Department of the Epidemiology of Microbial Diseases, Yale School of Public Health, 350 George Street, 3rd Floor, New Haven, CT, 06511, United States, 1 2036069149, gregg.gonsalves@yale.edu %K active surveillance %K bandit algorithms %K infectious disease %K community health %K reinforcement learning %K COVID-19 %K SARS-CoV-2 %D 2023 %7 15.8.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field. Objective: This study’s objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021. Methods: The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives—including gift cards and take-home rapid antigen tests—were offered to those who approached the pop-up sites to encourage their participation. Results: Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located. Conclusions: This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV. %M 37581924 %R 10.2196/39754 %U https://publichealth.jmir.org/2023/1/e39754 %U https://doi.org/10.2196/39754 %U http://www.ncbi.nlm.nih.gov/pubmed/37581924 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46026 %T Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study %A Schooley,Benjamin L %A Ahmed,Abdulaziz %A Maxwell,Justine %A Feldman,Sue S %+ University of Alabama at Birmingham, 1716 9th Street South, SHP 590K, Birmingham, AL, 35294, United States, 1 205 975 0809, sfeldman@uab.edu %K COVID-19 %K risk assessment %K symptom tracker %K passport application %K surveillance %K mobile app %K multiple linear regression %K healthcheck %K public health informatics %K decision support system %K health information technology %D 2023 %7 25.7.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Some of the most vexing issues with the COVID-19 pandemic were the inability of facilities and events, such as schools and work areas, to track symptoms to mitigate the spread of the disease. To combat these challenges, many turned to the implementation of technology. Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to influence behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy. This paper presents the implementation and early evaluation of a return-to-work COVID-19 symptom and risk assessment tool. The system was implemented across 34 institutions of health and education in Alabama, including more than 174,000 users with over 4 million total uses and more than 86,000 reports of exposure risk between July 2020 and April 2021. Objective: This study aimed to explore the usage of technology, specifically a COVID-19 symptom and risk assessment tool, to mitigate exposure to COVID-19 within public spaces. More specifically, the objective was to assess the relationship between user-reported symptoms and exposure via a mobile health app, with confirmed COVID-19 cases reported by the Alabama Department of Public Health (ADPH). Methods: This cross-sectional study evaluated the relationship between confirmed COVID-19 cases and user-reported COVID-19 symptoms and exposure reported through the Healthcheck web-based mobile application. A dependent variable for confirmed COVID-19 cases in Alabama was obtained from ADPH. Independent variables (ie, health symptoms and exposure) were collected through Healthcheck survey data and included measures assessing COVID-19–related risk levels and symptoms. Multiple linear regression was used to examine the relationship between ADPH-confirmed diagnosis of COVID-19 and self-reported health symptoms and exposure via Healthcheck that were analyzed across the state population but not connected at the individual patient level. Results: Regression analysis showed that the self-reported information collected by Healthcheck significantly affects the number of COVID-19–confirmed cases. The results demonstrate that the average number of confirmed COVID-19 cases increased by 5 (high risk: β=5.10; P=.001), decreased by 24 (sore throat: β=−24.03; P=.001), and increased by 21 (nausea or vomiting: β=21.67; P=.02) per day for every additional self-report of symptoms by Healthcheck survey respondents. Congestion or runny nose was the most frequently reported symptom. Sore throat, low risk, high risk, nausea, or vomiting were all statistically significant factors. Conclusions: The use of technology allowed organizations to remotely track a population as it is related to COVID-19. Healthcheck was a platform that aided in symptom tracking, risk assessment, and evaluation of status for admitting individuals into public spaces for people in the Alabama area. The confirmed relationship between symptom and exposure self-reporting using an app and population-wide confirmed cases suggests that further investigation is needed to determine the opportunity for such apps to mitigate disease spread at a community and individual level. %M 37490320 %R 10.2196/46026 %U https://www.jmir.org/2023/1/e46026 %U https://doi.org/10.2196/46026 %U http://www.ncbi.nlm.nih.gov/pubmed/37490320 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e46792 %T The Combined Effects of Short-Term Exposure to Multiple Meteorological Factors on Unintentional Drowning Mortality: Large Case-Crossover Study %A Liu,Yingyin %A Dong,Xiaomei %A Li,Zhixing %A Zhu,Sui %A Lin,Ziqiang %A He,Guanhao %A Gong,Weiwei %A Hu,Jianxiong %A Hou,Zhulin %A Meng,Ruilin %A Zhou,Chunliang %A Yu,Min %A Huang,Biao %A Lin,Lifeng %A Xiao,Jianpeng %A Zhong,Jieming %A Jin,Donghui %A Xu,Yiqing %A Lv,Lingshuang %A Huang,Cunrui %A Liu,Tao %A Ma,Wenjun %+ Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Avenue, Tianhe District, Guangzhou, 510632, China, 86 13622280436, mawj@gdiph.org.cn %K drowning %K exposure mixture %K quantile g-computation %K environmental epidemiology %K meteorological factor %D 2023 %7 20.7.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Drowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. Objective: We aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. Methods: Unintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. Results: A total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and –9.5% to the overall effects, respectively. Conclusions: This study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future. %M 37471118 %R 10.2196/46792 %U https://publichealth.jmir.org/2023/1/e46792 %U https://doi.org/10.2196/46792 %U http://www.ncbi.nlm.nih.gov/pubmed/37471118 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e47110 %T Survival and Trends in Annualized Hazard Function by Age at Diagnosis Among Chinese Breast Cancer Patients Aged ≤40 Years: Case Analysis Study %A Wang,Jun %A Luo,Ting %A Xiang,Zhong-zheng %A He,Ming-min %A Zeng,Yuan-yuan %A Yang,Tian %A Wei,Xiao-yuan %A Yu,Siting %A Dai,Ze-lei %A Xu,Ning-yue %A Tan,Chen-feng %A Liu,Lei %+ Department of Head and Neck Oncology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, 610044, China, 86 18805053037, liuleihx@gmail.com %K breast cancer %K young age %K age strata %K survival %K annual hazard function %K China %D 2023 %7 18.7.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Young breast cancer patients are more likely to develop aggressive tumor characteristics and a worse prognosis than older women, and different races and ethnicities have distinct epidemiologies and prognoses. However, few studies have evaluated the clinical biological features and relapse patterns in different age strata of young women in Asia. Objective: We aimed to explore survival differences and the hazard function in young Chinese patients with breast cancer (BC) by age. Methods: The patients were enrolled from West China Hospital, Sichuan University. The chi-squared test, a Kaplan-Meier analysis, a log-rank test, a Cox multivariate hazards regression model, and a hazard function were applied for data analysis. Locoregional recurrence–free survival (LRFS), distant metastasis–free survival (DMFS), breast cancer–specific survival (BCSS), and overall survival (OS) were defined as end points. Results: We included 1928 young BC patients diagnosed between 2008 and 2019. Patients aged 18 to 25, 26 to 30, 31 to 35, and 36 to 40 years accounted for 2.7% (n=53), 11.8% (n=228), 27.7% (n=535), and 57.7% (n=1112) of the patients, respectively. The diagnosis of young BC significantly increased from 2008 to 2019. Five-year LRFS, DMFS, BCSS, and OS for the entire population were 98.3%, 93.4%, 94.4%, and 94%, respectively. Patients aged 18 to 25 years had significantly poorer 5-year LRFS (P<.001), 5-year DMFS (P<.001), 5-year BCSS (P=.04), and 5-year OS (P=.04) than those aged 31 to 35, 26 to 30, and 36 to 40 years. The hazard curves for recurrence and metastasis for the whole cohort continuously increased over the years, while the BC mortality risk peaked at 2 to 3 years and then slowly decreased. When stratified by age, the annualized hazard function for recurrence, metastasis, and BC mortality in different age strata showed significantly different trends, especially for BC mortality. Conclusions: The annual diagnosis of young BC seemed to increase in Chinese patients, and the distinct age strata of young BC patients did not differ in survival outcome or failure pattern. Our results might provide strategies for personalized management of young BC. %M 37463020 %R 10.2196/47110 %U https://publichealth.jmir.org/2023/1/e47110 %U https://doi.org/10.2196/47110 %U http://www.ncbi.nlm.nih.gov/pubmed/37463020 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e42149 %T Dashboard With Bump Charts to Visualize the Changes in the Rankings of Leading Causes of Death According to Two Lists: National Population-Based Time-Series Cross-Sectional Study %A Tai,Shu-Yu %A Chi,Ying-Chen %A Chien,Yu-Wen %A Kawachi,Ichiro %A Lu,Tsung-Hsueh %+ Department of Public Health, College of Medicine, National Cheng Kung University, No. 1, Dah Hsueh Road, East District, Tainan, 701, Taiwan, 886 0928389971, robertlu@mail.ncku.edu.tw %K COVID-19 %K dashboard %K data visualization %K leading causes of death %K mortality/trend %K ranking %K surveillance %K cause of mortality %K cause of death %K monitoring %K surveillance indicator %K health statistics %K mortality data %D 2023 %7 27.6.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Health advocates and the media often use the rankings of the leading causes of death (CODs) to draw attention to health issues with relatively high mortality burdens in a population. The National Center for Health Statistics (NCHS) publishes “Deaths: leading causes” annually. The ranking list used by the NCHS and statistical offices in several countries includes broad categories such as cancer, heart disease, and accidents. However, the list used by the World Health Organization (WHO) subdivides broad categories (17 for cancer, 8 for heart disease, and 6 for accidents) and classifies Alzheimer disease and related dementias and hypertensive diseases more comprehensively compared to the NCHS list. Regarding the data visualization of the rankings of leading CODs, the bar chart is the most commonly used graph; nevertheless, bar charts may not effectively reveal the changes in the rankings over time. Objective: The aim of this study is to use a dashboard with bump charts to visualize the changes in the rankings of the leading CODs in the United States by sex and age from 1999 to 2021, according to 2 lists (NCHS vs WHO). Methods: Data on the number of deaths in each category from each list for each year were obtained from the Wide-ranging Online Data for Epidemiologic Research system, maintained by the Center for Disease Control and Prevention. Rankings were based on the absolute number of deaths. The dashboard enables users to filter by list (NCHS or WHO) and demographic characteristics (sex and age) and highlight a particular COD. Results: Several CODs that were only on the WHO list, including brain, breast, colon, hematopoietic, lung, pancreas, prostate, and uterus cancer (all classified as cancer on the NCHS list); unintentional transport injury; poisoning; drowning; and falls (all classified as accidents on the NCHS list), were among the 10 leading CODs in several sex and age subgroups. In contrast, several CODs that appeared among the 10 leading CODs according to the NCHS list, such as pneumonia, kidney disease, cirrhosis, and sepsis, were excluded from the 10 leading CODs if the WHO list was used. The rank of Alzheimer disease and related dementias and hypertensive diseases according to the WHO list was higher than their ranks according to the NCHS list. A marked increase in the ranking of unintentional poisoning among men aged 45-64 years was noted from 2008 to 2021. Conclusions: A dashboard with bump charts can be used to improve the visualization of the changes in the rankings of leading CODs according to the WHO and NCHS lists as well as demographic characteristics; the visualization can help users make informed decisions regarding the most appropriate ranking list for their needs. %M 37368475 %R 10.2196/42149 %U https://publichealth.jmir.org/2023/1/e42149 %U https://doi.org/10.2196/42149 %U http://www.ncbi.nlm.nih.gov/pubmed/37368475 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e46357 %T Vector Autoregression for Forecasting the Number of COVID-19 Cases and Analyzing Behavioral Indicators in the Philippines: Ecologic Time-Trend Study %A Latorre,Angelica Anne Eligado %A Nakamura,Keiko %A Seino,Kaoruko %A Hasegawa,Takanori %+ Department of Global Health Entrepreneurship, Tokyo Medical and Dental University, Yushima 1-5-45, Bunkyo-ku, Tokyo, 113-8510, Japan, 81 3 5803 4048, nakamura.ith@tmd.ac.jp %K COVID-19 %K forecasting %K interest by the general public %K mobility %K surveillance %K vector autoregression %D 2023 %7 27.6.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Traditional surveillance systems rely on routine collection of data. The inherent delay in retrieval and analysis of data leads to reactionary rather than preventive measures. Forecasting and analysis of behavior-related data can supplement the information from traditional surveillance systems. Objective: We assessed the use of behavioral indicators, such as the general public’s interest in the risk of contracting SARS-CoV-2 and changes in their mobility, in building a vector autoregression model for forecasting and analysis of the relationships of these indicators with the number of COVID-19 cases in the National Capital Region. Methods: An etiologic, time-trend, ecologic study design was used to forecast the daily number of cases in 3 periods during the resurgence of COVID-19. We determined the lag length by combining knowledge on the epidemiology of SARS-CoV-2 and information criteria measures. We fitted 2 models to the training data set and computed their out-of-sample forecasts. Model 1 contains changes in mobility and number of cases with a dummy variable for the day of the week, while model 2 also includes the general public’s interest. The forecast accuracy of the models was compared using mean absolute percentage error. Granger causality test was performed to determine whether changes in mobility and public’s interest improved the prediction of cases. We tested the assumptions of the model through the Augmented Dickey-Fuller test, Lagrange multiplier test, and assessment of the moduli of eigenvalues. Results: A vector autoregression (8) model was fitted to the training data as the information criteria measures suggest the appropriateness of 8. Both models generated forecasts with similar trends to the actual number of cases during the forecast period of August 11-18 and September 15-22. However, the difference in the performance of the 2 models became substantial from January 28 to February 4, as the accuracy of model 2 remained within reasonable limits (mean absolute percentage error [MAPE]=21.4%) while model 1 became inaccurate (MAPE=74.2%). The results of the Granger causality test suggest that the relationship of public interest with number of cases changed over time. During the forecast period of August 11-18, only change in mobility (P=.002) improved the forecasting of cases, while public interest was also found to Granger-cause the number of cases during September 15-22 (P=.001) and January 28 to February 4 (P=.003). Conclusions: To the best of our knowledge, this is the first study that forecasted the number of COVID-19 cases and explored the relationship of behavioral indicators with the number of COVID-19 cases in the Philippines. The resemblance of the forecasts from model 2 with the actual data suggests its potential in providing information about future contingencies. Granger causality also implies the importance of examining changes in mobility and public interest for surveillance purposes. %M 37368473 %R 10.2196/46357 %U https://formative.jmir.org/2023/1/e46357 %U https://doi.org/10.2196/46357 %U http://www.ncbi.nlm.nih.gov/pubmed/37368473 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44966 %T Digital Public Health Solutions in Response to the COVID-19 Pandemic: Comparative Analysis of Contact Tracing Solutions Deployed in Japan and Germany %A Louw,Candice %+ Department of Epidemiology, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, 38124, Germany, 49 53161813124, candice.louw@helmholtz-hzi.de %K contact tracing %K COVID-19 %K digital health %K digitalization %K open-source software %K pandemic preparedness %K pandemic technologies %D 2023 %7 14.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: In response to the COVID-19 pandemic, numerous countries, including the likes of Japan and Germany, initiated, developed, and deployed digital contact tracing solutions in an effort to detect and interrupt COVID-19 transmission chains. These initiatives indicated the willingness of both the Japanese and German governments to support eHealth solution development for public health; however, end user acceptance, trust, and willingness to make use of the solutions delivered through these initiatives are critical to their success. Through a case-based analysis of contact tracing solutions deployed in Japan and Germany during the COVID-19 pandemic we may gain valuable perspectives on the transnational role of digital technologies in crises, while also projecting possible directions for future pandemic technologies. Objective: In this study, we investigate (1) which types of digital contact tracing solutions were developed and deployed by the Japanese and German governments in response to the COVID-19 pandemic and (2) how many of these solutions are open-source software (OSS) solutions. Our objective is to establish not only the type of applications that may be needed in response to a pandemic from the perspective of 2 geographically diverse, world-leading economies but also how prevalent OSS pandemic technology development has been in this context. Methods: We analyze the official government websites of Japan and Germany to identify digital solutions that are developed and deployed for contact tracing purposes (for any length of time) during the timeframe January-December 2021, specifically in response to the COVID-19 pandemic. We subsequently perform a case-oriented comparative analysis, also identifying which solutions are published as open-source. Results: In Japan, a proximity tracing tool (COVID-19 Contact-Confirming Application [COCOA]) and an outbreak management tool (Health Center Real-time Information-sharing System on COVID-19 [HER-SYS]) with an integrated symptom tracking tool (My HER-SYS) were developed. In Germany, a proximity tracing tool (Corona-Warn-App) and an outbreak management tool (Surveillance Outbreak Response Management and Analysis System [SORMAS]) were developed. From these identified solutions, COCOA, Corona-Warn-App, and SORMAS were published as open-source, indicating support by both the Japanese and German governments for OSS pandemic technology development in the context of public health. Conclusions: Japan and Germany showed support for developing and deploying not only digital contact tracing solutions but also OSS digital contact tracing solutions in response to the COVID-19 pandemic. Despite the open nature of such OSS solutions’ source code, software solutions (both OSS and non-OSS) are only as transparent as the live or production environment where their processed data is hosted or stored. Software development and live software hosting are thus 2 sides of the same coin. It is nonetheless arguable that OSS pandemic technology solutions for public health are a step in the right direction for enhanced transparency in the interest of the greater public good. %M 37314852 %R 10.2196/44966 %U https://www.jmir.org/2023/1/e44966 %U https://doi.org/10.2196/44966 %U http://www.ncbi.nlm.nih.gov/pubmed/37314852 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44647 %T Prediction of Multimorbidity in Brazil: Latest Fifth of a Century Population Study %A Li,Xi-liang %A Huang,Hang %A Lu,Ying %A Stafford,Randall S %A Lima,Simone Maria %A Mota,Caroline %A Shi,Xin %+ School of Mathematics and Information Science, Shandong Technology and Business University, Binhai Road, Yantai, 264005, China, 86 18059892450, jasonshi510@hotmail.com %K Brazil %K demographic factors %K logistic regression analysis %K multimorbidity %K nomogram prediction %K prevalence %D 2023 %7 30.5.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Multimorbidity is characterized by the co-occurrence of 2 or more chronic diseases and has been a focus of the health care sector and health policy makers due to its severe adverse effects. Objective: This paper aims to use the latest 2 decades of national health data in Brazil to analyze the effects of demographic factors and predict the impact of various risk factors on multimorbidity. Methods: Data analysis methods include descriptive analysis, logistic regression, and nomogram prediction. The study makes use of a set of national cross-sectional data with a sample size of 877,032. The study used data from 1998, 2003, and 2008 from the Brazilian National Household Sample Survey, and from 2013 and 2019 from the Brazilian National Health Survey. We developed a logistic regression model to assess the influence of risk factors on multimorbidity and predict the influence of the key risk factors in the future, based on the prevalence of multimorbidity in Brazil. Results: Overall, females were 1.7 times more likely to experience multimorbidity than males (odds ratio [OR] 1.72, 95% CI 1.69-1.74). The prevalence of multimorbidity among unemployed individuals was 1.5 times that of employed individuals (OR 1.51, 95% CI 1.49-1.53). Multimorbidity prevalence increased significantly with age. People over 60 years of age were about 20 times more likely to have multiple chronic diseases than those between 18 and 29 years of age (OR 19.6, 95% CI 19.15-20.07). The prevalence of multimorbidity in illiterate individuals was 1.2 times that in literate ones (OR 1.26, 95% CI 1.24-1.28). The subjective well-being of seniors without multimorbidity was 15 times that among people with multimorbidity (OR 15.29, 95% CI 14.97-15.63). Adults with multimorbidity were more than 1.5 times more likely to be hospitalized than those without (OR 1.53, 95% CI 1.50-1.56) and 1.9 times more likely need medical care (OR 1.94, 95% CI 1.91-1.97). These patterns were similar in all 5 cohort studies and remained stable for over 21 years. A nomogram model was used to predict multimorbidity prevalence under the influence of various risk factors. The prediction results were consistent with the effects of logistic regression; older age and poorer participant well-being had the strongest correlation with multimorbidity. Conclusions: Our study shows that multimorbidity prevalence varied little in the past 2 decades but varies widely across social groups. Identifying populations with higher rates of multimorbidity prevalence may improve policy making around multimorbidity prevention and management. The Brazilian government can create public health policies targeting these groups, and provide more medical treatment and health services to support and protect the multimorbidity population. %M 37252771 %R 10.2196/44647 %U https://publichealth.jmir.org/2023/1/e44647 %U https://doi.org/10.2196/44647 %U http://www.ncbi.nlm.nih.gov/pubmed/37252771 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e43017 %T Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation %A Mason,Joseph A %A Friedman,Eleanor E %A Devlin,Samantha A %A Schneider,John A %A Ridgway,Jessica P %+ The Chicago Center for HIV Elimination, Department of Medicine, University of Chicago, 5837 S. Maryland Ave., Chicago, IL, 60637, United States, 1 773 702 9586, joemason@bsd.uchicago.edu %K HIV %K predictive model %K lapse in care %K retention in care %K people living with HIV %K Chicago %K HIV care continuum %K electronic health record %K EHR %D 2023 %7 17.5.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Reducing care lapses for people living with HIV is critical to ending the HIV epidemic and beneficial for their health. Predictive modeling can identify clinical factors associated with HIV care lapses. Previous studies have identified these factors within a single clinic or using a national network of clinics, but public health strategies to improve retention in care in the United States often occur within a regional jurisdiction (eg, a city or county). Objective: We sought to build predictive models of HIV care lapses using a large, multisite, noncurated database of electronic health records (EHRs) in Chicago, Illinois. Methods: We used 2011-2019 data from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database including multiple health systems, covering the majority of 23,580 people with an HIV diagnosis living in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago health care systems with different EHRs, providing a unique citywide view of retention in HIV care. From the database, we used diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, elastic net logistic regression, and XGBoost models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history. Results: We included people living with HIV with at least 2 HIV care encounters in the database, yielding 16,930 people living with HIV with 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the XGBoost model (area under the receiver operating characteristic curve 0.776, 95% CI 0.768-0.784 vs 0.674, 95% CI 0.664-0.683; P<.001). Top predictors included the history of care lapses, being seen by an infectious disease provider (vs a primary care provider), site of care, Hispanic ethnicity, and previous HIV laboratory testing. The random forest model (area under the receiver operating characteristic curve 0.751, 95% CI 0.742-0.759) revealed age, insurance type, and chronic comorbidities (eg, hypertension), as important variables in predicting a care lapse. Conclusions: We used a real-world approach to leverage the full scope of data available in modern EHRs to predict HIV care lapses. Our findings reinforce previously known factors, such as the history of prior care lapses, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and clinic-specific factors for predicting care lapses for people living with HIV in Chicago. We provide a framework for others to use data from multiple different health care systems within a single city to examine lapses in care using EHR data, which will aid in jurisdictional efforts to improve retention in HIV care. %M 37195750 %R 10.2196/43017 %U https://publichealth.jmir.org/2023/1/e43017 %U https://doi.org/10.2196/43017 %U http://www.ncbi.nlm.nih.gov/pubmed/37195750 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44186 %T Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study %A Wang,Zhaohan %A He,Jun %A Jin,Bolin %A Zhang,Lizhi %A Han,Chenyu %A Wang,Meiqi %A Wang,Hao %A An,Shuqi %A Zhao,Meifang %A Zhen,Qing %A Tiejun,Shui %A Zhang,Xinyao %+ Department of Social Medicine and Health Management, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China, 86 0431 85619442, 15892560@qq.com %K Baidu index %K chickenpox %K support vector machine regression model %K disease surveillance %K disease %K infectious %K vaccine %K surveillance system %K model %K prevention %K control %K monitoring %K epidemic %D 2023 %7 16.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. Objective: This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. Methods: Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. Results: The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as “chickenpox,” “chickenpox treatment,” “treatment of chickenpox,” “chickenpox symptoms,” and “chickenpox virus,” trend consistently. Some BDI search terms, such as “chickenpox pictures,” “symptoms of chickenpox,” “chickenpox vaccine,” and “is chickenpox vaccine necessary,” appeared earlier than the trend of “chickenpox virus.” The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. Conclusions: These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems. %M 37191983 %R 10.2196/44186 %U https://www.jmir.org/2023/1/e44186 %U https://doi.org/10.2196/44186 %U http://www.ncbi.nlm.nih.gov/pubmed/37191983 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e37540 %T Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study %A Ondrikova,Nikola %A Harris,John P %A Douglas,Amy %A Hughes,Helen E %A Iturriza-Gomara,Miren %A Vivancos,Roberto %A Elliot,Alex J %A Cunliffe,Nigel A %A Clough,Helen E %+ Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool, L69 7BE, United Kingdom, 44 7445422498, nikola.ondrikova@liverpool.ac.uk %K syndromic data %K syndromic surveillance %K surveillance %K infodemiology %K norovirus %K Google Trends %K Wikipedia %K prediction %K variable importance %K mental model %K infoveillance %K trend %K gastroenteritis %K gastroenterology %K gastroenterologist %K internal medicine %K viral disease %K viral %K virus %K communicable disease %K infection prevention %K infection control %K infectious disease %K viral infection %K disease spread %K big data %K Granger causality framework %K predict %K model %K web-based data %K internet data %K transmission %D 2023 %7 8.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective: This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods: We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results: Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including “flu symptoms,” “norovirus in pregnancy,” and norovirus activity in specific years, such as “norovirus 2016.” Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions: Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information–seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual’s conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies. %M 37155231 %R 10.2196/37540 %U https://www.jmir.org/2023/1/e37540 %U https://doi.org/10.2196/37540 %U http://www.ncbi.nlm.nih.gov/pubmed/37155231 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40138 %T Maintaining Adherence to COVID-19 Preventive Practices and Policies Pertaining to Masking and Distancing in the District of Columbia and Other US States: Systematic Observational Study %A Ruiz,Monica S %A McMahon,Mercedes V %A Latif,Hannah %A Vyas,Amita %+ Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW, Suite 300, Washington, DC, 20052, United States, 1 202 994 3676, msruiz@gwu.edu %K COVID-19 %K mask adherence %K social distancing %K public health %K health policy %K public health mandates %D 2023 %7 25.4.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Prior to the development of effective vaccines against SARS-CoV-2, masking and social distancing emerged as important strategies for infection control. Locations across the United States required or recommended face coverings where distancing was not possible, but it is unclear to what extent people complied with these policies. Objective: This study provides descriptive information about adherence to public health policies pertaining to mask wearing and social distancing and examines differences in adherence to these policies among different population groups in the District of Columbia and 8 US states. Methods: This study was part of a national systematic observational study using a validated research protocol for recording adherence to correct mask wearing and maintaining social distance (6 feet/1.83 meters) from other individuals. Data were collected from December 2020 to August 2021 by research team members who stationed themselves in outdoor areas with high pedestrian traffic, observed individuals crossing their paths, and collected data on whether individuals’ masks were present (visible or not visible) or worn (correctly, incorrectly, not at all) and whether social distance was maintained if other individuals were present. Observational data were entered electronically into Google Forms and were exported in Excel format for analysis. All data analyses were conducted using SPSS. Information on local COVID-19 protection policies (eg, mask wearing requirements) was obtained by examining city and state health department websites for the locations where data were being collected. Results: At the time these data were collected, most locations in our study required (5937/10,308, 57.6%) or recommended (4207/10,308, 40.8%) masking. Despite this, more than 30% of our sample were unmasked (2889/10136, 28.5%) or masked incorrectly (636/10136, 6.3%). Masking policy was significantly related to correct masking with locations that required or recommended masking (66% correct masking vs 28/164, 17.1% in locations that did not require masking, P<.001). Participants who maintained social distance from others were more likely to be correctly masked than those who were not (P<.001). Adherence to masking policy by location was significant (P<.001); however, this was driven by 100% compliance in Georgia, which did not require masks at any point during the data collection period. When the same analysis was conducted for compliance with mask requirements and recommendations, there was no significant difference by location. Overall adherence to masking policies was 66.9% Conclusions: Despite a clear relationship between mask policies and masking behavior, one-third of our sample was nonadherent to those policies, and approximately 23% of our sample did not have any mask, either on or visible. This may speak to the confusion surrounding “risk” and protective behaviors, as well as pandemic fatigue. These results underscore the importance of clear public health communication, particularly given variations in public health policies across states and localities. %M 36888910 %R 10.2196/40138 %U https://publichealth.jmir.org/2023/1/e40138 %U https://doi.org/10.2196/40138 %U http://www.ncbi.nlm.nih.gov/pubmed/36888910 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e39409 %T Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions %A Rich,Shannan N %A Richards,Veronica %A Mavian,Carla %A Rife Magalis,Brittany %A Grubaugh,Nathan %A Rasmussen,Sonja A %A Dellicour,Simon %A Vrancken,Bram %A Carrington,Christine %A Fisk-Hoffman,Rebecca %A Danso-Odei,Demi %A Chacreton,Daniel %A Shapiro,Jerne %A Seraphin,Marie Nancy %A Hepp,Crystal %A Black,Allison %A Dennis,Ann %A Trovão,Nídia Sequeira %A Vandamme,Anne-Mieke %A Rasmussen,Angela %A Lauzardo,Michael %A Dean,Natalie %A Salemi,Marco %A Prosperi,Mattia %+ Department of Epidemiology, College of Public Health and Health Professions, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, United States, 1 3522735468, shannanrich@ufl.edu %K COVID-19 %K SARS-CoV-2 %K molecular epidemiology %K genomic surveillance %K variants %K pandemic %K phylogenetic %K genomic %K epidemiology %K data %K virology %K bioinformatics %K response %K phylodynamic %K monitoring %K surveillance %K transmission %D 2023 %7 21.4.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. Objective: The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics—many of whom were actively engaged in the COVID-19 response—to discuss and report on the application of phylodynamic tools to inform pandemic responses. Methods: In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. Results: We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. Conclusions: This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses. %M 36848460 %R 10.2196/39409 %U https://formative.jmir.org/2023/1/e39409 %U https://doi.org/10.2196/39409 %U http://www.ncbi.nlm.nih.gov/pubmed/36848460 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e43061 %T Defining County-Level Terrestrial Rabies Freedom Using the US National Rabies Surveillance System: Surveillance Data Analysis %A Kunkel,Amber %A Veytsel,Gabriella %A Bonaparte,Sarah %A Meek,Haillie %A Ma,Xiaoyue %A Davis,Amy J %A Bonwitt,Jesse %A Wallace,Ryan M %+ Division of High-Consequence Pathogens and Pathology, National Center for Emerging & Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States, 1 4046393538, sarahcbonaparte@gmail.com %K rabies %K surveillance %K zoonoses %K zoonosis %K model %K disease spread %K infection spread %K animal %K predict %K public health %D 2023 %7 7.4.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Rabies is a deadly zoonotic disease with nearly 100% fatality rate. In the United States, rabies virus persists in wildlife reservoirs, with occasional spillover into humans and domestic animals. The distribution of reservoir hosts in US counties plays an important role in public health decision-making, including the recommendation of lifesaving postexposure prophylaxis upon suspected rabies exposures. Furthermore, in surveillance data, it is difficult to discern whether counties have no cases reported because rabies was not present or because counties have an unreported rabies presence. These epizootics are monitored by the National Rabies Surveillance System (NRSS), to which approximately 130 state public health, agriculture, and academic laboratories report animal rabies testing statistics. Historically, the NRSS classifies US counties as free from terrestrial rabies if, over the previous 5 years, they and any adjacent counties did not report any rabies cases and they tested ≥15 reservoir animals or 30 domestic animals. Objective: This study aimed to describe and evaluate the historical NRSS rabies-free county definition, review possibilities for improving this definition, and develop a model to achieve more precise estimates of the probability of terrestrial rabies freedom and the number of reported county-level terrestrial rabies cases. Methods: Data submitted to the NRSS by state and territorial public health departments and the US Department of Agriculture Wildlife Services were analyzed to evaluate the historical rabies-free definition. A zero-inflated negative binomial model created county-level predictions of the probability of rabies freedom and the expected number of rabies cases reported. Data analyzed were from all animals submitted for laboratory diagnosis of rabies in the United States from 1995 to 2020 in skunk and raccoon reservoir territories, excluding bats and bat variants. Results: We analyzed data from 14,642 and 30,120 county-years in the raccoon and skunk reservoir territories, respectively. Only 0.85% (9/1065) raccoon county-years and 0.79% (27/3411) skunk county-years that met the historical rabies-free criteria reported a case in the following year (99.2% negative predictive value for each), of which 2 were attributed to unreported bat variants. County-level model predictions displayed excellent discrimination for detecting zero cases and good estimates of reported cases in the following year. Counties classified as rabies free rarely (36/4476, 0.8%) detected cases in the following year. Conclusions: This study concludes that the historical rabies freedom definition is a reasonable approach for identifying counties that are truly free from terrestrial raccoon and skunk rabies virus transmission. Gradations of risk can be measured using the rabies prediction model presented in this study. However, even counties with a high probability of rabies freedom should maintain rabies testing capacity, as there are numerous examples of translocations of rabies-infected animals that can cause major changes in the epidemiology of rabies. %M 37027194 %R 10.2196/43061 %U https://publichealth.jmir.org/2023/1/e43061 %U https://doi.org/10.2196/43061 %U http://www.ncbi.nlm.nih.gov/pubmed/37027194 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e45697 %T US Adults Practicing Healthy Lifestyles Before and During COVID-19: Comparative Analysis of National Surveys %A Tam,Hon Lon %A Chair,Sek Ying %A Leung,Isaac Sze Him %A Leung,Leona Yuen Ling %A Chan,Alex Siu Wing %+ The Nethersole School of Nursing, The Chinese University of Hong Kong, Rm 704B, 7/F, Esther Lee Building, Shatin, NT, China (Hong Kong), 852 39439306, hltam@cuhk.edu.hk %K healthy lifestyle %K health risk behaviors %K habits %K noncommunicable diseases %K population surveillance %K Behavioural Risk Factor Surveillance System %K BRFSS %D 2023 %7 31.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Practicing healthy lifestyles can reduce the risk to develop noncommunicable diseases and the related mortality. Studies showed that practicing healthy lifestyles could enhance disease-free life expectancy and preserve bodily functions. However, engagement in healthy lifestyle behavior was suboptimal. Objective: This study aimed to define individuals’ lifestyle characteristics before and during COVID-19 and determine the factors associated with practicing a healthy lifestyle. This cross-sectional study was conducted using data from the 2019 and 2021 Behavioral Risk Factor Surveillance System surveys. Methods: US individuals aged ≥18 years were interviewed via phone call. Healthy lifestyles were assessed through corresponding questions regarding the maintenance of optimal body weight, physical activity, daily consumption of at least five portions of fruits and vegetables, current smoking status, and alcohol consumption. Missing data were imputed using a package in the R statistical software. The effects of practicing a healthy lifestyle on cases without missing data and those with imputation were reported. Results: There were 550,607 respondents (272,543 and 278,064 from 2019 and 2021, respectively) included in this analysis. The rates of practicing a healthy lifestyle were 4% (10,955/272,543) and 3.6% (10,139/278,064) in 2019 and 2021, respectively. Although 36.6% (160,629/438,693) of all 2021 respondents had missing data, the results of the logistic regression analysis for cases without missing data and those with imputation were similar. Of the cases with imputation, women (odds ratio [OR] 1.87) residing in urban areas (OR 1.24) with high education levels (OR 1.73) and good or better health status (OR 1.59) were more likely to practice healthier lifestyles than young individuals (OR 0.51-0.67) with a low household income (OR 0.74-0.78) and chronic health conditions (OR 0.48-0.74). Conclusions: A healthy lifestyle should be strongly promoted at the community level. In particular, factors associated with a low rate of practice of healthy lifestyles should be targeted. %M 36940169 %R 10.2196/45697 %U https://publichealth.jmir.org/2023/1/e45697 %U https://doi.org/10.2196/45697 %U http://www.ncbi.nlm.nih.gov/pubmed/36940169 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 12 %N %P e42292 %T Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda %A Amusa,Lateef Babatunde %A Twinomurinzi,Hossana %A Phalane,Edith %A Phaswana-Mafuya,Refilwe Nancy %+ Centre for Applied Data Science, University of Johannesburg, Bunting Road Campus, Auckland Park, Johannesburg, 2006, South Africa, 27 659904686, amusasuxes@gmail.com %K big data %K bibliometrics %K infectious disease %K COVID-19 %K disease surveillance %K disease %K pandemic %K data %K surveillance %K hotspot %K epidemiology %K social media %K utility %K electronic health records %D 2023 %7 31.3.2023 %9 Original Paper %J Interact J Med Res %G English %X Background: Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling. Objective: The aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology. Methods: Bibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents. Results: The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes. Conclusions: Proposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology. %M 36913554 %R 10.2196/42292 %U https://www.i-jmr.org/2023/1/e42292 %U https://doi.org/10.2196/42292 %U http://www.ncbi.nlm.nih.gov/pubmed/36913554 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40177 %T Monitoring and Combating Waterpipe Tobacco Smoking Through Surveillance and Taxation %A Saad,Randa K %A Maiteh,Adna %A Nakkash,Rima %A Salloum,Ramzi G %A Chalak,Ali %A Abu-Rmeileh,Niveen M E %A Khader,Yousef %A Al Nsour,Mohannad %+ Global Health Development | Eastern Mediterranean Public Health Network, Abdallah Ben Abbas St 42, Amman, 11196, Jordan, 962 781665060, randaksaad@gmail.com %K waterpipe tobacco %K smoking %K tobacco taxation %K Global Tobacco Surveillance System %K GTSS %K Eastern Mediterranean Region %K tobacco %K public health %K surveillance %K taxation %D 2023 %7 23.3.2023 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Waterpipe tobacco smoking (WTS) is a traditional tobacco use method that originated in the Eastern Mediterranean Region (EMR) and has resurged in recent decades. WTS rates in the EMR are the highest worldwide, especially among youth, exceeding cigarette-smoking rates in select jurisdictions. Despite its documented harm, the growing prevalence of WTS has been met with a poor regulatory response globally. At the epicenter of the WTS epidemic, countries in the EMR are in urgent need of effective tobacco control strategies that consider the particularities of WTS. A roundtable session, titled “Monitoring and Combating WTS Through Taxation and the Global Tobacco Surveillance System (GTSS),” was held as part of the 7th Eastern Mediterranean Public Health Network’s regional conference. The session provided an overview of evidence to date about WTS policy control, the taxation of WTS, volumetric choice experiments for tobacco control research, and monitoring WTS patterns and control policies among adults and youth through the GTSS. The session highlighted the need to update the regulation of WTS in the current global tobacco control policy frameworks and the need for developing tailored, evidence-based, and WTS-specific regulations to complement current tobacco control policy frameworks. Raising taxes to increase the price of tobacco products is the single most effective tobacco control measure, and these taxes can fund expanded government health programs. The effectiveness of taxation can be measured via volumetric choice experiments, which allow for the estimation of a complete set of own-price and cross-price elasticities that are instrumental for fiscal policy simulations. Finally, the surveillance of WTS (for example, through the GTSS) is critical to informing policy and decision makers. The Global Youth Tobacco Survey (GYTS) and Global Adult Tobacco Survey (GATS) are 2 GTSS products that provide nationally representative data among students aged 13-15 years and persons ≥15 years, respectively. %M 36951907 %R 10.2196/40177 %U https://publichealth.jmir.org/2023/1/e40177 %U https://doi.org/10.2196/40177 %U http://www.ncbi.nlm.nih.gov/pubmed/36951907 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e38633 %T Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study %A Cevasco,Kevin E %A Roess,Amira A %+ College of Health and Human Services, George Mason University, 4400 University Dr, Fairfax, VA, 22030, United States, 1 703 993 1923, aroess@gmu.edu %K COVID-19 %K contact tracing %K postmarketing %K mobile apps %K public health %K digital %K interventions %K tool %K adoption %K effectiveness %K prevention %K application %K transmission %D 2023 %7 22.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers. Objective: The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework. Methods: We analyzed data from a nationally representative survey of US adults about their COVID-19–related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates. Results: An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12). Conclusions: Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters’ friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety. %M 36947135 %R 10.2196/38633 %U https://publichealth.jmir.org/2023/1/e38633 %U https://doi.org/10.2196/38633 %U http://www.ncbi.nlm.nih.gov/pubmed/36947135 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45419 %T Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study %A Wu,Jiageng %A Wang,Lumin %A Hua,Yining %A Li,Minghui %A Zhou,Li %A Bates,David W %A Yang,Jie %+ School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou, 310058, China, 86 057187077982, jieynlp@gmail.com %K social media %K network analysis %K public health %K data mining %K COVID-19 %D 2023 %7 14.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. Objective: Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. Methods: This retrospective study included 471,553,966 COVID-19–related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. Results: This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive). Conclusions: This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies. %M 36812402 %R 10.2196/45419 %U https://www.jmir.org/2023/1/e45419 %U https://doi.org/10.2196/45419 %U http://www.ncbi.nlm.nih.gov/pubmed/36812402 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e43049 %T The Impact of the COVID-19 Pandemic on the Registration and Care Provision of Mental Health Problems in General Practice: Registry-Based Study %A Vandamme,Jan %A Beerten,Simon Gabriël %A Crèvecoeur,Jonas %A Van den Bulck,Steve %A Aertgeerts,Bert %A Delvaux,Nicolas %A Van Pottelbergh,Gijs %A Vermandere,Mieke %A Tops,Laura %A Neyens,Thomas %A Vaes,Bert %+ Academic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7 blok h, bus 7001, Leuven, 3000, Belgium, 32 16 19 40 50, simon.beerten@kuleuven.be %K COVID-19 %K mental health %K care provision %K general practice %K socioeconomic status %D 2023 %7 10.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The impact of the COVID-19 pandemic on mental health in general practice remains uncertain. Several studies showed an increase in terms of mental health problems during the pandemic. In Belgium, especially during the first waves of the pandemic, access to general practice was limited. Specifically, it is unclear how this impacted not only the registration of mental health problems itself but also the care for patients with an existing mental health problem. Objective: This study aimed to know the impact of the COVID-19 pandemic on (1) the incidence of newly registered mental health problems and (2) the provision of care for patients with mental health problems in general practice, both using a pre–COVID-19 baseline. Methods: The prepandemic volume of provided care (care provision) for patients with mental health problems was compared to that from 2020-2021 by using INTEGO, a Belgian general practice morbidity registry. Care provision was defined as the total number of new registrations in a patient’s electronic medical record. Regression models evaluated the association of demographic factors and care provision in patients with mental health problems, both before and during the pandemic. Results: During the COVID-19 pandemic as compared to before the COVID-19 pandemic, the incidence of registered mental health problems showed a fluctuating course, with a sharp drop in registrations during the first wave. Registrations for depression and anxiety increased, whereas the incidence of registered eating disorders, substance abuse, and personality problems decreased. During the 5 COVID-19 waves, the overall incidence of registered mental health problems dropped during the wave and rose again when measures were relaxed. A relative rise of 8.7% and 40% in volume of provided care, specifically for patients with mental health problems, was seen during the first and second years of the COVID-19 pandemic, respectively. Care provision for patients with mental health problems was higher in older patients, male patients, patients living in center cities (centrumsteden), patients with lower socioeconomic status (SES), native Belgian patients, and patients with acute rather than chronic mental health problems. Compared to prepandemic care provision, a reduction of 10% was observed in people with a low SES. Conclusions: This study showed (1) a relative overall increase in the registrations of mental health problems in general practice and (2) an increase in care provision for patients with mental health problems in the first 2 years of the COVID-19 pandemic. Low SES remained a determining factor for more care provision, but care provision dropped significantly in people with mental health problems with a low SES. Our findings suggest that the pandemic in Belgium was also largely a “syndemic,” affecting different layers of the population disproportionately. %M 36599160 %R 10.2196/43049 %U https://publichealth.jmir.org/2023/1/e43049 %U https://doi.org/10.2196/43049 %U http://www.ncbi.nlm.nih.gov/pubmed/36599160 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e45121 %T Technology, Training, and Task Shifting at the World’s Largest Mass Gathering in 2025: An Opportunity for Antibiotic Stewardship in India %A Chan,Isaac H Y %A Gofine,Miriam %A Arora,Shitij %A Shaikh,Ahmed %A Balsari,Satchit %+ Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 1 Deaconess Road, Boston, MA, 02215, United States, 1 6177542400, sbalsari@bidmc.harvard.edu %K digital tools %K mass gathering %K Kumbh Mela %K antibiotics %K antimicrobial %K stewardship %K surveillance %K public health %K informatics %K India %D 2023 %7 8.3.2023 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The role of antibiotic overuse in intensifying selection pressures and contributing to the emergence of antimicrobial resistance is well established. The Kumbh Mela, a religious festival that occurs in 4 Indian cities of spiritual significance, is the world’s largest mass gathering, attracting over 80 million pilgrims in 2013. Digital syndromic surveillance from the 2013 and 2015 Melas demonstrated a consistent pattern of antibiotic overuse, with an antibiotic prescribing rate of up to 31% for all patient encounters. As preparations for the 2025 Kumbh Mela begin, task shifting, point-of-care diagnostic and digital tools, robust clinician training, and community awareness can promote the restrained and evidence-based use of antibiotics, minimizing the potential for the emergence of antimicrobial resistance at the world’s largest mass gathering. %M 36805363 %R 10.2196/45121 %U https://publichealth.jmir.org/2023/1/e45121 %U https://doi.org/10.2196/45121 %U http://www.ncbi.nlm.nih.gov/pubmed/36805363 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e42425 %T Spatiotemporal Changes in Pulmonary Tuberculosis Incidence in a Low-Epidemic Area of China in 2005-2020: Retrospective Spatiotemporal Analysis %A Zhang,Qi %A Ding,Huan %A Gao,Song %A Zhang,Shipeng %A Shen,Shiya %A Chen,Xiaoyan %A Xu,Zhuping %+ Department of Chronic Communicable Disease, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, 499 Jin Cheng Road, Wuxi, 214023, China, 86 0510 82726760, Xzp15951589696@163.com %K pulmonary tuberculosis %K spatial analysis %K temporal analysis %K epidemiology %K China %D 2023 %7 8.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In China, tuberculosis (TB) is still a major public health problem, and the incidence of TB has significant spatial heterogeneity. Objective: This study aimed to investigate the temporal trends and spatial patterns of pulmonary tuberculosis (PTB) in a low-epidemic area of eastern China, Wuxi city, from 2005 to 2020. Methods: The data of PTB cases from 2005 to 2020 were obtained from the Tuberculosis Information Management System. The joinpoint regression model was used to identify the changes in the secular temporal trend. Kernel density analysis and hot spot analysis were used to explore the spatial distribution characteristics and clusters of the PTB incidence rate. Results: A total of 37,592 cases were registered during 2005-2020, with an average annual incidence rate of 34.6 per 100,000 population. The population older than 60 years had the highest incidence rate of 59.0 per 100,000 population. In the study period, the incidence rate decreased from 50.4 to 23.9 per 100,000 population, with an average annual percent change of –4.9% (95% CI –6.8% to –2.9%). The incidence rate of pathogen-positive patients increased during 2017-2020, with an annual percent change of 13.4% (95% CI 4.3%-23.2%). The TB cases were mainly concentrated in the city center, and the incidence of hot spots areas gradually changed from rural areas to urban areas during the study period. Conclusions: The PTB incidence rate in Wuxi city has been declining rapidly with the effective implementation of strategies and projects. The populated urban centers will become key areas of TB prevention and control, especially in the older population. %M 36884278 %R 10.2196/42425 %U https://publichealth.jmir.org/2023/1/e42425 %U https://doi.org/10.2196/42425 %U http://www.ncbi.nlm.nih.gov/pubmed/36884278 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44552 %T Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study %A Wittenborn,John %A Lee,Aaron %A Lundeen,Elizabeth A %A Lamuda,Phoebe %A Saaddine,Jinan %A Su,Grace L %A Lu,Randy %A Damani,Aashka %A Zawadzki,Jonathan S %A Froines,Colin P %A Shen,Jolie Z %A Kung,Timothy-Paul H %A Yanagihara,Ryan T %A Maring,Morgan %A Takahashi,Melissa M %A Blazes,Marian %A Rein,David B %+ Public Health Analytics, National Opinion Research Center at the University of Chicago, 55 East Monroe Street, 30th Floor, Chicago, IL, 60603, United States, 1 3125195718, wittenborn-john@norc.org %K vision %K blindness %K surveillance %K survey %K acuity %K validation %K visual health %K optometry clinic %K eye disease %K vision loss %D 2023 %7 7.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. Objective: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. Methods: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. Results: The survey question, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of “fair,” “poor,” or “very poor” to the question, “At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor” (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. Conclusions: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA. %M 36881468 %R 10.2196/44552 %U https://publichealth.jmir.org/2023/1/e44552 %U https://doi.org/10.2196/44552 %U http://www.ncbi.nlm.nih.gov/pubmed/36881468 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44251 %T Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis %A Chong,Ka Chun %A Li,Kehang %A Guo,Zihao %A Jia,Katherine Min %A Leung,Eman Yee Man %A Zhao,Shi %A Hung,Chi Tim %A Yam,Carrie Ho Kwan %A Chow,Tsz Yu %A Dong,Dong %A Wang,Huwen %A Wei,Yuchen %A Yeoh,Eng Kiong %+ Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Room 415, School of Public Health Building, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, 852 22528702, yeoh_ek@cuhk.edu.hk %K COVID-19 %K contact tracing %K unlinked %K superspreading %K dispersion %K public health %K surveillance %K digital health surveillance %K digital surveillance %K disease spread %D 2023 %7 7.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. Objective: We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. Methods: We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. Results: A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). Conclusions: We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events. %M 36811849 %R 10.2196/44251 %U https://publichealth.jmir.org/2023/1/e44251 %U https://doi.org/10.2196/44251 %U http://www.ncbi.nlm.nih.gov/pubmed/36811849 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e40554 %T A Platform for Data-Centric, Continuous Epidemiological Analyses (EpiGraphHub): Descriptive Analysis %A Coelho,Flávio %A Câmara,Daniel Cardoso Portela %A Araújo,Eduardo Correa %A Bianchi,Lucas Monteiro %A Ogasawara,Ivan %A Dalal,Jyoti %A James,Ananthu %A Abbate,Jessica L %A Merzouki,Aziza %A dos Reis,Izabel Cristina %A Nwosu,Kene David %A Keiser,Olivia %+ School of Applied Mathematics, Fundação Getulio Vargas, Praia de Botafogo, 190 s508, Rio de Janeiro, 22250-900, Brazil, 55 37995735, fccoelho@fgv.br %K epidemiology %K data analysis %K disease surveillance %K data science %K public health %K durability %K accessibility %K data set %K public %K public health %K platform %K data %K application %K decision %K decision-making %D 2023 %7 6.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Guaranteeing durability, provenance, accessibility, and trust in open data sets can be challenging for researchers and organizations that rely on public repositories of data critical for epidemiology and other health analytics. The required data repositories are often difficult to locate and may require conversion to a standard data format. Data-hosting websites may also change or become unavailable without warning. A single change to the rules in one repository can hinder updating a public dashboard reliant on data pulled from external sources. These concerns are particularly challenging at the international level, because policies on systems aimed at harmonizing health and related data are typically dictated by national governments to serve their individual needs. Objective: In this paper, we introduce a comprehensive public health data platform, EpiGraphHub, that aims to provide a single interoperable repository for open health and related data. Methods: The platform, curated by the international research community, allows secure local integration of sensitive data while facilitating the development of data-driven applications and reports for decision-makers. Its main components include centrally managed databases with fine-grained access control to data, fully automated and documented data collection and transformation, and a powerful web-based data exploration and visualization tool. Results: EpiGraphHub is already being used for hosting a growing collection of open data sets and for automating epidemiological analyses based on them. The project has also released an open-source software library with the analytical methods used in the platform. Conclusions: The platform is fully open source and open to external users. It is in active development with the goal of maximizing its value for large-scale public health studies. %M 36877539 %R 10.2196/40554 %U https://www.jmir.org/2023/1/e40554 %U https://doi.org/10.2196/40554 %U http://www.ncbi.nlm.nih.gov/pubmed/36877539 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e39798 %T Updating the Data on Malaria Vectors in Malaysia: Protocol for a Scoping Review %A Ali,Rafidah %A Wan Mohamad Ali,Wan Najdah %A Wilson Putit,Perada %+ Institute for Public Health, National Institutes of Health, Jalan Setia Murni U13/52, Seksyen U13 Setia Alam, Selangor, 40170, Malaysia, 60 333628762, rafidahali1984@gmail.com %K malaria vector %K Anopheles %K Malaysia %K scoping review %K malaria %K vector %K transmission %K prevention %K entomology %K eliminating malaria %D 2023 %7 6.3.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Malaria is still a public health threat. From 2015 to 2021, a total of 23,214 malaria cases were recorded in Malaysia. Thus, effective intervention and key entomological information are vital for interrupting or preventing malaria transmission. Therefore, the availability of malaria vector information is desperately needed. Objective: The objective of our study is to update the list of human and zoonotic malaria vectors in Malaysia. This work will include (1) the characterization of the key behavioral traits and breeding sites of malaria vectors and (2) the determination of new and potential malaria vectors in Malaysia. The findings of our scoping review will serve as decision-making evidence that stakeholders and decision makers can use to strengthen and intensify malaria surveillance in Malaysia. Methods: The scoping review will be conducted based on the following four electronic databases: Scopus, PubMed, Google Scholar, and Science Direct. A search strategy was conducted for articles published from database inception to March 2022. The criteria for article inclusion were any malaria vector–related studies conducted in Malaysia (with no time frame restrictions) and peer-reviewed studies. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) will be used to guide our systematic approach. Data from published research literature will be extracted by using a standardized data extraction framework, including the titles, abstracts, characteristics, and main findings of the included studies. To assess the risk of bias, articles will be screened independently by 2 reviewers, and a third reviewer will make the final decision if disagreements occur. Results: The study commenced in June 2021, and it is planned to be completed at end of 2022. As of early 2022, we identified 631 articles. After accessing and evaluating the articles, 48 were found to be eligible. Full-text screening will be conducted in mid-2022. The results of the scoping review will be published as an open-access article in a peer-reviewed journal. Conclusions: Our novel scoping review of malaria vectors in Malaysia will provide a comprehensive evidence summary of updated, relevant information. An understanding of the status of Anopheles as malaria vectors and the knowledge generated from the behavioral characteristics of malaria vectors are the key components in making effective interventions for eliminating malaria. International Registered Report Identifier (IRRID): DERR1-10.2196/39798 %M 36877567 %R 10.2196/39798 %U https://www.researchprotocols.org/2023/1/e39798 %U https://doi.org/10.2196/39798 %U http://www.ncbi.nlm.nih.gov/pubmed/36877567 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40552 %T Changes in Mobile Health Apps Usage Before and After the COVID-19 Outbreak in China: Semilongitudinal Survey %A Yang,Le %A Wu,Jiadong %A Mo,Xiaoxiao %A Chen,Yaqin %A Huang,Shanshan %A Zhou,Linlin %A Dai,Jiaqi %A Xie,Linna %A Chen,Siyu %A Shang,Hao %A Rao,Beibei %A Weng,Bingtao %A Abulimiti,Ayiguli %A Wu,Siying %A Xie,Xiaoxu %+ School of Public Health, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou University New District, Fuzhou, 350122, China, 86 059122862023, xiexiaoxu@aliyun.com %K application %K China %K COVID-19 %K mHealth %K health management %K mobile health %K technology %K app %K survey %K data %K user %K user experience %K vaccination %K download %K healthcare %K development %D 2023 %7 22.2.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Mobile health (mHealth) apps are rapidly emerging technologies in China due to strictly controlled medical needs during the COVID-19 pandemic while continuing essential services for chronic diseases. However, there have been no large-scale, systematic efforts to evaluate relevant apps. Objective: We aim to provide a landscape of mHealth apps in China by describing and comparing digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and analyze the impact of COVID-19 on mHealth apps. Methods: We conducted a semilongitudinal survey of 1593 mHealth apps to study the app data flow and clarify usage changes and influencing factors. We selected mHealth apps in app markets, web pages from the Baidu search engine, the 2018 top 100 hospitals with internet hospitals, and online shopping sites with apps that connect to smart devices. For user experience, we recruited residents from a community in southeastern China from October 2019 to November 2019 (before the outbreak) and from June 2020 to August 2020 (after the outbreak) comparing the attention of the population to apps. We also examined associations between app characteristics, functions, and outcomes at specific quantiles of distribution in download changes using quantile regression models. Results: Rehabilitation medical support was the top-ranked functionality, with a median 1.44 million downloads per app prepandemic and a median 2.74 million downloads per app postpandemic. Among the top 10 functions postpandemic, 4 were related to maternal and child health: pregnancy preparation (ranked second; fold change 4.13), women's health (ranked fifth; fold change 5.16), pregnancy (ranked sixth; fold change 5.78), and parenting (ranked tenth; fold change 4.03). Quantile regression models showed that rehabilitation (P75, P90), pregnancy preparation (P90), bodybuilding (P50, P90), and vaccination (P75) were positively associated with an increase in downloads after the outbreak. In the user experience survey, the attention given to health information (prepandemic: 249/375, 66.4%; postpandemic: 146/178, 82.0%; P=.006) steadily increased after the outbreak. Conclusions: mHealth apps are an effective health care approach gaining in popularity among the Chinese population following the COVID-19 outbreak. This research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference, which may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion. %M 36634256 %R 10.2196/40552 %U https://publichealth.jmir.org/2023/1/e40552 %U https://doi.org/10.2196/40552 %U http://www.ncbi.nlm.nih.gov/pubmed/36634256 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e41404 %T Multimorbidity and SARS-CoV-2–Related Outcomes: Analysis of a Cohort of Italian Patients %A Catalano,Alberto %A Dansero,Lucia %A Gilcrease,Winston %A Macciotta,Alessandra %A Saugo,Carlo %A Manfredi,Luca %A Gnavi,Roberto %A Strippoli,Elena %A Zengarini,Nicolás %A Caramello,Valeria %A Costa,Giuseppe %A Sacerdote,Carlotta %A Ricceri,Fulvio %+ Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, Orbassano (TO), 10043, Italy, 39 0116705443, alberto.catalano@unito.it %K multimorbidity %K SARS-CoV-2 %K mortality %K intensive care unit %K epidemiology %K COVID-19 %K pandemic %K severity %K cardiovascular %K respiratory %K disease %K risk %K public health %K intervention %D 2023 %7 9.2.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Since the outbreak of the COVID-19 pandemic, identifying the main risk factors has been imperative to properly manage the public health challenges that the pandemic exposes, such as organizing effective vaccination campaigns. In addition to gender and age, multimorbidity seems to be 1 of the predisposing factors coming out of many studies investigating the possible causes of increased susceptibility to SARS-CoV-2 infection and adverse outcomes. However, only a few studies conducted have used large samples. Objective: The objective is to evaluate the association between multimorbidity, the probability to be tested, susceptibility, and the severity of SARS-CoV-2 infection in the Piedmont population (Northern Italy, about 4 million inhabitants). For this purpose, we considered 5 main outcomes: access to the swab, positivity to SARS-CoV-2, hospitalization, intensive care unit (ICU) admission, and death within 30 days from the first positive swab. Methods: Data were obtained from different Piedmont health administrative databases. Subjects aged from 45 to 74 years and infections diagnosed from February to May 2020 were considered. Multimorbidity was defined both with the Charlson Comorbidity Index (CCI) and by identifying patients with previous comorbidities, such as diabetes and oncological, cardiovascular, and respiratory diseases. Multivariable logistic regression models (adjusted for age and month of infection and stratified by gender) were performed for each outcome. Analyses were also conducted by separating 2 age groups (45-59 and 60-74 years). Results: Of 1,918,549 subjects, 85,348 (4.4%) performed at least 1 swab, of whom 12,793 (14.9%) tested positive for SARS-CoV-2. Of these 12,793 subjects, 4644 (36.3%) were hospitalized, 1508 (11.8%) were admitted to the ICU, and 749 (5.9%) died within 30 days from the first positive swab. Individuals with a higher CCI had a higher probability of being swabbed but a lower probability of testing positive. We observed the same results when analyzing subjects with previous oncological and cardiovascular diseases. Moreover, especially in the youngest group, we identified a greater risk of being hospitalized and dying. Among comorbidities considered in the study, respiratory diseases seemed to be the most likely to increase the risk of having a positive swab and worse disease outcomes. Conclusions: Our study shows that patients with multimorbidity, although swabbed more frequently, are less likely to get infected with SARS-CoV-2, probably due to greater attention on protective methods. Moreover, a history of respiratory diseases is a risk factor for a worse prognosis of COVID-19. Nonetheless, whatever comorbidities affect the patients, a strong dose-response effect was observed between an increased CCI score and COVID-19 hospitalization, ICU admission, and death. These results are important in terms of public health because they help in identifying a group of subjects who are more prone to worse SARS-CoV-2 outcomes. This information is important for promoting targeted prevention and developing policies for the prioritization of public health interventions. %M 36626821 %R 10.2196/41404 %U https://publichealth.jmir.org/2023/1/e41404 %U https://doi.org/10.2196/41404 %U http://www.ncbi.nlm.nih.gov/pubmed/36626821 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42519 %T Long Short-term Memory–Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation %A Athanasiou,Maria %A Fragkozidis,Georgios %A Zarkogianni,Konstantia %A Nikita,Konstantina S %+ School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Str., Zografos, Athens, 15780, Greece, 30 2107722149, mathanasiou@biosim.ntua.gr %K influenza-like illness %K epidemiological surveillance %K machine learning %K deep learning %K social media %K Twitter %K meteorological parameters %D 2023 %7 6.2.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. Objective: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. Methods: The model’s input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models’ LSTM layers for the latter to feed a dense layer. Results: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). Conclusions: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics. %M 36745490 %R 10.2196/42519 %U https://www.jmir.org/2023/1/e42519 %U https://doi.org/10.2196/42519 %U http://www.ncbi.nlm.nih.gov/pubmed/36745490 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40958 %T Associations of Country-Specific and Sociodemographic Factors With Self-Reported COVID-19–Related Symptoms: Multivariable Analysis of Data From the CoronaCheck Mobile Health Platform %A Humer,Elke %A Keil,Thomas %A Stupp,Carolin %A Schlee,Winfried %A Wildner,Manfred %A Heuschmann,Peter %A Winter,Michael %A Probst,Thomas %A Pryss,Rüdiger %+ Institute of Clinical Epidemiology and Biometry, University of Würzburg, Am Schwarzenberg Haus A15, Würzburg, 97078, Germany, 49 931201 ext 46471, pryss_r@ukw.de %K COVID-19 %K COVID-19 symptoms %K gender %K India %K South Africa %K Germany %K symptoms %K app %K information %K English %K sociodemographic %K weakness %K muscle pain %K pain %K age %K education %D 2023 %7 3.2.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 symptom-monitoring apps provide direct feedback to users about the suspected risk of infection with SARS-CoV-2 and advice on how to proceed to prevent the spread of the virus. We have developed the CoronaCheck mobile health (mHealth) platform, the first free app that provides easy access to valid information about the risk of infection with SARS-CoV-2 in English and German. Previous studies have suggested that the clinical characteristics of individuals infected with SARS-CoV-2 vary by age, gender, and viral variant; however, potential differences between countries have not been adequately studied. Objective: The aim of this study is to describe the characteristics of the users of the CoronaCheck mHealth platform and to determine country-specific and sociodemographic associations of COVID-19–related symptoms and previous contacts with individuals infected with COVID-19. Methods: Between April 8, 2020, and February 3, 2022, data on sociodemographic characteristics, symptoms, and reports of previous close contacts with individuals infected with COVID-19 were collected from CoronaCheck users in different countries. Multivariable logistic regression analyses were performed to examine whether self-reports of COVID-19–related symptoms and recent contact with a person infected with COVID-19 differed between countries (Germany, India, South Africa), gender identities, age groups, education, and calendar year. Results: Most app users (N=23,179) were from Germany (n=8116, 35.0%), India (n=6622, 28.6%), and South Africa (n=3705, 16.0%). Most data were collected in 2020 (n=19,723, 85.1%). In addition, 64% (n=14,842) of the users were male, 52.1% (n=12,077) were ≥30 years old, and 38.6% (n=8953) had an education level of more than 11 years of schooling. Headache, muscle pain, fever, loss of smell, loss of taste, and previous contacts with individuals infected with COVID-19 were reported more frequently by users in India (adjusted odds ratios [aORs] 1.3-8.3, 95% CI 1.2-9.2) and South Africa (aORs 1.1-2.6, 95% CI 1.0-3.0) than those in Germany. Cough, general weakness, sore throat, and shortness of breath were more frequently reported in India (aORs 1.3-2.6, 95% CI 1.2-2.9) compared to Germany. Gender-diverse users reported symptoms and contacts with confirmed COVID-19 cases more often compared to male users. Conclusions: Patterns of self-reported COVID-19–related symptoms and awareness of a previous contact with individuals infected with COVID-19 seemed to differ between India, South Africa, and Germany, as well as by gender identity in these countries. Viral symptom–collecting apps, such as the CoronaCheck mHealth platform, may be promising tools for pandemics to support appropriate assessments. Future mHealth research on country-specific differences during a pandemic should aim to recruit representative samples. %M 36515987 %R 10.2196/40958 %U https://publichealth.jmir.org/2023/1/e40958 %U https://doi.org/10.2196/40958 %U http://www.ncbi.nlm.nih.gov/pubmed/36515987 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e44234 %T The Association Between Pediatric COVID-19 Vaccination and Socioeconomic Position: Nested Case-Control Study From the Pedianet Veneto Cohort %A Batzella,Erich %A Cantarutti,Anna %A Caranci,Nicola %A Giaquinto,Carlo %A Barbiellini Amidei,Claudio %A Canova,Cristina %+ Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padova, Via Loredan 18, Padua, 35131, Italy, 39 0498275391, cristina.canova@unipd.it %K SEP %K socioeconomic position %K quantile-g-computation %K nested case-control study %K COVID-19 vaccine %K children %K area deprivation index %D 2023 %7 1.2.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The success of pediatric COVID-19 vaccination strongly depends on parents' willingness to vaccinate their children. To date, the role of socioeconomic position (SEP) in pediatric COVID-19 vaccination has not been thoroughly examined. Objective: We evaluated the association between COVID-19 vaccination and SEP in a large pediatric cohort. Methods: A case-control study design nested into a pediatric cohort of children born between 2007 and 2017, living in the Veneto Region and followed up to at least January 1, 2022, was adopted. Data on children were collected from the Pedianet database and linked with the regional COVID-19 registry. Each child vaccinated with at least one dose of any COVID-19 vaccine between July 1, 2021, and March 31, 2022, was matched by sex, year of birth, and family pediatrician to up to 5 unvaccinated children. Unvaccinated children with a positive outcome on the swab test within 180 days before the index date were excluded from the analyses. Children were geo-referenced to determine their area deprivation index (ADI)—a social and material deprivation measure calculated at the census block level and consisting of 5 socioeconomic items. The index was then categorized in quintiles based on the regional ADI level. The association between ADI quintiles and vaccination status was measured using conditioned logistic regression models to estimate odds ratios and the corresponding 95% CIs. Quantile-g-computation regression models were applied to develop a weighted combination of the individual items to estimate how much each component influenced the likelihood of vaccination. All analyses were stratified by age at vaccination (5-11 and 12-14 years). Results: The study population consisted of 6475 vaccinated children, who were matched with 32,124 unvaccinated children. Increasing area deprivation was associated with a lower probability of being vaccinated, with approximately a linear dose-response relationship. Children in the highest deprivation quintile were 36% less likely to receive a COVID-19 vaccine than those with the lowest area deprivation (95% CI 0.59-0.70). The results were similar in the 2 age groups, with a slightly stronger association in 5-11–year-old children. When assessing the effects of the weighted combination of the individual items, a quintile increase was associated with a 17% decrease in the probability of being vaccinated (95% CI 0.80-0.86). The conditions that influenced the probability of vaccination the most were living on rent, being unemployed, and being born in single-parent families. Conclusions: This study has shown a significant reduction in the likelihood of receiving a COVID-19 vaccine among children living in areas characterized by a lower SEP. Findings were robust among multiple analyses and definitions of the deprivation index. These findings suggest that SEP plays an important role in vaccination coverage, emphasizing the need to promote targeted public health efforts to ensure global vaccine equity. %M 36645419 %R 10.2196/44234 %U https://publichealth.jmir.org/2023/1/e44234 %U https://doi.org/10.2196/44234 %U http://www.ncbi.nlm.nih.gov/pubmed/36645419 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e34982 %T Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study %A Poirier,Canelle %A Bouzillé,Guillaume %A Bertaud,Valérie %A Cuggia,Marc %A Santillana,Mauricio %A Lavenu,Audrey %+ Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, United States, 1 617 355 6000, canelle.poirier@outlook.fr %K infectious disease %K acute gastroenteritis %K modeling %K modeling disease outbreaks %K machine learning %K public health %K machine learning in public health %K forecasting %K digital data %D 2023 %7 31.1.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics. Objective: The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks). Methods: We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity. Results: Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease. Conclusions: The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks. %M 36719726 %R 10.2196/34982 %U https://publichealth.jmir.org/2023/1/e34982 %U https://doi.org/10.2196/34982 %U http://www.ncbi.nlm.nih.gov/pubmed/36719726 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40036 %T Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil %A Boaventura,Viviane S %A Grave,Malú %A Cerqueira-Silva,Thiago %A Carreiro,Roberto %A Pinheiro,Adélia %A Coutinho,Alvaro %A Barral Netto,Manoel %+ Laboratório de Doenças Infecciosas Transmitidas por Vetores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Rua Waldemar Falcao, 121, Salvador, 40296-710, Brazil, 55 71 9619 4245, viviane.boaventura@fiocruz.br %K telehealth %K telemedicine %K disease surveillance %K mathematical model %K COVID-19 %K prediction %K cases %K detection %K monitoring %K surveillance %K computational modeling %K spread %K transmission %K disease %K infectious diseases %K syndromic %D 2023 %7 24.1.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region. Objective: Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread. Methods: We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19–like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19–like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called “susceptible, exposed, infected, recovered, deceased.” Results: For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19–like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the “susceptible, exposed, infected, recovered, deceased” model to simulate the spatiotemporal spread of the disease. Conclusions: Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics. %M 36692925 %R 10.2196/40036 %U https://publichealth.jmir.org/2023/1/e40036 %U https://doi.org/10.2196/40036 %U http://www.ncbi.nlm.nih.gov/pubmed/36692925 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e39002 %T Resident Willingness to Participate in Digital Contact Tracing in a COVID-19 Hotspot: Findings From a Detroit Panel Study %A Wileden,Lydia %A Anthony,Denise %A Campos-Castillo,Celeste %A Morenoff,Jeffrey %+ Mansueto Institute for Urban Innovation, University of Chicago, 1155 E 60th St, Chicago, IL, United States, 1 773 702 7894, lwileden@uchicago.edu %K COVID-19 %K contact tracing %K surveillance %K informatics %K trust %K racial disparities %D 2023 %7 19.1.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Digital surveillance tools and health informatics show promise in counteracting diseases but have limited uptake. A notable illustration of the limits of such tools is the general failure of digital contact tracing in the United States in response to COVID-19. Objective: We investigated the associations between individual characteristics and the willingness to use app-based contact tracing in Detroit, a majority-minority city that experienced multiple waves of COVID-19 outbreaks and deaths since the start of the pandemic. The aim of this study was to examine variations among residents in the willingness to download a contact tracing app on their phones to provide public health officials with information about close COVID-19 contact during summer 2020. Methods: To examine residents’ willingness to participate in digital contact tracing, we analyzed data from 2 waves of the Detroit Metro Area Communities Study, a population-based survey of Detroit, Michigan residents. The data captured 1873 responses from 991 Detroit residents collected in June and July 2020. We estimated a series of multilevel logit models to gain insights into differences in the willingness to participate in digital contact tracing across a variety of individual attributes, including race/ethnicity, degree of trust in the government, and level of education, as well as interactions among these variables. Results: Our results reflected widespread reluctance to participate in digital contact tracing in response to COVID-19, as less than half (826/1873, 44.1%) of the respondents said they would be willing to participate in app-based contact tracing. Compared to White respondents, Black (odds ratio [OR] 0.45, 95% CI 0.23-0.86) and Latino (OR 0.32, 95% CI 0.11-0.99) respondents were significantly less willing to participate in digital contact tracing. Trust in the government was positively associated with the willingness to participate in digital contact tracing (OR 1.17, 95% CI 1.07-1.27), but this effect was the strongest for White residents (OR 2.14, 95% CI 1.55-2.93). We found similarly divergent patterns of the effects of education by race. While there were no significant differences among noncollege-educated residents, White college-educated residents showed greater willingness to use app-based contact tracing (OR 6.12, 95% CI 1.86-20.15) and Black college-educated residents showed less willingness (OR 0.46, 95% CI 0.26-0.81). Conclusions: Trust in the government and education contribute to Detroit residents’ wariness of digital contact tracing, reflecting concerns about surveillance that cut across race but likely arise from different sources. These findings point to the importance of a culturally informed understanding of health hesitancy for future efforts hoping to leverage digital contact tracing. Though contact tracing technologies have the potential to advance public health, unequal uptake may exacerbate disparate impacts of health crises. %M 36240029 %R 10.2196/39002 %U https://publichealth.jmir.org/2023/1/e39002 %U https://doi.org/10.2196/39002 %U http://www.ncbi.nlm.nih.gov/pubmed/36240029 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e40659 %T The Environmental and Socioeconomic Effects and Prediction of Patients With Tuberculosis in Different Age Groups in Southwest China: A Population-Based Study %A Wei,Wen %A Xia,Lan %A Wu,Jianlin %A Zhou,Zonglei %A Zhang,Wenqiang %A Luan,Rongsheng %+ Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, 16#, Section 3, Renmin Nan Lu, Chengdu, 610041, China, 86 028 85501604, luan_rs@scu.edu.cn %K tuberculosis %K risk factors %K age %K sex %K prediction %K TB control %K tuberculosis control %D 2023 %7 13.1.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: While the End Tuberculosis (TB) Strategy has been implemented worldwide, the cause of the TB epidemic is multifactorial and not fully understood. Objective: This study aims to investigate the risk factors of TB and incorporate these factors to forecast the incidence of TB infection across different age groups in Sichuan, China. Methods: Correlation and linear regression analyses were conducted to assess the relationships between TB cases and ecological factors, including environmental, economic, and social factors, in Sichuan Province from 2006 to 2017. The transfer function-noise model was used to forecast trends, considering both time and multifactor effects. Results: From 2006 to 2017, Sichuan Province had a reported cumulative incidence rate of 1321.08 cases per 100,000 individuals in male patients and 583.04 cases per 100,000 individuals in female patients. There were significant sex differences in the distribution of cases among age groups (trend χ225=12,544.4; P<.001). Ganzi Tibetan Autonomous Prefecture had the highest incidence rates of TB in both male and female patients in Sichuan. Correlation and regression analyses showed that the total illiteracy rate and average pressure at each measuring station (for individuals aged 15-24 years) were risk factors for TB. The protective factors were as follows: the number of families with the minimum living standard guarantee in urban areas, the average wind speed, the number of discharged patients with invasive TB, the number of people with the minimum living standard guarantee in rural areas, the total health expenditure as a percentage of regional gross domestic product, and being a single male individual (for those aged 0-14 years); the number of hospitals and number of health workers in infectious disease hospitals (for individuals aged 25-64 years); and the amount of daily morning and evening exercise, the number of people with the urban minimum living standard guarantee, and being married (for female individuals aged ≥65 years). The transfer function-noise model indicated that the incidence of TB in male patients aged 0-14 and 15-24 years will continue to increase, and the incidence of TB in female patients aged 0-14 and ≥65 years will continue to increase rapidly in Sichuan by 2035. Conclusions: The End TB Strategy in Sichuan should consider environmental, educational, medical, social, personal, and other conditions, and further substantial efforts are needed especially for male patients aged 0-24 years, female patients aged 0-14 years, and female patients older than 64 years. %M 36456535 %R 10.2196/40659 %U https://publichealth.jmir.org/2023/1/e40659 %U https://doi.org/10.2196/40659 %U http://www.ncbi.nlm.nih.gov/pubmed/36456535 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 12 %P e39336 %T Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study %A Braun,David %A Ingram,Daniel %A Ingram,David %A Khan,Bilal %A Marsh,Jessecae %A McAndrew,Thomas %+ Department of Psychology, Lehigh University, 17 Memorial Dr E, Bethlehem, PA, 18015, United States, 1 6107583000, dab414@lehigh.edu %K crowdsourcing %K COVID-19 %K forecasting %K human judgment %D 2022 %7 30.12.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Past research has shown that various signals associated with human behavior (eg, social media engagement) can benefit computational forecasts of COVID-19. One behavior that has been shown to reduce the spread of infectious agents is compliance with nonpharmaceutical interventions (NPIs). However, the extent to which the public adheres to NPIs is difficult to measure and consequently difficult to incorporate into computational forecasts of infectious diseases. Soliciting judgments from many individuals (ie, crowdsourcing) can lead to surprisingly accurate estimates of both current and future targets of interest. Therefore, asking a crowd to estimate community-level compliance with NPIs may prove to be an accurate and predictive signal of an infectious disease such as COVID-19. Objective: We aimed to show that crowdsourced perceptions of compliance with NPIs can be a fast and reliable signal that can predict the spread of an infectious agent. We showed this by measuring the correlation between crowdsourced perceptions of NPIs and US incident cases of COVID-19 1-4 weeks ahead, and evaluating whether incorporating crowdsourced perceptions improves the predictive performance of a computational forecast of incident cases. Methods: For 36 weeks from September 2020 to April 2021, we asked 2 crowds 21 questions about their perceptions of community adherence to NPIs and public health guidelines, and collected 10,120 responses. Self-reported state residency was compared to estimates from the US census to determine the representativeness of the crowds. Crowdsourced NPI signals were mapped to 21 mean perceived adherence (MEPA) signals and analyzed descriptively to investigate features, such as how MEPA signals changed over time and whether MEPA time series could be clustered into groups based on response patterns. We investigated whether MEPA signals were associated with incident cases of COVID-19 1-4 weeks ahead by (1) estimating correlations between MEPA and incident cases, and (2) including MEPA into computational forecasts. Results: The crowds were mostly geographically representative of the US population with slight overrepresentation in the Northeast. MEPA signals tended to converge toward moderate levels of compliance throughout the survey period, and an unsupervised analysis revealed signals clustered into 4 groups roughly based on the type of question being asked. Several MEPA signals linearly correlated with incident cases of COVID-19 1-4 weeks ahead at the US national level. Including questions related to social distancing, testing, and limiting large gatherings increased out-of-sample predictive performance for probabilistic forecasts of incident cases of COVID-19 1-3 weeks ahead when compared to a model that was trained on only past incident cases. Conclusions: Crowdsourced perceptions of nonpharmaceutical adherence may be an important signal to improve forecasts of the trajectory of an infectious agent and increase public health situational awareness. %M 36219845 %R 10.2196/39336 %U https://publichealth.jmir.org/2022/12/e39336 %U https://doi.org/10.2196/39336 %U http://www.ncbi.nlm.nih.gov/pubmed/36219845 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 12 %P e41834 %T Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study %A Mackey,Tim Ken %A Jarmusch,Alan K %A Xu,Qing %A Sun,Kunyang %A Lu,Aileen %A Aguirre,Shaden %A Lim,Jessica %A Bhakta,Simran %A Dorrestein,Pieter C %+ Global Health Program, Department of Anthropology, University of California, San Diego, 9500 Gilman Drive, MC: 0505, La Jolla, CA, 92093, United States, 1 9514914161, tkmackey@ucsd.edu %K online pharmacy %K antimicrobial resistance %K drug safety %K cyberpharmacies %K public health %K health website %K online health %K web surveillance %K patient safety %D 2022 %7 23.12.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Antimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription. Objective: The aim of this paper is to identify and characterize “no prescription” online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys. Methods: We first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants. Results: A total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants. Conclusions: Our study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients. %M 36563038 %R 10.2196/41834 %U https://publichealth.jmir.org/2022/12/e41834 %U https://doi.org/10.2196/41834 %U http://www.ncbi.nlm.nih.gov/pubmed/36563038 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 12 %P e24938 %T Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study %A Lokala,Usha %A Lamy,Francois %A Daniulaityte,Raminta %A Gaur,Manas %A Gyrard,Amelie %A Thirunarayan,Krishnaprasad %A Kursuncu,Ugur %A Sheth,Amit %+ AI Institute, University of South Carolina, 1112 Greene St, Columbia, SC, 29208, United States, 1 803 777 9707, nlokala@email.sc.edu %K ontology %K knowledge graph %K semantic web %K illicit drugs %K cryptomarket %K social media %D 2022 %7 23.12.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports. Methods: The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. Results: The current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces. Conclusions: The DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research. %M 36563032 %R 10.2196/24938 %U https://publichealth.jmir.org/2022/12/e24938 %U https://doi.org/10.2196/24938 %U http://www.ncbi.nlm.nih.gov/pubmed/36563032 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 12 %P e40825 %T State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data %A Turvy,Alex %+ City, Culture, and Community, Department of Sociology, Tulane University, 6823 St Charles Ave, New Orleans, LA, 70118, United States, 1 504 865 5231, aturvy@tulane.edu %K COVID-19 %K search trends %K prediction %K case %K political %K symptom %K pandemic %K data %K google %K disease %K prevention %K model %D 2022 %7 23.12.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Across each state, the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often corresponded to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state but also the subjective individual experience of the pandemic. Objective: This study leverages state-level data from Google Search Trends and Centers for Disease Control and Prevention (CDC) daily case data to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the 4 spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2). Methods: Using publicly available data from Google Trends and the CDC, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the 4 spikes in individual states. Google offers a data set that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC’s definition of the 11 most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives. Results: The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically significant differences in lag time by state but that no individual variable measuring political climate was a statistically significant predictor of these differences. Conclusions: Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, or level of geographic modeling. %M 36446048 %R 10.2196/40825 %U https://formative.jmir.org/2022/12/e40825 %U https://doi.org/10.2196/40825 %U http://www.ncbi.nlm.nih.gov/pubmed/36446048 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 12 %P e37122 %T The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study %A Sylvestre,Emmanuelle %A Cécilia-Joseph,Elsa %A Bouzillé,Guillaume %A Najioullah,Fatiha %A Etienne,Manuel %A Malouines,Fabrice %A Rosine,Jacques %A Julié,Sandrine %A Cabié,André %A Cuggia,Marc %+ Centre de Données Cliniques, Centre Hospitalier Universitaire Martinique, CS 90632, Fort-de-France, 97261, Martinique, 596 596553507, emmanuelle.sylvestre@chu-martinique.fr %K dengue %K surveillance %K real-word data %K Big Data %K Caribbean %K dengue-endemic region %D 2022 %7 22.12.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. Objective: The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Methods: Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. Results: The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of −3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of −2 weeks. The most correlated query from Google Trends was the “Dengue” topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of −3 weeks. Conclusions: Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories. %M 36548023 %R 10.2196/37122 %U https://publichealth.jmir.org/2022/12/e37122 %U https://doi.org/10.2196/37122 %U http://www.ncbi.nlm.nih.gov/pubmed/36548023 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 12 %P e42781 %T The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data %A Ravkin,Hersh D %A Yom-Tov,Elad %A Nesher,Lior %+ Microsoft Research, 3 Alan Turing, Herzeliya, 4672415, Israel, 972 779391359, eladyt@microsoft.com %K RSV %K respiratory syncytial virus %K search engine %K Google Trends %K Google %K respiratory %K children %K pharmaceutical %K intervention %K COVID-19 %K pandemic %K virus %K infection %K health %D 2022 %7 21.12.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Respiratory syncytial virus (RSV) is a major cause of respiratory infection in children. Despite usually following a consistent seasonal pattern, the 2020-2021 RSV season in many countries was delayed and changed in magnitude. Objective: This study aimed to test if these changes can be attributed to nonpharmaceutical interventions (NPIs) instituted around the world to combat SARS-CoV-2. Methods: We used the internet search volume for RSV, as obtained from Google Trends, as a proxy to investigate these abnormalities. Results: Our analysis shows a breakdown of the usual correlation between peak latency and magnitude during the year of the pandemic. Analyzing latency and magnitude separately, we found that the changes therein are associated with implemented NPIs. Among several important interventions, NPIs affecting population mobility are shown to be particularly relevant to RSV incidence. Conclusions: The 2020-2021 RSV season served as a natural experiment to test NPIs that are likely to restrict RSV spread, and our findings can be used to guide health authorities to possible interventions. %M 36476385 %R 10.2196/42781 %U https://www.jmir.org/2022/12/e42781 %U https://doi.org/10.2196/42781 %U http://www.ncbi.nlm.nih.gov/pubmed/36476385 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 12 %P e37669 %T Digitalizing and Upgrading Severe Acute Respiratory Infections Surveillance in Malta: System Development %A Cauchi,John Paul %A Borg,Maria-Louise %A Džiugytė,Aušra %A Attard,Jessica %A Melillo,Tanya %A Zahra,Graziella %A Barbara,Christopher %A Spiteri,Michael %A Drago,Allan %A Zammit,Luke %A Debono,Joseph %A Souness,Jorgen %A Agius,Steve %A Young,Sharon %A Dimech,Alan %A Chetcuti,Ian %A Camenzuli,Mark %A Borg,Ivan %A Calleja,Neville %A Tabone,Lorraine %A Gauci,Charmaine %A Vassallo,Pauline %A Baruch,Joaquin %+ Health Promotion and Disease Prevention Directorate, 1, St. Luke's Square, Gwardamanġa, Msida, PTA 1010, Malta, 356 79899999, john-paul.cauchi@gov.mt %K surveillance %K public health %K epidemiology %K COVID-19 %K disease prevention %K disease surveillance %K digital health %K health system %K pandemic %K public hospital %K patient data %K health data %K electronic record %K monitoring %D 2022 %7 5.12.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In late 2020, the European Centre for Disease Prevention and Control and Epiconcept started implementing a surveillance system for severe acute respiratory infections (SARI) across Europe. Objective: We sought to describe the process of digitizing and upgrading SARI surveillance in Malta, an island country with a centralized health system, during the COVID-19 pandemic from February to November 2021. We described the characteristics of people included in the surveillance system and compared different SARI case definitions, including their advantages and disadvantages. This study also discusses the process, output, and future for SARI and other public health surveillance opportunities. Methods: Malta has one main public hospital where, on admission, patient data are entered into electronic records as free text. Symptoms and comorbidities are manually extracted from these records, whereas other data are collected from registers. Collected data are formatted to produce weekly and monthly reports to inform public health actions. From October 2020 to February 2021, we established an analogue incidence-based system for SARI surveillance. From February 2021 onward, we mapped key stakeholders and digitized most surveillance processes. Results: By November 30, 2021, 903 SARI cases were reported, with 380 (42.1%) positive for SARS-CoV-2. Of all SARI hospitalizations, 69 (7.6%) were admitted to the intensive care unit, 769 (85.2%) were discharged, 27 (3%) are still being treated, and 107 (11.8%) died. Among the 107 patients who died, 96 (89.7%) had more than one underlying condition, the most common of which were hypertension (n=57, 53.3%) and chronic heart disease (n=49, 45.8%). Conclusions: The implementation of enhanced SARI surveillance in Malta was completed by the end of May 2021, allowing the monitoring of SARI incidence and patient characteristics. A future shift to register-based surveillance should improve SARI detection through automated processes. %M 36227157 %R 10.2196/37669 %U https://publichealth.jmir.org/2022/12/e37669 %U https://doi.org/10.2196/37669 %U http://www.ncbi.nlm.nih.gov/pubmed/36227157 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 11 %P e40866 %T New Surveillance Metrics for Alerting Community-Acquired Outbreaks of Emerging SARS-CoV-2 Variants Using Imported Case Data: Bayesian Markov Chain Monte Carlo Approach %A Yen,Amy Ming-Fang %A Chen,Tony Hsiu-Hsi %A Chang,Wei-Jung %A Lin,Ting-Yu %A Jen,Grace Hsiao-Hsuan %A Hsu,Chen-Yang %A Wang,Sen-Te %A Dang,Huong %A Chen,Sam Li-Sheng %+ School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, No 250, Wuxing St, Taipei, 110, Taiwan, 886 27361661 ext 5211, samchen@tmu.edu.tw %K COVID-19 %K imported case %K surveillance metric %K early detection %K community-acquired outbreak %D 2022 %7 25.11.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Global transmission from imported cases to domestic cluster infections is often the origin of local community-acquired outbreaks when facing emerging SARS-CoV-2 variants. Objective: We aimed to develop new surveillance metrics for alerting emerging community-acquired outbreaks arising from new strains by monitoring the risk of small domestic cluster infections originating from few imported cases of emerging variants. Methods: We used Taiwanese COVID-19 weekly data on imported cases, domestic cluster infections, and community-acquired outbreaks. The study period included the D614G strain in February 2020, the Alpha and Delta variants of concern (VOCs) in 2021, and the Omicron BA.1 and BA.2 VOCs in April 2022. The number of cases arising from domestic cluster infection caused by imported cases (Dci/Imc) per week was used as the SARS-CoV-2 strain-dependent surveillance metric for alerting local community-acquired outbreaks. Its upper 95% credible interval was used as the alert threshold for guiding the rapid preparedness of containment measures, including nonpharmaceutical interventions (NPIs), testing, and vaccination. The 2 metrics were estimated by using the Bayesian Monte Carlo Markov Chain method underpinning the directed acyclic graphic diagram constructed by the extra-Poisson (random-effect) regression model. The proposed model was also used to assess the most likely week lag of imported cases prior to the current week of domestic cluster infections. Results: A 1-week lag of imported cases prior to the current week of domestic cluster infections was considered optimal. Both metrics of Dci/Imc and the alert threshold varied with SARS-CoV-2 variants and available containment measures. The estimates were 9.54% and 12.59%, respectively, for D614G and increased to 14.14% and 25.10%, respectively, for the Alpha VOC when only NPIs and testing were available. The corresponding figures were 10.01% and 13.32% for the Delta VOC, but reduced to 4.29% and 5.19% for the Omicron VOC when NPIs, testing, and vaccination were available. The rapid preparedness of containment measures guided by the estimated metrics accounted for the lack of community-acquired outbreaks during the D614G period, the early Alpha VOC period, the Delta VOC period, and the Omicron VOC period between BA.1 and BA.2. In contrast, community-acquired outbreaks of the Alpha VOC in mid-May 2021, Omicron BA.1 VOC in January 2022, and Omicron BA.2 VOC from April 2022 onwards, were indicative of the failure to prepare containment measures guided by the alert threshold. Conclusions: We developed new surveillance metrics for estimating the risk of domestic cluster infections with increasing imported cases and its alert threshold for community-acquired infections varying with emerging SARS-CoV-2 strains and the availability of containment measures. The use of new surveillance metrics is important in the rapid preparedness of containment measures for averting large-scale community-acquired outbreaks arising from emerging imported SARS-CoV-2 variants. %M 36265134 %R 10.2196/40866 %U https://publichealth.jmir.org/2022/11/e40866 %U https://doi.org/10.2196/40866 %U http://www.ncbi.nlm.nih.gov/pubmed/36265134 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 11 %P e40977 %T Outcomes of a Community Engagement and Information Gathering Program to Support Telephone-Based COVID-19 Contact Tracing: Descriptive Analysis %A Udeagu,Chi-Chi N %A Pitiranggon,Masha %A Misra,Kavita %A Huang,Jamie %A Terilli,Thomas %A Ramos,Yasmin %A Alexander,Martha %A Kim,Christine %A Lee,David %A Blaney,Kathleen %A Keeley,Chris %A Long,Theodore %A Vora,Neil M %+ New York City Test & Trace Corps, New York City Department of Health & Mental Hygiene, 42-09 28th Street, Queens, NY, 11101, United States, 1 3473864909, cudeagu@health.nyc.gov %K COVID-19 %K contact tracing %K home visits %K community health workers %K health equity %D 2022 %7 15.11.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Contact tracing is an important public health tool for curbing the spread of infectious diseases. Effective and efficient contact tracing involves the rapid identification of individuals with infection and their exposed contacts and ensuring their isolation or quarantine, respectively. Manual contact tracing via telephone call and digital proximity app technology have been key strategies in mitigating the spread of COVID-19. However, many people are not reached for COVID-19 contact tracing due to missing telephone numbers or nonresponse to telephone calls. The New York City COVID-19 Trace program augmented the efforts of telephone-based contact tracers with information gatherers (IGs) to search and obtain telephone numbers or residential addresses, and community engagement specialists (CESs) made home visits to individuals that were not contacted via telephone calls. Objective: The aim of this study was to assess the contribution of information gathering and home visits to the yields of COVID-19 contact tracing in New York City. Methods: IGs looked for phone numbers or addresses when records were missing phone numbers to locate case-patients or contacts. CESs made home visits to case-patients and contacts with no phone numbers or those who were not reached by telephone-based tracers. Contact tracing management software was used to triage and queue assignments for the telephone-based tracers, IGs, and CESs. We measured the outcomes of contact tracing–related tasks performed by the IGs and CESs from July 2020 to June 2021. Results: Of 659,484 cases and 861,566 contact records in the Trace system, 28% (185,485) of cases and 35% (303,550) of contacts were referred to IGs. IGs obtained new phone numbers for 33% (61,804) of case-patients and 11% (31,951) of contacts; 50% (31,019) of the case-patients and 46% (14,604) of the contacts with new phone numbers completed interviews; 25% (167,815) of case-patients and 8% (72,437) of contacts were referred to CESs. CESs attempted 80% (132,781) of case and 69% (49,846) of contact investigations, of which 47% (62,733) and 50% (25,015) respectively, completed interviews. An additional 12,192 contacts were identified following IG investigations and 13,507 following CES interventions. Conclusions: Gathering new or missing locating information and making home visits increased the number of case-patients and contacts interviewed for contact tracing and resulted in additional contacts. When possible, contact tracing programs should add information gathering and home visiting strategies to increase COVID-19 contact tracing coverage and yields as well as promote equity in the delivery of this public health intervention. %M 36240019 %R 10.2196/40977 %U https://publichealth.jmir.org/2022/11/e40977 %U https://doi.org/10.2196/40977 %U http://www.ncbi.nlm.nih.gov/pubmed/36240019 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 10 %P e38647 %T Entomological Surveillance of the Invasive Aedes Species at Higher-Priority Entry Points in Northern Iran: Exploratory Report on a Field Study %A Nikookar,Seyed Hassan %A Maleki,Alireza %A Fazeli-Dinan,Mahmoud %A Shabani Kordshouli,Razieh %A Enayati,Ahmadali %+ Department of Medical Entomology and Vector Control, School of Public Health and Health Sciences Research Center, Mazandaran University of Medical Sciences, Khazar Abad Road (Km-17th), Sari, P.O. Box 48175-1553, Iran, 98 9111522209, aenayati1372@gmail.com %K mosquito surveillance %K Aedes %K biodiversity %K Guilan %K Northern Iran %D 2022 %7 31.10.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Arboviral diseases such as dengue, Zika, and chikungunya are transmitted by Aedes aegypti and Ae albopictus and are emerging global public health concerns. Objective: This study aimed to provide up-to-date data on the occurrence of the invasive Aedes species in a given area as this is essential for planning and implementing timely control strategies. Methods: Entomological surveillance was planned and carried out monthly from May 2018 to December 2019 at higher-priority entry points in Guilan Province, Northern Iran, using ovitraps, larval collection, and human-baited traps. Species richness (R), Simpson (D), evenness (E), and Shannon-Wiener indexes (H̕) were measured to better understand the diversity of the Aedes species. The Spearman correlation coefficient and regression models were used for data analysis. Results: We collected a total of 3964 mosquito samples including 17.20% (682/3964) belonging to the Aedes species, from 3 genera and 13 species, and morphologically identified them from May 2018 to December 2019. Ae vexans and Ae geniculatus, which showed a peak in activity levels and population in October (226/564, 40.07% and 26/103, 25.2%), were the eudominant species (D=75.7%; D=21.2%) with constant (C=100) and frequent (C=66.7%) distributions, respectively. The population of Ae vexans had a significant positive correlation with precipitation (r=0.521; P=.009) and relative humidity (r=0.510; P=.01), whereas it was inversely associated with temperature (r=−0.432; P=.04). The Shannon-Wiener Index was up to 0.84 and 1.04 in the city of Rasht and in July, respectively. The rarefaction curve showed sufficiency in sampling efforts by reaching the asymptotic line at all spatial and temporal scales, except in Rasht and in October. Conclusions: Although no specimens of the Ae aegypti and Ae albopictus species were collected, this surveillance provides a better understanding of the native Aedes species in the northern regions of Iran. These data will assist the health system in future arbovirus research, and in the implementation of effective vector control and prevention strategies, should Ae aegypti and Ae albopictus be found in the province. %M 36315230 %R 10.2196/38647 %U https://publichealth.jmir.org/2022/10/e38647 %U https://doi.org/10.2196/38647 %U http://www.ncbi.nlm.nih.gov/pubmed/36315230 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 10 %P e40233 %T Effectiveness, Policy, and User Acceptance of COVID-19 Contact-Tracing Apps in the Post–COVID-19 Pandemic Era: Experience and Comparative Study %A Liu,MingXin %A Zhou,SiYu %A Jin,Qun %A Nishimura,Shoji %A Ogihara,Atsushi %+ Graduate School of Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, 3591192, Japan, 81 04 2949 8111, liumingxin98@akane.waseda.jp %K COVID-19 %K contact-tracing app %K digital contact tracing %K mobile phone %D 2022 %7 27.10.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In the post–COVID-19 pandemic era, many countries have launched apps to trace contacts of COVID-19 infections. Each contact-tracing app (CTA) faces a variety of issues owing to different national policies or technologies for tracing contacts. Objective: In this study, we aimed to investigate all the CTAs used to trace contacts in various countries worldwide, including the technology used by each CTA, the availability of knowledge about the CTA from official websites, the interoperability of CTAs in various countries, and the infection detection rates and policies of the specific country that launched the CTA, and to summarize the current problems of the apps based on the information collected. Methods: We investigated CTAs launched in all countries through Google, Google Scholar, and PubMed. We experimented with all apps that could be installed and compiled information about apps that could not be installed or used by consulting official websites and previous literature. We compared the information collected by us on CTAs with relevant previous literature to understand and analyze the data. Results: After screening 166 COVID-19 apps developed in 197 countries worldwide, we selected 98 (59%) apps from 95 (48.2%) countries, of which 63 (66.3%) apps were usable. The methods of contact tracing are divided into 3 main categories: Bluetooth, geolocation, and QR codes. At the technical level, CTAs face 3 major problems. First, the distance and time for Bluetooth- and geolocation-based CTAs to record contact are generally set to 2 meters and 15 minutes; however, this distance should be lengthened, and the time should be shortened for more infectious variants. Second, Bluetooth- or geolocation-based CTAs also face the problem of lack of accuracy. For example, individuals in 2 adjacent vehicles during traffic jams may be at a distance of ≤2 meters to make the CTA trace contact, but the 2 users may actually be separated by car doors, which could prevent transmission and infection. In addition, we investigated infection detection rates in 33 countries, 16 (48.5%) of which had significantly low infection detection rates, wherein CTAs could have lacked effectiveness in reducing virus propagation. Regarding policy, CTAs in most countries can only be used in their own countries and lack interoperability among other countries. In addition, 7 countries have already discontinued CTAs, but we believe that it was too early to discontinue them. Regarding user acceptance, 28.6% (28/98) of CTAs had no official source of information that could reduce user acceptance. Conclusions: We surveyed all CTAs worldwide, identified their technological policy and acceptance issues, and provided solutions for each of the issues we identified. This study aimed to provide useful guidance and suggestions for updating the existing CTAs and the subsequent development of new CTAs. %M 36190741 %R 10.2196/40233 %U https://publichealth.jmir.org/2022/10/e40233 %U https://doi.org/10.2196/40233 %U http://www.ncbi.nlm.nih.gov/pubmed/36190741 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 10 %P e37177 %T Pilot Influenza Syndromic Surveillance System Based on Absenteeism and Temperature in China: Development and Usability Study %A Yang,Zhen %A Jiang,Chenghua %+ Dongfang Hospital, Tongji University, No 1239 Siping Road, Yangpu District, Shanghai, 200092, China, 86 18917266778, jchtongji@163.com %K influenza %K syndromic surveillance system %K face recognition %K infrared thermometer %K absenteeism %K temperature %D 2022 %7 14.10.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Shortcomings of the current school-based infectious disease syndromic surveillance system (SSS) in China include relying on school physicians to collect data manually and ignoring the health information of students in attendance. Objective: This study aimed to design and implement an influenza SSS based on the absenteeism (collected by face recognition) and temperature of attending students (measured by thermal imaging). Methods: An SSS was implemented by extending the functionality of an existing application. The system was implemented in 2 primary schools and 1 junior high school in the Yangtze River Delta, with a total of 3535 students. The examination period was from March 1, 2021, to January 14, 2022, with 174 effective days. The daily and weekly absenteeism and fever rates reported by the system (DAR1 and DFR; WAR1 and WFR) were calculated. The daily and weekly absenteeism rates reported by school physicians (DAR2 and WAR2) and the weekly positive rate of influenza virus (WPRIV, released by the Chinese National Influenza Center) were used as standards to evaluate the quality of the data reported by the system. Results: Absenteeism reported by school physicians (completeness 86.7%) was 36.5% of that reported by this system (completeness 100%), and a significant positive correlation between them was detected (r=0.372, P=.002). When the influenza activity level was moderate, DAR1s were significantly positively correlated among schools (rab=0.508, P=.004; rbc=0.427, P=.02; rac=0.447, P=.01). During the influenza breakout, the gap of DAR1s widened. WAR1 peaked 2 weeks earlier in schools A and B than in school C. Variables significantly positively correlated with the WPRIV were the WAR1 and WAR2 of school A, WAR1 of school C, and WFR of school B. The correlation between the WAR1 and WPRIV was greater than that between the WAR2 and WPRIV in school A. Addition of the WFR to the WAR1 of school B increased the correlation between the WAR1 and WPRIV. Conclusions: Data demonstrated that absenteeism calculation based on face recognition was reliable, but the accuracy of the temperature recorded by the infrared thermometer should be enhanced. Compared with similar SSSs, this system has superior simplicity, cost-effectiveness, data quality, sensitivity, and timeliness. %M 36239991 %R 10.2196/37177 %U https://publichealth.jmir.org/2022/10/e37177 %U https://doi.org/10.2196/37177 %U http://www.ncbi.nlm.nih.gov/pubmed/36239991 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 9 %P e39033 %T Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study %A Huang,Yuru %A Burgoine,Thomas %A Essman,Michael %A Theis,Dolly R Z %A Bishop,Tom R P %A Adams,Jean %+ Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, United Kingdom, 44 01223 330315, Yuru.Huang@mrc-epid.cam.ac.uk %K nutritional database %K web scraping %K food prepared out of the home %K out-of-home %K data science %K chains %D 2022 %7 8.9.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Hand transcribing nutrient composition data from websites requires extensive human resources and is prone to error. As a result, there are limited nutrient composition data on food prepared out of the home in the United Kingdom. Such data are crucial for understanding and monitoring the out-of-home food environment, which aids policy making. Automated data collection from publicly available sources offers a potential low-resource solution to address this gap. Objective: In this paper, we describe the first UK longitudinal nutritional database of food prepared out of the home, MenuTracker. As large chains will be required to display calorie information on their UK menus from April 2022, we also aimed to identify which chains reported their nutritional information online in November 2021. In a case study to demonstrate the utility of MenuTracker, we estimated the proportions of menu items exceeding recommended energy and nutrient intake (eg, >600 kcal per meal). Methods: We have collated nutrient composition data of menu items sold by large chain restaurants quarterly since March 2021. Large chains were defined as those with 250 employees or more (those covered by the new calorie labeling policy) or belonging to the top 100 restaurants based on sales volume. We developed scripts in Python to automate the data collection process from business websites. Various techniques were used to harvest web data and extract data from nutritional tables in PDF format. Results: Automated Python programs reduced approximately 85% of manual work, totaling 500 hours saved for each wave of data collection. As of January 2022, MenuTracker has 76,405 records from 88 large out-of-home food chains at 4 different time points (ie, March, June, September, and December) in 2021. In constructing the database, we found that one-quarter (24.5%, 256/1043) of large chains, which are likely to be subject to the United Kingdom’s calorie menu labeling regulations, provided their nutritional information online in November 2021. Across these chains, 24.7% (16,391/66,295) of menu items exceeded the UK government’s recommendation of a maximum of 600 kcal for a single meal. Comparable figures were 46.4% (29,411/63,416) for saturated fat, 34.7% (21,964/63,388) for total fat, 17.6% (11,260/64,051) for carbohydrates, 17.8% (11,434/64,059) for sugar, and 35.2% (22,588/64,086) for salt. Furthermore, 0.7% to 7.1% of the menu items exceeded the maximum daily recommended intake for these nutrients. Conclusions: MenuTracker is a valuable resource that harnesses the power of data science techniques to use publicly available data online. Researchers, policy makers, and consumers can use MenuTracker to understand and assess foods available from out-of-home food outlets. The methods used in development are available online and can be used to establish similar databases elsewhere. %M 36074559 %R 10.2196/39033 %U https://publichealth.jmir.org/2022/9/e39033 %U https://doi.org/10.2196/39033 %U http://www.ncbi.nlm.nih.gov/pubmed/36074559 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 8 %P e34589 %T Colorectal Cancer Incidence, Inequalities, and Prevention Priorities in Urban Texas: Surveillance Study With the “surveil” Software Package %A Donegan,Connor %A Hughes,Amy E %A Lee,Simon J Craddock %+ Department of Population and Data Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, United States, 1 214 648 9400, Connor.Donegan@UTSouthwestern.edu %K Bayesian analysis %K cancer prevention %K colorectal cancer %K health equity %K open source software %K public health monitoring %K time-series analysis %D 2022 %7 16.8.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Monitoring disease incidence rates over time with population surveillance data is fundamental to public health research and practice. Bayesian disease monitoring methods provide advantages over conventional methods including greater flexibility in model specification and the ability to conduct formal inference on model-derived quantities of interest. However, software platforms for Bayesian inference are often inaccessible to nonspecialists. Objective: To increase the accessibility of Bayesian methods among health surveillance researchers, we introduce a Bayesian methodology and open source software package, surveil, for time-series modeling of disease incidence and mortality. Given case count and population-at-risk data, the software enables health researchers to draw inferences about underlying risk and derivative quantities including age-standardized rates, annual and cumulative percent change, and measures of inequality. Methods: We specify a Poisson likelihood for case counts and model trends in log-risk using the first-difference (random-walk) prior. Models in the surveil R package were built using the Stan modeling language. We demonstrate the methodology and software by analyzing age-standardized colorectal cancer (CRC) incidence rates by race and ethnicity for non-Latino Black (Black), non-Latino White (White), and Hispanic/Latino (of any race) adults aged 50-79 years in Texas’s 4 largest metropolitan statistical areas between 1999 and 2018. Results: Our analysis revealed a cumulative decline of 31% (95% CI –37% to –25%) in CRC risk among Black adults, 17% (95% CI –23% to –11%) for Latino adults, and 35% (95% CI –38% to –31%) for White adults from 1999 to 2018. None of the 3 observed groups experienced significant incidence reduction in the final 4 years of the study (2015-2018). The Black-White rate difference (per 100,000) was 44 (95% CI 30-57) in 1999 and 35 (95% CI 28-43) in 2018. Cumulatively, the Black-White gap accounts for 3983 CRC cases (95% CI 3746-4219) or 31% (95% CI 29%-32%) of total CRC incidence among Black adults in this period. Conclusions: Stalled progress on CRC prevention and excess CRC risk among Black residents warrant special attention as cancer prevention and control priorities in urban Texas. Our methodology and software can help the public and health agencies monitor health inequalities and evaluate progress toward disease prevention goals. Advantages of the methodology over current common practice include the following: (1) the absence of piecewise linearity constraints on the model space, and (2) formal inference can be undertaken on any model-derived quantities of interest using Bayesian methods. %M 35972778 %R 10.2196/34589 %U https://publichealth.jmir.org/2022/8/e34589 %U https://doi.org/10.2196/34589 %U http://www.ncbi.nlm.nih.gov/pubmed/35972778 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 8 %P e35840 %T Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method %A Luo,Wei %A Liu,Zhaoyin %A Zhou,Yuxuan %A Zhao,Yumin %A Li,Yunyue Elita %A Masrur,Arif %A Yu,Manzhu %+ Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore, 65 65163851, geowl@nus.edu.sg %K COVID-19 %K Delta variant %K space-time scan %K intervention %K Southeast Asia %D 2022 %7 9.8.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective: The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods: We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results: We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions: The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19. %M 35861674 %R 10.2196/35840 %U https://publichealth.jmir.org/2022/8/e35840 %U https://doi.org/10.2196/35840 %U http://www.ncbi.nlm.nih.gov/pubmed/35861674 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 8 %P e38551 %T The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review %A McNeil,Carrie %A Verlander,Sarah %A Divi,Nomita %A Smolinski,Mark %+ Ending Pandemics, 870 Market Street, Suite 528, San Francisco, CA, 94102, United States, 1 415 571 2175, carrie@endingpandemics.org %K participatory surveillance %K One Health %K citizen science %K community-based surveillance %K infectious disease %K digital disease detection %K community participation %K mobile phone %D 2022 %7 5.8.2022 %9 Review %J JMIR Public Health Surveill %G English %X Background: Participatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum. Objective: The aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum. Methods: This landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs. Results: The landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48%) include data on human health, 26 (43%) include data on environmental health, and 24 (40%) include data on animal health. In total, 16 (27%) systems are multisectoral; of these, 9 (56%) collect animal and environmental health data; 3 (19%) collect human, animal, and environmental health data; 2 (13%) collect human and environmental health data; and 2 (13%) collect human and animal health data. Out of 60 systems, 31 (52%) are designed to cover a national scale, compared to those with a subnational (n=19, 32%) or multinational (n=10, 17%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67%) and smartphones (n=29, 48%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52%), photographs (n=29, 48%), and videos (n=6, 10%), and can incorporate lab data or sample collection (n=15, 25%). In sharing information back with users, most use visualization, such as maps (n=43, 72%); training and educational materials (n=37, 62%); newsletters, blogs, and emails (n=34, 57%); and disease prevention information (n=32, 53%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78%) noted “improved community knowledge and understanding” and 31 (67%) noted “earlier detection.” Conclusions: The landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly. %M 35930345 %R 10.2196/38551 %U https://publichealth.jmir.org/2022/8/e38551 %U https://doi.org/10.2196/38551 %U http://www.ncbi.nlm.nih.gov/pubmed/35930345 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 7 %P e34782 %T Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study %A Abdullah,Abu Yousuf Md %A Law,Jane %A Perlman,Christopher M %A Butt,Zahid A %+ School of Planning, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 226 978 2838, aymabdul@uwaterloo.ca %K mental health disorders %K vegetation cover %K age- and sex- specific association %K Enhanced Vegetation Index %K Bayesian %K spatial %K hierarchical modeling %K marginalization %K latent covariates %D 2022 %7 28.7.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. Objective: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. Methods: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. Results: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (−7.009; 95% CI −13.130 to −0.980) and for both males (−4.544; 95% CI −8.224 to −0.895) and females (−3.513; 95% CI −6.289 to −0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. Conclusions: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being. %M 35900816 %R 10.2196/34782 %U https://publichealth.jmir.org/2022/7/e34782 %U https://doi.org/10.2196/34782 %U http://www.ncbi.nlm.nih.gov/pubmed/35900816 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 7 %P e32164 %T Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework %A Megahed,Fadel M %A Jones-Farmer,L Allison %A Ma,Yinjiao %A Rigdon,Steven E %+ Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, 3545 Lafayette Ave, St Louis, MO, 63104, United States, 1 3149772781, steve.rigdon@slu.edu %K explanatory modeling %K multinomial regression %K SARS-CoV-2 %K COVID-19 %K socioeconomic analyses %K time series analysis %D 2022 %7 19.7.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. Objective: Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. Methods: We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021. Results: Four distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties’ outbreak patterns/clusters. Conclusions: Our results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors. %M 35476722 %R 10.2196/32164 %U https://publichealth.jmir.org/2022/7/e32164 %U https://doi.org/10.2196/32164 %U http://www.ncbi.nlm.nih.gov/pubmed/35476722 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 7 %P e34583 %T Linguistic Pattern–Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases–Mail Database: Algorithm Development Study %A Chang,Yung-Chun %A Chiu,Yu-Wen %A Chuang,Ting-Wu %+ Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, No 250, Wu-Hsing Street, Taipei, 110, Taiwan, 886 27361661 ext 3123, chtingwu@tmu.edu.tw %K ProMED-mail %K natural language processing %K dengue %K dual channel %K bidirectional long short-term memory %D 2022 %7 13.7.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)–mail is the most comprehensive international expert–curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the reports makes analysis for further application difficult. Objective: To make monitoring the epidemic situation in Southeast Asia more efficient, this study aims to develop an automatic summary of the alert articles from ProMED-mail, a huge textual data source. In this paper, we proposed a text summarization method that uses natural language processing technology to automatically extract important sentences from alert articles in ProMED-mail emails to generate summaries. Using our method, we can quickly capture crucial information to help make important decisions regarding epidemic surveillance. Methods: Our data, which span a period from 1994 to 2019, come from the ProMED-mail website. We analyzed the collected data to establish a unique Taiwan dengue corpus that was validated with professionals’ annotations to achieve almost perfect agreement (Cohen κ=90%). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long short-term memory with attention mechanism with infused latent syntactic features to identify key sentences from the alerting article. Results: Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macroaverage F1 score of 93%. Moreover, it can successfully extract the relevant correct information on dengue fever from a ProMED-mail alerting article, which can help researchers or general users to quickly understand the essence of the alerting article at first glance. In addition to verifying the model, we also recruited 3 professional experts and 2 students from related fields to participate in a satisfaction survey on the generated summaries, and the results show that 84% (63/75) of the summaries received high satisfaction ratings. Conclusions: The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze the syntactic, semantic, and contextual information in the text. It then exploits the derived information to identify crucial sentences in the ProMED-mail alerting article. The experiment results show that the proposed method is not only effective but also outperforms the compared methods. Our approach also demonstrates the potential for case summary generation from ProMED-mail alerting articles. In terms of practical application, when a new alerting article arrives, our method can quickly identify the relevant case information, which is the most critical part, to use as a reference or for further analysis. %M 35830225 %R 10.2196/34583 %U https://publichealth.jmir.org/2022/7/e34583 %U https://doi.org/10.2196/34583 %U http://www.ncbi.nlm.nih.gov/pubmed/35830225 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 7 %P e34605 %T Assessing the Implementation of Digital Innovations in Response to the COVID-19 Pandemic to Address Key Public Health Functions: Scoping Review of Academic and Nonacademic Literature %A Francombe,Joseph %A Ali,Gemma-Claire %A Gloinson,Emily Ryen %A Feijao,Carolina %A Morley,Katherine I %A Gunashekar,Salil %A de Carvalho Gomes,Helena %+ European Centre for Disease Prevention and Control, Gustav III:s Boulevard 40, Solna, Stockholm, 169 73, Sweden, 46 858601000, helena.gomes@ecdc.europa.eu %K digital technologies %K COVID-19 %K key public health functions %K scoping review %K digital health %K pandemic %K surveillance %K mobile phone %D 2022 %7 6.7.2022 %9 Review %J JMIR Public Health Surveill %G English %X Background: Digital technologies have been central to efforts to respond to the COVID-19 pandemic. In this context, a range of literature has reported on developments regarding the implementation of new digital technologies for COVID-19–related surveillance, prevention, and control. Objective: In this study, scoping reviews of academic and nonacademic literature were undertaken to obtain an overview of the evidence regarding digital innovations implemented to address key public health functions in the context of the COVID-19 pandemic. This study aimed to expand on the work of existing reviews by drawing on additional data sources (including nonacademic sources) by considering literature published over a longer time frame and analyzing data in terms of the number of unique digital innovations. Methods: We conducted a scoping review of the academic literature published between January 1, 2020, and September 15, 2020, supplemented by a further scoping review of selected nonacademic literature published between January 1, 2020, and October 13, 2020. Both reviews followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. Results: A total of 226 academic articles and 406 nonacademic articles were included. The included articles provided evidence of 561 (academic literature) and 497 (nonacademic literature) unique digital innovations. The most common implementation settings for digital innovations were the United States, China, India, and the United Kingdom. Technologies most commonly used by digital innovations were those belonging to the high-level technology group of integrated and ubiquitous fixed and mobile networks. The key public health functions most commonly addressed by digital innovations were communication and collaboration and surveillance and monitoring. Conclusions: Digital innovations implemented in response to the COVID-19 pandemic have been wide ranging in terms of their implementation settings, the digital technologies used, and the public health functions addressed. However, evidence gathered through this study also points to a range of barriers that have affected the successful implementation of digital technologies for public health functions. It is also evident that many digital innovations implemented in response to the COVID-19 pandemic are yet to be formally evaluated or assessed. %M 35605152 %R 10.2196/34605 %U https://publichealth.jmir.org/2022/7/e34605 %U https://doi.org/10.2196/34605 %U http://www.ncbi.nlm.nih.gov/pubmed/35605152 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 7 %P e37920 %T Use of Random Domain Intercept Technology to Track COVID-19 Vaccination Rates in Real Time Across the United States: Survey Study %A Sargent,Rikki H %A Laurie,Shaelyn %A Weakland,Leo F %A Lavery,James V %A Salmon,Daniel A %A Orenstein,Walter A %A Breiman,Robert F %+ RIWI Corp, 180 Bloor Street West, Suite 1000, Toronto, ON, M5S2V6, Canada, 1 888 505 7494, rsargent@riwi.com %K COVID-19 %K vaccination rates %K Random Domain Intercept Technology %K health technology %K vaccination %K vaccine tracker %K web-based survey %K epidemiology %K health data %K digital tool %K online intercept sampling %K health service %D 2022 %7 1.7.2022 %9 Short Paper %J J Med Internet Res %G English %X Background: Accurate and timely COVID-19 vaccination coverage data are vital for informing targeted, effective messaging and outreach and identifying barriers to equitable health service access. However, gathering vaccination rate data is challenging, and efforts often result in information that is either limited in scope (eg, limited to administrative data) or delayed (impeding the ability to rapidly respond). The evaluation of innovative technologies and approaches that can assist in addressing these limitations globally are needed. Objective: The objective of this survey study was to assess the validity of Random Domain Intercept Technology (RDIT; RIWI Corp) for tracking self-reported vaccination rates in real time at the US national and state levels. RDIT—a form of online intercept sampling—has the potential to address the limitations of current vaccination tracking systems by allowing for the measurement of additional data (eg, attitudinal data) and real-time, rapid data collection anywhere there is web access. Methods: We used RDIT from June 30 to July 26, 2021, to reach a broad sample of US adult (aged ≥18 years) web users and asked questions related to COVID-19 vaccination. Self-reported vaccination status was used as the focus of this validation exercise. National- and state-level RDIT-based vaccination rates were compared to Centers for Disease Control and Prevention (CDC)–reported national and state vaccination rates. Johns Hopkins University’s and Emory University’s institutional review boards designated this project as public health practice to inform message development (not human subjects research). Results: By using RDIT, 63,853 adult web users reported their vaccination status (6.2% of the entire 1,026,850 American web-using population that was exposed to the survey). At the national level, the RDIT-based estimate of adult COVID-19 vaccine coverage was slightly higher (44,524/63,853, 69.7%; 95% CI 69.4%-70.1%) than the CDC-reported estimate (67.9%) on July 15, 2021 (ie, midway through data collection; t63,852=10.06; P<.001). The RDIT-based and CDC-reported state-level estimates were strongly and positively correlated (r=0.90; P<.001). RDIT-based estimates were within 5 percentage points of the CDC’s estimates for 29 states. Conclusions: This broad-reaching, real-time data stream may provide unique advantages for tracking the use of a range of vaccines and for the timely evaluation of vaccination interventions. Moreover, RDIT could be harnessed to rapidly assess demographic, attitudinal, and behavioral constructs that are not available in administrative data, which could allow for deeper insights into the real-time predictors of vaccine uptake–enabling targeted and timely interventions. %M 35709335 %R 10.2196/37920 %U https://www.jmir.org/2022/7/e37920 %U https://doi.org/10.2196/37920 %U http://www.ncbi.nlm.nih.gov/pubmed/35709335 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 6 %P e35343 %T Changes in Temporal Properties of Notifiable Infectious Disease Epidemics in China During the COVID-19 Pandemic: Population-Based Surveillance Study %A Zhao,Xixi %A Li,Meijia %A Haihambo,Naem %A Jin,Jianhua %A Zeng,Yimeng %A Qiu,Jinyi %A Guo,Mingrou %A Zhu,Yuyao %A Li,Zhirui %A Liu,Jiaxin %A Teng,Jiayi %A Li,Sixiao %A Zhao,Ya-nan %A Cao,Yanxiang %A Wang,Xuemei %A Li,Yaqiong %A Gao,Michel %A Feng,Xiaoyang %A Han,Chuanliang %+ Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Nanshan District, Shenzhen, 518055, China, 86 18800129802, hanchuanliang2014@163.com %K class B infectious disease %K COVID-19 %K event-related trough %K infection selectivity %K oscillation %K public health interventions %K pandemic %K surveillance %K health policy %K epidemiology %K prevention policy %K public health %K risk prevention %D 2022 %7 23.6.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic. Objective: Nonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China. Methods: Time series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People’s Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak. Results: We found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude. Conclusions: Our results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases. %M 35649394 %R 10.2196/35343 %U https://publichealth.jmir.org/2022/6/e35343 %U https://doi.org/10.2196/35343 %U http://www.ncbi.nlm.nih.gov/pubmed/35649394 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 6 %P e33099 %T Characterizing Anchoring Bias in Vaccine Comparator Selection Due to Health Care Utilization With COVID-19 and Influenza: Observational Cohort Study %A Ostropolets,Anna %A Ryan,Patrick B %A Schuemie,Martijn J %A Hripcsak,George %+ Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, New York, NY, 10032, United States, 1 2123055334, gh13@cumc.columbia.edu %K COVID-19 %K vaccine %K anchoring %K comparator selection %K time-at-risk %K vaccination %K bias %K observational %K utilization %K flu %K influenza %K index %K cohort %D 2022 %7 17.6.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Observational data enables large-scale vaccine safety surveillance but requires careful evaluation of the potential sources of bias. One potential source of bias is the index date selection procedure for the unvaccinated cohort or unvaccinated comparison time (“anchoring”). Objective: Here, we evaluated the different index date selection procedures for 2 vaccinations: COVID-19 and influenza. Methods: For each vaccine, we extracted patient baseline characteristics on the index date and up to 450 days prior and then compared them to the characteristics of the unvaccinated patients indexed on (1) an arbitrary date or (2) a date of a visit. Additionally, we compared vaccinated patients indexed on the date of vaccination and the same patients indexed on a prior date or visit. Results: COVID-19 vaccination and influenza vaccination differ drastically from each other in terms of the populations vaccinated and their status on the day of vaccination. When compared to indexing on a visit in the unvaccinated population, influenza vaccination had markedly higher covariate proportions, and COVID-19 vaccination had lower proportions of most covariates on the index date. In contrast, COVID-19 vaccination had similar covariate proportions when compared to an arbitrary date. These effects attenuated, but were still present, with a longer lookback period. The effect of day 0 was present even when the patients served as their own controls. Conclusions: Patient baseline characteristics are sensitive to the choice of the index date. In vaccine safety studies, unexposed index event should represent vaccination settings. Study designs previously used to assess influenza vaccination must be reassessed for COVID-19 to account for a potentially healthier population and lack of medical activity on the day of vaccination. %M 35482996 %R 10.2196/33099 %U https://publichealth.jmir.org/2022/6/e33099 %U https://doi.org/10.2196/33099 %U http://www.ncbi.nlm.nih.gov/pubmed/35482996 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e38113 %T Design and Preliminary Findings of Adherence to the Self-Testing for Our Protection From COVID-19 (STOP COVID-19) Risk-Based Testing Protocol: Prospective Digital Study %A Herbert,Carly %A Kheterpal,Vik %A Suvarna,Thejas %A Broach,John %A Marquez,Juan Luis %A Gerber,Ben %A Schrader,Summer %A Nowak,Christopher %A Harman,Emma %A Heetderks,William %A Fahey,Nisha %A Orvek,Elizabeth %A Lazar,Peter %A Ferranto,Julia %A Noorishirazi,Kamran %A Valpady,Shivakumar %A Shi,Qiming %A Lin,Honghuang %A Marvel,Kathryn %A Gibson,Laura %A Barton,Bruce %A Lemon,Stephenie %A Hafer,Nathaniel %A McManus,David %A Soni,Apurv %+ Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA, 01655, United States, 1 774 442 4107, apurv.soni@umassmed.edu %K COVID-19 %K rapid antigen tests %K COVID-19 testing %K infectious disease %K disease spread %K prevention %K coronavirus %K adherence %K reporting %K mHealth %K health application %K mobile health %K digital health %K public health %K surveillance %K health care %K smartphone app %K vaccination %K digital surveillance %D 2022 %7 16.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments. Objective: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments. Methods: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments. Results: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%). Conclusions: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance. %M 35649180 %R 10.2196/38113 %U https://formative.jmir.org/2022/6/e38113 %U https://doi.org/10.2196/38113 %U http://www.ncbi.nlm.nih.gov/pubmed/35649180 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 6 %P e37327 %T COVID-19 Surveillance in the Biobank at the Colorado Center for Personalized Medicine: Observational Study %A Johnson,Randi K %A Marker,Katie M %A Mayer,David %A Shortt,Jonathan %A Kao,David %A Barnes,Kathleen C %A Lowery,Jan T %A Gignoux,Christopher R %+ Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, 13001 E 17th Place, Mail Stop 563, Aurora, CO, 80045, United States, 1 3037245375, randi.johnson@cuanschutz.edu %K COVID-19 %K surveillance %K pandemic %K biobank %K EHR %K public health %K integrated data %K population health %K health monitoring %K electronic health record %K eHealth %K health record %K emergency response %K vaccination status %K vaccination %K testing %K symptom %K disease impact %D 2022 %7 13.6.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys. Objective: This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources. Methods: In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a “confirmed case” of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses. Results: Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic. Conclusions: We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic. %M 35486493 %R 10.2196/37327 %U https://publichealth.jmir.org/2022/6/e37327 %U https://doi.org/10.2196/37327 %U http://www.ncbi.nlm.nih.gov/pubmed/35486493 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 6 %P e37479 %T Public Health Implications of Adapting HIV Pre-exposure Prophylaxis Programs for Virtual Service Delivery in the Context of the COVID-19 Pandemic: Systematic Review %A Patel,Pragna %A Kerzner,Michael %A Reed,Jason B %A Sullivan,Patrick Sean %A El-Sadr,Wafaa M %+ COVID-19 Response Team, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States, 1 4046396132, plp3@cdc.gov %K HIV %K pre-exposure prophylaxis %K COVID-19 %K virtual service delivery %K HIV prevention %K public health %K systematic review %K virtual service %K health intervention %K digital intervention %K health technology %K social media platform %K telehealth %K public health message %D 2022 %7 7.6.2022 %9 Review %J JMIR Public Health Surveill %G English %X Background: The novel coronavirus disease COVID-19 caused by SARS-CoV-2 threatens to disrupt global progress toward HIV epidemic control. Opportunities exist to leverage ongoing public health responses to mitigate the impacts of COVID-19 on HIV services, and novel approaches to care provision might help address both epidemics. Objective: As the COVID-19 pandemic continues, novel approaches to maintain comprehensive HIV prevention service delivery are needed. The aim of this study was to summarize the related literature to highlight adaptations that could address potential COVID-19–related service interruptions. Methods: We performed a systematic review and searched six databases, OVID/Medline, Scopus, Cochrane Library, CINAHL, PsycINFO, and Embase, for studies published between January 1, 2010, and October 26, 2021, related to recent technology-based interventions for virtual service delivery. Search terms included “telemedicine,” “telehealth,” “mobile health,” “eHealth,” “mHealth,” “telecommunication,” “social media,” “mobile device,” and “internet,” among others. Of the 6685 abstracts identified, 1259 focused on HIV virtual service delivery, 120 of which were relevant for HIV prevention efforts; 48 pertained to pre-exposure prophylaxis (PrEP) and 19 of these focused on evaluations of interventions for the virtual service delivery of PrEP. Of the 16 systematic reviews identified, three were specific to PrEP. All 35 papers were reviewed for outcomes of efficacy, feasibility, and/or acceptability. Limitations included heterogeneity of the studies’ methodological approaches and outcomes; thus, a meta-analysis was not performed. We considered the evidence-based interventions found in our review and developed a virtual service delivery model for HIV prevention interventions. We also considered how this platform could be leveraged for COVID-19 prevention and care. Results: We summarize 19 studies of virtual service delivery of PrEP and 16 relevant reviews. Examples of technology-based interventions that were effective, feasible, and/or acceptable for PrEP service delivery include: use of SMS, internet, and smartphone apps such as iText (50% [95% CI 16%-71%] reduction in discontinuation of PrEP) and PrEPmate (OR 2.62, 95% CI 1.24-5.5.4); telehealth and eHealth platforms for virtual visits such as PrEPTECH and IowaTelePrEP; and platforms for training of health care workers such as Extension for Community Healthcare Outcomes (ECHO). We suggest a virtual service delivery model for PrEP that can be leveraged for COVID-19 using the internet and social media for demand creation, community-based self-testing, telehealth platforms for risk assessment and follow-up, applications for support groups and adherence/appointment reminders, and applications for monitoring. Conclusions: Innovations in the virtual service provision of PrEP occurred before COVID-19 but have new relevance during the COVID-19 pandemic. The innovations we describe might strengthen HIV prevention service delivery during the COVID-19 pandemic and in the long run by engaging traditionally hard-to-reach populations, reducing stigma, and creating a more accessible health care platform. These virtual service delivery platforms can mitigate the impacts of the COVID-19 pandemic on HIV services, which can be leveraged to facilitate COVID-19 pandemic control now and for future responses. %M 35486813 %R 10.2196/37479 %U https://publichealth.jmir.org/2022/6/e37479 %U https://doi.org/10.2196/37479 %U http://www.ncbi.nlm.nih.gov/pubmed/35486813 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 6 %P e37377 %T Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates %A Lundberg,Alexander L %A Lorenzo-Redondo,Ramon %A Hultquist,Judd F %A Hawkins,Claudia A %A Ozer,Egon A %A Welch,Sarah B %A Prasad,P V Vara %A Achenbach,Chad J %A White,Janine I %A Oehmke,James F %A Murphy,Robert L %A Havey,Robert J %A Post,Lori A %+ Buehler Center for Health Policy and Economics, Robert J Havey, MD Institute for Global Health, Northwestern University, 750 N. Lake Shore Drive, Chicago, IL, 60611, United States, 1 3125031706, lori.post@northwestern.edu %K Omicron variant of concern %K Delta %K COVID-19 %K SARS-CoV-2 %K B.1.1.529 %K outbreak %K Arellano-Bond estimator %K dynamic panel data %K stringency index %K surveillance %K disease transmission metrics %D 2022 %7 3.6.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta. Objective: The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred. Methods: We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate. Results: Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak. Conclusions: These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts. %M 35500140 %R 10.2196/37377 %U https://publichealth.jmir.org/2022/6/e37377 %U https://doi.org/10.2196/37377 %U http://www.ncbi.nlm.nih.gov/pubmed/35500140 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 6 %P e34296 %T Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study %A Couture,Alexia %A Iuliano,A Danielle %A Chang,Howard H %A Patel,Neha N %A Gilmer,Matthew %A Steele,Molly %A Havers,Fiona P %A Whitaker,Michael %A Reed,Carrie %+ Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States, 1 4044985984, njh6@cdc.gov %K COVID-19 %K SARS-CoV-2 %K hospitalization %K Bayesian %K COVID-NET %K extrapolation %K hospital %K estimation %K prediction %K United States %K surveillance %K data %K model %K modeling %K hierarchical %K rate %K novel %K framework %K monitoring %D 2022 %7 2.6.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. Objective: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. Methods: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. Results: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. Conclusions: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data. %M 35452402 %R 10.2196/34296 %U https://publichealth.jmir.org/2022/6/e34296 %U https://doi.org/10.2196/34296 %U http://www.ncbi.nlm.nih.gov/pubmed/35452402 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 5 %P e34438 %T A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment %A Silenou,Bernard C %A Verset,Carolin %A Kaburi,Basil B %A Leuci,Olivier %A Ghozzi,Stéphane %A Duboudin,Cédric %A Krause,Gérard %+ Department of Epidemiology, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, 38124, Germany, 49 5316181 3100, Gerard.Krause@helmholtz-hzi.de %K COVID-19 %K disease outbreak %K contact tracing %K serial interval %K basic reproduction number %K infectious disease incubation period %K superspreading events %K telemedicine %K public health %K epidemiology %K surveillance tool %K outbreak response %K pandemic %K digital health application %K response strategy %D 2022 %7 31.5.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application. Objective: This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak. Methods: Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data. Results: Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61. Conclusions: We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators. %M 35486812 %R 10.2196/34438 %U https://publichealth.jmir.org/2022/5/e34438 %U https://doi.org/10.2196/34438 %U http://www.ncbi.nlm.nih.gov/pubmed/35486812 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 4 %P e32411 %T Design of a Vaccine Passport Validation System Using Blockchain-based Architecture: Development Study %A Lee,Hsiu An %A Wu,Wei-Chen %A Kung,Hsin-Hua %A Udayasankaran,Jai Ganesh %A Wei,Yu-Chih %A Kijsanayotin,Boonchai %A Marcelo,Alvin B %A Hsu,Chien-Yeh %+ Department of Information Management, National Taipei University of Nursing and Health Sciences, No.365, Ming-te Road, Peitou District, Taipei, 112, Taiwan, 886 0939193212, cyhsu@ntunhs.edu.tw %K COVID-19 %K vaccine passport %K global border control %K health policy %K international infectious disease strategy %K vaccine %K policy %K strategy %K blockchain %K privacy %K security %K testing %K verification %K certification %K Fast Healthcare Interoperability Resource %D 2022 %7 26.4.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 is an ongoing global pandemic caused by SARS-CoV-2. As of June 2021, 5 emergency vaccines were available for COVID-19 prevention, and with the improvement of vaccination rates and the resumption of activities in each country, verification of vaccination has become an important issue. Currently, in most areas, vaccination and reverse transcription polymerase chain reaction (RT-PCR) test results are certified and validated on paper. This leads to the problem of counterfeit documents. Therefore, a global vaccination record is needed. Objective: The main objective of this study is to design a vaccine passport (VP) validation system based on a general blockchain architecture for international use in a simulated environment. With decentralized characteristics, the system is expected to have the advantages of low cost, high interoperability, effectiveness, security, and verifiability through blockchain architecture. Methods: The blockchain decentralized mechanism was used to build an open and anticounterfeiting information platform for VPs. The contents of a vaccination card are recorded according to international Fast Healthcare Interoperability Resource (FHIR) standards, and blockchain smart contracts (SCs) are used for authorization and authentication to achieve hierarchical management of various international hospitals and people receiving injections. The blockchain stores an encrypted vaccination path managed by the user who manages the private key. The blockchain uses the proof-of-authority (PoA) public chain and can access all information through the specified chain. This will achieve the goal of keeping development costs low and streamlining vaccine transit management so that countries in different economies can use them. Results: The openness of the blockchain helps to create transparency and data accuracy. This blockchain architecture contains a total of 3 entities. All approvals are published on Open Ledger. Smart certificates enable authorization and authentication, and encryption and decryption mechanisms guarantee data protection. This proof of concept demonstrates the design of blockchain architecture, which can achieve accurate global VP verification at an affordable price. In this study, an actual VP case was established and demonstrated. An open blockchain, an individually approved certification mechanism, and an international standard vaccination record were introduced. Conclusions: Blockchain architecture can be used to build a viable international VP authentication process with the advantages of low cost, high interoperability, effectiveness, security, and verifiability. %M 35377316 %R 10.2196/32411 %U https://publichealth.jmir.org/2022/4/e32411 %U https://doi.org/10.2196/32411 %U http://www.ncbi.nlm.nih.gov/pubmed/35377316 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 3 %P e35253 %T Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study %A Wang,Alex %A McCarron,Robert %A Azzam,Daniel %A Stehli,Annamarie %A Xiong,Glen %A DeMartini,Jeremy %+ Department of Psychiatry and Human Behavior, University of California, Irvine, 101 The City Dr S, Orange, CA, 92868, United States, 1 8584059768, wangaj3@uci.edu %K depression %K epidemiology %K internet %K google trends %K big data %K mental health %D 2022 %7 31.3.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies. Objective: This study aimed to map depression search intent in the United States based on internet-based mental health queries. Methods: Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide.” Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix.” Heat maps of population depression were generated based on search intent. Results: Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South. Conclusions: The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States. %M 35357320 %R 10.2196/35253 %U https://mental.jmir.org/2022/3/e35253 %U https://doi.org/10.2196/35253 %U http://www.ncbi.nlm.nih.gov/pubmed/35357320 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e33404 %T Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Protocol for a Scoping Review %A Maaß,Laura %A Pan,Chen-Chia %A Freye,Merle %+ Department of Health, Long-Term Care and Pensions, Research Center on Inequality and Social Policy, Mary-Somerville-Straße 3, Bremen, 28359, Germany, 49 421 218 58610, laura.maass@uni-bremen.de %K digital public health %K telemedicine %K electronic health records %K ePrescription %K eReferral %K eConsultation %K eSurveillance %K eVaccination registries %K scoping review %K protocol %D 2022 %7 31.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Rapid developments and implementation of digital technologies in public health domains throughout the last decades have changed the landscape of health delivery and disease prevention globally. A growing number of countries are introducing interventions such as online consultations, electronic health records, or telemedicine to their health systems to improve their populations’ health and improve access to health care. Despite multiple definitions for digital public health and the development of different digital interventions, no study has analyzed whether the utilized technologies fit the definition or the core characteristics of digital public health interventions. A scoping review is therefore needed to explore the extent of the literature on this topic. Objective: The main aim of this scoping review is to outline real-world digital public health interventions on all levels of health care, prevention, and health. The second objective will be the mapping of reported intervention characteristics. These will include nontechnical elements and the technical features of an intervention. Methods: We searched for relevant literature in the following databases: PubMed, Web of Science, CENTRAL (Cochrane Central Register of Controlled Trials), IEEE (Institute of Electrical and Electronics Engineers) Xplore, and the Association for Computing Machinery (ACM) Full-Text Collection. All original study types (observational studies, experimental trials, qualitative studies, and health-economic analyses), as well as governmental reports, books, book chapters, or peer-reviewed full-text conference papers were included when the evaluation and description of a digital health intervention was the primary intervention component. Two authors screened the articles independently in three stages (title, abstract, and full text). Two independent authors will also perform the data charting. We will report our results following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Results: An additional systematic search in IEEE Xplore and ACM, performed on December 1, 2021, identified another 491 titles. We identified a total of 13,869 papers after deduplication. As of March 2022, the abstract screening state is complete, and we are in the state of screening the 1417 selected full texts for final inclusion. We estimate completing the review in April 2022. Conclusions: To our knowledge, this will be the first scoping review to fill the theoretical definitions of digital public health with concrete interventions and their characteristics. Our scoping review will display the landscape of worldwide existing digital public health interventions that use information and communication technologies. The results of this review will be published in a peer-reviewed journal in early 2022, which can serve as a blueprint for the development of future digital public health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/33404 %M 35357321 %R 10.2196/33404 %U https://www.researchprotocols.org/2022/3/e33404 %U https://doi.org/10.2196/33404 %U http://www.ncbi.nlm.nih.gov/pubmed/35357321 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e30032 %T Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique %A Zhou,Lexin %A Romero-García,Nekane %A Martínez-Miranda,Juan %A Conejero,J Alberto %A García-Gómez,Juan M %A Sáez,Carlos %+ Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain, 34 963877000 ext 75278, carsaesi@upv.es %K COVID-19 %K subphenotypes %K clustering %K characterization %K observational %K epidemiology %K Mexico %D 2022 %7 30.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. Objective: We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. Methods: We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. Results: In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. Conclusions: The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments. %M 35144239 %R 10.2196/30032 %U https://publichealth.jmir.org/2022/3/e30032 %U https://doi.org/10.2196/30032 %U http://www.ncbi.nlm.nih.gov/pubmed/35144239 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e25658 %T A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study %A Huang,Yun %A Luo,Chongliang %A Jiang,Ying %A Du,Jingcheng %A Tao,Cui %A Chen,Yong %A Hao,Yuantao %+ Department of Medical Statistics, Sun Yat-Sen University, No. 74 Zhongshan II Road, Guangzhou, 510080, China, 86 02087331587, haoyt@mail.sysu.edu.cn %K adverse events %K Bayesian network %K Guillain-Barré syndrome %K risk prediction %K trivalent influenza vaccine %D 2022 %7 25.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Identifying the key factors of Guillain-Barré syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction. Objective: The aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk. Methods: Large-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained from the EudraVigilance system between 2003 and 2016) adverse events (AEs) reports, were extracted for model development and validation. GBS, age, gender, and the top 50 prevalent AEs were included for initial BN construction using the R package bnlearn. Results: Age, gender, and 10 AEs were identified as the most significant factors of GBS. The posttest probability of GBS suggested that male vaccinees aged 50-64 years and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also experience symptoms of asthenia, hypesthesia, muscular weakness, or paresthesia. The established BN model achieved an area under the receiver operating characteristic curve of 0.866 (95% CI 0.865-0.867), sensitivity of 0.752 (95% CI 0.749-0.756), specificity of 0.882 (95% CI 0.879-0.885), and accuracy of 0.882 (95% CI 0.879-0.884) for predicting GBS risk during the internal validation and obtained values of 0.829, 0.673, 0.854, and 0.843 for area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, respectively, during the external validation. Conclusions: The findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions among different postvaccination symptoms through its graphical representation, and accurately predict the risk of GBS. The established BN model could further assist clinical decision-making by providing an estimated risk of GBS for a specific vaccinee or be developed into an open-access platform for vaccinees’ self-monitoring. %M 35333192 %R 10.2196/25658 %U https://publichealth.jmir.org/2022/3/e25658 %U https://doi.org/10.2196/25658 %U http://www.ncbi.nlm.nih.gov/pubmed/35333192 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e22544 %T Leveraging Polio Geographic Information System Platforms in the African Region for Mitigating COVID-19 Contact Tracing and Surveillance Challenges: Viewpoint %A Akpan,Godwin Ubong %A Bello,Isah Mohammed %A Touray,Kebba %A Ngofa,Reuben %A Oyaole,Daniel Rasheed %A Maleghemi,Sylvester %A Babona,Marie %A Chikwanda,Chanda %A Poy,Alain %A Mboussou,Franck %A Ogundiran,Opeayo %A Impouma,Benido %A Mihigo,Richard %A Yao,Nda Konan Michel %A Ticha,Johnson Muluh %A Tuma,Jude %A A Mohamed,Hani Farouk %A Kanmodi,Kehinde %A Ejiofor,Nonso Ephraim %A Kipterer,John Kapoi %A Manengu,Casimir %A Kasolo,Francis %A Seaman,Vincent %A Mkanda,Pascal %+ Regional Office of Africa, World Health Organization, World Health Organization,Regional Office for Africa, Cite Du Djoue, Brazzaville, 500101, Congo, 242 055736476, akpang@who.int %K contact tracing %K GIS %K COVID-19 %K surveillance %D 2022 %7 17.3.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: The ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic. Objective: This paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO’s polio program in the African region. Methods: We repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights. Results: The contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility–based surveillance app has been used more extensively, as it has been used in 27 countries in the region. Conclusions: In light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries’ efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19. %M 34854813 %R 10.2196/22544 %U https://mhealth.jmir.org/2022/3/e22544 %U https://doi.org/10.2196/22544 %U http://www.ncbi.nlm.nih.gov/pubmed/34854813 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e27921 %T Lessons From a Rapid Project Management Exercise in the Time of Pandemic: Methodology for a Global COVID-19 VIRUS Registry Database %A Turek,Janice R %A Bansal,Vikas %A Tekin,Aysun %A Singh,Shuchita %A Deo,Neha %A Sharma,Mayank %A Bogojevic,Marija %A Qamar,Shahraz %A Singh,Romil %A Kumar,Vishakha %A Kashyap,Rahul %+ Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 1216 2nd St SW, Rochester, MN, 55902, United States, 1 507 255 7196, kashyap.rahul@mayo.edu %K COVID-19 %K critical care %K global %K program management %K registry %D 2022 %7 15.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: The rapid emergence of the COVID-19 pandemic globally collapsed health care organizations worldwide. Incomplete knowledge of best practices, progression of disease, and its impact could result in fallible care. Data on symptoms and advancement of the SARS-CoV-2 virus leading to critical care admission have not been captured or communicated well between international organizations experiencing the same impact from the virus. This led to the expedited need for establishing international communication and data collection on the critical care patients admitted with COVID-19. Objective: Developing a global registry to collect patient data in the critical care setting was imperative with the goal of analyzing and ameliorating outcomes. Methods: A prospective, observational global registry database was put together to record extensive deidentified clinical information for patients hospitalized with COVID-19. Results: Project management was crucial for prompt implementation of the registry for synchronization, improving efficiency, increasing innovation, and fostering global collaboration for valuable data collection. The Society of Critical Care Medicine Discovery VIRUS (Viral Infection and Respiratory Illness Universal Study): COVID-19 Registry would compile data for crucial longitudinal outcomes for disease, treatment, and research. The agile project management approach expedited establishing the registry in 15 days and submission of institutional review board agreement for 250 participating sites. There has been enrollment of sites every month with a total of 306 sites from 28 countries and 64,114 patients enrolled (as of June 7, 2021). Conclusions: This protocol addresses project management lessons in a time of crises which can be a precept for rapid project management for a large-scale health care data registry. We aim to discuss the approach and methodology for establishing the registry, the challenges faced, and the factors contributing to successful outcomes. Trial Registration: ClinicalTrials.gov NCT04323787; https://clinicaltrials.gov/ct2/show/NCT04323787 %M 34762062 %R 10.2196/27921 %U https://www.researchprotocols.org/2022/3/e27921 %U https://doi.org/10.2196/27921 %U http://www.ncbi.nlm.nih.gov/pubmed/34762062 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e30691 %T Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework %A Tsvyatkova,Damyanka %A Buckley,Jim %A Beecham,Sarah %A Chochlov,Muslim %A O’Keeffe,Ian R %A Razzaq,Abdul %A Rekanar,Kaavya %A Richardson,Ita %A Welsh,Thomas %A Storni,Cristiano %A , %+ Department of Computer Science and Information Systems, University of Limerick, Computer Science Building, Plassey Park Rd, Limerick, V94 T9PX, Ireland, 353 61 202632, Cristiano.Storni@ul.ie %K COVID-19 %K mHealth %K digital contact tracing apps %K framework %K evaluation %K mobile health %K health apps %K digital health %K contact tracing %D 2022 %7 11.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The silent transmission of COVID-19 has led to an exponential growth of fatal infections. With over 4 million deaths worldwide, the need to control and stem transmission has never been more critical. New COVID-19 vaccines offer hope. However, administration timelines, long-term protection, and effectiveness against potential variants are still unknown. In this context, contact tracing and digital contact tracing apps (CTAs) continue to offer a mechanism to help contain transmission, keep people safe, and help kickstart economies. However, CTAs must address a wide range of often conflicting concerns, which make their development/evolution complex. For example, the app must preserve citizens’ privacy while gleaning their close contacts and as much epidemiological information as possible. Objective: In this study, we derived a compare-and-contrast evaluative framework for CTAs that integrates and expands upon existing works in this domain, with a particular focus on citizen adoption; we call this framework the Citizen-Focused Compare-and-Contrast Evaluation Framework (C3EF) for CTAs. Methods: The framework was derived using an iterative approach. First, we reviewed the literature on CTAs and mobile health app evaluations, from which we derived a preliminary set of attributes and organizing pillars. These attributes and the probing questions that we formulated were iteratively validated, augmented, and refined by applying the provisional framework against a selection of CTAs. Each framework pillar was then subjected to internal cross-team scrutiny, where domain experts cross-checked sufficiency, relevancy, specificity, and nonredundancy of the attributes, and their organization in pillars. The consolidated framework was further validated on the selected CTAs to create a finalized version of C3EF for CTAs, which we offer in this paper. Results: The final framework presents seven pillars exploring issues related to CTA design, adoption, and use: (General) Characteristics, Usability, Data Protection, Effectiveness, Transparency, Technical Performance, and Citizen Autonomy. The pillars encompass attributes, subattributes, and a set of illustrative questions (with associated example answers) to support app design, evaluation, and evolution. An online version of the framework has been made available to developers, health authorities, and others interested in assessing CTAs. Conclusions: Our CTA framework provides a holistic compare-and-contrast tool that supports the work of decision-makers in the development and evolution of CTAs for citizens. This framework supports reflection on design decisions to better understand and optimize the design compromises in play when evolving current CTAs for increased public adoption. We intend this framework to serve as a foundation for other researchers to build on and extend as the technology matures and new CTAs become available. %M 35084338 %R 10.2196/30691 %U https://mhealth.jmir.org/2022/3/e30691 %U https://doi.org/10.2196/30691 %U http://www.ncbi.nlm.nih.gov/pubmed/35084338 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e36119 %T Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline %A Caskey,John %A McConnell,Iain L %A Oguss,Madeline %A Dligach,Dmitriy %A Kulikoff,Rachel %A Grogan,Brittany %A Gibson,Crystal %A Wimmer,Elizabeth %A DeSalvo,Traci E %A Nyakoe-Nyasani,Edwin E %A Churpek,Matthew M %A Afshar,Majid %+ University of Wisconsin–Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, United States, 1 3125459462, majid.afshar@wisc.edu %K natural language processing %K public health informatics %K named entity recognition %K contact tracing %K COVID-19 %K outbreaks %K neural language model %K disease surveillance %K digital health %K electronic surveillance %K public health %K digital surveillance tool %D 2022 %7 8.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted policy making. We developed an automated pipeline to ingest the free text into a pretrained neural language model to identify businesses and facilities as outbreaks. Objective: We aimed to examine the precision and recall of our natural language processing pipeline against existing outbreaks and potentially new clusters. Methods: Data on cases of COVID-19 were extracted from the Wisconsin Electronic Disease Surveillance System (WEDSS) for Dane County between July 1, 2020, and June 30, 2021. Features from the case interview forms were fed into a Bidirectional Encoder Representations from Transformers (BERT) model that was fine-tuned for named entity recognition (NER). We also developed a novel location-mapping tool to provide addresses for relevant NER. Precision and recall were measured against manually verified outbreaks and valid addresses in WEDSS. Results: There were 46,798 cases of COVID-19, with 4,183,273 total BERT tokens and 15,051 unique tokens. The recall and precision of the NER tool were 0.67 (95% CI 0.66-0.68) and 0.55 (95% CI 0.54-0.57), respectively. For the location-mapping tool, the recall and precision were 0.93 (95% CI 0.92-0.95) and 0.93 (95% CI 0.92-0.95), respectively. Across monthly intervals, the NER tool identified more potential clusters than were verified in WEDSS. Conclusions: We developed a novel pipeline of tools that identified existing outbreaks and novel clusters with associated addresses. Our pipeline ingests data from a statewide database and may be deployed to assist local health departments for targeted interventions. %M 35144241 %R 10.2196/36119 %U https://publichealth.jmir.org/2022/3/e36119 %U https://doi.org/10.2196/36119 %U http://www.ncbi.nlm.nih.gov/pubmed/35144241 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e36026 %T HIV Surveillance and Research for Migrant Populations: Protocol Integrating Respondent-Driven Sampling, Case Finding, and Medicolegal Services for Venezuelans Living in Colombia %A Wirtz,Andrea L %A Page,Kathleen R %A Stevenson,Megan %A Guillén,José Rafael %A Ortíz,Jennifer %A López,Jhon Jairo %A Ramírez,Jhon Fredy %A Quijano,Cindy %A Vela,Alejandra %A Moreno,Yessenia %A Rigual,Francisco %A Case,James %A Hakim,Avi J %A Hladik,Wolfgang %A Spiegel,Paul B %+ Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 4109292881, awirtz1@jhu.edu %K HIV %K epidemiology %K migrant %K Venezuela %K Colombia %K respondent-driven sampling %K case finding %K HIV treatment %K HIV surveillance %K research %D 2022 %7 8.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Epidemiologic research among migrant populations is limited by logistical, methodological, and ethical challenges, but it is necessary for informing public health and humanitarian programming. Objective: We describe a methodology to estimate HIV prevalence among Venezuelan migrants in Colombia. Methods: Respondent-driven sampling, a nonprobability sampling method, was selected for attributes of reaching highly networked populations without sampling frames and analytic methods that permit estimation of population parameters. Respondent-driven sampling was modified to permit electronic referral of peers via SMS text messaging and WhatsApp. Participants complete sociobehavioral surveys and rapid HIV and syphilis screening tests with confirmatory testing. HIV treatment is not available for migrants who have entered Colombia through irregular pathways; thus, medicolegal services integrated into posttest counseling provide staff lawyers and legal assistance to participants diagnosed with HIV or syphilis for sustained access to treatment through the national health system. Case finding is integrated into respondent-driven sampling to allow partner referral. This study is implemented by a local community-based organization providing HIV support services and related legal services for Venezuelans in Colombia. Results: Data collection was launched in 4 cities in July and August 2021. As of November 2021, 3105 of the target 6100 participants were enrolled, with enrollment expected to end by February/March 2022. Conclusions: Tailored methods that combine community-led efforts with innovations in sampling and linkage to care can aid in advancing health research for migrant and displaced populations. Worldwide trends in displacement and migration underscore the value of improved methods for translation to humanitarian and public health programming. International Registered Report Identifier (IRRID): DERR1-10.2196/36026 %M 35258458 %R 10.2196/36026 %U https://www.researchprotocols.org/2022/3/e36026 %U https://doi.org/10.2196/36026 %U http://www.ncbi.nlm.nih.gov/pubmed/35258458 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 3 %P e32364 %T United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study %A Cai,Owen %A Sousa-Pinto,Bernardo %+ Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Rua Plácido Costa s/n, Porto, 4200-450, Portugal, 351 225513622, bernardosousapinto@protonmail.com %K COVID-19 %K influenza %K surveillance %K media coverage %K Google Trends %K infodemiology %K monitoring %K trend %K United States %K information-seeking %K online health information %D 2022 %7 3.3.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. Objective: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. Methods: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. Results: We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707). Conclusions: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool. %M 34878996 %R 10.2196/32364 %U https://publichealth.jmir.org/2022/3/e32364 %U https://doi.org/10.2196/32364 %U http://www.ncbi.nlm.nih.gov/pubmed/34878996 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e28737 %T COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling %A Oehmke,Theresa B %A Moss,Charles B %A Oehmke,James F %+ Department of Civil and Environmental Engineering, University of California, Berkeley, 202 O'Brien Hall, Berkeley, CA, 94720, United States, 1 5108986406, toehmke@berkeley.edu %K surveillance system %K COVID-19 %K coronavirus %K Sars-CoV-2 %K Houston %K dynamic panel data model %K speed %K jerk %K acceleration %K 7-Day persistence %K modeling %K data %K surveillance %K monitoring %K public health %K United States %K transmission %K response %D 2022 %7 24.2.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the “Delta wave” appears to have peaked nationally, some states and municipalities continue to see elevated numbers of new cases. Vigilant surveillance including at a metropolitan level can help identify any reignition and validate continued and strong public health policy responses in problem localities. Objective: This surveillance report aimed to provide up-to-date information for the 25 largest US metropolitan areas about the rapidity of descent in the number of new cases following the Delta wave peak, as well as any potential reignition of the pandemic associated with declining vaccine effectiveness over time, new variants, or other factors. Methods: COVID-19 pandemic dynamics for the 25 largest US metropolitan areas were analyzed through September 19, 2021, using novel metrics of speed, acceleration, jerk, and 7-day persistence, calculated from the observed data on the cumulative number of cases as reported by USAFacts. Statistical analysis was conducted using dynamic panel data models estimated with the Arellano-Bond regression techniques. The results are presented in tabular and graphic forms for visual interpretation. Results: On average, speed in the 25 largest US metropolitan areas declined from 34 new cases per day per 100,000 population, during the week ending August 15, 2021, to 29 new cases per day per 100,000 population, during the week ending September 19, 2021. This average masks important differences across metropolitan areas. For example, Miami’s speed decreased from 105 for the week ending August 15, 2021, to 40 for the week ending September 19, 2021. Los Angeles, San Francisco, Riverside, and San Diego had decreasing speed over the sample period and ended with single-digit speeds for the week ending September 19, 2021. However, Boston, Washington DC, Detroit, Minneapolis, Denver, and Charlotte all had their highest speed of the sample during the week ending September 19, 2021. These cities, as well as Houston and Baltimore, had positive acceleration for the week ending September 19, 2021. Conclusions: There is great variation in epidemiological curves across US metropolitan areas, including increasing numbers of new cases in 8 of the largest 25 metropolitan areas for the week ending September 19, 2021. These trends, including the possibility of waning vaccine effectiveness and the emergence of resistant variants, strongly indicate the need for continued surveillance and perhaps a return to more restrictive public health guidelines for some areas. %M 34882569 %R 10.2196/28737 %U https://publichealth.jmir.org/2022/2/e28737 %U https://doi.org/10.2196/28737 %U http://www.ncbi.nlm.nih.gov/pubmed/34882569 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e32426 %T The Use of Cremation Data for Timely Mortality Surveillance During the COVID-19 Pandemic in Ontario, Canada: Validation Study %A Postill,Gemma %A Murray,Regan %A Wilton,Andrew S %A Wells,Richard A %A Sirbu,Renee %A Daley,Mark J %A Rosella,Laura %+ Epidemiology Division, Dalla Lana School of Public Health, 155 College Street, Suite 600, Toronto, ON, M5T 3M7, Canada, 1 416 978 0901, laura.rosella@utoronto.ca %K excess deaths %K real-time mortality %K cremation %K COVID-19 %K SARS-CoV-2 %K mortality %K estimate %K impact %K public health %K validation %K pattern %K trend %K utility %K Canada %K mortality data %K pandemic %K death %K cremation data %K cause of death %K vital statistics %K excess mortality %D 2022 %7 21.2.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions, including across Canada. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating real-time mortality data that encompasses the majority of deaths within the province. Objective: This study aimed to validate the use of cremation data as a timely surveillance tool for all-cause mortality during a public health emergency in a jurisdiction with delays in vital statistics data. Specifically, this study aimed to validate this surveillance tool by determining the stability, timeliness, and robustness of its real-time estimation of all-cause mortality. Methods: Cremation records from January 2020 until April 2021 were compared to the historical records from 2017 to 2019, grouped according to week, age, sex, and whether COVID-19 was the cause of death. Cremation data were compared to Ontario’s provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years (2017-2019) to determine whether there was excess mortality within various age groups and whether deaths attributed to COVID-19 accounted for the entirety of the excess mortality. Results: Between 2017 and 2019, cremations were performed for 67.4% (95% CI 67.3%-67.5%) of deaths. The proportion of cremated deaths remained stable throughout 2020, even within age and sex categories. Cremation records are 99% complete within 3 weeks of the date of death, which precedes the compilation of vital statistics data by several months. Consequently, during the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI 14.6%-19.3%) in all-cause mortality, a finding that was confirmed several months later with cremation data. Conclusions: The percentage of Ontarians cremated and the completion of cremation data several months before vital statistics did not change meaningfully during the COVID-19 pandemic period, establishing that the pandemic did not significantly alter cremation practices. Cremation data can be used to accurately estimate all-cause mortality in near real-time, particularly when real-time mortality estimates are needed to inform policy decisions for public health measures. The accuracy of this excess mortality estimation was confirmed by comparing it with official vital statistics data. These findings demonstrate the utility of cremation data as a complementary data source for timely mortality information during public health emergencies. %M 35038302 %R 10.2196/32426 %U https://publichealth.jmir.org/2022/2/e32426 %U https://doi.org/10.2196/32426 %U http://www.ncbi.nlm.nih.gov/pubmed/35038302 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e30524 %T Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World %A Kilgallon,John L %A Tewarie,Ishaan Ashwini %A Broekman,Marike L D %A Rana,Aakanksha %A Smith,Timothy R %+ Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, United States, 1 9145395441, jkilgallon@bwh.harvard.edu %K passive data %K public health surveillance %K digital public health surveillance %K pandemic response %K data privacy %K digital phenotyping %K smartphone %K mobile phone %K mHealth %K digital health %K informed consent %K data equity %K data ownership %D 2022 %7 15.2.2022 %9 Viewpoint %J J Med Internet Res %G English %X There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership. %M 35166676 %R 10.2196/30524 %U https://www.jmir.org/2022/2/e30524 %U https://doi.org/10.2196/30524 %U http://www.ncbi.nlm.nih.gov/pubmed/35166676 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e32355 %T Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research %A Shakeri Hossein Abad,Zahra %A Butler,Gregory P %A Thompson,Wendy %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K digital public health surveillance %K social media analysis %K physical activity %K sedentary behavior %K sleep %K machine learning %K online health information %K infodemiology %K public health database %D 2022 %7 14.2.2022 %9 Open Source/Open Data %J JMIR Public Health Surveill %G English %X Background: Advances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public’s trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage. Objective: The main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata. Methods: We collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set. Results: LPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet. Conclusions: Publicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners. %M 35156938 %R 10.2196/32355 %U https://publichealth.jmir.org/2022/2/e32355 %U https://doi.org/10.2196/32355 %U http://www.ncbi.nlm.nih.gov/pubmed/35156938 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e32638 %T Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study %A Yan,Mengqing %A Kang,Wenjun %A Guo,Zhifeng %A Wang,Qi %A Wang,Peizhong Peter %A Zhu,Yun %A Yang,Yongli %A Wang,Wei %+ Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, No.100, Science Avenue, Zhengzhou, 450001, China, 86 371 67781466, ww375@zzu.edu.cn %K COVID-19 %K case fatality rate %K discharged case fatality rate %K new infectious diseases %D 2022 %7 10.2.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR). Objective: The main objective of this study was to explore the value of the discharged CFR (DCFR) to make more accurate forecasts of epidemic trends of COVID-19 in Italy. Methods: We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of deaths to discharged cases（including deaths and recovered cases） to calculate the total DCFR (tDCFR), monthly DCFR (mDCFR), and stage DCFR (sDCFR). Furthermore, we analyzed the trend in the mDCFR between January and December 2020 using joinpoint regression analysis, used ArcGIS version 10.7 to visualize the spatial distribution of the epidemic CFR, and assigned different colors to each province based on the CFR or tDCFR. Results: We calculated the numbers and obtained the new indices of the tDCFR and mDCFR for calculating the fatality rate. The results showed that the tDCFR and mDCFR fluctuated greatly from January to May. They first showed a rapid increase followed by a rapid decline after reaching the peak. The map showed that the provinces with a high tDCFR were Emilia-Romagna, Puglia, and Lombardia. The change trend of the mDCFR over time was divided into the following 2 stages: the first stage (from January to May) and the second stage (from June to December). With regard to worldwide COVID-19 statistics, among 6 selected countries, the United States had the highest tDCFR (4.26%), while the tDCFR of the remaining countries was between 0.98% and 2.72%. Conclusions: We provide a new perspective for assessing the fatality of COVID-19 in Italy, which can use ever-changing data to calculate a more accurate CFR and scientifically predict the development trend of the epidemic. %M 34963659 %R 10.2196/32638 %U https://publichealth.jmir.org/2022/2/e32638 %U https://doi.org/10.2196/32638 %U http://www.ncbi.nlm.nih.gov/pubmed/34963659 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e33522 %T America’s HIV Epidemic Analysis Dashboard: Protocol for a Data Resource to Support Ending the HIV Epidemic in the United States %A Sullivan,Patrick Sean %A Woodyatt,Cory R %A Kouzouian,Oskian %A Parrish,Kristen J %A Taussig,Jennifer %A Conlan,Chris %A Phillips,Harold %+ Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA, 30329, United States, 1 4047272038, pssulli@emory.edu %K HIV %K dashboard %K data %K data dashboard %K infectious disease %K infodemiology %K surveillance %K public health %K United States %K monitoring %D 2022 %7 10.2.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Ending the HIV Epidemic (EHE) plan aims to end the HIV epidemic in the United States by 2030. Having timely and accessible data to assess progress toward EHE goals at the local level is a critical resource to achieve this goal. Objective: The aim of this paper was to introduce America’s HIV Epidemic Analysis Dashboard (AHEAD), a data visualization tool that displays relevant data on the 6 HIV indicators provided by the Centers for Disease Control and Prevention. AHEAD can be used to monitor progress toward ending the HIV epidemic in local communities across the United States. Its objective is to make data available to stakeholders, which can be used to measure national and local progress toward 2025 and 2030 EHE goals and to help jurisdictions make local decisions that are grounded in high-quality data. Methods: AHEAD displays data from public health data systems (eg, surveillance systems and census data), organized around the 6 EHE indicators (HIV incidence, knowledge of HIV status, HIV diagnoses, linkage to HIV medical care, viral HIV suppression, and preexposure prophylaxis coverage). Data are displayed for each of the EHE priority areas (48 counties in Washington, District of Columbia, and San Juan, Puerto Rico) which accounted for more than 50% of all US HIV diagnoses in 2016 and 2017 and 7 primarily southern states with high rates of HIV in rural communities. AHEAD also displays data for the 43 remaining states for which data are available. Data features prioritize interactive data visualization tools that allow users to compare indicator data stratified by sex at birth, race or ethnicity, age, and transmission category within a jurisdiction (when available) or compare data on EHE indicators between jurisdictions. Results: AHEAD was launched on August 14, 2020. In the 11 months since its launch, the Dashboard has been visited 26,591 times by 17,600 unique users. About one-quarter of all users returned to the Dashboard at least once. On average, users engaged with 2.4 pages during their visit to the Dashboard, indicating that the average user goes beyond the informational landing page to engage with 1 or more pages of data and content. The most frequently visited content pages are the jurisdiction webpages. Conclusions: The Ending the HIV Epidemic plan is described as a “whole of society” effort. Societal public health initiatives require objective indicators and require that all societal stakeholders have transparent access to indicator data at the level of the health jurisdictions responsible for meeting the goals of the plan. Data transparency empowers local stakeholders to track movement toward EHE goals, identify areas with needs for improvement, and make data-informed adjustments to deploy the expertise and resources required to locally tailor and implement strategies to end the HIV epidemic in their jurisdiction. %M 35142639 %R 10.2196/33522 %U https://publichealth.jmir.org/2022/2/e33522 %U https://doi.org/10.2196/33522 %U http://www.ncbi.nlm.nih.gov/pubmed/35142639 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e31131 %T An Association of Influenza Epidemics in Children With Mobile App Data: Population-Based Observational Study in Osaka, Japan %A Katayama,Yusuke %A Kiyohara,Kosuke %A Hirose,Tomoya %A Ishida,Kenichiro %A Tachino,Jotaro %A Nakao,Shunichiro %A Noda,Tomohiro %A Ojima,Masahiro %A Kiguchi,Takeyuki %A Matsuyama,Tasuku %A Kitamura,Tetsuhisa %+ Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, 2-15 Yamada-oka, Suita, Japan, 81 6 6879 5707, orion13@hp-emerg.med.osaka-u.ac.jp %K syndromic surveillance %K mobile app %K influenza %K epidemic %K children %D 2022 %7 10.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Early surveillance to prevent the spread of influenza is a major public health concern. If there is an association of influenza epidemics with mobile app data, it may be possible to forecast influenza earlier and more easily. Objective: We aimed to assess the relationship between seasonal influenza and the frequency of mobile app use among children in Osaka Prefecture, Japan. Methods: This was a retrospective observational study that was performed over a three-year period from January 2017 to December 2019. Using a linear regression model, we calculated the R2 value of the regression model to evaluate the relationship between the number of “fever” events selected in the mobile app and the number of influenza patients ≤14 years of age. We conducted three-fold cross-validation using data from two years as the training data set and the data of the remaining year as the test data set to evaluate the validity of the regression model. And we calculated Spearman correlation coefficients between the calculated number of influenza patients estimated using the regression model and the number of influenza patients, limited to the period from December to April when influenza is prevalent in Japan. Results: We included 29,392 mobile app users. The R2 value for the linear regression model was 0.944, and the adjusted R2 value was 0.915. The mean Spearman correlation coefficient for the three regression models was 0.804. During the influenza season (December–April), the Spearman correlation coefficient between the number of influenza patients and the calculated number estimated using the linear regression model was 0.946 (P<.001). Conclusions: In this study, the number of times that mobile apps were used was positively associated with the number of influenza patients. In particular, there was a good association of the number of influenza patients with the number of “fever” events selected in the mobile app during the influenza epidemic season. %M 35142628 %R 10.2196/31131 %U https://formative.jmir.org/2022/2/e31131 %U https://doi.org/10.2196/31131 %U http://www.ncbi.nlm.nih.gov/pubmed/35142628 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 2 %P e32680 %T An Evaluation of the Text Illness Monitoring (TIM) Platform for COVID-19: Cross-sectional Online Survey of Public Health Users %A Joseph,Heather A %A Ingber,Susan Z %A Austin,Chelsea %A Westnedge,Caroline %A Strona,F V %A Lee,Leslie %A Shah,Ami B %A Roper,Lauren %A Patel,Anita %+ Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, Mailstop 106, 4770 Buford Hwy, Chamblee, GA, 30341, United States, 1 4046392636, hbj7@cdc.gov %K COVID-19 %K contact tracing %K SMS text system %K symptom monitoring %D 2022 %7 7.2.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The US public health response to the COVID-19 pandemic has required contact tracing and symptom monitoring at an unprecedented scale. The US Centers for Disease Control and Prevention and several partners created the Text Illness Monitoring (TIM) platform in 2015 to assist US public health jurisdictions with symptom monitoring for potential novel influenza virus outbreaks. Since May 2020, 142 federal, state, and local public health agencies have deployed TIM for COVID-19 symptom monitoring. Objective: The aim of this study was to evaluate the utility, benefits, and challenges of TIM to help guide decision-making for improvements and expansion to support future public health emergency response efforts. Methods: We conducted a brief online survey of previous and current TIM administrative users (admin users) from November 28 through December 21, 2020. Closed- and open-ended questions inquired about the onboarding process, decision to use TIM, groups monitored with TIM, comparison of TIM to other symptom monitoring systems, technical challenges and satisfaction with TIM, and user support. A total of 1479 admin users were invited to participate. Results: A total of 97 admin users from 43 agencies responded to the survey. Most admin users represented the Indian Health Service (35/97, 36%), state health departments (26/97, 27%), and local or county health departments (18/97, 19%), and almost all were current users of TIM (85/94, 90%). Among the 43 agencies represented, 11 (26%) used TIM for monitoring staff exclusively, 13 (30%) monitored community members exclusively, and 19 (44%) monitored both staff and community members. Agencies most frequently used TIM to monitor symptom development in contacts of cases among community members (28/43, 65%), followed by symptom development among staff (27/43, 63%) and among staff contacts of cases (24/43, 56%). Agencies also reported using TIM to monitor patients with COVID-19 for the worsening of symptoms among staff (21/43, 49%) and community members (18/43, 42%). When asked to compare TIM to previous monitoring systems, 78% (40/51) of respondents rated TIM more favorably than their previous monitoring system, 20% (10/51) said there was no difference, and 2% (1/51) rated the previous monitoring system more favorably than TIM. Most respondents found TIM favorable in terms of time burden, staff burden, timeliness of the data, and the ability to monitor large population sizes. TIM compared negatively to other systems in terms of effort to enroll participants (ie, persons TIM monitors) and accuracy of the data. Most respondents (76/85, 89%) reported that they would highly or somewhat recommend TIM to others for symptom monitoring. Conclusions: This evaluation of TIM showed that agencies used TIM for a variety of purposes and rated TIM favorably compared to previously used monitoring systems. We also identified opportunities to improve TIM; for example, enhancing the flexibility of alert deliveries would better meet admin users’ varying needs. We also suggest continuous program evaluation practices to assess and respond to implementation gaps. %M 34882572 %R 10.2196/32680 %U https://publichealth.jmir.org/2022/2/e32680 %U https://doi.org/10.2196/32680 %U http://www.ncbi.nlm.nih.gov/pubmed/34882572 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e35763 %T Has Omicron Changed the Evolution of the Pandemic? %A Lundberg,Alexander L %A Lorenzo-Redondo,Ramon %A Ozer,Egon A %A Hawkins,Claudia A %A Hultquist,Judd F %A Welch,Sarah B %A Prasad,PV Vara %A Oehmke,James F %A Achenbach,Chad J %A Murphy,Robert L %A White,Janine I %A Havey,Robert J %A Post,Lori Ann %+ Buehler Center for Health Policy and Economics, Robert J. Havey, MD Institute for Global Health, Northwestern University, 750 N. Lake Shore Drive, Chicago, IL, 60611, United States, 1 312 503 5659, lori.post@northwestern.edu %K Omicron %K SARS-CoV-2 %K public health surveillance %K VOC %K variant of concern %K Delta %K Beta %K COVID-19 %K sub-Saharan Africa %K public health %K pandemic %K epidemiology %D 2022 %7 31.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Variants of the SARS-CoV-2 virus carry differential risks to public health. The Omicron (B.1.1.529) variant, first identified in Botswana on November 11, 2021, has spread globally faster than any previous variant of concern. Understanding the transmissibility of Omicron is vital in the development of public health policy. Objective: The aim of this study is to compare SARS-CoV-2 outbreaks driven by Omicron to those driven by prior variants of concern in terms of both the speed and magnitude of an outbreak. Methods: We analyzed trends in outbreaks by variant of concern with validated surveillance metrics in several southern African countries. The region offers an ideal setting for a natural experiment given that most outbreaks thus far have been driven primarily by a single variant at a time. With a daily longitudinal data set of new infections, total vaccinations, and cumulative infections in countries in sub-Saharan Africa, we estimated how the emergence of Omicron has altered the trajectory of SARS-CoV-2 outbreaks. We used the Arellano-Bond method to estimate regression coefficients from a dynamic panel model, in which new infections are a function of infections yesterday and last week. We controlled for vaccinations and prior infections in the population. To test whether Omicron has changed the average trajectory of a SARS-CoV-2 outbreak, we included an interaction between an indicator variable for the emergence of Omicron and lagged infections. Results: The observed Omicron outbreaks in this study reach the outbreak threshold within 5-10 days after first detection, whereas other variants of concern have taken at least 14 days and up to as many as 35 days. The Omicron outbreaks also reach peak rates of new cases that are roughly 1.5-2 times those of prior variants of concern. Dynamic panel regression estimates confirm Omicron has created a statistically significant shift in viral spread. Conclusions: The transmissibility of Omicron is markedly higher than prior variants of concern. At the population level, the Omicron outbreaks occurred more quickly and with larger magnitude, despite substantial increases in vaccinations and prior infections, which should have otherwise reduced susceptibility to new infections. Unless public health policies are substantially altered, Omicron outbreaks in other countries are likely to occur with little warning. %M 35072638 %R 10.2196/35763 %U https://publichealth.jmir.org/2022/1/e35763 %U https://doi.org/10.2196/35763 %U http://www.ncbi.nlm.nih.gov/pubmed/35072638 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e32455 %T Added Value of Electronic Immunization Registries in Low- and Middle-Income Countries: Observational Case Study in Tanzania %A Secor,Andrew M %A Mtenga,Hassan %A Richard,John %A Bulula,Ngwegwe %A Ferriss,Ellen %A Rathod,Mansi %A Ryman,Tove K %A Werner,Laurie %A Carnahan,Emily %+ PATH, 2201 Westlake Avenue, Seattle, WA, , United States, 1 206 285 3500, ecarnahan@path.org %K immunization %K immunization information system %K electronic immunization registry %K digital health %K eHealth %D 2022 %7 21.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: There is growing interest and investment in electronic immunization registries (EIRs) in low- and middle-income countries. EIRs provide ready access to patient- and aggregate-level service delivery data that can be used to improve patient care, identify spatiotemporal trends in vaccination coverage and dropout, inform resource allocation and program operations, and target quality improvement measures. The Government of Tanzania introduced the Tanzania Immunization Registry (TImR) in 2017, and the system has since been rolled out in 3736 facilities in 15 regions. Objective: The aims of this study are to conceptualize the additional ways in which EIRs can add value to immunization programs (beyond measuring vaccine coverage) and assess the potential value-add using EIR data from Tanzania as a case study. Methods: This study comprised 2 sequential phases. First, a comprehensive list of ways EIRs can potentially add value to immunization programs was developed through stakeholder interviews. Second, the added value was evaluated using descriptive and regression analyses of TImR data for a prioritized subset of program needs. Results: The analysis areas prioritized through stakeholder interviews were population movement, missed opportunities for vaccination (MOVs), continuum of care, and continuous quality improvement. The included TImR data comprised 958,870 visits for 559,542 patients from 2359 health facilities. Our analyses revealed that few patients sought care outside their assigned facility (44,733/810,568, 5.52% of applicable visits); however, this varied by region; facility urbanicity, type, ownership, patient volume, and duration of TImR system use; density of facilities in the immediate area; and patient age. Analyses further showed that MOVs were highest among children aged <12 months (215,576/831,018, 25.94% of visits included an MOV and were applicable visits); however, there were few significant differences based on other individual or facility characteristics. Nearly half (133,337/294,464, 45.28%) of the children aged 12 to 35 months were fully vaccinated or had received all doses except measles-containing vaccine–1 of the 14-dose under-12-month schedule (ie, through measles-containing vaccine–1), and facility and patient characteristics associated with dropout varied by vaccine. The continuous quality improvement analysis showed that most quality issues (eg, MOVs) were concentrated in <10% of facilities, indicating the potential for EIRs to target quality improvement efforts. Conclusions: EIRs have the potential to add value to immunization stakeholders at all levels of the health system. Individual-level electronic data can enable new analyses to understand service delivery or care-seeking patterns, potential risk factors for underimmunization, and where challenges occur. However, to achieve this potential, country programs need to leverage and strengthen the capacity to collect, analyze, interpret, and act on the data. As EIRs are introduced and scaled in low- and middle-income countries, implementers and researchers should continue to share real-world examples and build an evidence base for how EIRs can add value to immunization programs, particularly for innovative uses. %M 35060919 %R 10.2196/32455 %U https://publichealth.jmir.org/2022/1/e32455 %U https://doi.org/10.2196/32455 %U http://www.ncbi.nlm.nih.gov/pubmed/35060919 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e27270 %T Risk Factors of Dengue Fever in Urban Areas of Rawalpindi District in Pakistan During 2017: A Case Control Study %A Awan,Najma Javed %A Chaudhry,Ambreen %A Hussain,Zakir %A Baig,Zeeshan Iqbal %A Baig,Mirza Amir %A Asghar,Rana Jawad %A Khader,Yousef %A Ikram,Aamer %+ Field Epidemiology and Laboratory Training Program, National Institute of Health Pakistan, Park Road, Chak Shehzad, Islamabad, 44000, Pakistan, 92 519255302, ambr.chaudhry@gmail.com %K dengue fever %K outbreak %K Rawalpindi %K risk factors %K stored water %K urban %D 2022 %7 19.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: During August 2017, increased numbers of suspected dengue fever cases were reported in the hospitals of Rawalpindi district. A case control study was conducted to determine the risk factors among urban areas, dengue serotype, and recommend preventive measures. Objective: The objective of the investigation was to determine the risk factors among urban areas, dengue serotype, and recommend preventive measures. Methods: A case was defined as having acute febrile illness with one or more of the following symptoms: retro-orbital pain, headache, rash, myalgia, arthralgia, and hemorrhage. The cases were residents of Rawalpindi and were confirmed for dengue fever from August 30, 2017, to October 30, 2017. All NS1 confirmed cases from urban areas of Rawalpindi were recruited from tertiary care hospitals. Age- and sex-matched controls were selected from the same community with a 1:1 ratio. Frequency, univariate, and multivariate analyses were performed at 95% CI with P<.05 considered statistically significant. Results: Totally 373 cases were recruited. The mean age was 36 (SD 2.9) years (range 10-69 years), and 280 cases (75%) were male. The most affected age group was 21-30 years (n=151, attack rate [AR] 40%), followed by 31-40 years (n=66, AR 23%). Further, 2 deaths were reported (case fatality rate of 0.53%). The most frequent signs or symptoms were fever (n=373, 100%), myalgia and headache (n=320, 86%), and retro-orbital pain (n=272, 73%). Serotype identification was carried out in 322 cases, and DEN-2 was the dominant serotype (n=126, 34%). Contact with a confirmed dengue case (odds ratio [OR] 4.27; 95% CI 3.14-5.81; P<.001), stored water in open containers at home (OR 2.04; 95% CI 1.53-2.73; P<.001), and travel to a dengue outbreak area (OR 2.88; 95% CI 2.12-3.92; P<.001) were the main reasons for the outbreak, whereas use of mosquito repellents (OR 0.12; 95% CI 0.09-0.18; P<.001) and regular water supply at home (OR 0.03; 95% CI 0.02-0.04; P<.001) showed protective effects. The geographical distribution of cases was limited to densely populated areas and all the 5 randomly collected water samples tested positive for dengue larvae. Conclusions: Stored water in containers inside houses and subsequent mosquito breeding were the most probable causes of this outbreak. Based on the study findings, undertaking activities to improve the use of mosquito repellents and removing sources of breeding (uncovered water stored indoors) are some recommendations for preventing dengue outbreaks. %M 35044313 %R 10.2196/27270 %U https://publichealth.jmir.org/2022/1/e27270 %U https://doi.org/10.2196/27270 %U http://www.ncbi.nlm.nih.gov/pubmed/35044313 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e28749 %T Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk %A Shakeri Hossein Abad,Zahra %A Butler,Gregory P %A Thompson,Wendy %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K crowdsourcing %K machine learning %K digital public health surveillance %K public health database %K social media analysis %D 2022 %7 18.1.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. Objective: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Methods: We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning–based classification model, and a hybrid convolutional neural network model. Results: Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. Conclusions: Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models. %M 35040794 %R 10.2196/28749 %U https://www.jmir.org/2022/1/e28749 %U https://doi.org/10.2196/28749 %U http://www.ncbi.nlm.nih.gov/pubmed/35040794 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e22113 %T Reasons for Nonuse, Discontinuation of Use, and Acceptance of Additional Functionalities of a COVID-19 Contact Tracing App: Cross-sectional Survey Study %A Walrave,Michel %A Waeterloos,Cato %A Ponnet,Koen %+ MIOS Research Group and GOVTRUST Centre of Excellence, Department of Communication Studies, Faculty of Social Sciences, University of Antwerp, Sint-Jacobstraat 2, Antwerp, 2000, Belgium, 32 475459785, michel.walrave@uantwerp.be %K COVID-19 %K SARS-CoV-2 %K coronavirus %K contact tracing %K proximity tracing %K mHealth %K mobile app %K user acceptability %K surveillance %K privacy %D 2022 %7 14.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In several countries, contact tracing apps (CTAs) have been introduced to warn users if they have had high-risk contacts that could expose them to SARS-CoV-2 and could, therefore, develop COVID-19 or further transmit the virus. For CTAs to be effective, a sufficient critical mass of users is needed. Until now, adoption of these apps in several countries has been limited, resulting in questions on which factors prevent app uptake or stimulate discontinuation of app use. Objective: The aim of this study was to investigate individuals’ reasons for not using, or stopping use of, a CTA, in particular, the Coronalert app. Users’ and nonusers’ attitudes toward the app’s potential impact was assessed in Belgium. To further stimulate interest and potential use of a CTA, the study also investigated the population’s interest in new functionalities. Methods: An online survey was administered in Belgium to a sample of 1850 respondents aged 18 to 64 years. Data were collected between October 30 and November 2, 2020. Sociodemographic differences were assessed between users and nonusers. We analyzed both groups’ attitudes toward the potential impact of CTAs and their acceptance of new app functionalities. Results: Our data showed that 64.9% (1201/1850) of our respondents were nonusers of the CTA under study; this included individuals who did not install the app, those who downloaded but did not activate the app, and those who uninstalled the app. While we did not find any sociodemographic differences between users and nonusers, attitudes toward the app and its functionalities seemed to differ. The main reasons for not downloading and using the app were a perceived lack of advantages (308/991, 31.1%), worries about privacy (290/991, 29.3%), and, to a lesser extent, not having a smartphone (183/991, 18.5%). Users of the CTA agreed more with the potential of such apps to mitigate the consequences of the pandemic. Overall, nonusers found the possibility of extending the CTA with future functionalities to be less acceptable than users. However, among users, acceptability also tended to differ. Among users, functionalities relating to access and control, such as digital certificates or “green cards” for events, were less accepted (358/649, 55.2%) than functionalities focusing on informing citizens about the spread of the virus (453/649, 69.8%) or making an appointment to get tested (525/649, 80.9%). Conclusions: Our results show that app users were more convinced of the CTA’s utility and more inclined to accept new app features than nonusers. Moreover, nonusers had more CTA-related privacy concerns. Therefore, to further stimulate app adoption and use, its potential advantages and privacy-preserving mechanisms need to be stressed. Building further knowledge on the forms of resistance among nonusers is important for responding to these barriers through the app’s further development and communication campaigns. %M 34794117 %R 10.2196/22113 %U https://publichealth.jmir.org/2022/1/e22113 %U https://doi.org/10.2196/22113 %U http://www.ncbi.nlm.nih.gov/pubmed/34794117 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e27952 %T Applying Human-Centered Design Principles to Digital Syndromic Surveillance at a Mass Gathering in India: Viewpoint %A Shaikh,Ahmed %A Bhatia,Abhishek %A Yadav,Ghanshyam %A Hora,Shashwat %A Won,Chung %A Shankar,Mark %A Heerboth,Aaron %A Vemulapalli,Prakash %A Navalkar,Paresh %A Oswal,Kunal %A Heaton,Clay %A Saunik,Sujata %A Khanna,Tarun %A Balsari,Satchit %+ Department of Global Health and Population, Harvard TH Chan School of Public Health, 651 Huntington Avenue, 703C, Boston, MA, 02115, United States, 1 6174951000, balsari@hsph.harvard.edu %K mHealth %K design %K human centered design %K intervention %K syndromic surveillance %K digital health %D 2022 %7 10.1.2022 %9 Viewpoint %J J Med Internet Res %G English %X In the wake of the COVID-19 pandemic, digital health tools have been deployed by governments around the world to advance clinical and population health objectives. Few interventions have been successful or have achieved sustainability or scale. In India, government agencies are proposing sweeping changes to India’s digital health architecture. Underpinning these initiatives is the assumption that mobile health solutions will find near universal acceptance and uptake, though the observed reticence of clinicians to use electronic health records suggests otherwise. In this practice article, we describe our experience with implementing a digital surveillance tool at a large mass gathering, attended by nearly 30 million people. Deployed with limited resources and in a dynamic chaotic setting, the adherence to human-centered design principles resulted in near universal adoption and high end-user satisfaction. Through this use case, we share generalizable lessons in the importance of contextual relevance, stakeholder participation, customizability, and rapid iteration, while designing digital health tools for individuals or populations. %M 35006088 %R 10.2196/27952 %U https://www.jmir.org/2022/1/e27952 %U https://doi.org/10.2196/27952 %U http://www.ncbi.nlm.nih.gov/pubmed/35006088 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e30006 %T Individuals With SARS-CoV-2 Infection During the First and Second Waves in Catalonia, Spain: Retrospective Observational Study Using Daily Updated Data %A Alves-Cabratosa,Lia %A Comas-Cufí,Marc %A Blanch,Jordi %A Martí-Lluch,Ruth %A Ponjoan,Anna %A Castro-Guardiola,Antoni %A Hurtado-Ganoza,Abelardo %A Pérez-Jaén,Ana %A Rexach-Fumaña,Maria %A Faixedas-Brunsoms,Delfi %A Gispert-Ametller,Maria Angels %A Guell-Cargol,Anna %A Rodriguez-Batista,Maria %A Santaularia-Font,Ferran %A Orriols,Ramon %A Bonnin-Vilaplana,Marc %A Calderón López,Juan Carlos %A Sabater-Talaverano,Gladis %A Queralt Moles,Francesc Xavier %A Rodriguez-Requejo,Sara %A Avellana-Revuelta,Esteve %A Balló,Elisabet %A Fages-Masmiquel,Ester %A Sirvent,Josep-Maria %A Lorencio,Carol %A Morales-Pedrosa,Josep Miquel %A Ortiz-Ballujera,Patricia %A Ramos,Rafel %+ Vascular Health Research Group of Girona, Institut Universitari per a la Recerca en Atenció Primària Jordi Gol i Gurina, Maluquer Salvador, 11, Girona, 17002, Spain, 34 972487968, rramos.girona.ics@gencat.cat %K epidemiology %K SARS-CoV-2 %K COVID-19 %K timeline %K comparison %K pandemic %K waves %K population characteristics %D 2022 %7 6.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A description of individuals with SARS-CoV-2 infection comparing the first and second waves could help adapt health services to manage this highly transmissible infection. Objective: We aimed to describe the epidemiology of individuals with suspected SARS-CoV-2 infection, and the characteristics of patients with a positive test comparing the first and second waves in Catalonia, Spain. Methods: This study had 2 stages. First, we analyzed daily updated data on SARS-CoV-2 infection in individuals from Girona (Catalonia). Second, we compared 2 retrospective cohorts of patients with a positive reverse-transcription polymerase chain reaction or rapid antigen test for SARS-CoV-2. The severity of patients with a positive test was defined by their admission to hospital, admission to intermediate respiratory care, admission to the intensive care unit, or death. The first wave was from March 1, 2020, to June 24, 2020, and the second wave was from June 25, 2020, to December 8, 2020. Results: The numbers of tests and cases were lower in the first wave than in the second wave (26,096 tests and 3140 cases in the first wave versus 140,332 tests and 11,800 cases in the second wave), but the percentage of positive results was higher in the first wave than in the second wave (12.0% versus 8.4%). Among individuals with a positive diagnostic test, 818 needed hospitalization in the first wave and 680 in the second; however, the percentage of hospitalized individuals was higher in the first wave than in the second wave (26.1% versus 5.8%). The group that was not admitted to hospital included older people and those with a higher percentage of comorbidities in the first wave, whereas the characteristics of the groups admitted to hospital were more alike. Conclusions: Screening systems for SARS-CoV-2 infection were scarce during the first wave, but were more adequate during the second wave, reflecting the usefulness of surveillance systems to detect a high number of asymptomatic infected individuals and their contacts, to help control this pandemic. The characteristics of individuals with SARS-CoV-2 infection in the first and second waves differed substantially; individuals in the first wave were older and had a worse health condition. %M 34797774 %R 10.2196/30006 %U https://publichealth.jmir.org/2022/1/e30006 %U https://doi.org/10.2196/30006 %U http://www.ncbi.nlm.nih.gov/pubmed/34797774 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e30106 %T Availability and Suitability of Digital Health Tools in Africa for Pandemic Control: Scoping Review and Cluster Analysis %A Silenou,Bernard C %A Nyirenda,John L Z %A Zaghloul,Ahmed %A Lange,Berit %A Doerrbecker,Juliane %A Schenkel,Karl %A Krause,Gérard %+ Department of Epidemiology, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, 38124, Germany, 49 5316181 3100, Gerard.Krause@helmholtz-hzi.de %K mobile applications %K mHealth %K epidemiological surveillance %K communicable diseases %K outbreak response %K health information management %K public health %K review %K transmission network %D 2021 %7 23.12.2021 %9 Review %J JMIR Public Health Surveill %G English %X Background: Gaining oversight into the rapidly growing number of mobile health tools for surveillance or outbreak management in Africa has become a challenge. Objective: The aim of this study is to map the functional portfolio of mobile health tools used for surveillance or outbreak management of communicable diseases in Africa. Methods: We conducted a scoping review by combining data from a systematic review of the literature and a telephone survey of experts. We applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines by searching for articles published between January 2010 and December 2020. In addition, we used the respondent-driven sampling method and conducted a telephone survey from October 2019 to February 2020 among representatives from national public health institutes from all African countries. We combined the findings and used a hierarchical clustering method to group the tools based on their functionalities (attributes). Results: We identified 30 tools from 1914 publications and 45 responses from 52% (28/54) of African countries. Approximately 13% of the tools (4/30; Surveillance Outbreak Response Management and Analysis System, Go.Data, CommCare, and District Health Information Software 2) covered 93% (14/15) of the identified attributes. Of the 30 tools, 17 (59%) tools managed health event data, 20 (67%) managed case-based data, and 28 (97%) offered a dashboard. Clustering identified 2 exceptional attributes for outbreak management, namely contact follow-up (offered by 8/30, 27%, of the tools) and transmission network visualization (offered by Surveillance Outbreak Response Management and Analysis System and Go.Data). Conclusions: There is a large range of tools in use; however, most of them do not offer a comprehensive set of attributes, resulting in the need for public health workers having to use multiple tools in parallel. Only 13% (4/30) of the tools cover most of the attributes, including those most relevant for response to the COVID-19 pandemic, such as laboratory interface, contact follow-up, and transmission network visualization. %M 34941551 %R 10.2196/30106 %U https://publichealth.jmir.org/2021/12/e30106 %U https://doi.org/10.2196/30106 %U http://www.ncbi.nlm.nih.gov/pubmed/34941551 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e34178 %T Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study %A Husnayain,Atina %A Shim,Eunha %A Fuad,Anis %A Su,Emily Chia-Yu %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 172-1 Keelung Rd, Sec 2, Taipei, 106, Taiwan, 886 266382736 ext 1515, emilysu@tmu.edu.tw %K prediction %K internet search %K COVID-19 %K South Korea %K infodemiology %D 2021 %7 22.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. Objective: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. Methods: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. Results: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. Conclusions: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions. %M 34762064 %R 10.2196/34178 %U https://www.jmir.org/2021/12/e34178 %U https://doi.org/10.2196/34178 %U http://www.ncbi.nlm.nih.gov/pubmed/34762064 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e26644 %T Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries %A Ming,Wai-kit %A Huang,Fengqiu %A Chen,Qiuyi %A Liang,Beiting %A Jiao,Aoao %A Liu,Taoran %A Wu,Huailiang %A Akinwunmi,Babatunde %A Li,Jia %A Liu,Guan %A Zhang,Casper J P %A Huang,Jian %A Liu,Qian %+ School of Journalism and Communication, National Media Experimental Teaching Demonstration Center, Jinan University, 601 Huangpu Dadao West, Guangzhou, 510632, China, 86 13302292599, tsusanliu@jnu.edu.cn %K COVID-19 %K Google Trends %K search peaks %K news coverage %K public concerns %D 2021 %7 21.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19–related health information during the pandemic. Objective: The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage. Methods: To achieve the study objectives, we analyzed the public’s information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19–related “diseases,” “treatments and medical resources,” “symptoms and signs,” and “public measures.” The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage. Results: Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19–related “diseases,” in most countries, the RSV of the term “coronavirus” increased earlier than that of “covid-19”; however, around April 2020, the search volume of the term “covid-19” surpassed that of “coronavirus.” Regarding the topic “treatments and medical resources,” the most and least searched terms were “mask” and “ventilator,” respectively. Regarding the topic “symptoms and signs,” “fever” and “cough” were the most searched terms. The RSV for the term “lockdown” was significantly higher than that for “social distancing” under the topic “public health measures.” In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic “treatments and medical resources” and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic “diseases” and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage. Conclusions: Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics. %M 34591781 %R 10.2196/26644 %U https://publichealth.jmir.org/2021/12/e26644 %U https://doi.org/10.2196/26644 %U http://www.ncbi.nlm.nih.gov/pubmed/34591781 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e29187 %T Web-Based Discussion and Illicit Street Sales of Tapentadol and Oxycodone in Australia: Epidemiological Surveillance Study %A Black,Joshua %A Margolin,Zachary R %A Bau,Gabrielle %A Olson,Richard %A Iwanicki,Janetta L %A Dart,Richard C %+ Rocky Mountain Poison and Drug Safety, 1391 N Speer Blvd, #600, M/C 0180, Denver, CO, 80204, United States, 1 303 389 1652, joshua.black@rmpds.org %K Australia %K opioids %K web-based discussion %K diversion %D 2021 %7 20.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Opioid use disorder and its consequences are a persistent public health concern for Australians. Web activity has been used to understand the perception of drug safety and diversion of drugs in contexts outside of Australia. The anonymity of the internet offers several advantages for surveilling and inquiring about specific covert behaviors, such as diversion or discussion of sensitive subjects where traditional surveillance approaches might be limited. Objective: This study aims to characterize the content of web posts and compare reports of illicit sales of tapentadol and oxycodone from sources originating in Australia. First, post content is evaluated to determine whether internet discussion encourages or discourages proper therapeutic use of the drugs. Second, we hypothesize that tapentadol would have lower street price and fewer illicit sales than oxycodone. Methods: Web posts originating in Australia between 2017 and 2019 were collected using the Researched Abuse, Diversion, and Addiction-Related Surveillance System Web Monitoring Program. Using a manual coding process, unstructured post content from social media, blogs, and forums was categorized into topics of discussion related to the harms and behaviors that could lead to harm. Illicit sales data in a structured format were collected through a crowdsourcing website between 2016 and 2019 using the Researched Abuse, Diversion, and Addiction-Related Surveillance System StreetRx Program. In total, 2 multivariable regression models assessed the differences in illicit price and number of sales. Results: A total of 4.7% (28/600) of tapentadol posts discussed an adverse event, whereas 10.27% (95% CI 9.32-11.21) of oxycodone posts discussed this topic. A total of 10% (60/600) of tapentadol posts discussed unsafe use or side effects, whereas 20.17% (95% CI 18.92-21.41) of oxycodone posts discussed unsafe use or side effects. There were 31 illicit sales reports for tapentadol (geometric mean price per milligram: Aus $0.12 [US $0.09]) and 756 illicit sales reports for oxycodone (Aus $1.28 [US $0.91]). Models detected no differences in the street price or number of sales between the drugs when covariates were included, although the potency of the pill significantly predicted the street price (P<.001) and availability predicted the number of sales (P=.03). Conclusions: Australians searching the web for opinions could judge tapentadol as safer than oxycodone because of the web post content. The illicit sales market for tapentadol was smaller than that of oxycodone, and drug potency and licit availability are likely important factors influencing the illicit market. %M 34932012 %R 10.2196/29187 %U https://publichealth.jmir.org/2021/12/e29187 %U https://doi.org/10.2196/29187 %U http://www.ncbi.nlm.nih.gov/pubmed/34932012 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e34286 %T EpiHacks, a Process for Technologists and Health Experts to Cocreate Optimal Solutions for Disease Prevention and Control: User-Centered Design Approach %A Divi,Nomita %A Smolinski,Mark %+ Ending Pandemics, 870 Market Street, Suite 528, San Francisco, CA, 94102, United States, 1 6173591733, nomita@endingpandemics.org %K epidemiology %K public health %K diagnostic %K tool %K disease surveillance %K technology solution %K innovative approaches to disease surveillance %K One Health %K surveillance %K hack %K innovation %K expert %K solution %K prevention %K control %D 2021 %7 15.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Technology-based innovations that are created collaboratively by local technology specialists and health experts can optimize the addressing of priority needs for disease prevention and control. An EpiHack is a distinct, collaborative approach to developing solutions that combines the science of epidemiology with the format of a hackathon. Since 2013, a total of 12 EpiHacks have collectively brought together over 500 technology and health professionals from 29 countries. Objective: We aimed to define the EpiHack process and summarize the impacts of the technology-based innovations that have been created through this approach. Methods: The key components and timeline of an EpiHack were described in detail. The focus areas, outputs, and impacts of the twelve EpiHacks that were conducted between 2013 and 2021 were summarized. Results: EpiHack solutions have served to improve surveillance for influenza, dengue, and mass gatherings, as well as laboratory sample tracking and One Health surveillance, in rural and urban communities. Several EpiHack tools were scaled during the COVID-19 pandemic to support local governments in conducting active surveillance. All tools were designed to be open source to allow for easy replication and adaptation by other governments or parties. Conclusions: EpiHacks provide an efficient, flexible, and replicable new approach to generating relevant and timely innovations that are locally developed and owned, are scalable, and are sustainable. %M 34807832 %R 10.2196/34286 %U https://www.jmir.org/2021/12/e34286 %U https://doi.org/10.2196/34286 %U http://www.ncbi.nlm.nih.gov/pubmed/34807832 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e33617 %T Utility of Facebook’s Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study %A Prusaczyk,Beth %A Pietka,Kathryn %A Landman,Joshua M %A Luke,Douglas A %+ Center for Population Health Informatics, Institute for Informatics, Washington University School of Medicine in St. Louis, 660 S. Euclid Avenue, Saint Louis, MO, 63110, United States, 1 314 330 0537, beth.prusaczyk@wustl.edu %K COVID-19 %K social media %K social networks %K network analysis %K public health %K utility %K Facebook %K connection %K modeling %K spread %K United States %K belief %D 2021 %7 15.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI). Objective: Building off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique. Methods: In September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county’s total population, percent rurality, and distance between each county pair. Results: We found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county’s total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26). Conclusions: These results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices. %M 34797775 %R 10.2196/33617 %U https://publichealth.jmir.org/2021/12/e33617 %U https://doi.org/10.2196/33617 %U http://www.ncbi.nlm.nih.gov/pubmed/34797775 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e32203 %T Presenting Characteristics, Comorbidities, and Outcomes Among Patients With COVID-19 Hospitalized in Pakistan: Retrospective Observational Study %A Akhtar,Hashaam %A Khalid,Sundas %A Rahman,Fazal ur %A Umar,Muhammad %A Ali,Sabahat %A Afridi,Maham %A Hassan,Faheem %A Saleh Khader,Yousef %A Akhtar,Nasim %A Khan,Muhammad Mujeeb %A Ikram,Aamer %+ Yusra Institute of Pharmaceutical Sciences, Zaraj Housing Society, Opposite DHA Phase 2 Gate III, Main G T Road, Islamabad, 44000, Pakistan, 92 3215272489, hashaamakhtar@gmail.com %K COVID-19 %K indicators %K symptoms %K risk factors %K comorbidities %K severity %K Pakistan %D 2021 %7 14.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 became a pandemic rapidly after its emergence in December 2019. It belongs to the coronavirus family of viruses, which have struck a few times before in history. Data based on previous research regarding etiology and epidemiology of other viruses from this family helped played a vital role in formulating prevention and precaution strategies during the initial stages of this pandemic. Data related to COVID-19 in Pakistan were not initially documented on a large scale. In addition, due to a weak health care system and low economic conditions, Pakistan’s population, in general, already suffers from many comorbidities, which can severely affect the outcome of patients infected with COVID-19. Objective: COVID-19 infections are coupled with a manifestation of various notable outcomes that can be documented and characterized clinically. The aim of this study was to examine these clinical manifestations, which can serve as indicators for early detection as well as severity prognosis for COVID-19 infections, especially in high-risk groups. Methods: A retrospective observational study involving abstraction of demographic features, presenting symptoms, and adverse clinical outcomes for 1812 patients with COVID-19 was conducted. Patients were admitted to the four major hospitals in the Rawalpindi-Islamabad region of Pakistan, and the study was conducted from February to August 2020. Multivariate regression analysis was carried out to identify significant indicators of COVID-19 severity, intensive care unit (ICU) admission, ventilator aid, and mortality. The study not only relates COVID-19 infection with comorbidities, but also examines other related factors, such as age and gender. Results: This study identified fever (1592/1812, 87.9%), cough (1433/1812, 79.1%), and shortness of breath (998/1812, 55.1%) at the time of hospital admission as the most prevalent symptoms for patients with COVID-19. These symptoms were common but not conclusive of the outcome of infection. Out of 1812 patients, 24.4% (n=443) required ICU admission and 21.5% (n=390) required ventilator aid at some point of disease progression during their stay at the hospital; 25.9% (n=469) of the patients died. Further analysis revealed the relationship of the presented symptoms and comorbidities with the progression of disease severity in these patients. Older adult patients with comorbidities, such as hypertension, diabetes, chronic kidney disease, and asthma, were significantly affected in higher proportions, resulting in requirement of ICU admission and ventilator aid in some cases and, in many cases, even mortality. Conclusions: Older adult patients with comorbidities, such as hypertension, diabetes, asthma, chronic obstructive pulmonary disorder, and chronic kidney disease, are at increased risk of developing severe COVID-19 infections, with an increased likelihood of adverse clinical outcomes. %M 34710053 %R 10.2196/32203 %U https://publichealth.jmir.org/2021/12/e32203 %U https://doi.org/10.2196/32203 %U http://www.ncbi.nlm.nih.gov/pubmed/34710053 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e33296 %T The Standardization of Hospital-Acquired Infection Rates Using Prediction Models in Iran: Observational Study of National Nosocomial Infection Registry Data %A Izadi,Neda %A Etemad,Koorosh %A Mehrabi,Yadollah %A Eshrati,Babak %A Hashemi Nazari,Seyed Saeed %+ Prevention of Cardiovascular Disease Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Velenjak, 2th Floor, Tehran, Iran, 98 09125779300, saeedh_1999@yahoo.com %K hospital-acquired infections %K standardized infection ratio %K prediction model %K Iran %D 2021 %7 7.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Many factors contribute to the spreading of hospital-acquired infections (HAIs). Objective: This study aimed to standardize the HAI rate using prediction models in Iran based on the National Healthcare Safety Network (NHSN) method. Methods: In this study, the Iranian nosocomial infections surveillance system (INIS) was used to gather data on patients with HAIs (126,314 infections). In addition, the hospital statistics and information system (AVAB) was used to collect data on hospital characteristics. First, well-performing hospitals, including 357 hospitals from all over the country, were selected. Data were randomly split into training (70%) and testing (30%) sets. Finally, the standardized infection ratio (SIR) and the corrected SIR were calculated for the HAIs. Results: The mean age of the 100,110 patients with an HAI was 40.02 (SD 23.56) years. The corrected SIRs based on the observed and predicted infections for respiratory tract infections (RTIs), urinary tract infections (UTIs), surgical site infections (SSIs), and bloodstream infections (BSIs) were 0.03 (95% CI 0-0.09), 1.02 (95% CI 0.95-1.09), 0.93 (95% CI 0.85-1.007), and 0.91 (95% CI 0.54-1.28), respectively. Moreover, the corrected SIRs for RTIs in the infectious disease, burn, obstetrics and gynecology, and internal medicine wards; UTIs in the burn, infectious disease, internal medicine, and intensive care unit wards; SSIs in the burn and infectious disease wards; and BSIs in most wards were >1, indicating that more HAIs were observed than expected. Conclusions: The results of this study can help to promote preventive measures based on scientific evidence. They can also lead to the continuous improvement of the monitoring system by collecting and systematically analyzing data on HAIs and encourage the hospitals to better control their infection rates by establishing a benchmarking system. %M 34879002 %R 10.2196/33296 %U https://publichealth.jmir.org/2021/12/e33296 %U https://doi.org/10.2196/33296 %U http://www.ncbi.nlm.nih.gov/pubmed/34879002 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e31961 %T Analyzing Citizens’ and Health Care Professionals’ Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs %A Mukka,Milla %A Pesälä,Samuli %A Hammer,Charlotte %A Mustonen,Pekka %A Jormanainen,Vesa %A Pelttari,Hanna %A Kaila,Minna %A Helve,Otto %+ University of Helsinki, Temppelikatu 17 A 5, Helsinki, 00100, Finland, 358 0504397177, milla.mukka@helsinki.fi %K COVID-19 %K SARS-CoV-2 %K smell disorders %K taste disorders %K information-seeking behavior %K health personnel %K statistical models %K medical informatics %D 2021 %7 7.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks. Objective: The aim of this study was (1) to assess whether citizens’ and professionals’ searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and (2) to test our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model). Methods: We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 2020 (49 weeks). Two major medical internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician’s Database (PD), a database widely used among HCPs. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs. Results: We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database. Conclusions: Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases. %M 34727525 %R 10.2196/31961 %U https://publichealth.jmir.org/2021/12/e31961 %U https://doi.org/10.2196/31961 %U http://www.ncbi.nlm.nih.gov/pubmed/34727525 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e30004 %T Using Venn Diagrams to Evaluate Digital Contact Tracing: Panel Survey Analysis %A Daniore,Paola %A Nittas,Vasileios %A Moser,André %A Höglinger,Marc %A von Wyl,Viktor %+ Institute for Implementation Science in Healthcare, University of Zurich, Universitätstrasse 84, Zurich, 8006, Switzerland, 41 446346380, viktor.vonwyl@uzh.ch %K digital contact tracing %K exposure notification %K COVID-19 %K SARS-CoV-2 %K contact tracing %K digital health %K tracing apps %K mHealth %K mobile apps %K key performance indicators %K Venn diagram approach %D 2021 %7 6.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Mitigation of the spread of infection relies on targeted approaches aimed at preventing nonhousehold interactions. Contact tracing in the form of digital proximity tracing apps has been widely adopted in multiple countries due to its perceived added benefits of tracing speed and breadth in comparison to traditional manual contact tracing. Assessments of user responses to exposure notifications through a guided approach can provide insights into the effect of digital proximity tracing app use on managing the spread of SARS-CoV-2. Objective: The aim of this study was to demonstrate the use of Venn diagrams to investigate the contributions of digital proximity tracing app exposure notifications and subsequent mitigative actions in curbing the spread of SARS-CoV-2 in Switzerland. Methods: We assessed data from 4 survey waves (December 2020 to March 2021) from a nationwide panel study (COVID-19 Social Monitor) of Swiss residents who were (1) nonusers of the SwissCovid app, (2) users of the SwissCovid app, or (3) users of the SwissCovid app who received exposure notifications. A Venn diagram approach was applied to describe the overlap or nonoverlap of these subpopulations and to assess digital proximity tracing app use and its associated key performance indicators, including actions taken to prevent SARS-CoV-2 transmission. Results: We included 12,525 assessments from 2403 participants, of whom 50.9% (1222/2403) reported not using the SwissCovid digital proximity tracing app, 49.1% (1181/2403) reported using the SwissCovid digital proximity tracing app and 2.5% (29/1181) of the digital proximity tracing app users reported having received an exposure notification. Most digital proximity tracing app users (75.9%, 22/29) revealed taking at least one recommended action after receiving an exposure notification, such as seeking SARS-CoV-2 testing (17/29, 58.6%) or calling a federal information hotline (7/29, 24.1%). An assessment of key indicators of mitigative actions through a Venn diagram approach reveals that 30% of digital proximity tracing app users (95% CI 11.9%-54.3%) also tested positive for SARS-CoV-2 after having received exposure notifications, which is more than 3 times that of digital proximity tracing app users who did not receive exposure notifications (8%, 95% CI 5%-11.9%). Conclusions: Responses in the form of mitigative actions taken by 3 out of 4 individuals who received exposure notifications reveal a possible contribution of digital proximity tracing apps in mitigating the spread of SARS-CoV-2. The application of a Venn diagram approach demonstrates its value as a foundation for researchers and health authorities to assess population-level digital proximity tracing app effectiveness by providing an intuitive approach for calculating key performance indicators. %M 34874890 %R 10.2196/30004 %U https://publichealth.jmir.org/2021/12/e30004 %U https://doi.org/10.2196/30004 %U http://www.ncbi.nlm.nih.gov/pubmed/34874890 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e34016 %T Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model %A Taira,Kazuya %A Hosokawa,Rikuya %A Itatani,Tomoya %A Fujita,Sumio %+ Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, 53, Shogoinkawara-cho, Sakyo-ku, Kyoto, 606-8507, Japan, 81 75 751 3927, taira.kazuya.5m@kyoto-u.ac.jp %K suicide %K internet search engine %K infoveillance %K query %K time series analysis %K vector autoregression model %K COVID-19 %K suicide-related terms %K internet %K information seeking %K time series %K model %K loneliness %K mental health %K prediction %K Japan %K behavior %K trend %D 2021 %7 3.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures. Objective: This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword “suicide.” Methods: This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with “suicide” on “Yahoo! JAPAN Search” from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword “suicide” and extracted and used for analyses: “abuse”; “work, don’t want to go”; “company, want to quit”; “divorce”; and “no money.” The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results: In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: −9.24; max tau 3: −5.38) and women (minimum tau 3: −9.24; max tau 3: −5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were “divorce” for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and “no money” for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, “divorce” was significant for both men (F104=3.29; P=.04) and women (F104=3.23; P=.04). Conclusions: The number of suicides can be predicted by search queries related to the keyword “suicide.” Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on “no money” and “divorce” predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary. %M 34823225 %R 10.2196/34016 %U https://publichealth.jmir.org/2021/12/e34016 %U https://doi.org/10.2196/34016 %U http://www.ncbi.nlm.nih.gov/pubmed/34823225 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e30648 %T Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach %A Donnat,Claire %A Bunbury,Freddy %A Kreindler,Jack %A Liu,David %A Filippidis,Filippos T %A Esko,Tonu %A El-Osta,Austen %A Harris,Matthew %+ Department of Statistics, University of Chicago, 5747 South Ellis Avenue, Chicago, IL, 60637, United States, 1 773 702 9890, cdonnat@uchicago.edu %K COVID-19 %K transmission dynamics %K live event management %K Monte Carlo simulation %D 2021 %7 1.12.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Modelling COVID-19 transmission at live events and public gatherings is essential to controlling the probability of subsequent outbreaks and communicating to participants their personalized risk. Yet, despite the fast-growing body of literature on COVID-19 transmission dynamics, current risk models either neglect contextual information including vaccination rates or disease prevalence or do not attempt to quantitatively model transmission. Objective: This paper attempted to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty. Methods: Building upon existing models, our approach ties together 3 main components: (1) reliable modelling of the number of infectious cases at the time of the event, (2) evaluation of the efficiency of pre-event screening, and (3) modelling of the event’s transmission dynamics and their uncertainty using Monte Carlo simulations. Results: We illustrated the application of our pipeline for a concert at the Royal Albert Hall and highlighted the risk’s dependency on factors such as prevalence, mask wearing, and event duration. We demonstrate how this event held on 3 different dates (August 20, 2020; January 20, 2021; and March 20, 2021) would likely lead to transmission events that are similar to community transmission rates (0.06 vs 0.07, 2.38 vs 2.39, and 0.67 vs 0.60, respectively). However, differences between event and background transmissions substantially widened in the upper tails of the distribution of the number of infections (as denoted by their respective 99th quantiles: 1 vs 1, 19 vs 8, and 6 vs 3, respectively, for our 3 dates), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event. Conclusions: Despite the unknowns surrounding COVID-19 transmission, our estimation pipeline opens the discussion on contextualized risk assessment by combining the best tools at hand to assess the order of magnitude of the risk. Our model can be applied to any future event and is presented in a user-friendly RShiny interface. Finally, we discussed our model’s limitations as well as avenues for model evaluation and improvement. %M 34583317 %R 10.2196/30648 %U https://publichealth.jmir.org/2021/12/e30648 %U https://doi.org/10.2196/30648 %U http://www.ncbi.nlm.nih.gov/pubmed/34583317 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e27626 %T Evaluation of the National Tuberculosis Surveillance System in Sana’a, Yemen, 2018: Observational Study %A Al kalali,Fadwa Salem Ahmed %A Mahyoub,Essam %A Al-Hammadi,Abdulbary %A Anam,Labiba %A Khader,Yousef %+ Yemen Field Epidemiology Training Program, Ministry of Public Health and Population, Mazda St, Al Hasaba, Sana’a, 12093, Yemen, 967 778005123, fadwa20102011@yahoo.com %K evaluation %K surveillance system %K tuberculosis %K Yemen %D 2021 %7 30.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Tuberculosis remains a public problem that is considered one of the top causes of morbidity and mortality worldwide. The National Tuberculosis Control Program in Yemen was established in 1970 and included in the national health policy under the leadership of the Ministry of Public Health and Population to monitor tuberculosis control. The surveillance system must be evaluated periodically to produce recommendations for improving performance and usefulness. Objective: This study aims to assess the usefulness and the performance of the tuberculosis surveillance system attributes and to identify the strengths and weaknesses of the system. Methods: A quantitative and qualitative evaluation of the national tuberculosis surveillance system was conducted using the Centers for Disease Control and Prevention’s updated guidelines. The study was carried out in 10 districts in Sana’a City. A total of 28 public health facilities providing tuberculosis services for the whole population in their assigned catchment areas were purposively selected. All participants were interviewed based on their involvement with key aspects of tuberculosis surveillance activities. Results: The tuberculosis surveillance system was found to have an average performance in usefulness (57/80, 71%), flexibility (30/40, 75%), acceptability (174/264, 66%), data quality (4/6, 67%), and positive predictive value (78/107, 73%), and poor performance in simplicity (863/1452, 59%) and stability (15%, 3/20). In addition, the system also had a good performance in sensitivity (78/81, 96%). Conclusions: The tuberculosis surveillance system was found to be useful. The flexibility, positive predictive value, and data quality were average. Stability and simplicity were poor. The sensitivity was good. The main weaknesses in the tuberculosis surveillance system include a lack of governmental financial support, a paper-based system, and a lack of regular staff training. Developing an electronic system, securing governmental finances, and training the staff on tuberculosis surveillance are strongly recommended to improve the system performance. %M 34851294 %R 10.2196/27626 %U https://publichealth.jmir.org/2021/11/e27626 %U https://doi.org/10.2196/27626 %U http://www.ncbi.nlm.nih.gov/pubmed/34851294 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e30529 %T Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning %A Jarynowski,Andrzej %A Semenov,Alexander %A Kamiński,Mikołaj %A Belik,Vitaly %+ System Modeling Group, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Königsweg 67, Berlin, 14163, Germany, 49 30 838 61129, vitaly.belik@fu-berlin.de %K adverse events %K Sputnik V %K Gam-COVID-Vac %K social media %K Telegram %K COVID-19 %K Sars-CoV-2 %K deep learning %K vaccine safety %D 2021 %7 29.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines. %M 34662291 %R 10.2196/30529 %U https://www.jmir.org/2021/11/e30529 %U https://doi.org/10.2196/30529 %U http://www.ncbi.nlm.nih.gov/pubmed/34662291 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e33231 %T Predictive Modeling of Vaccination Uptake in US Counties: A Machine Learning–Based Approach %A Cheong,Queena %A Au-yeung,Martin %A Quon,Stephanie %A Concepcion,Katsy %A Kong,Jude Dzevela %+ Africa-Canada Artificial Intelligence and Data Innovation Consortium, Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada, 1 416 736 2100 ext 66093, jdkong@yorku.ca %K COVID-19 %K vaccine %K public health %K machine learning %K XGBoost %K SARS-CoV-2 %K sociodemographic factors %K United States %K sociodemographic %K prediction %K model %K uptake %D 2021 %7 25.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Although the COVID-19 pandemic has left an unprecedented impact worldwide, countries such as the United States have reported the most substantial incidence of COVID-19 cases worldwide. Within the United States, various sociodemographic factors have played a role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modeling strategies to inform public health officials and reduce the burden on health care systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the United States, vaccination rates have become stagnant, necessitating predictive modeling to identify important factors impacting vaccination uptake. Objective: This study aims to determine the association between sociodemographic factors and vaccine uptake across counties in the United States. Methods: Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases such as the US Centers for Disease Control and Prevention and the US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data. Results: Our model predicted COVID-19 vaccination uptake across US counties with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by health care authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns. Conclusions: Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rates across counties in the United States and, if leveraged appropriately, can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them. %M 34751650 %R 10.2196/33231 %U https://www.jmir.org/2021/11/e33231 %U https://doi.org/10.2196/33231 %U http://www.ncbi.nlm.nih.gov/pubmed/34751650 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e29693 %T Prevalence of Multimorbidity of Chronic Noncommunicable Diseases in Brazil: Population-Based Study %A Shi,Xin %A Lima,Simone Maria da Silva %A Mota,Caroline Maria de Miranda %A Lu,Ying %A Stafford,Randall S %A Pereira,Corintho Viana %+ Management Engineering Department, Universidade Federal de Pernambuco, Av Prof Moraes Rego, 1235, Cidade Universitaria, Recife, 50670-901, Brazil, 55 8138795574, caroline.mota@ufpe.br %K multimorbidity %K prevalence %K health care %K public health %K Brazil %K logistic regression %D 2021 %7 25.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Multimorbidity is the co-occurrence of two or more chronic diseases. Objective: This study, based on self-reported medical diagnosis, aims to investigate the dynamic distribution of multimorbidity across sociodemographic levels and its impacts on health-related issues over 15 years in Brazil using national data. Methods: Data were analyzed using descriptive statistics, hypothesis tests, and logistic regression. The study sample comprised 679,572 adults (18-59 years of age) and 115,699 elderly people (≥60 years of age) from the two latest cross-sectional, multiple-cohort, national-based studies: the National Sample Household Survey (PNAD) of 1998, 2003, and 2008, and the Brazilian National Health Survey (PNS) of 2013. Results: Overall, the risk of multimorbidity in adults was 1.7 times higher in women (odds ratio [OR] 1.73, 95% CI 1.67-1.79) and 1.3 times higher among people without education (OR 1.34, 95% CI 1.28-1.41). Multiple chronic diseases considerably increased with age in Brazil, and people between 50 and 59 years old were about 12 times more likely to have multimorbidity than adults between 18 and 29 years of age (OR 11.89, 95% CI 11.27-12.55). Seniors with multimorbidity had more than twice the likelihood of receiving health assistance in community services or clinics (OR 2.16, 95% CI 2.02-2.31) and of being hospitalized (OR 2.37, 95% CI 2.21-2.56). The subjective well-being of adults with multimorbidity was often worse than people without multiple chronic diseases (OR=12.85, 95% CI: 12.07-13.68). These patterns were similar across all 4 cohorts analyzed and were relatively stable over 15 years. Conclusions: Our study shows little variation in the prevalence of the multimorbidity of chronic diseases in Brazil over time, but there are differences in the prevalence of multimorbidity across different social groups. It is hoped that the analysis of multimorbidity from the two latest Brazil national surveys will support policy making on epidemic prevention and management. %M 34842558 %R 10.2196/29693 %U https://publichealth.jmir.org/2021/11/e29693 %U https://doi.org/10.2196/29693 %U http://www.ncbi.nlm.nih.gov/pubmed/34842558 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e27880 %T Electronic Monitoring Systems for Hand Hygiene: Systematic Review of Technology %A Wang,Chaofan %A Jiang,Weiwei %A Yang,Kangning %A Yu,Difeng %A Newn,Joshua %A Sarsenbayeva,Zhanna %A Goncalves,Jorge %A Kostakos,Vassilis %+ School of Computing and Information Systems, The University of Melbourne, 700 Swanston Street, Carlton, 3053, Australia, 61 390358966, chaofanw@student.unimelb.edu.au %K hand hygiene %K hand hygiene compliance %K hand hygiene quality %K electronic monitoring systems %K systematic review %K mobile phone %D 2021 %7 24.11.2021 %9 Review %J J Med Internet Res %G English %X Background: Hand hygiene is one of the most effective ways of preventing health care–associated infections and reducing their transmission. Owing to recent advances in sensing technologies, electronic hand hygiene monitoring systems have been integrated into the daily routines of health care workers to measure their hand hygiene compliance and quality. Objective: This review aims to summarize the latest technologies adopted in electronic hand hygiene monitoring systems and discuss the capabilities and limitations of these systems. Methods: A systematic search of PubMed, ACM Digital Library, and IEEE Xplore Digital Library was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were initially screened and assessed independently by the 2 authors, and disagreements between them were further summarized and resolved by discussion with the senior author. Results: In total, 1035 publications were retrieved by the search queries; of the 1035 papers, 89 (8.60%) fulfilled the eligibility criteria and were retained for review. In summary, 73 studies used electronic monitoring systems to monitor hand hygiene compliance, including application-assisted direct observation (5/73, 7%), camera-assisted observation (10/73, 14%), sensor-assisted observation (29/73, 40%), and real-time locating system (32/73, 44%). A total of 21 studies evaluated hand hygiene quality, consisting of compliance with the World Health Organization 6-step hand hygiene techniques (14/21, 67%) and surface coverage or illumination reduction of fluorescent substances (7/21, 33%). Conclusions: Electronic hand hygiene monitoring systems face issues of accuracy, data integration, privacy and confidentiality, usability, associated costs, and infrastructure improvements. Moreover, this review found that standardized measurement tools to evaluate system performance are lacking; thus, future research is needed to establish standardized metrics to measure system performance differences among electronic hand hygiene monitoring systems. Furthermore, with sensing technologies and algorithms continually advancing, more research is needed on their implementation to improve system performance and address other hand hygiene–related issues. %M 34821565 %R 10.2196/27880 %U https://www.jmir.org/2021/11/e27880 %U https://doi.org/10.2196/27880 %U http://www.ncbi.nlm.nih.gov/pubmed/34821565 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e33576 %T Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study %A Leal-Neto,Onicio %A Egger,Thomas %A Schlegel,Matthias %A Flury,Domenica %A Sumer,Johannes %A Albrich,Werner %A Babouee Flury,Baharak %A Kuster,Stefan %A Vernazza,Pietro %A Kahlert,Christian %A Kohler,Philipp %+ Department of Economics, University of Zurich, Schönberggasse 1, Zurich, 8001, Switzerland, 41 783242116, onicio@gmail.com %K digital epidemiology %K SARS-CoV-2 %K COVID-19 %K health care workers %D 2021 %7 22.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis. Objective: The objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19. Methods: A prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children’s Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest. Results: From March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%). Conclusions: Loss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level—using machine learning–based random forest classification—reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19. %M 34727046 %R 10.2196/33576 %U https://publichealth.jmir.org/2021/11/e33576 %U https://doi.org/10.2196/33576 %U http://www.ncbi.nlm.nih.gov/pubmed/34727046 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 11 %P e32285 %T Seroepidemiological Survey on the Impact of Smoking on SARS-CoV-2 Infection and COVID-19 Outcomes: Protocol for the Troina Study %A Polosa,Riccardo %A Tomaselli,Venera %A Ferrara,Pietro %A Romeo,Alba Corina %A Rust,Sonja %A Saitta,Daniela %A Caraci,Filippo %A Romano,Corrado %A Thangaraju,Murugesan %A Zuccarello,Pietro %A Rose,Jed %A Cantone,Giulio Giacomo %A Ferrante,Margherita %A Belsey,Jonathan %A Cibella,Fabio %A Interlandi,Elisa %A Ferri,Raffaele %+ Institute of Internal Medicine, Azienda Ospedaliera Universitaria “Policlinico - V. Emanuele”, Via S Sofia, 78, Catania, 95123, Italy, 39 0953781566, polosa@unict.it %K antibody persistence %K cotinine %K COVID-19 %K SARS-CoV-2 %K seroprevalence %K smoking impact %K smoking status %D 2021 %7 22.11.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: After the global spread of SARS-CoV-2, research has highlighted several aspects of the pandemic, focusing on clinical features and risk factors associated with infection and disease severity. However, emerging results on the role of smoking in SARS-CoV-2 infection susceptibility or COVID-19 outcomes are conflicting, and their robustness remains uncertain. Objective: In this context, this study aims at quantifying the proportion of SARS-CoV-2 antibody seroprevalence, studying the changes in antibody levels over time, and analyzing the association between the biochemically verified smoking status and SARS-CoV-2 infection. Methods: The research design involves a 6-month prospective cohort study with a serial sampling of the same individuals. Each participant will be surveyed about their demographics and COVID-19–related information, and blood sampling will be collected upon recruitment and at specified follow-up time points (ie, after 8 and 24 weeks). Blood samples will be screened for the presence of SARS-CoV-2–specific antibodies and serum cotinine, being the latter of the principal metabolite of nicotine, which will be used to assess participants’ smoking status. Results: The study is ongoing. It aims to find a higher antibody prevalence in individuals at high risk for viral exposure (ie, health care personnel) and to refine current estimates on the association between smoking status and SARS-CoV-2/COVID-19. Conclusions: The added value of this research is that the current smoking status of the population to be studied will be biochemically verified to avoid the bias associated with self-reported smoking status. As such, the results from this survey may provide an actionable metric to study the role of smoking in SARS-CoV-2 infection and COVID-19 outcomes, and therefore to implement the most appropriate public health measures to control the pandemic. Results may also serve as a reference for future clinical research, and the methodology could be exploited in public health sectors and policies. International Registered Report Identifier (IRRID): DERR1-10.2196/32285 %M 34678752 %R 10.2196/32285 %U https://www.researchprotocols.org/2021/11/e32285 %U https://doi.org/10.2196/32285 %U http://www.ncbi.nlm.nih.gov/pubmed/34678752 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e33509 %T Central COVID-19 Coordination Centers in Germany: Description, Economic Evaluation, and Systematic Review %A Schopow,Nikolas %A Osterhoff,Georg %A von Dercks,Nikolaus %A Girrbach,Felix %A Josten,Christoph %A Stehr,Sebastian %A Hepp,Pierre %+ Department for Orthopedics, Trauma Surgery and Plastic Surgery, University Hospital Leipzig, Liebigstr. 20, Leipzig, 04103, Germany, 49 341 9717849, schopow@medizin.uni-leipzig.de %K telemedical consultation %K patient allocation %K algorithm-based treatment %K telemedicine %K telehealth %K consultation %K allocation %K algorithm %K treatment %K COVID-19 %K coordination %K Germany %K economic %K review %K establishment %K management %D 2021 %7 18.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: During the COVID-19 pandemic, Central COVID-19 Coordination Centers (CCCCs) have been established at several hospitals across Germany with the intention to assist local health care professionals in efficiently referring patients with suspected or confirmed SARS-CoV-2 infection to regional hospitals and therefore to prevent the collapse of local health system structures. In addition, these centers coordinate interhospital transfers of patients with COVID-19 and provide or arrange specialized telemedical consultations. Objective: This study describes the establishment and management of a CCCC at a German university hospital. Methods: We performed economic analyses (cost, cost-effectiveness, use, and utility) according to the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) criteria. Additionally, we conducted a systematic review to identify publications on similar institutions worldwide. The 2 months with the highest local incidence of COVID-19 cases (December 2020 and January 2021) were considered. Results: During this time, 17.3 requests per day were made to the CCCC regarding admission or transfer of patients with COVID-19. The majority of requests were made by emergency medical services (601/1068, 56.3%), patients with an average age of 71.8 (SD 17.2) years were involved, and for 737 of 1068 cases (69%), SARS-CoV-2 had already been detected by a positive polymerase chain reaction test. In 59.8% (639/1068) of the concerned patients, further treatment by a general practitioner or outpatient presentation in a hospital could be initiated after appropriate advice, 27.2% (291/1068) of patients were admitted to normal wards, and 12.9% (138/1068) were directly transmitted to an intensive care unit. The operating costs of the CCCC amounted to more than €52,000 (US $60,031) per month. Of the 334 patients with detected SARS-CoV-2 who were referred via EMS or outpatient physicians, 302 (90.4%) were triaged and announced in advance by the CCCC. No other published economic analysis of COVID-19 coordination or management institutions at hospitals could be found. Conclusions: Despite the high cost of the CCCC, we were able to show that it is a beneficial concept to both the providing hospital and the public health system. However, the most important benefits of the CCCC are that it prevents hospitals from being overrun by patients and that it avoids situations in which physicians must weigh one patient’s life against another’s. %M 34623955 %R 10.2196/33509 %U https://publichealth.jmir.org/2021/11/e33509 %U https://doi.org/10.2196/33509 %U http://www.ncbi.nlm.nih.gov/pubmed/34623955 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e25976 %T Long-Term Survival Among Histological Subtypes in Advanced Epithelial Ovarian Cancer: Population-Based Study Using the Surveillance, Epidemiology, and End Results Database %A Yang,Shi-Ping %A Su,Hui-Luan %A Chen,Xiu-Bei %A Hua,Li %A Chen,Jian-Xian %A Hu,Min %A Lei,Jian %A Wu,San-Gang %A Zhou,Juan %+ Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xiamen University, 55 Zhenhai Road, Xiamen, 361003, China, 86 5922139531, zhoujuan@xmu.edu.cn %K ovarian epithelial carcinoma %K survivors %K histology %K survival rate %K survival %K ovarian %K cancer %K surveillance %K epidemiology %K women’s health %K reproductive health %K Surveillance, Epidemiology, and End Results %K ovary %K oncology %K survivorship %K long-term outcome %K epithelial %D 2021 %7 17.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Actual long-term survival rates for advanced epithelial ovarian cancer (EOC) are rarely reported. Objective: This study aimed to assess the role of histological subtypes in predicting the prognosis among long-term survivors (≥5 years) of advanced EOC. Methods: We performed a retrospective analysis of data among patients with stage III-IV EOC diagnosed from 2000 to 2014 using the Surveillance, Epidemiology, and End Results cancer data of the United States. We used the chi-square test, Kaplan–Meier analysis, and multivariate Cox proportional hazards model for the analyses. Results: We included 8050 patients in this study, including 6929 (86.1%), 743 (9.2%), 237 (2.9%), and 141 (1.8%) patients with serous, endometrioid, clear cell, and mucinous tumors, respectively. With a median follow-up of 91 months, the most common cause of death was primary ovarian cancer (80.3%), followed by other cancers (8.1%), other causes of death (7.3%), cardiac-related death (3.2%), and nonmalignant pulmonary disease (3.2%). Patients with the serous subtype were more likely to die from primary ovarian cancer, and patients with the mucinous subtype were more likely to die from other cancers and cardiac-related disease. Multivariate Cox analysis showed that patients with endometrioid (hazard ratio [HR] 0.534, P<.001), mucinous (HR 0.454, P<.001), and clear cell (HR 0.563, P<.001) subtypes showed better ovarian cancer-specific survival than those with the serous subtype. Similar results were found regarding overall survival. However, ovarian cancer–specific survival and overall survival were comparable among those with endometrioid, clear cell, and mucinous tumors. Conclusions: Ovarian cancer remains the primary cause of death in long-term ovarian cancer survivors. Moreover, the probability of death was significantly different among those with different histological subtypes. It is important for clinicians to individualize the surveillance program for long-term ovarian cancer survivors. %M 34787583 %R 10.2196/25976 %U https://publichealth.jmir.org/2021/11/e25976 %U https://doi.org/10.2196/25976 %U http://www.ncbi.nlm.nih.gov/pubmed/34787583 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e30642 %T COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies %A Muric,Goran %A Wu,Yusong %A Ferrara,Emilio %+ Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA, 90292, United States, 1 213 740 2467, gmuric@isi.edu %K vaccine hesitancy %K COVID-19 vaccines %K dataset %K COVID-19 %K SARS-CoV-2 %K social media %K network analysis %K hesitancy %K vaccine %K Twitter %K misinformation %K conspiracy %K trust %K public health %K utilization %D 2021 %7 17.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data. Objective: In this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning. Methods: We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared. Results: We gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword–centered data collection with more than 1.8 million tweets, and (2) a historical account–level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility. Conclusions: The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy. %M 34653016 %R 10.2196/30642 %U https://publichealth.jmir.org/2021/11/e30642 %U https://doi.org/10.2196/30642 %U http://www.ncbi.nlm.nih.gov/pubmed/34653016 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e30968 %T Characterization of Unlinked Cases of COVID-19 and Implications for Contact Tracing Measures: Retrospective Analysis of Surveillance Data %A Chong,Ka Chun %A Jia,Katherine %A Lee,Shui Shan %A Hung,Chi Tim %A Wong,Ngai Sze %A Lai,Francisco Tsz Tsun %A Chau,Nancy %A Yam,Carrie Ho Kwan %A Chow,Tsz Yu %A Wei,Yuchen %A Guo,Zihao %A Yeoh,Eng Kiong %+ Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Central Avenue, Hong Kong, Hong Kong, 852 22528716, yeoh_ek@cuhk.edu.hk %K COVID-19 %K contact tracing %K unlinked %K superspreading %K dispersion %K surveillance %K monitoring %K digital health %K testing %K transmission %K epidemiology %K outbreak %K spread %D 2021 %7 16.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Contact tracing and intensive testing programs are essential for controlling the spread of COVID-19. However, conventional contact tracing is resource intensive and may not result in the tracing of all cases due to recall bias and cases not knowing the identity of some close contacts. Few studies have reported the epidemiological features of cases not identified by contact tracing (“unlinked cases”) or described their potential roles in seeding community outbreaks. Objective: For this study, we characterized the role of unlinked cases in the epidemic by comparing their epidemiological profile with the linked cases; we also estimated their transmission potential across different settings. Methods: We obtained rapid surveillance data from the government, which contained the line listing of COVID-19 confirmed cases during the first three waves in Hong Kong. We compared the demographics, history of chronic illnesses, epidemiological characteristics, clinical characteristics, and outcomes of linked and unlinked cases. Transmission potentials in different settings were assessed by fitting a negative binomial distribution to the observed offspring distribution. Results: Time interval from illness onset to hospital admission was longer among unlinked cases than linked cases (median 5.00 days versus 3.78 days; P<.001), with a higher proportion of cases whose condition was critical or serious (13.0% versus 8.2%; P<.001). The proportion of unlinked cases was associated with an increase in the weekly number of local cases (P=.049). Cluster transmissions from the unlinked cases were most frequently identified in household settings, followed by eateries and workplaces, with the estimated probability of cluster transmissions being around 0.4 for households and 0.1-0.3 for the latter two settings. Conclusions: The unlinked cases were positively associated with time to hospital admission, severity of infection, and epidemic size—implying a need to design and implement digital tracing methods to complement current conventional testing and tracing. To minimize the risk of cluster transmissions from unlinked cases, digital tracing approaches should be effectively applied in high-risk socioeconomic settings, and risk assessments should be conducted to review and adjust the policies. %M 34591778 %R 10.2196/30968 %U https://publichealth.jmir.org/2021/11/e30968 %U https://doi.org/10.2196/30968 %U http://www.ncbi.nlm.nih.gov/pubmed/34591778 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e31337 %T Predicting COVID-19–Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development Study %A Kasturi,Suranga N %A Park,Jeremy %A Wild,David %A Khan,Babar %A Haggstrom,David A %A Grannis,Shaun %+ Regenstrief Institute, 1101 W 10th St, Indianapolis, IN, 46202, United States, 1 (317) 274 9000, snkasthu@iu.edu %K COVID-19 %K machine learning %K population health %K health care utilization %K health disparities %K health information %K epidemiology %K public health %K digital health %K health data %K pandemic %K decision models %K health informatics %K healthcare resources %D 2021 %7 15.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. Objective: This study aims to inform the feasibility of leveraging broad, statewide datasets for population health–driven decision-making by developing robust analytical models that predict COVID-19–related health care resource utilization across patients served by Indiana’s statewide Health Information Exchange. Methods: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). Results: For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). Conclusions: This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them. %M 34581671 %R 10.2196/31337 %U https://www.jmir.org/2021/11/e31337 %U https://doi.org/10.2196/31337 %U http://www.ncbi.nlm.nih.gov/pubmed/34581671 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e29504 %T Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study %A Murtas,Rossella %A Morici,Nuccia %A Cogliati,Chiara %A Puoti,Massimo %A Omazzi,Barbara %A Bergamaschi,Walter %A Voza,Antonio %A Rovere Querini,Patrizia %A Stefanini,Giulio %A Manfredi,Maria Grazia %A Zocchi,Maria Teresa %A Mangiagalli,Andrea %A Brambilla,Carla Vittoria %A Bosio,Marco %A Corradin,Matteo %A Cortellaro,Francesca %A Trivelli,Marco %A Savonitto,Stefano %A Russo,Antonio Giampiero %+ Epidemiology Unit, Agency for the Protection of Health of the Metropolitan Area of Milan, Via Conca del Naviglio 45, Milan, 20123, Italy, 39 0285782111, agrusso@ats-milano.it %K COVID-19 %K severe outcome %K prediction %K monitoring system %K symptoms %K risk prediction %K risk %K algorithms %K prediction models %K pandemic %K digital data %K health records %D 2021 %7 15.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. Objective: This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. Methods: A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. Results: The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. Conclusions: A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic. %M 34543227 %R 10.2196/29504 %U https://publichealth.jmir.org/2021/11/e29504 %U https://doi.org/10.2196/29504 %U http://www.ncbi.nlm.nih.gov/pubmed/34543227 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e25897 %T Online Newspaper Reports on Ambulance Accidents in Austria, Germany, and Switzerland: Retrospective Cross-sectional Review %A Boldt,Johanna %A Steinfort,Femke %A Müller,Martin %A Exadaktylos,Aristomenis K %A Klukowska-Roetzler,Jolanta %+ Department of Emergency Medicine, Inselspital, Bern University Hospital, Bern University, Freiburgerstrasse 16C, Berne, 3010, Switzerland, 41 31 632 33 96, jolanta.klukowska-roetzler@insel.ch %K ambulance accidents %K ambulance collisions %K ambulance crashes %K media-based %K media-based review %K newspaper review %K Austria %K Germany %K Switzerland %K German-speaking European countries %K retrospective %K cross-sectional %K review %K ambulance %K accident %K data %K media %K newspaper %D 2021 %7 12.11.2021 %9 Review %J JMIR Public Health Surveill %G English %X Background: Ambulance accidents are an unfortunate indirect result of ambulance emergency calls, which create hazardous environments for personnel, patients, and bystanders. However, in European German-speaking countries, factors contributing to ambulance accidents have not been optimally researched and analyzed. Objective: The objective of this study was to extract, analyze, and compare data from online newspaper articles on ambulance accidents for Austria, Germany, and Switzerland. We hope to highlight future strategies to offset the deficit in research data and official registers for prevention of ambulance and emergency vehicle accidents. Methods: Ambulance accident data were collected from Austrian, German, and Swiss free web-based daily newspapers, as listed in Wikipedia, for the period between January 2014 and January 2019. All included newspapers were searched for articles reporting ambulance accidents using German terms representing “ambulance” and “ambulance accident.” Characteristics of the accidents were compiled and analyzed. Only ground ambulance accidents were covered. Results: In Germany, a total of 597 ambulance accidents were recorded, corresponding to 0.719 (95% CI 0.663-0.779) per 100,000 inhabitants; 453 of these accidents left 1170 people injured, corresponding to 1.409 (95% CI 1.330-1.492) per 100,000 inhabitants, and 28 of these accidents caused 31 fatalities, corresponding to 0.037 (95% CI 0.025-0.053) per 100,000 inhabitants. In Austria, a total of 62 ambulance accidents were recorded, corresponding to 0.698 (95% CI 0.535-0.894) per 100,000 inhabitants; 47 of these accidents left 115 people injured, corresponding to 1.294 (95% CI 1.068-1.553) per 100,000 inhabitants, and 6 of these accidents caused 7 fatalities, corresponding to 0.079 (95% CI 0.032-0.162) per 100,000 inhabitants. In Switzerland, a total of 25 ambulance accidents were recorded, corresponding to 0.293 (95% CI 0.189-0.432) per 100,000 inhabitants; 11 of these accidents left 18 people injured, corresponding to 0.211(95% CI 0.113-0.308) per 100,000 inhabitants. There were no fatalities. In each of the three countries, the majority of the accidents involved another car (77%-81%). In Germany and Switzerland, most accidents occurred at an intersection. In Germany, Austria, and Switzerland, 38.7%, 26%, and 4%, respectively, of ambulance accidents occurred at intersections for which the ambulance had a red light (P<.001). In all three countries, most of the casualties were staff and not uncommonly a third party. Most accidents took place on weekdays and during the daytime. Ambulance accidents were evenly distributed across the four seasons. The direction of travel was reported in 28%-37% of the accidents and the patient was in the ambulance approximately 50% of the time in all countries. The cause of the ambulance accidents was reported to be the ambulance itself in 125 (48.1% of accidents where the cause was reported), 22 (42%), and 8 (40%) accidents in Germany, Austria, and Switzerland, respectively (P=.02), and another vehicle in 118 (45.4%), 29 (56%), and 9 (45%) accidents, respectively (P<.001). A total of 292 accidents occurred while blue lights and sirens were used, which caused 3 deaths and 577 injuries. Conclusions: This study draws attention to much needed auxiliary sources of data that may allow for creation of a contemporary registry of all ambulance accidents in Austria, Germany, and Switzerland. To improve risk management and set European standards, it should be mandatory to collect standardized goal-directed and representative information using various sources (including the wide range presented by the press and social media), which should then be made available for audit, analysis, and research. %M 34766915 %R 10.2196/25897 %U https://publichealth.jmir.org/2021/11/e25897 %U https://doi.org/10.2196/25897 %U http://www.ncbi.nlm.nih.gov/pubmed/34766915 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e30462 %T The Influence of Normative Perceptions on the Uptake of the COVID-19 TraceTogether Digital Contact Tracing System: Cross-sectional Study %A Lee,Jeong Kyu %A Lin,Lavinia %A Kang,Hyunjin %+ Saw Swee Hock School of Public Health, National University of Singapore, Tahir Foundation Building, 12 Science Drive 2, 10-01, Singapore, 117549, Singapore, 65 66015838, lee.jeongkyu@gmail.com %K COVID-19 %K social norms %K TraceTogether %K Singapore %K contact tracing %K mobile app %K token %D 2021 %7 12.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In 2020, the Singapore government rolled out the TraceTogether program, a digital system to facilitate contact tracing efforts in response to the COVID-19 pandemic. This system is available as a smartphone app and Bluetooth-enabled token to help identify close contacts. As of February 1, 2021, more than 80% of the population has either downloaded the mobile app or received the token in Singapore. Despite the high adoption rate of the TraceTogether mobile app and token (ie, device), it is crucial to understand the role of social and normative perceptions in uptake and usage by the public, given the collective efforts for contact tracing. Objective: This study aimed to examine normative influences (descriptive and injunctive norms) on TraceTogether device use for contact tracing purposes, informed by the theory of normative social behavior, a theoretical framework to explain how perceived social norms are related to behaviors. Methods: From January to February 2021, cross-sectional data were collected by a local research company through emailing their panel members who were (1) Singapore citizens or permanent residents aged 21 years or above; (2) able to read English; and (3) internet users with access to a personal email account. The study sample (n=1137) was restricted to those who had either downloaded the TraceTogether mobile app or received the token. Results: Multivariate (linear and ordinal logistic) regression analyses were carried out to assess the relationships of the behavioral outcome variables (TraceTogether device usage and intention of TraceTogether device usage) with potential correlates, including perceived social norms, perceived community, and interpersonal communication. Multivariate regression analyses indicated that descriptive norms (unstandardized regression coefficient β=0.31, SE=0.05; P<.001) and injunctive norms (unstandardized regression coefficient β=0.16, SE=0.04; P<.001) were significantly positively associated with the intention to use the TraceTogether device. It was also found that descriptive norms were a significant correlate of TraceTogether device use frequency (adjusted odds ratio [aOR] 2.08, 95% CI 1.66-2.61; P<.001). Though not significantly related to TraceTogether device use frequency, injunctive norms moderated the relationship between descriptive norms and the outcome variable (aOR 1.12, 95% CI 1.03-1.21; P=.005). Conclusions: This study provides useful implications for the design of effective intervention strategies to promote the uptake and usage of digital methods for contact tracing in a multiethnic Asian population. Our findings highlight that influence from social networks plays an important role in developing normative perceptions in relation to TraceTogether device use for contact tracing. To promote the uptake of the TraceTogether device and other preventive behaviors for COVID-19, it would be useful to devise norm-based interventions that address these normative perceptions by presenting high prevalence and approval of important social referents, such as family and close friends. %M 34623956 %R 10.2196/30462 %U https://publichealth.jmir.org/2021/11/e30462 %U https://doi.org/10.2196/30462 %U http://www.ncbi.nlm.nih.gov/pubmed/34623956 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e29020 %T Population Health Surveillance Using Mobile Phone Surveys in Low- and Middle-Income Countries: Methodology and Sample Representativeness of a Cross-sectional Survey of Live Poultry Exposure in Bangladesh %A Berry,Isha %A Mangtani,Punam %A Rahman,Mahbubur %A Khan,Iqbal Ansary %A Sarkar,Sudipta %A Naureen,Tanzila %A Greer,Amy L %A Morris,Shaun K %A Fisman,David N %A Flora,Meerjady Sabrina %+ Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada, 1 416 978 0901, isha.berry@mail.utoronto.ca %K mobile telephone survey %K health surveillance %K survey methodology %K Bangladesh %D 2021 %7 12.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Population-based health surveys are typically conducted using face-to-face household interviews in low- and middle-income countries (LMICs). However, telephone-based surveys are cheaper, faster, and can provide greater access to hard-to-reach or remote populations. The rapid growth in mobile phone ownership in LMICs provides a unique opportunity to implement novel data collection methods for population health surveys. Objective: This study aims to describe the development and population representativeness of a mobile phone survey measuring live poultry exposure in urban Bangladesh. Methods: A population-based, cross-sectional, mobile phone survey was conducted between September and November 2019 in North and South Dhaka City Corporations (DCC), Bangladesh, to measure live poultry exposure using a stratified probability sampling design. Data were collected using a computer-assisted telephone interview platform. The call operational data were summarized, and the participant data were weighted by age, sex, and education to the 2011 census. The demographic distribution of the weighted sample was compared with external sources to assess population representativeness. Results: A total of 5486 unique mobile phone numbers were dialed, with 1047 respondents completing the survey. The survey had an overall response rate of 52.2% (1047/2006) and a co-operation rate of 89.0% (1047/1176). Initial results comparing the sociodemographic profile of the survey sample to the census population showed that mobile phone sampling slightly underrepresented older individuals and overrepresented those with higher secondary education. After weighting, the demographic profile of the sample population matched well with the latest DCC census population profile. Conclusions: Probability-based mobile phone survey sampling and data collection methods produced a population-representative sample with minimal adjustment in DCC, Bangladesh. Mobile phone–based surveys can offer an efficient, economic, and robust way to conduct surveillance for population health outcomes, which has important implications for improving population health surveillance in LMICs. %M 34766914 %R 10.2196/29020 %U https://publichealth.jmir.org/2021/11/e29020 %U https://doi.org/10.2196/29020 %U http://www.ncbi.nlm.nih.gov/pubmed/34766914 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e28951 %T Validation of a Mobile Health Technology Platform (FeverTracker) for Malaria Surveillance in India: Development and Usability Study %A Pal Bhowmick,Ipsita %A Chutia,Dibyajyoti %A Chouhan,Avinash %A Nishant,Nilay %A Raju,P L N %A Narain,Kanwar %A Kaur,Harpreet %A Pebam,Rocky %A Debnath,Jayanta %A Tripura,Rabindra %A Gogoi,Kongkona %A Ch Nag,Suman %A Nath,Aatreyee %A Tripathy,Debabrata %A Debbarma,Jotish %A Das,Nirapada %A Sarkar,Ujjwal %A Debbarma,Rislyn %A Roy,Rajashree %A Debnath,Bishal %A Dasgupta,Dipanjan %A Debbarma,Suraj %A Joy Tripura,Kamal %A Reang,Guneram %A Sharma,Amit %A Rahi,Manju %A Chhibber-Goel,Jyoti %+ International Centre for Genetic Engineering and Biotechnology, Vasant Kunj, New Delhi, 110067, India, 91 011 26741358 ext 170, jyotichhibbergoel@gmail.com %K fever %K health system %K mHealth app %K malaria %K surveillance %K mobile phone %D 2021 %7 10.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: A surveillance system is the foundation for disease prevention and control. Malaria surveillance is crucial for tracking regional and temporal patterns in disease incidence, assisting in recorded details, timely reporting, and frequency of analysis. Objective: In this study, we aim to develop an integrated surveillance graphical app called FeverTracker, which has been designed to assist the community and health care workers in digital surveillance and thereby contribute toward malaria control and elimination. Methods: FeverTracker uses a geographic information system and is linked to a web app with automated data digitization, SMS text messaging, and advisory instructions, thereby allowing immediate notification of individual cases to district and state health authorities in real time. Results: The use of FeverTracker for malaria surveillance is evident, given the archaic paper-based surveillance tools used currently. The use of the app in 19 tribal villages of the Dhalai district in Tripura, India, assisted in the surveillance of 1880 suspected malaria patients and confirmed malaria infection in 93.4% (114/122; Plasmodium falciparum), 4.9% (6/122; P vivax), and 1.6% (2/122; P falciparum/P vivax mixed infection) of cases. Digital tools such as FeverTracker will be critical in integrating disease surveillance, and they offer instant data digitization for downstream processing. Conclusions: The use of this technology in health care and research will strengthen the ongoing efforts to eliminate malaria. Moreover, FeverTracker provides a modifiable template for deployment in other disease systems. %M 34757321 %R 10.2196/28951 %U https://formative.jmir.org/2021/11/e28951 %U https://doi.org/10.2196/28951 %U http://www.ncbi.nlm.nih.gov/pubmed/34757321 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e26523 %T Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study %A Cawley,Caoimhe %A Bergey,François %A Mehl,Alicia %A Finckh,Ashlee %A Gilsdorf,Andreas %+ Ada Health GmbH, Karl-Liebknecht Strasse 1, Berlin, 10178, Germany, 49 17680765335, caoimhecawley@gmail.com %K ILI %K influenza %K syndromic surveillance %K participatory surveillance %K digital surveillance %K mobile phone %D 2021 %7 4.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Participatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms. Objective: The objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. Methods: We extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient. Results: In total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation—2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years. Conclusions: With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems. %M 34734836 %R 10.2196/26523 %U https://publichealth.jmir.org/2021/11/e26523 %U https://doi.org/10.2196/26523 %U http://www.ncbi.nlm.nih.gov/pubmed/34734836 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e32639 %T Public Health Surveillance Systems in the Eastern Mediterranean Region: Bibliometric Analysis of Scientific Literature %A Saad,Randa K %A Al Nsour,Mohannad %A Khader,Yousef %A Al Gunaid,Magid %+ Global Health Development|Eastern Mediterranean Public Health Network, Abdallah Ben Abbas St, Building No. 42, Amman, Jordan, 962 781665060, randaksaad@gmail.com %K public health %K surveillance %K Eastern Mediterranean Region %K bibliometric analysis %K literature %K research %K review %D 2021 %7 1.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Eastern Mediterranean Region (EMR) hosts some of the world’s worst humanitarian and health crises. The implementation of health surveillance in this region has faced multiple constraints. New and novel approaches in surveillance are in a constant state of high and immediate demand. Identifying the existing literature on surveillance helps foster an understanding of scientific development and thus potentially supports future development directions. Objective: This study aims to illustrate the scientific production, quantify the scholarly impact, and highlight the characteristics of publications on public health surveillance in the EMR over the past decade. Methods: We performed a Scopus search using keywords related to public health surveillance or its disciplines, cross-referenced with EMR countries, from 2011 to July 2021. Data were exported and analyzed using Microsoft Excel and Visualization of Similarities Viewer. Quality of journals was determined using SCImago Journal Rank and CiteScore. Results: We retrieved 1987 documents, of which 1927 (96.98%) were articles or reviews. There has been an incremental increase in the number of publications (exponential growth, R2=0.80) over the past decade. Publications were mostly affiliated with Iran (501/1987, 25.21%), the United States (468/1987, 23.55%), Pakistan (243/1987, 12.23%), Egypt (224/1987, 11.27%), and Saudi Arabia (209/1987, 10.52%). However, Iran only had links with 40 other countries (total link strength 164), and the biggest collaborator from the EMR was Egypt, with 67 links (total link strength 402). Within the other EMR countries, only Morocco, Lebanon, and Jordan produced ≥79 publications in the 10-year period. Most publications (1551/1987, 78.06%) were affiliated with EMR universities. Most journals were categorized as medical journals, and the highest number of articles were published in the Eastern Mediterranean Health Journal (SCImago Journal Rank 0.442; CiteScore 1.5). Retrieved documents had an average of 18.4 (SD 125.5) citations per document and an h-index of 66. The top-3 most cited documents were from the Global Burden of Diseases study. We found 70 high-frequency terms, occurring ≥10 times in author keywords, connected in 3 clusters. COVID-19, SARS-CoV-2, and pandemic represented the most recent 2020 cluster. Conclusions: This is the first research study to quantify the published literature on public health surveillance and its disciplines in the EMR. Research productivity has steadily increased over the past decade, and Iran has been the leading country publishing relevant research. Recurrent recent surveillance themes included COVID-19 and SARS-CoV-2. This study also sheds light on the gaps in surveillance research in the EMR, including inadequate publications on noncommunicable diseases and injury-related surveillance. %M 34723831 %R 10.2196/32639 %U https://publichealth.jmir.org/2021/11/e32639 %U https://doi.org/10.2196/32639 %U http://www.ncbi.nlm.nih.gov/pubmed/34723831 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 10 %P e29954 %T Implementation of the World Health Organization Global Antimicrobial Resistance Surveillance System in Uganda, 2015-2020: Mixed-Methods Study Using National Surveillance Data %A Nabadda,Susan %A Kakooza,Francis %A Kiggundu,Reuben %A Walwema,Richard %A Bazira,Joel %A Mayito,Jonathan %A Mugerwa,Ibrahimm %A Sekamatte,Musa %A Kambugu,Andrew %A Lamorde,Mohammed %A Kajumbula,Henry %A Mwebasa,Henry %+ Global Health Security Department, Infectious Diseases Institute, Makerere University College of Health Sciences, Hall Lane, Makerere University-Main Campus, Kampala, PO Box 22418, Uganda, 256 772437100, francis.kakooza1@gmail.com %K antimicrobial resistance %K surveillance %K microbiology %K laboratory %K Uganda %K implementation %K WHO %K collection %K analysis %K data %K antimicrobial %K progress %K bacteria %K feasibility %K resistance %K antibiotic %D 2021 %7 21.10.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Antimicrobial resistance (AMR) is an emerging public health crisis in Uganda. The World Health Organization (WHO) Global Action Plan recommends that countries should develop and implement National Action Plans for AMR. We describe the establishment of the national AMR program in Uganda and present the early microbial sensitivity results from the program. Objective: The aim of this study is to describe a national surveillance program that was developed to perform the systematic and continuous collection, analysis, and interpretation of AMR data. Methods: A systematic qualitative description of the process and progress made in the establishment of the national AMR program is provided, detailing the progress made from 2015 to 2020. This is followed by a report of the findings of the isolates that were collected from AMR surveillance sites. Identification and antimicrobial susceptibility testing (AST) of the bacterial isolates were performed using standard methods at both the surveillance sites and the reference laboratory. Results: Remarkable progress has been achieved in the establishment of the national AMR program, which is guided by the WHO Global Laboratory AMR Surveillance System (GLASS) in Uganda. A functional national coordinating center for AMR has been established with a supporting designated reference laboratory. WHONET software for AMR data management has been installed in the surveillance sites and laboratory staff trained on data quality assurance. Uganda has progressively submitted data to the WHO GLASS reporting system. Of the 19,216 isolates from WHO GLASS priority specimens collected from October 2015 to June 2020, 22.95% (n=4411) had community-acquired infections, 9.46% (n=1818) had hospital-acquired infections, and 68.57% (n=12,987) had infections of unknown origin. The highest proportion of the specimens was blood (12,398/19,216, 64.52%), followed by urine (5278/19,216, 27.47%) and stool (1266/19,216, 6.59%), whereas the lowest proportion was urogenital swabs (274/19,216, 1.4%). The mean age was 19.1 (SD 19.8 years), whereas the median age was 13 years (IQR 28). Approximately 49.13% (9440/19,216) of the participants were female and 50.51% (9706/19,216) were male. Participants with community-acquired infections were older (mean age 28, SD 18.6 years; median age 26, IQR 20.5 years) than those with hospital-acquired infections (mean age 17.3, SD 20.9 years; median age 8, IQR 26 years). All gram-negative (Escherichia coli, Klebsiella pneumoniae, and Neisseria gonorrhoeae) and gram-positive (Staphylococcus aureus and Enterococcus sp) bacteria with AST showed resistance to each of the tested antibiotics. Conclusions: Uganda is the first African country to implement a structured national AMR surveillance program in alignment with the WHO GLASS. The reported AST data indicate very high resistance to the recommended and prescribed antibiotics for treatment of infections. More effort is required regarding quality assurance of laboratory testing methodologies to ensure optimal adherence to WHO GLASS–recommended pathogen-antimicrobial combinations. The current AMR data will inform the development of treatment algorithms and clinical guidelines. %M 34673531 %R 10.2196/29954 %U https://publichealth.jmir.org/2021/10/e29954 %U https://doi.org/10.2196/29954 %U http://www.ncbi.nlm.nih.gov/pubmed/34673531 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 10 %P e30824 %T Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study %A Gwon,Hansle %A Ahn,Imjin %A Kim,Yunha %A Kang,Hee Jun %A Seo,Hyeram %A Cho,Ha Na %A Choi,Heejung %A Jun,Tae Joon %A Kim,Young-Hak %+ Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, 82 2 3010 0955, mdyhkim@amc.seoul.kr %K self-training %K artificial intelligence %K electronic medical records %K imputation %D 2021 %7 13.10.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imputations by chained equations (MICE) as well as machine learning methods such as multilayer perceptron, k-nearest neighbor, and decision tree. Objective: The objective of this study was to impute numeric medical data such as physical data and laboratory data. We aimed to effectively impute data using a progressive method called self-training in the medical field where training data are scarce. Methods: In this paper, we propose a self-training method that gradually increases the available data. Models trained with complete data predict the missing values in incomplete data. Among the incomplete data, the data in which the missing value is validly predicted are incorporated into the complete data. Using the predicted value as the actual value is called pseudolabeling. This process is repeated until the condition is satisfied. The most important part of this process is how to evaluate the accuracy of pseudolabels. They can be evaluated by observing the effect of the pseudolabeled data on the performance of the model. Results: In self-training using random forest (RF), mean squared error was up to 12% lower than pure RF, and the Pearson correlation coefficient was 0.1% higher. This difference was confirmed statistically. In the Friedman test performed on MICE and RF, self-training showed a P value between .003 and .02. A Wilcoxon signed-rank test performed on the mean imputation showed the lowest possible P value, 3.05e-5, in all situations. Conclusions: Self-training showed significant results in comparing the predicted values and actual values, but it needs to be verified in an actual machine learning system. And self-training has the potential to improve performance according to the pseudolabel evaluation method, which will be the main subject of our future research. %M 34643539 %R 10.2196/30824 %U https://publichealth.jmir.org/2021/10/e30824 %U https://doi.org/10.2196/30824 %U http://www.ncbi.nlm.nih.gov/pubmed/34643539 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 10 %P e31161 %T Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study %A Bos,Véronique L L C %A Jansen,Tessa %A Klazinga,Niek S %A Kringos,Dionne S %+ Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, 1105 AZ, Netherlands, 31 020 5661449, v.l.bos@amsterdamumc.nl %K COVID-19 %K dashboard %K performance intelligence %K Netherlands %K actionability %K communication %K government %K pandemic %K public health %D 2021 %7 12.10.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Web-based public reporting by means of dashboards has become an essential tool for governments worldwide to monitor COVID-19 information and communicate it to the public. The actionability of such dashboards is determined by their fitness for purpose—meeting a specific information need—and fitness for use—placing the right information into the right hands at the right time and in a manner that can be understood. Objective: The aim of this study was to identify specific areas where the actionability of the Dutch government’s COVID-19 dashboard could be improved, with the ultimate goal of enhancing public understanding of the pandemic. Methods: The study was conducted from February 2020 to April 2021. A mixed methods approach was carried out, using (1) a descriptive checklist over time to monitor changes made to the dashboard, (2) an actionability scoring of the dashboard to pinpoint areas for improvement, and (3) a reflection meeting with the dashboard development team to contextualize findings and discuss areas for improvement. Results: The dashboard predominantly showed epidemiological information on COVID-19. It had been developed and adapted by adding more in-depth indicators, more geographic disaggregation options, and new indicator themes. It also changed in target audience from policy makers to the general public; thus, a homepage was added with the most important information, using news-like items to explain the provided indicators and conducting research to enhance public understanding of the dashboard. However, disaggregation options such as sex, socioeconomic status, and ethnicity and indicators on dual-track health system management and social and economic impact that have proven to give important insights in other countries are missing from the Dutch COVID-19 dashboard, limiting its actionability. Conclusions: The Dutch COVID-19 dashboard developed over time its fitness for purpose and use in terms of providing epidemiological information to the general public as a target audience. However, to strengthen the Dutch health system’s ability to cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, we advise (1) establishing timely indicators relating to health system capacity, (2) including relevant data disaggregation options (eg, sex, socioeconomic status), and (3) enabling interoperability between social, health, and economic data sources. %M 34543229 %R 10.2196/31161 %U https://publichealth.jmir.org/2021/10/e31161 %U https://doi.org/10.2196/31161 %U http://www.ncbi.nlm.nih.gov/pubmed/34543229 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e31400 %T International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study %A Weber,Griffin M %A Zhang,Harrison G %A L'Yi,Sehi %A Bonzel,Clara-Lea %A Hong,Chuan %A Avillach,Paul %A Gutiérrez-Sacristán,Alba %A Palmer,Nathan P %A Tan,Amelia Li Min %A Wang,Xuan %A Yuan,William %A Gehlenborg,Nils %A Alloni,Anna %A Amendola,Danilo F %A Bellasi,Antonio %A Bellazzi,Riccardo %A Beraghi,Michele %A Bucalo,Mauro %A Chiovato,Luca %A Cho,Kelly %A Dagliati,Arianna %A Estiri,Hossein %A Follett,Robert W %A García Barrio,Noelia %A Hanauer,David A %A Henderson,Darren W %A Ho,Yuk-Lam %A Holmes,John H %A Hutch,Meghan R %A Kavuluru,Ramakanth %A Kirchoff,Katie %A Klann,Jeffrey G %A Krishnamurthy,Ashok K %A Le,Trang T %A Liu,Molei %A Loh,Ne Hooi Will %A Lozano-Zahonero,Sara %A Luo,Yuan %A Maidlow,Sarah %A Makoudjou,Adeline %A Malovini,Alberto %A Martins,Marcelo Roberto %A Moal,Bertrand %A Morris,Michele %A Mowery,Danielle L %A Murphy,Shawn N %A Neuraz,Antoine %A Ngiam,Kee Yuan %A Okoshi,Marina P %A Omenn,Gilbert S %A Patel,Lav P %A Pedrera Jiménez,Miguel %A Prudente,Robson A %A Samayamuthu,Malarkodi Jebathilagam %A Sanz Vidorreta,Fernando J %A Schriver,Emily R %A Schubert,Petra %A Serrano Balazote,Pablo %A Tan,Byorn WL %A Tanni,Suzana E %A Tibollo,Valentina %A Visweswaran,Shyam %A Wagholikar,Kavishwar B %A Xia,Zongqi %A Zöller,Daniela %A , %A Kohane,Isaac S %A Cai,Tianxi %A South,Andrew M %A Brat,Gabriel A %+ Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA, 02115, United States, 1 6175488520, tcai@hsph.harvard.edu %K SARS-CoV-2 %K electronic health records %K federated study %K retrospective cohort study %K meta-analysis %K COVID-19 %K severe COVID-19 %K laboratory trajectory %D 2021 %7 11.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve. %M 34533459 %R 10.2196/31400 %U https://www.jmir.org/2021/10/e31400 %U https://doi.org/10.2196/31400 %U http://www.ncbi.nlm.nih.gov/pubmed/34533459 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 10 %P e32559 %T Excess Mortality During the COVID-19 Pandemic in Jordan: Secondary Data Analysis %A Khader,Yousef %A Al Nsour,Mohannad %+ Department of Public Health, Faculty of Medicine, Jordan University of Science and Technology, Alramtha-Amman Street, Irbid, 22110, Jordan, 962 796802040, yskhader@just.edu.jo %K COVID-19 %K excess mortality %K pandemic %D 2021 %7 7.10.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: All-cause mortality and estimates of excess deaths are commonly used in different countries to estimate the burden of COVID-19 and assess its direct and indirect effects. Objective: This study aimed to analyze the excess mortality during the COVID-19 pandemic in Jordan in April-December 2020. Methods: Official data on deaths in Jordan for 2020 and previous years (2016-2019) were obtained from the Department of Civil Status. We contrasted mortality rates in 2020 with those in each year and the pooled period 2016-2020 using a standardized mortality ratio (SMR) measure. Expected deaths for 2020 were estimated by fitting the overdispersed Poisson generalized linear models to the monthly death counts for the period of 2016-2019. Results: Overall, a 21% increase in standardized mortality (SMR 1.21, 95% CI 1.19-1.22) occurred in April-December 2020 compared with the April-December months in the pooled period 2016-2019. The SMR was more pronounced for men than for women (SMR 1.26, 95% CI 1.24-1.29 vs SMR 1.12, 95% CI 1.10-1.14), and it was statistically significant for both genders (P<.05). Using overdispersed Poisson generalized linear models, the number of expected deaths in April-December 2020 was 12,845 (7957 for women and 4888 for men). The total number of excess deaths during this period was estimated at 4583 (95% CI 4451-4716), with higher excess deaths in men (3112, 95% CI 3003-3221) than in women (1503, 95% CI 1427-1579). Almost 83.66% of excess deaths were attributed to COVID-19 in the Ministry of Health database. The vast majority of excess deaths occurred in people aged 60 years or older. Conclusions: The reported COVID-19 death counts underestimated mortality attributable to COVID-19. Excess deaths could reflect the increased deaths secondary to the pandemic and its containment measures. The majority of excess deaths occurred among old age groups. It is, therefore, important to maintain essential services for the elderly during pandemics. %M 34617910 %R 10.2196/32559 %U https://publichealth.jmir.org/2021/10/e32559 %U https://doi.org/10.2196/32559 %U http://www.ncbi.nlm.nih.gov/pubmed/34617910 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 10 %P e32468 %T Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review %A Thomas Craig,Kelly Jean %A Rizvi,Rubina %A Willis,Van C %A Kassler,William J %A Jackson,Gretchen Purcell %+ Center for AI, Research, and Evaluation, IBM Watson Health, IBM Corporation, 75 Binney Street, Cambridge, MA, 02142, United States, 1 9702613366, kelly.jean.craig@ibm.com %K contact tracing %K non-pharmaceutical interventions %K pandemic %K epidemic %K viral disease %K COVID-19 %K isolation %K testing %K surveillance %K monitoring %K review %K intervention %K effectiveness %K mitigation %K transmission %K spread %K protection %K outcome %D 2021 %7 6.10.2021 %9 Review %J JMIR Public Health Surveill %G English %X Background: Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown. Objective: This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature. Methods: An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality. Results: Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread. Conclusions: Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality. %M 34612841 %R 10.2196/32468 %U https://publichealth.jmir.org/2021/10/e32468 %U https://doi.org/10.2196/32468 %U http://www.ncbi.nlm.nih.gov/pubmed/34612841 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 10 %P e30444 %T Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study %A De Ridder,David %A Loizeau,Andrea Jutta %A Sandoval,José Luis %A Ehrler,Frédéric %A Perrier,Myriam %A Ritch,Albert %A Violot,Guillemette %A Santolini,Marc %A Greshake Tzovaras,Bastian %A Stringhini,Silvia %A Kaiser,Laurent %A Pradeau,Jean-François %A Joost,Stéphane %A Guessous,Idris %+ Division of Primary Care, Geneva University Hospitals, 4 Rue Gabrielle-Perret-Gentil, Geneva, 1205, Switzerland, 41 22 305 58 61, Idris.Guessous@hcuge.ch %K participatory surveillance %K infectious disease %K COVID-19 %K SARS-CoV-2 %K space-time clustering %K digital health %K mobile app %K mHealth %K epidemiology %K surveillance %K digital surveillance %K public health %D 2021 %7 6.10.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The early detection of clusters of infectious diseases such as the SARS-CoV-2–related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic. Objective: The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19–associated symptoms, and to examine (a posteriori) the association between the clusters’ characteristics and sociodemographic and environmental determinants. Methods: This report presents the methodology and development of the @choum (English: “achoo”) study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19–associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19–associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm. Results: The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool’s user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool’s launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022. Conclusions: The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19–associated symptoms. @choum collects precise geographic information while protecting the user’s privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing. International Registered Report Identifier (IRRID): DERR1-10.2196/30444 %M 34449403 %R 10.2196/30444 %U https://www.researchprotocols.org/2021/10/e30444 %U https://doi.org/10.2196/30444 %U http://www.ncbi.nlm.nih.gov/pubmed/34449403 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 9 %P e29544 %T Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach %A Wong,Kenneth Chi-Yin %A Xiang,Yong %A Yin,Liangying %A So,Hon-Cheong %+ School of Biomedical Sciences, The Chinese University of Hong Kong, RM 520A, Lo Kwee Seong Biomedical Sciences Buildiing, Chinese University of Hong Kong, Hong Kong, China, 86 39439255, hcso@cuhk.edu.hk %K prediction %K COVID-19 %K risk factors %K machine learning %K pandemic %K biobank %K public health %K prediction models %K medical informatics %D 2021 %7 30.9.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance. Objective: Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved. Methods: We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes. Results: A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM. Conclusions: We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings. %M 34591027 %R 10.2196/29544 %U https://publichealth.jmir.org/2021/9/e29544 %U https://doi.org/10.2196/29544 %U http://www.ncbi.nlm.nih.gov/pubmed/34591027 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e30157 %T COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation %A Sankaranarayanan,Saranya %A Balan,Jagadheshwar %A Walsh,Jesse R %A Wu,Yanhong %A Minnich,Sara %A Piazza,Amy %A Osborne,Collin %A Oliver,Gavin R %A Lesko,Jessica %A Bates,Kathy L %A Khezeli,Kia %A Block,Darci R %A DiGuardo,Margaret %A Kreuter,Justin %A O’Horo,John C %A Kalantari,John %A Klee,Eric W %A Salama,Mohamed E %A Kipp,Benjamin %A Morice,William G %A Jenkinson,Garrett %+ Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, United States, 1 507 293 9457, Jenkinson.William@mayo.edu %K COVID-19 %K mortality %K prediction %K recurrent neural networks %K missing data %K time series %K deep learning %K machine learning %K neural network %K electronic health record %K EHR %K algorithm %K development %K validation %D 2021 %7 28.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. Objective: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. Methods: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test result. Results: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). Conclusions: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19–positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result. %M 34449401 %R 10.2196/30157 %U https://www.jmir.org/2021/9/e30157 %U https://doi.org/10.2196/30157 %U http://www.ncbi.nlm.nih.gov/pubmed/34449401 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 9 %P e31236 %T Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection %A Greenleaf,Abigail R %A Mwima,Gerald %A Lethoko,Molibeli %A Conkling,Martha %A Keefer,George %A Chang,Christiana %A McLeod,Natasha %A Maruyama,Haruka %A Chen,Qixuan %A Farley,Shannon M %A Low,Andrea %+ ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Avenue, New York, NY, United States, 1 2123050398, arg2177@cumc.columbia.edu %K COVID-19 %K cell phones %K mHealth %K Africa south of the Sahara %K surveillance %D 2021 %7 27.9.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The increase in cell phone ownership in low- and middle-income countries (LMIC) has created an opportunity for low-cost, rapid data collection by calling participants on their cell phones. Cell phones can be mobilized for a myriad of data collection purposes, including surveillance. In LMIC, cell phone–based surveillance has been used to track Ebola, measles, acute flaccid paralysis, and diarrheal disease, as well as noncommunicable diseases. Phone-based surveillance in LMIC is a particularly pertinent, burgeoning approach in the context of the COVID-19 pandemic. Participatory surveillance via cell phone could allow governments to assess burden of disease and complements existing surveillance systems. Objective: We describe the protocol for the LeCellPHIA (Lesotho Cell Phone PHIA) project, a cell phone surveillance system that collects weekly population-based data on influenza-like illness (ILI) in Lesotho by calling a representative sample of a recent face-to-face survey. Methods: We established a phone-based surveillance system to collect ILI symptoms from approximately 1700 participants who had participated in a recent face-to-face survey in Lesotho, the Population-based HIV Impact Assessment (PHIA) Survey. Of the 15,267 PHIA participants who were over 18 years old, 11,975 (78.44%) consented to future research and provided a valid phone number. We followed the PHIA sample design and included 342 primary sampling units from 10 districts. We randomly selected 5 households from each primary sampling unit that had an eligible participant and sampled 1 person per household. We oversampled the elderly, as they are more likely to be affected by COVID-19. A 3-day Zoom training was conducted in June 2020 to train LeCellPHIA interviewers. Results: The surveillance system launched July 1, 2020, beginning with a 2-week enrollment period followed by weekly calls that will continue until September 30, 2022. Of the 11,975 phone numbers that were in the sample frame, 3020 were sampled, and 1778 were enrolled. Conclusions: The surveillance system will track COVID-19 in a resource-limited setting. The novel approach of a weekly cell phone–based surveillance system can be used to track other health outcomes, and this protocol provides information about how to implement such a system. International Registered Report Identifier (IRRID): DERR1-10.2196/31236 %M 34351866 %R 10.2196/31236 %U https://www.researchprotocols.org/2021/9/e31236 %U https://doi.org/10.2196/31236 %U http://www.ncbi.nlm.nih.gov/pubmed/34351866 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e30854 %T Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective %A Hu,Tao %A Wang,Siqin %A Luo,Wei %A Zhang,Mengxi %A Huang,Xiao %A Yan,Yingwei %A Liu,Regina %A Ly,Kelly %A Kacker,Viraj %A She,Bing %A Li,Zhenlong %+ Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore, 65 65163851, geowl@nus.edu.sg %K Twitter %K public opinion %K COVID-19 vaccines %K sentiment analysis %K emotion analysis %K topic modeling %K COVID-19 %D 2021 %7 10.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines. %M 34346888 %R 10.2196/30854 %U https://www.jmir.org/2021/9/e30854 %U https://doi.org/10.2196/30854 %U http://www.ncbi.nlm.nih.gov/pubmed/34346888 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e30800 %T Framing of and Attention to COVID-19 on Twitter: Thematic Analysis of Hashtags %A Tahamtan,Iman %A Potnis,Devendra %A Mohammadi,Ehsan %A Miller,Laura E %A Singh,Vandana %+ School of Information Sciences, The University of Tennessee, 1345 Circle Park Drive 451 Communications Building, Knoxville, TN, 37996-0332, United States, 1 865 974 2148, iman.tahamtan@gmail.com %K COVID-19 %K framing %K Twitter %K social media %K public opinion %K engagement %K public attention %K thematic analysis %K public health %D 2021 %7 10.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Although past research has focused on COVID-19–related frames in the news media, such research may not accurately capture and represent the perspectives of people from diverse backgrounds. Additionally, research on the public attention to COVID-19 as reflected through frames on social media is scarce. Objective: This study identified the frames about the COVID-19 pandemic in the public discourse on Twitter, which voices diverse opinions. This study also investigated the amount of public attention to those frames on Twitter. Methods: We collected 22 trending hashtags related to COVID-19 in the United States and 694,582 tweets written in English containing these hashtags in March 2020 and analyzed them via thematic analysis. Public attention to these frames was measured by evaluating the amount of public engagement with frames and public adoption of those frames. Results: We identified 9 frames including “public health guidelines,” “quarantine life,” “solidarity,” “evidence and facts,” “call for action,” “politics,” “post-pandemic life,” “shortage panic,” and “conflict.” Results showed that some frames such as “call for action” are more appealing than others during a global pandemic, receiving greater public adoption and engagement. The “call for action” frame had the highest engagement score, followed by “conflict” and “evidence and facts.” Additionally, “post-pandemic life” had the highest adoption score, followed by “call for action” and “shortage panic.” The findings indicated that the frequency of a frame on social media does not necessarily mean greater public adoption of or engagement with the frame. Conclusions: This study contributes to framing theory and research by demonstrating how trending hashtags can be used as new user-generated data to identify frames on social media. This study concludes that the identified frames such as “quarantine life” and “conflict” and themes such as “isolation” and “toilet paper panic” represent the consequences of the COVID-19 pandemic. The consequences could be (1) exclusively related to COVID-19, such as hand hygiene or isolation; (2) related to any health crisis such as social support of vulnerable groups; and (3) generic that are irrespective of COVID-19, such as homeschooling or remote working. %M 34406961 %R 10.2196/30800 %U https://www.jmir.org/2021/9/e30800 %U https://doi.org/10.2196/30800 %U http://www.ncbi.nlm.nih.gov/pubmed/34406961 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e24295 %T Data Empowerment of Decision-Makers in an Era of a Pandemic: Intersection of “Classic” and Artificial Intelligence in the Service of Medicine %A Geva,Gil A %A Ketko,Itay %A Nitecki,Maya %A Simon,Shoham %A Inbar,Barr %A Toledo,Itay %A Shapiro,Michael %A Vaturi,Barak %A Votta,Yoni %A Filler,Daniel %A Yosef,Roey %A Shpitzer,Sagi A %A Hir,Nabil %A Peri Markovich,Michal %A Shapira,Shachar %A Fink,Noam %A Glasberg,Elon %A Furer,Ariel %+ Medical Corps, Israel Defense Force, IDF Medical Corps Headquarters, Tel HaShomer, Ramat Gan, 02149, Israel, 972 529277372, furera@gmail.com %K COVID-19 %K medical informatics %K decision-making %K pandemic %K data %K policy %K validation %K accuracy %K data analysis %D 2021 %7 10.9.2021 %9 Viewpoint %J J Med Internet Res %G English %X Background: The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. Objective: We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. Methods: Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. Results: Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). Conclusions: In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making. %M 34313589 %R 10.2196/24295 %U https://www.jmir.org/2021/9/e24295 %U https://doi.org/10.2196/24295 %U http://www.ncbi.nlm.nih.gov/pubmed/34313589 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 9 %P e26318 %T User Experiences of the NZ COVID Tracer App in New Zealand: Thematic Analysis of Interviews %A Tretiakov,Alexei %A Hunter,Inga %+ School of Management, Massey University, Tennent Drive, Palmerston North, 4474, New Zealand, 64 6 951 7905, A.Tretiakov@massey.ac.nz %K COVID-19 %K contact tracing %K app %K New Zealand %K adoption %K use %K civic responsibility %K privacy %D 2021 %7 8.9.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: For mobile app–based COVID-19 contact tracing to be fully effective, a large majority of the population needs to be using the app on an ongoing basis. However, there is a paucity of studies of users, as opposed to potential adopters, of mobile contact tracing apps and of their experiences. New Zealand, a high-income country with western political culture, was successful in managing the COVID-19 pandemic, and its experience is valuable for informing policy responses in similar contexts. Objective: This study asks the following research questions: (1) How do users experience the app in their everyday contexts? and (2) What drives the use of the app? Methods: Residents of New Zealand’s Auckland region, which encompasses the country’s largest city, were approached via Facebook, and 34 NZ COVID Tracer app users were interviewed. Interview transcripts were analyzed using thematic analysis. Results: Interviews ranged in duration from 15 to 50 minutes. Participants ranged in age from those in their late teens to those in their early sixties. Even though about half of the participants identified as White New Zealanders of European origin, different ethnicities were represented, including New Zealanders of South Pacific, Indian, Middle Eastern, South American, and Southeast Asian descent. Out of 34 participants, 2 (6%) identified as Māori (Indigenous New Zealanders). A broad range of careers were represented, from top-middle management to health support work and charity work. Likewise, educational backgrounds ranged broadly, from high school completion to master’s degrees. Out of 34 participants, 2 (6%) were unemployed, having recently lost their jobs because of the pandemic. The thematic analysis resulted in five major themes: perceived benefits, patterns of use, privacy, social influence, and need for collective action. Benefits of using the app to society in general were more salient to the participants than immediate health benefits to the individual. Use, however, depended on the alert level and tended to decline for many participants at low alert levels. Privacy considerations played a small role in shaping adoption and use, even though the participants were highly aware of privacy discourse around the app. Participants were aware of the need for high levels of adoption and use of the app to control the pandemic. Attempts to encourage others to use the app were common, although not always successful. Conclusions: Appeals to civic responsibility are likely to drive the use of a mobile contact tracing app under the conditions of high threat. Under the likely scenario of COVID-19 remaining endemic and requiring ongoing vigilance over the long term, other mechanisms promoting the use of mobile contact tracing apps may be needed, such as offering incentives. As privacy is not an important concern for many users, flexible privacy settings in mobile contact tracing apps allowing users to set their optimal levels of privacy may be appropriate. %M 34292868 %R 10.2196/26318 %U https://mhealth.jmir.org/2021/9/e26318 %U https://doi.org/10.2196/26318 %U http://www.ncbi.nlm.nih.gov/pubmed/34292868 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 9 %P e26503 %T Assessing the Electronic Evidence System Needs of Canadian Public Health Professionals: Cross-sectional Study %A Dhaliwal,Bandna %A Neil-Sztramko,Sarah E %A Boston-Fisher,Nikita %A Buckeridge,David L %A Dobbins,Maureen %+ National Collaborating Centre for Methods and Tools, McMaster University, 175 Longwood Road South, Suite 210a, Hamilton, ON, L8P 0A1, Canada, 1 905 525 9140 ext 20450, dobbinsm@mcmaster.ca %K population surveillance %K evidence-informed decision-making %K needs assessment %K public health %K precision public health %D 2021 %7 7.9.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: True evidence-informed decision-making in public health relies on incorporating evidence from a number of sources in addition to traditional scientific evidence. Lack of access to these types of data as well as ease of use and interpretability of scientific evidence contribute to limited uptake of evidence-informed decision-making in practice. An electronic evidence system that includes multiple sources of evidence and potentially novel computational processing approaches or artificial intelligence holds promise as a solution to overcoming barriers to evidence-informed decision-making in public health. Objective: This study aims to understand the needs and preferences for an electronic evidence system among public health professionals in Canada. Methods: An invitation to participate in an anonymous web-based survey was distributed via listservs of 2 Canadian public health organizations in February 2019. Eligible participants were English- or French-speaking individuals currently working in public health. The survey contained both multiple-choice and open-ended questions about the needs and preferences relevant to an electronic evidence system. Quantitative responses were analyzed to explore differences by public health role. Inductive and deductive analysis methods were used to code and interpret the qualitative data. Ethics review was not required by the host institution. Results: Respondents (N=371) were heterogeneous, spanning organizations, positions, and areas of practice within public health. Nearly all (364/371, 98.1%) respondents indicated that an electronic evidence system would support their work. Respondents had high preferences for local contextual data, research and intervention evidence, and information about human and financial resources. Qualitative analyses identified several concerns, needs, and suggestions for the development of such a system. Concerns ranged from the personal use of such a system to the ability of their organization to use such a system. Recognized needs spanned the different sources of evidence, including local context, research and intervention evidence, and resources and tools. Additional suggestions were identified to improve system usability. Conclusions: Canadian public health professionals have positive perceptions toward an electronic evidence system that would bring together evidence from the local context, scientific research, and resources. Elements were also identified to increase the usability of an electronic evidence system. %M 34491205 %R 10.2196/26503 %U https://publichealth.jmir.org/2021/9/e26503 %U https://doi.org/10.2196/26503 %U http://www.ncbi.nlm.nih.gov/pubmed/34491205 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e28975 %T Association Between What People Learned About COVID-19 Using Web Searches and Their Behavior Toward Public Health Guidelines: Empirical Infodemiology Study %A Akpan,Ikpe Justice %A Aguolu,Obianuju Genevieve %A Kobara,Yawo Mamoua %A Razavi,Rouzbeh %A Akpan,Asuama A %A Shanker,Murali %+ Department of Management & Information Systems, Kent State University, 330 University Drive Northeast, New Philadelphia, OH, 44663, United States, 1 3303393391 ext 7572, iakpan@kent.edu %K internet %K novel coronavirus %K SARS-CoV-2 %K COVID-19 %K infodemiology %K misinformation %K conspiracy theories %K public health %D 2021 %7 2.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to “Google it.” As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people’s deviant behaviors toward public health safety measures. Objective: The goals of this study were to determine what people learned about the COVID-19 pandemic through web searches, examine any association between what people learned about COVID-19 and behavior toward public health guidelines, and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people’s behavior toward public health measures. Methods: This infodemiology study used Google Trends’ worldwide search index, covering the first 6 months after the SARS-CoV-2 outbreak (January 1 to June 30, 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis (PCA), and predictive models. Results: The PCA identified two latent variables comprising past coronavirus epidemics (pastCoVepidemics: keywords that address previous epidemics) and the ongoing COVID-19 pandemic (presCoVpandemic: keywords that explain the ongoing pandemic). Both principal components were used significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78% of the variability. Three principal components fuelled misinformation about COVID-19: misinformation (keywords “biological weapon,” “virus hoax,” “common cold,” “COVID-19 hoax,” and “China virus”), conspiracy theory 1 (ConspTheory1; keyword “5G” or “@5G”), and conspiracy theory 2 (ConspTheory2; keyword “ingest bleach”). These principal components explained 84.85% of the variability. The principal components represent two measurements of public health safety guidelines—public health measures 1 (PubHealthMes1; keywords “social distancing,” “wash hands,” “isolation,” and “quarantine”) and public health measures 2 (PubHealthMes2; keyword “wear mask”)—which explained 84.7% of the variability. Based on the PCA results and the log-linear and predictive models, ConspTheory1 (keyword “@5G”) was identified as a predictor of people’s behavior toward public health measures (PubHealthMes2). Although correlations of misinformation (keywords “COVID-19,” “hoax,” “virus hoax,” “common cold,” and more) and ConspTheory2 (keyword “ingest bleach”) with PubHealthMes1 (keywords “social distancing,” “hand wash,” “isolation,” and more) were r=0.83 and r=–0.11, respectively, neither was statistically significant (P=.27 and P=.13, respectively). Conclusions: Several studies focused on the impacts of social media and related platforms on the spreading of misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web searches to learn about SARS-CoV-2 and COVID-19. %M 34280117 %R 10.2196/28975 %U https://www.jmir.org/2021/9/e28975 %U https://doi.org/10.2196/28975 %U http://www.ncbi.nlm.nih.gov/pubmed/34280117 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 9 %P e29310 %T COVID-19 Data Utilization in North Carolina: Qualitative Analysis of Stakeholder Experiences %A Patterson,Jenny Rees %A Shaw,Donna %A Thomas,Sharita R %A Hayes,Julie A %A Daley,Christopher R %A Knight,Stefania %A Aikat,Jay %A Mieczkowska,Joanna O %A Ahalt,Stanley C %A Krishnamurthy,Ashok K %+ University of Iowa, 145 N. Riverside Drive, Iowa City, IA, 52242, United States, 1 3147490050, jennifer-patterson@uiowa.edu %K qualitative research %K interview %K COVID-19 %K SARS-CoV-2 %K pandemic %K data collection %K data reporting %K data %K public health %K coronavirus disease 2019 %D 2021 %7 2.9.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: As the world faced the pandemic caused by the novel coronavirus disease 2019 (COVID-19), medical professionals, technologists, community leaders, and policy makers sought to understand how best to leverage data for public health surveillance and community education. With this complex public health problem, North Carolinians relied on data from state, federal, and global health organizations to increase their understanding of the pandemic and guide decision-making. Objective: We aimed to describe the role that stakeholders involved in COVID-19–related data played in managing the pandemic in North Carolina. The study investigated the processes used by organizations throughout the state in using, collecting, and reporting COVID-19 data. Methods: We used an exploratory qualitative study design to investigate North Carolina’s COVID-19 data collection efforts. To better understand these processes, key informant interviews were conducted with employees from organizations that collected COVID-19 data across the state. We developed an interview guide, and open-ended semistructured interviews were conducted during the period from June through November 2020. Interviews lasted between 30 and 45 minutes and were conducted by data scientists by videoconference. Data were subsequently analyzed using qualitative data analysis software. Results: Results indicated that electronic health records were primary sources of COVID-19 data. Often, data were also used to create dashboards to inform the public or other health professionals, to aid in decision-making, or for reporting purposes. Cross-sector collaboration was cited as a major success. Consistency among metrics and data definitions, data collection processes, and contact tracing were cited as challenges. Conclusions: Findings suggest that, during future outbreaks, organizations across regions could benefit from data centralization and data governance. Data should be publicly accessible and in a user-friendly format. Additionally, established cross-sector collaboration networks are demonstrably beneficial for public health professionals across the state as these established relationships facilitate a rapid response to evolving public health challenges. %M 34298500 %R 10.2196/29310 %U https://publichealth.jmir.org/2021/9/e29310 %U https://doi.org/10.2196/29310 %U http://www.ncbi.nlm.nih.gov/pubmed/34298500 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 8 %P e29957 %T Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis %A Kishore,Kamal %A Jaswal,Vidushi %A Verma,Madhur %A Koushal,Vipin %+ All India Institute of Medical Sciences, Jodhpur Romana Road, Bathinda, 151001, India, 91 9466445513, drmadhurverma@gmail.com %K COVID-19 %K lockdown %K nonpharmaceutical Interventions %K social distancing %K digital surveillance %K Google Community Mobility Reports %K community mobility %D 2021 %7 30.8.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices. Objective: The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India. Methods: In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity. Results: At the national level, mobility decreased from –38% to –77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a –60% reduction in mobility depicted more mobility compared to –82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (–0.05 to –0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase. Conclusions: Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance. %M 34174780 %R 10.2196/29957 %U https://publichealth.jmir.org/2021/8/e29957 %U https://doi.org/10.2196/29957 %U http://www.ncbi.nlm.nih.gov/pubmed/34174780 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e24408 %T Image Processing for Public Health Surveillance of Tobacco Point-of-Sale Advertising: Machine Learning–Based Methodology %A English,Ned %A Anesetti-Rothermel,Andrew %A Zhao,Chang %A Latterner,Andrew %A Benson,Adam F %A Herman,Peter %A Emery,Sherry %A Schneider,Jordan %A Rose,Shyanika W %A Patel,Minal %A Schillo,Barbara A %+ NORC at the University of Chicago, 55 E Monroe St, Ste 3100, Chicago, IL, 60603, United States, 1 3127594010, english-ned@norc.org %K machine learning %K image classification %K convolutional neural network %K object detection %K crowdsourcing %K tobacco point of sale %K public health surveillance %D 2021 %7 27.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: With a rapidly evolving tobacco retail environment, it is increasingly necessary to understand the point-of-sale (POS) advertising environment as part of tobacco surveillance and control. Advances in machine learning and image processing suggest the ability for more efficient and nuanced data capture than previously available. Objective: The study aims to use machine learning algorithms to discover the presence of tobacco advertising in photographs of tobacco POS advertising and their location in the photograph. Methods: We first collected images of the interiors of tobacco retailers in West Virginia and the District of Columbia during 2016 and 2018. The clearest photographs were selected and used to create a training and test data set. We then used a pretrained image classification network model, Inception V3, to discover the presence of tobacco logos and a unified object detection system, You Only Look Once V3, to identify logo locations. Results: Our model was successful in identifying the presence of advertising within images, with a classification accuracy of over 75% for 8 of the 42 brands. Discovering the location of logos within a given photograph was more challenging because of the relatively small training data set, resulting in a mean average precision score of 0.72 and an intersection over union score of 0.62. Conclusions: Our research provides preliminary evidence for a novel methodological approach that tobacco researchers and other public health practitioners can apply in the collection and processing of data for tobacco or other POS surveillance efforts. The resulting surveillance information can inform policy adoption, implementation, and enforcement. Limitations notwithstanding, our analysis shows the promise of using machine learning as part of a suite of tools to understand the tobacco retail environment, make policy recommendations, and design public health interventions at the municipal or other jurisdictional scale. %M 34448700 %R 10.2196/24408 %U https://www.jmir.org/2021/8/e24408 %U https://doi.org/10.2196/24408 %U http://www.ncbi.nlm.nih.gov/pubmed/34448700 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 8 %P e27892 %T The Roles of General Health and COVID-19 Proximity in Contact Tracing App Usage: Cross-sectional Survey Study %A Witteveen,Dirk %A de Pedraza,Pablo %+ European Commission, DG Joint Research Centre, Directorate I – Competences, Unit I.1 - Monitoring, Indicators and Impact Evaluation, Via E. Fermi 2749, TP 361, Ispra (VA), I-21027, Italy, 39 033278380, pablo.depedraza@ec.europa.eu %K COVID-19 %K contact tracing %K socioeconomic factors %K labor market status %K privacy %K data sharing %K pandemic %K mobile health %K public health %K smartphone %K mobile phone %D 2021 %7 18.8.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Contact tracing apps are considered useful means to monitor SARS-CoV-2 infections during the off-peak stages of the COVID-19 pandemic. Their effectiveness is, however, dependent on the uptake of such COVID-19 apps. Objective: We examined the role of individuals’ general health status in their willingness to use a COVID-19 tracing app as well as the roles of socioeconomic characteristics and COVID-19 proximity. Methods: We drew data from the WageIndicator Foundation Living and Working in Coronavirus Times survey. The survey collected data on labor market status as well as the potential confounders of the relationship between general health and COVID-19 tracing app usage, such as sociodemographics and regular smartphone usage data. The survey also contained information that allowed us to examine the role of COVID-19 proximity, such as whether an individual has contracted SARS-CoV-2, whether an individual has family members and colleagues with COVID-19, and whether an individual exhibits COVID-19 pandemic–induced depressive and anxiety symptoms. We selected data that were collected in Spain, Italy, Germany, and the Netherlands from individuals aged between 18 and 70 years (N=4504). Logistic regressions were used to measure individuals’ willingness to use a COVID-19 tracing app. Results: We found that the influence that socioeconomic factors have on COVID-19 tracing app usage varied dramatically between the four countries, although individuals experiencing forms of not being employed (ie, recent job loss and inactivity) consistently had a lower willingness to use a contact tracing app (effect size: 24.6%) compared to that of employees (effect size: 33.4%; P<.001). Among the selected COVID-19 proximity indicators, having a close family member with SARS-CoV-2 infection was associated with higher contact tracing app usage (effect size: 36.3% vs 27.1%; P<.001). After accounting for these proximity factors and the country-based variations therein, we found that having a poorer general health status was significantly associated with a much higher likelihood of contact tracing app usage; compared to a self-reported “very good” health status (estimated probability of contact tracing app use: 29.6%), the “good” (estimated probability: +4.6%; 95% CI 1.2%-8.1%) and “fair or bad” (estimated probability: +6.3%; 95% CI 2.3%-10.3%) health statuses were associated with a markedly higher willingness to use a COVID-19 tracing app. Conclusions: Current public health policies aim to promote the use of smartphone-based contact tracing apps during the off-peak periods of the COVID-19 pandemic. Campaigns that emphasize the health benefits of COVID-19 tracing apps may contribute the most to the uptake of such apps. Public health campaigns that rely on digital platforms would also benefit from seriously considering the country-specific distribution of privacy concerns. %M 34081602 %R 10.2196/27892 %U https://publichealth.jmir.org/2021/8/e27892 %U https://doi.org/10.2196/27892 %U http://www.ncbi.nlm.nih.gov/pubmed/34081602 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 8 %P e29309 %T Substandard and Falsified Medicines: Proposed Methods for Case Finding and Sentinel Surveillance %A Pisani,Elizabeth %A Hasnida,Amalia %A Rahmi,Mawaddati %A Kok,Maarten Olivier %A Harsono,Steven %A Anggriani,Yusi %+ Erasmus School of Health Policy and Management, Erasmus University, 3000 DR, Rotterdam, Netherlands, 31 2072541654, pisani@ternyata.org %K substandard drugs %K falsified medicine %K counterfeit medicine %K medicine quality %K sentinel surveillance %K public health surveillance %K substandard %K pharmaceuticals %K surveillance %K public health %D 2021 %7 16.8.2021 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The World Health Organization and others warn that substandard and falsified medicines harm health and waste money, especially in low- and middle-income countries. However, no country has measured the market-wide extent of the problem, and no standardized methods exist to estimate the prevalence of either substandard or falsified medicines. This is, in part, because the task seems overwhelming; medicine markets are huge and diverse, and testing medicines is expensive. Many countries do operate some form of postmarket surveillance of medicine, but their methods and goals differ. There is currently no clear guidance on which surveillance method is most appropriate to meet specific public health goals. In this viewpoint, we aimed to discuss the utility of both case finding and risk-based sentinel surveillance for substandard and falsified medicines, linking each to specific public health goals. We posit that choosing the system most appropriate to the goal, as well as implementing it with a clear understanding of the factors driving the production and sale of substandard and falsified medicines, will allow for surveillance resources to be concentrated most efficiently. We adapted principles used for disease outbreak responses to suggest a case-finding system that uses secondary data to flag poor-quality medicines, proposing risk-based indicators that differ for substandard and falsified medicines. This system potentially offers a cost-effective way of identifying “cases” for market withdrawal, enhanced oversight, or another immediate response. We further proposed a risk-based sentinel surveillance system that concentrates resources on measuring the prevalence of substandard and falsified medicines in the risk clusters where they are most likely to be found. The sentinel surveillance system provides base data for a transparent, spreadsheet-based model for estimating the national prevalence of substandard and falsified medicines. The methods we proposed are based on ongoing work in Indonesia, a large and diverse middle-income country currently aiming to achieve universal health coverage. Both the case finding and the sentinel surveillance system are designed to be adaptable to other resource-constrained settings. %M 34181563 %R 10.2196/29309 %U https://publichealth.jmir.org/2021/8/e29309 %U https://doi.org/10.2196/29309 %U http://www.ncbi.nlm.nih.gov/pubmed/34181563 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e29556 %T Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study %A Tozzi,Alberto Eugenio %A Gesualdo,Francesco %A Urbani,Emanuele %A Sbenaglia,Alessandro %A Ascione,Roberto %A Procopio,Nicola %A Croci,Ileana %A Rizzo,Caterina %+ Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Piazza S. Onofrio, 4, Rome, 00165, Italy, 39 3388437474, francesco.gesualdo@opbg.net %K COVID-19 %K public health %K surveillance %K digital surveillance %K internet %K online decision support system %K decision support %K support %K online tool %K Italy %K observational %D 2021 %7 13.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. Objective: This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. Methods: We compared the number of sessions by users with a COVID-19–positive contact and users with COVID-19–compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. Results: We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. Conclusions: Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic. %M 34292866 %R 10.2196/29556 %U https://www.jmir.org/2021/8/e29556 %U https://doi.org/10.2196/29556 %U http://www.ncbi.nlm.nih.gov/pubmed/34292866 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e26388 %T A Modified Public Health Automated Case Event Reporting Platform for Enhancing Electronic Laboratory Reports With Clinical Data: Design and Implementation Study %A Mishra,Ninad %A Duke,Jon %A Karki,Saugat %A Choi,Myung %A Riley,Michael %A Ilatovskiy,Andrey V %A Gorges,Marla %A Lenert,Leslie %+ Division of STD Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, US 12-2, Rm 2031, 1600 Clifton Road, Atlanta, GA, 30329, United States, 1 4047187483, oql7@cdc.gov %K public health surveillance %K sexually transmitted diseases %K gonorrhea %K chlamydia %K electronic case reporting %K electronic laboratory reporting %K health information interoperability %K fast healthcare interoperability resources %K electronic health records %K EHR %D 2021 %7 11.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Public health reporting is the cornerstone of public health practices that inform prevention and control strategies. There is a need to leverage advances made in the past to implement an architecture that facilitates the timely and complete public health reporting of relevant case-related information that has previously not easily been available to the public health community. Electronic laboratory reporting (ELR) is a reliable method for reporting cases to public health authorities but contains very limited data. In an earlier pilot study, we designed the Public Health Automated Case Event Reporting (PACER) platform, which leverages existing ELR infrastructure as the trigger for creating an electronic case report. PACER is a FHIR (Fast Health Interoperability Resources)-based system that queries the electronic health record from where the laboratory test was requested to extract expanded additional information about a case. Objective: This study aims to analyze the pilot implementation of a modified PACER system for electronic case reporting and describe how this FHIR-based, open-source, and interoperable system allows health systems to conduct public health reporting while maintaining the appropriate governance of the clinical data. Methods: ELR to a simulated public health department was used as the trigger for a FHIR-based query. Predetermined queries were translated into Clinical Quality Language logics. Within the PACER environment, these Clinical Quality Language logical statements were managed and evaluated against the providers’ FHIR servers. These predetermined logics were filtered, and only data relevant to that episode of the condition were extracted and sent to simulated public health agencies as an electronic case report. Design and testing were conducted at the Georgia Tech Research Institute, and the pilot was deployed at the Medical University of South Carolina. We evaluated this architecture by examining the completeness of additional information in the electronic case report, such as patient demographics, medications, symptoms, and diagnoses. This additional information is crucial for understanding disease epidemiology, but existing electronic case reporting and ELR architectures do not report them. Therefore, we used the completeness of these data fields as the metrics for enriching electronic case reports. Results: During the 8-week study period, we identified 117 positive test results for chlamydia. PACER successfully created an electronic case report for all 117 patients. PACER extracted demographics, medications, symptoms, and diagnoses from 99.1% (116/117), 72.6% (85/117), 70.9% (83/117), and 65% (76/117) of the cases, respectively. Conclusions: PACER deployed in conjunction with electronic laboratory reports can enhance public health case reporting with additional relevant data. The architecture is modular in design, thereby allowing it to be used for any reportable condition, including evolving outbreaks. PACER allows for the creation of an enhanced and more complete case report that contains relevant case information that helps us to better understand the epidemiology of a disease. %M 34383669 %R 10.2196/26388 %U https://www.jmir.org/2021/8/e26388 %U https://doi.org/10.2196/26388 %U http://www.ncbi.nlm.nih.gov/pubmed/34383669 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e29268 %T Self-Focused and Other-Focused Health Concerns as Predictors of the Uptake of Corona Contact Tracing Apps: Empirical Study %A große Deters,Fenne %A Meier,Tabea %A Milek,Anne %A Horn,Andrea B %+ Weizenbaum Institute for the Networked Society, Hardenbergstraße 32, Berlin, 10623, Germany, 49 331977 3710, fenne.grosse.deters@uni-potsdam.de %K COVID-19 %K corona contact tracing app %K digital proximity tracing %K preventive behavior %K health concern %K prosocial motivation %K public health %K risk perception, eHealth, Corona-Warn-App %K SwissCovid %K contact tracing app %K contact tracing %D 2021 %7 10.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Corona contact tracing apps are a novel and promising measure to reduce the spread of COVID-19. They can help to balance the need to maintain normal life and economic activities as much as possible while still avoiding exponentially growing case numbers. However, a majority of citizens need to be willing to install such an app for it to be effective. Hence, knowledge about drivers for app uptake is crucial. Objective: This study aimed to add to our understanding of underlying psychological factors motivating app uptake. More specifically, we investigated the role of concern for one’s own health and concern to unknowingly infect others. Methods: A two-wave survey with 346 German-speaking participants from Switzerland and Germany was conducted. We measured the uptake of two decentralized contact tracing apps officially launched by governments (Corona-Warn-App, Germany; SwissCovid, Switzerland), as well as concerns regarding COVID-19 and control variables. Results: Controlling for demographic variables and general attitudes toward the government and the pandemic, logistic regression analysis showed a significant effect of self-focused concerns (odds ratio [OR] 1.64, P=.002). Meanwhile, concern of unknowingly infecting others did not contribute significantly to the prediction of app uptake over and above concern for one’s own health (OR 1.01, P=.92). Longitudinal analyses replicated this pattern and showed no support for the possibility that app uptake provokes changes in levels of concern. Testing for a curvilinear relationship, there was no evidence that “too much” concern leads to defensive reactions and reduces app uptake. Conclusions: As one of the first studies to assess the installation of already launched corona tracing apps, this study extends our knowledge of the motivational landscape of app uptake. Based on this, practical implications for communication strategies and app design are discussed. %M 34227995 %R 10.2196/29268 %U https://www.jmir.org/2021/8/e29268 %U https://doi.org/10.2196/29268 %U http://www.ncbi.nlm.nih.gov/pubmed/34227995 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 8 %P e28195 %T Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence %A Nguyen,Hieu M %A Turk,Philip J %A McWilliams,Andrew D %+ Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28204, United States, 1 9706914892, hieu.nguyen@atriumhealth.org %K COVID-19 %K forecasting %K time-series model %K vector error correction model %K hospital census %K hospital resource utilization %K infection incidence %D 2021 %7 4.8.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. Objective: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. Methods: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. Results: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. Conclusions: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes. %M 34346897 %R 10.2196/28195 %U https://publichealth.jmir.org/2021/8/e28195 %U https://doi.org/10.2196/28195 %U http://www.ncbi.nlm.nih.gov/pubmed/34346897 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e28947 %T Evaluating Epidemiological Risk by Using Open Contact Tracing Data: Correlational Study %A Piotto,Stefano %A Di Biasi,Luigi %A Marrafino,Francesco %A Concilio,Simona %+ Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Italy, 39 3204230068, piotto@unisa.it %K SARS-CoV-2 %K COVID-19 %K contact tracing %K Bluetooth Low Energy %K transmission dynamics %K infection spread %K mobile apps %K mHealth %K digital apps %K mobile phone %D 2021 %7 2.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: During the 2020s, there has been extensive debate about the possibility of using contact tracing (CT) to contain the SARS-CoV-2 pandemic, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this paper, we present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. Objective: We aimed to discuss the technical and health aspects of using a centralized approach. We also aimed to show the correlation between the acquired contact data and the number of SARS-CoV-2–positive cases. Finally, we aimed to analyze CT data to define population behaviors and show the potential applications of real CT data. Methods: We collected, analyzed, and evaluated CT data on the duration, persistence, and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there was a correlation between indices of behavior that were calculated from the data and the number of new SARS-CoV-2 infections in the population (new SARS-CoV-2–positive cases). Results: We found evidence of a correlation between a weighted measure of contacts and the number of new SARS-CoV-2–positive cases (Pearson coefficient=0.86), thereby paving the road to better and more accurate data analyses and spread predictions. Conclusions: Our data have been used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system for simulating the effects of restrictions and vaccinations. Further, we demonstrated our system's ability to identify the physical locations where the probability of infection is the highest. All the data we collected are available to the scientific community for further analysis. %M 34227997 %R 10.2196/28947 %U https://www.jmir.org/2021/8/e28947 %U https://doi.org/10.2196/28947 %U http://www.ncbi.nlm.nih.gov/pubmed/34227997 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 7 %P e23528 %T Building an Interactive Geospatial Visualization Application for National Health Care–Associated Infection Surveillance: Development Study %A Zheng,Shuai %A Edwards,Jonathan R %A Dudeck,Margaret A %A Patel,Prachi R %A Wattenmaker,Lauren %A Mirza,Muzna %A Tejedor,Sheri Chernetsky %A Lemoine,Kent %A Benin,Andrea L %A Pollock,Daniel A %+ Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, United States, 1 4045614316, lgu3@cdc.gov %K data visualization %K geospatial information system %K health care–associated infection %D 2021 %7 30.7.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) is the most widely used health care–associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels. Objective: To extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data. Methods: We leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server–client model–based system enables users to access the application via a web browser. Results: We integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019. Conclusions: CDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions. %M 34328436 %R 10.2196/23528 %U https://publichealth.jmir.org/2021/7/e23528 %U https://doi.org/10.2196/23528 %U http://www.ncbi.nlm.nih.gov/pubmed/34328436 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 7 %P e29865 %T Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study %A Gao,Zhiwei %A Fujita,Sumio %A Shimizu,Nobuyuki %A Liew,Kongmeng %A Murayama,Taichi %A Yada,Shuntaro %A Wakamiya,Shoko %A Aramaki,Eiji %+ Social Computing Laboratory, Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho 8916-5, Ikoma, Nara 630-0192, Japan, 81 743 72 6053, aramaki@is.naist.jp %K COVID-19 %K search query %K infodemiology %K quantitative analysis %K concern %K rural %K urban %K Internet %K information-seeking behavior %K attitude %K Japan %D 2021 %7 20.7.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. Objective: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. Methods: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19–related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices—the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)—to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. Results: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures’ level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). Conclusions: We quantitatively defined Japanese Yahoo users’ concerns about COVID-19 by using the search counts of COVID-19–related search queries. Our results also showed that the LCI and LCIPP have external validity. %M 34174781 %R 10.2196/29865 %U https://publichealth.jmir.org/2021/7/e29865 %U https://doi.org/10.2196/29865 %U http://www.ncbi.nlm.nih.gov/pubmed/34174781 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 7 %P e27621 %T Performance of the Severe Acute Respiratory Illness Sentinel Surveillance System in Yemen: Mixed Methods Evaluation Study %A Alkholidy,Ghamdan Gamal %A Anam,Labiba Saeed %A Almahaqri,Ali Hamoud %A Khader,Yousef %+ Yemen Field Epidemiology Training Program, Ministry of Public Health and Population, Alsteen St, Bldg 23, Sana'a, Yemen, 967 771530764, ghamdangamal@gmail.com %K evaluation %K surveillance %K Centers for Disease Control and Prevention guidelines %K severe acute respiratory illness %K Yemen %D 2021 %7 9.7.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The national severe acute respiratory illness (SARI) surveillance system in Yemen was established in 2010 to monitor SARI occurrence in humans and provide a foundation for detecting SARI outbreaks. Objective: To ensure that the objectives of national surveillance are being met, this study aimed to examine the level of usefulness and the performance of the SARI surveillance system in Yemen. Methods: The updated Centers for Disease Control and Prevention guidelines were used for the purposes of our evaluation. Related documents and reports were reviewed. Data were collected from 4 central-level managers and stakeholders and from 10 focal points at 4 sentinel sites by using a semistructured questionnaire. For each attribute, percent scores were calculated and ranked as follows: very poor (≤20%), poor (20%-40%), average (40%-60%), good (60%-80%), and excellent (>80%). Results: As rated by the evaluators, the SARI surveillance system achieved its objectives. The system’s flexibility (percent score: 86%) and acceptability (percent score: 82%) were rated as “excellent,” and simplicity (percent score: 74%) and stability (percent score: 75%) were rated as “good.” The percent score for timeliness was 23% in 2018, which indicated poor timeliness. The overall data quality percent score of the SARI system was 98.5%. Despite its many strengths, the SARI system has some weaknesses. For example, it depends on irregular external financial support. Conclusions: The SARI surveillance system was useful in estimating morbidity and mortality, monitoring the trends of the disease, and promoting research for informing prevention and control measures. The overall performance of the SARI surveillance system was good. We recommend expanding the system by promoting private health facilities’ (eg, private hospitals and private health centers) engagement in SARI surveillance, establishing an electronic database at central and peripheral sites, and providing the National Central Public Health Laboratory with the reagents needed for disease confirmation. %M 34255695 %R 10.2196/27621 %U https://publichealth.jmir.org/2021/7/e27621 %U https://doi.org/10.2196/27621 %U http://www.ncbi.nlm.nih.gov/pubmed/34255695 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 7 %P e27627 %T Risk Factors of Cholera Transmission in Al Hudaydah, Yemen: Case-Control Study %A Qaserah,Abdulqawi Mohammed %A Al Amad,Mohammed Abdullah %A Al Serouri,Abdulwahed Abduljabbar %A Khader,Yousef Saleh %+ Rapid Response Team, Yemen Ministry of Public Health and Population, P.O. Box 299, Al-Hasabah, Sana'a, Yemen, 967 772435532, aabuhosam33@gmail.com %K cholera %K outbreak %K risk factors, Yemen %K Field Epidemiology Training Program %D 2021 %7 5.7.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Yemen has recently faced the largest cholera outbreak in the world, which started at the end of 2016. By the end of 2017, the cumulative reported cases from all governorates reached 777,229 with 2134 deaths. Al Hudaydah was one of the most strongly affected areas, with 88,741 (18%) cases and 244 (12%) deaths reported. Objective: The aim of this study was to determine the risk factors associated with cholera transmission in Al Hudaydah city, Yemen. Methods: From December 1, 2017 to January 10, 2018, a total of 104 patients with cholera (57 women and 47 men) who presented at cholera treatment centers in Al Hudaydah city with three or more watery stools in a 24-hour period and with moderate or severe dehydration were identified for inclusion in this study. Each case was matched by age and gender with two controls who were living in the neighboring house. A semistructured questionnaire was used to collect data on behavioral and environmental risk factors such as drinking water from public wells, storing water in containers, consumption of unwashed vegetables or fruits, and sharing a toilet. Results: The median age of the cases and controls was 20 years (range 5-80) and 23 years (range 5-85), respectively. Only 6% of cases and 4% of controls were employed. Multivariate analysis showed that eating unwashed vegetables or fruits (odds ratio [OR] 7.0, 95% CI 1.6-30.6, P=.01), storing water in containers (OR 3.0, 95% CI 1.3-7.3, P=.01), drinking water from a public well (OR 2.5, 95% CI 1.1-5.7, P=.02), and using a public toilet (OR 5.2, 95% CI 1.1-24.4, P=.04) were significantly associated with cholera infection risk. Conclusions: The cholera transmission risk factors in Al Hudaydah city were related to water and sanitation hygiene. Therefore, increasing awareness of the population on the importance of water chlorination, and washing fruits and vegetables through a health education campaign is strongly recommended. %M 36260393 %R 10.2196/27627 %U https://publichealth.jmir.org/2021/7/e27627 %U https://doi.org/10.2196/27627 %U http://www.ncbi.nlm.nih.gov/pubmed/36260393 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e27686 %T Defining the Scope of Digital Public Health and Its Implications for Policy, Practice, and Research: Protocol for a Scoping Review %A Iyamu,Ihoghosa %A Gómez-Ramírez,Oralia %A Xu,Alice X T %A Chang,Hsiu-Ju %A Haag,Devon %A Watt,Sarah %A Gilbert,Mark %+ British Columbia Centre for Disease Control, 655 W12th Avenue, Vancouver, BC, V5Z 4R4, Canada, 1 604 707 5619, Mark.Gilbert@bccdc.ca %K digital health %K public health %K prevention %K scoping review %K protocol %D 2021 %7 30.6.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: There has been rapid development and application of digital technologies in public health domains, which are considered to have the potential to transform public health. However, this growing interest in digital technologies in public health has not been accompanied by a clarity of scope to guide policy, practice, and research in this rapidly emergent field. Objective: This scoping review seeks to determine the scope of digital health as described by public health researchers and practitioners and to consolidate a conceptual framework of digital public health. Methods: The review follows Arksey and O’Malley’s framework for conducting scoping reviews with improvements as suggested by Levac et al. The search strategy will be applied to Embase, Medline, and Google Scholar. A grey literature search will be conducted on intergovernmental agency websites and country-specific websites. Titles and abstracts will be reviewed by independent reviewers, while full-text reviews will be conducted by 2 reviewers to determine eligibility based on prespecified inclusion and exclusion criteria. The data will be coded in an iterative approach using the best-fit framework analysis methodology. Results: This research project received funding from the British Columbia Centre for Disease Control Foundation for Population and Public Health on January 1, 2020. The initial search was conducted on June 1, 2020 and returned 6953 articles in total. After deduplication, 4523 abstracts were reviewed, and 227 articles have been included in the review. Ethical approval is not required for this review as it uses publicly available data. Conclusions: We anticipate that the findings of the scoping review will contribute relevant evidence to health policy makers and public health practitioners involved in planning, funding, and delivering health services that leverage digital technologies. Results of the review will be strategically disseminated through publications in scientific journals, conferences, and engagement with relevant stakeholders. International Registered Report Identifier (IRRID): DERR1-10.2196/27686 %M 34255717 %R 10.2196/27686 %U https://www.researchprotocols.org/2021/6/e27686 %U https://doi.org/10.2196/27686 %U http://www.ncbi.nlm.nih.gov/pubmed/34255717 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e28643 %T Making the COVID-19 Pandemic a Driver for Digital Health: Brazilian Strategies %A Donida,Bruna %A da Costa,Cristiano André %A Scherer,Juliana Nichterwitz %+ SOFTWARELAB - Software Innovation Laboratory, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, 93022-750, Brazil, 55 5135908161, cac@unisinos.br %K COVID-19 %K digital technology %K Brazil %K public health %K medical informatics %K digital health %K strategy %K outbreak %K system %K data %K health data %K implementation %K monitoring %D 2021 %7 29.6.2021 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The COVID-19 outbreak exposed several problems faced by health systems worldwide, especially concerning the safe and rapid generation and sharing of health data. However, this pandemic scenario has also facilitated the rapid implementation and monitoring of technologies in the health field. In view of the occurrence of the public emergency caused by SARS-CoV-2 in Brazil, the Department of Informatics of the Brazilian Unified Health System created a contingency plan. In this paper, we aim to report the digital health strategies applied in Brazil and the first results obtained during the fight against COVID-19. Conecte SUS, a platform created to store all the health data of an individual throughout their life, is the center point of the Brazilian digital strategy. Access to the platform can be obtained through an app by the patient and the health professionals involved in the case. Health data sharing became possible due to the creation of the National Health Data Network (Rede Nacional de Dados em Saúde, RNDS). A mobile app was developed to guide citizens regarding the need to go to a health facility and to assist in disseminating official news about the virus. The mobile app can also alert the user if they have had contact with an infected person. The official numbers of cases and available hospital beds are updated and published daily on a website containing interactive graphs. These data are obtained due to creating a web-based notification system that uses the RNDS to share information about the cases. Preclinical care through telemedicine has become essential to prevent overload in health facilities. The exchange of experiences between medical teams from large centers and small hospitals was made possible using telehealth. Brazil took a giant step toward digital health adoption, creating and implementing important initiatives; however, these initiatives do not yet cover the entire health system. It is expected that the sharing of health data that are maintained and authorized by the patient will become a reality in the near future. The intention is to obtain better clinical outcomes, cost reduction, and faster and better services in the public health network. %M 34101613 %R 10.2196/28643 %U https://publichealth.jmir.org/2021/6/e28643 %U https://doi.org/10.2196/28643 %U http://www.ncbi.nlm.nih.gov/pubmed/34101613 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e27623 %T Incidence, Trend, and Mortality of Human Exposure to Rabies in Yemen, 2011-2017: Observational Study %A Abdulmoghni,Rihana Taher %A Al-Ward,Ahmed Hasan %A Al-Moayed,Khaled Abdullah %A AL-Amad,Mohammed Abdullah %A Khader,Yousef S %+ Yemen Field Epidemiology Training Program, Ministry of Public Health and Population, Al-Asbahy Area, Al-Sabain District, Sana'a, Yemen, 967 775242320, dr.rihana@ymail.com %K rabies %K incidence %K trend %K mortality %D 2021 %7 22.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Rabies remains a neglected and poorly controlled disease throughout the developing world, particularly in Africa and Asia, where most human rabies deaths occur. Objective: This study aimed to describe the epidemiology of rabies exposures, its trend, and its geographical distribution in Yemen. Methods: Cumulative data from a rabies surveillance system for the period 2011-2017 were obtained from the National Rabies Control Program as paper-based annual reports. Data included the number of persons bitten by a suspected rabid animal, their gender and age, and the result of the animal’s laboratory test. Human cases were defined as those exposed to rabies virus bitten by a suspected rabid animal, exposed to a confirmed rabid animal and then received postexposure prophylaxis (PEP), and deaths occurred after exposure to a confirmed rabid animal after having rabies symptoms during 2011-2017. Results: From 2011 to 2017, a total of 76,049 persons were bitten by a suspected rabid animal. Of these, 21,927 (28.83%) were exposed to positively confirmed rabid animals and then received PEP, and 295 (0.38%) rabies-related deaths occurred. Of all cases with rabies exposure, 50,882 (66.91%) were males. The most affected age group by animal bites (31,816/76,041, 41.84%), positive exposure (8945/21,927, 40.79%), and rabies deaths (143/295, 48.47%) was 5-14 years. Rabies vaccines and immunoglobulins quantities were least available in 2016 and 2017. The annual incidence rate of exposure to animal bites and rabies exposure was 50 and 14 per 100,000, respectively. The annual mortality rate was 2 per 1,000,000. The highest incidence rate of animal bites was in Dhamar (112 per 100,000) and Ibb (94 per 100,000), whereas the highest incidence of exposed cases was in Amanat Al Asimah (40 per 100,000) and Ibb (37 per 100,000). Mortality rate was the highest in Amanat Al Asimah (6 deaths per 1,000,000) followed by Ibb and Dhamar (4 deaths per 1,000,000 in both). Conclusions: Rabies remains a worrying health problem in Yemen with higher percentage reported among children and males. Targeting school-age populations by education, communication, and information campaigns about preventive measures is strongly recommended. An electronic system should be introduced to improve reporting. It is important to have a sufficient supply of vaccines and immunoglobulins in control units, especially in the at-risk or impacted governorates. Future studies are suggested to determine incidences and risk factors of disease progression. %M 34156339 %R 10.2196/27623 %U https://publichealth.jmir.org/2021/6/e27623 %U https://doi.org/10.2196/27623 %U http://www.ncbi.nlm.nih.gov/pubmed/34156339 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e26303 %T Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications %A Burkom,Howard %A Loschen,Wayne %A Wojcik,Richard %A Holtry,Rekha %A Punjabi,Monika %A Siwek,Martina %A Lewis,Sheri %+ Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD, 20723, United States, 1 240 228 5000 ext 4361, Howard.Burkom@jhuapl.edu %K health surveillance %K outbreak detection %K population health %D 2021 %7 21.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats. Objective: This study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it. Methods: The features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users. Results: The resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse. Conclusions: With emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design. %M 34152271 %R 10.2196/26303 %U https://publichealth.jmir.org/2021/6/e26303 %U https://doi.org/10.2196/26303 %U http://www.ncbi.nlm.nih.gov/pubmed/34152271 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e27976 %T Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events %A Miller,Michele %A Romine,William %A Oroszi,Terry %+ Department of Pharmacology & Toxicology, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH, 45435, United States, 1 (937) 775 1000, millerme91@gmail.com %K anthrax %K big data %K internet %K infodemiology %K infoveillance %K social listening %K digital health %K biological weapon %K terrorism %K Federal Bureau of Investigation %K machine learning %K public health threat %K Twitter %D 2021 %7 18.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion. Results: Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F1-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories. Conclusions: This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats. %M 34142975 %R 10.2196/27976 %U https://publichealth.jmir.org/2021/6/e27976 %U https://doi.org/10.2196/27976 %U http://www.ncbi.nlm.nih.gov/pubmed/34142975 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e28269 %T An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study %A Brakefield,Whitney S %A Ammar,Nariman %A Olusanya,Olufunto A %A Shaban-Nejad,Arash %+ Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, 50 N Dunlap Street, Memphis, TN, 38103, United States, 1 901 287 583, ashabann@uthsc.edu %K causal inference %K COVID-19 surveillance %K COVID-19 %K digital health %K health disparities %K knowledge integration %K SARS-CoV-2 %K Social Determinants of Health %K surveillance %K urban health %D 2021 %7 16.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions. Objective: This study sought to redefine the Healthy People 2030’s SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data. Methods: The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes. Results: We generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users. Conclusions: UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health. %M 34081605 %R 10.2196/28269 %U https://publichealth.jmir.org/2021/6/e28269 %U https://doi.org/10.2196/28269 %U http://www.ncbi.nlm.nih.gov/pubmed/34081605 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e24285 %T Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation %A Peng,Yuanyuan %A Li,Cuilian %A Rong,Yibiao %A Pang,Chi Pui %A Chen,Xinjian %A Chen,Haoyu %+ Joint Shantou International Eye Center, Shantou University & the Chinese University of Hong Kong, Joint Shantou International Eye Center, North Dongxia Road, Shantou, 515041, China, 86 075488393560, drchenhaoyu@gmail.com %K COVID-19 %K daily incidence %K real-time prediction %K machine learning %K Google Trends %K infoveillance %K infodemiology %K digital health %K digital public health %K surveillance %K prediction %K incidence %K policy %K prevention %K model %D 2021 %7 14.6.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. Objective: We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. Methods: Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. Results: Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs <10 and 187 (87.0%) had Pearson correlation coefficients >0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). Conclusions: By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead. %M 34081607 %R 10.2196/24285 %U https://www.jmir.org/2021/6/e24285 %U https://doi.org/10.2196/24285 %U http://www.ncbi.nlm.nih.gov/pubmed/34081607 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e24887 %T Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging %A van Allen,Zack %A Bacon,Simon L %A Bernard,Paquito %A Brown,Heather %A Desroches,Sophie %A Kastner,Monika %A Lavoie,Kim %A Marques,Marta %A McCleary,Nicola %A Straus,Sharon %A Taljaard,Monica %A Thavorn,Kednapa %A Tomasone,Jennifer R %A Presseau,Justin %+ School of Psychology, University of Ottawa, 75 Laurier Ave E, Ottawa, ON, K1N 6N5, Canada, 1 6138660060, zvana030@uottawa.ca %K health behaviors %K multiple behaviors %K cluster analysis %K network analysis %K CLSA %D 2021 %7 11.6.2021 %9 Proposal %J JMIR Res Protoc %G English %X Background: Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life. Objective: The aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes. Methods: Using cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables. Results: Baseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H). Conclusions: This study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences. International Registered Report Identifier (IRRID): DERR1-10.2196/24887 %M 34114962 %R 10.2196/24887 %U https://www.researchprotocols.org/2021/6/e24887 %U https://doi.org/10.2196/24887 %U http://www.ncbi.nlm.nih.gov/pubmed/34114962 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e29528 %T Community Mitigation of COVID-19 and Portrayal of Testing on TikTok: Descriptive Study %A Basch,Corey H %A Mohlman,Jan %A Fera,Joseph %A Tang,Hao %A Pellicane,Alessia %A Basch,Charles E %+ Department of Public Health, William Paterson University, University Hall, Wayne, NJ, 07470, United States, 1 9737202603, baschc@wpunj.edu %K TikTok %K social media %K COVID-19 %K testing %K disgust %K anxiety %K content analysis %K communication %K infodemiology %K infoveillance %K public health %K digital public health %K digital health %K community mitigation %D 2021 %7 10.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: COVID-19 testing remains an essential element of a comprehensive strategy for community mitigation. Social media is a popular source of information about health, including COVID-19 and testing information. One of the most popular communication channels used by adolescents and young adults who search for health information is TikTok—an emerging social media platform. Objective: The purpose of this study was to describe TikTok videos related to COVID-19 testing. Methods: The hashtag #covidtesting was searched, and the first 100 videos were included in the study sample. At the time the sample was drawn, these 100 videos garnered more than 50% of the views for all videos cataloged under the hashtag #covidtesting. The content characteristics that were coded included mentions, displays, or suggestions of anxiety, COVID-19 symptoms, quarantine, types of tests, results of test, and disgust/unpleasantness. Additional data that were coded included the number and percentage of views, likes, and comments and the use of music, dance, and humor. Results: The 100 videos garnered more than 103 million views; 111,000 comments; and over 12.8 million likes. Even though only 44 videos mentioned or suggested disgust/unpleasantness and 44 mentioned or suggested anxiety, those that portrayed tests as disgusting/unpleasant garnered over 70% of the total cumulative number of views (73,479,400/103,071,900, 71.29%) and likes (9,354,691/12,872,505, 72.67%), and those that mentioned or suggested anxiety attracted about 60% of the total cumulative number of views (61,423,500/103,071,900, 59.59%) and more than 8 million likes (8,339,598/12,872,505, 64.79%). Independent one-tailed t tests (α=.05) revealed that videos that mentioned or suggested that COVID-19 testing was disgusting/unpleasant were associated with receiving a higher number of views and likes. Conclusions: Our finding of an association between TikTok videos that mentioned or suggested that COVID-19 tests were disgusting/unpleasant and these videos’ propensity to garner views and likes is of concern. There is a need for public health agencies to recognize and address connotations of COVID-19 testing on social media. %M 34081591 %R 10.2196/29528 %U https://publichealth.jmir.org/2021/6/e29528 %U https://doi.org/10.2196/29528 %U http://www.ncbi.nlm.nih.gov/pubmed/34081591 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e27102 %T Considerations for the Design and Implementation of COVID-19 Contact Tracing Apps: Scoping Review %A Osmanlliu,Esli %A Rafie,Edmond %A Bédard,Sylvain %A Paquette,Jesseca %A Gore,Genevieve %A Pomey,Marie-Pascale %+ Division of Pediatric Emergency Medicine, Department of Pediatrics, Montreal Children’s Hospital, McGill University Health Centre, MUHC Glen Site, 1001 Décarie Blvd, Montreal, QC, H4A 3J1, Canada, 1 514 412 4499, esli.osmanlliu@mcgill.ca %K COVID-19 %K contact tracing %K exposure notification %K app %K design %K implementation %K participatory %K eHealth %K surveillance %K monitoring %K review %D 2021 %7 9.6.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Given the magnitude and speed of SARS-CoV-2 transmission, achieving timely and effective manual contact tracing has been a challenging task. Early in the pandemic, contact tracing apps generated substantial enthusiasm due to their potential for automating tracing and reducing transmission rates while enabling targeted confinement strategies. However, although surveys demonstrate public interest in using such apps, their actual uptake remains limited. Their social acceptability is challenged by issues around privacy, fairness, and effectiveness, among other concerns. Objective: This study aims to examine the extent to which design and implementation considerations for contact tracing apps are detailed in the available literature, focusing on aspects related to participatory and responsible eHealth innovation, and synthesize recommendations that support the development of successful COVID-19 contact tracing apps and related eHealth technologies. Methods: Searches were performed on five databases, and articles were selected based on eligibility criteria. Papers pertaining to the design, implementation, or acceptability of contact tracing apps were included. Articles published since 2019, written in English or French, and for which the full articles were available were considered eligible for analysis. To assess the scope of the knowledge found in the current literature, we used three complementary frameworks: (1) the Holistic Framework to Improve the Uptake and Impact of eHealth Technologies, (2) the Montreal model, and (3) the Responsible Innovation in Health Assessment Tool. Results: A total of 63 articles qualified for the final analysis. Less than half of the selected articles cited the need for a participatory process (n=25, 40%), which nonetheless was the most frequently referenced item of the Framework to Improve the Uptake and Impact of eHealth Technologies. Regarding the Montreal model, stakeholder consultation was the most frequently described level of engagement in the development of contact tracing apps (n=24, 38%), while collaboration and partnership were cited the least (n=2, 3%). As for the Responsible Innovation in Health framework, all the articles (n=63, 100%) addressed population health, whereas only 2% (n=1) covered environmental considerations. Conclusions: Most studies lacked fundamental aspects of eHealth development and implementation. Our results demonstrate that stakeholders of COVID-19 contact tracing apps lack important information to be able to critically appraise this eHealth innovation. This may have contributed to the modest uptake of contact tracing apps worldwide. We make evidence-informed recommendations regarding data management, communication, stakeholder engagement, user experience, and implementation strategies for the successful and responsible development of contact tracing apps. %M 34038376 %R 10.2196/27102 %U https://mhealth.jmir.org/2021/6/e27102 %U https://doi.org/10.2196/27102 %U http://www.ncbi.nlm.nih.gov/pubmed/34038376 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e27888 %T How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis %A Castro,Lauren A %A Shelley,Courtney D %A Osthus,Dave %A Michaud,Isaac %A Mitchell,Jason %A Manore,Carrie A %A Del Valle,Sara Y %+ Information Systems & Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM, 87545, United States, 1 505 667 7544, lcastro@lanl.gov %K COVID-19 %K forecasting %K health care %K prediction %K forecast %K model %K quantitative %K hospital %K ICU %K ventilator %K intensive care unit %K probability %K trend %K plan %D 2021 %7 9.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. Objective: Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. Methods: We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. Results: Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. Conclusions: Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future. %M 34003763 %R 10.2196/27888 %U https://publichealth.jmir.org/2021/6/e27888 %U https://doi.org/10.2196/27888 %U http://www.ncbi.nlm.nih.gov/pubmed/34003763 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e27625 %T The Rotavirus Surveillance System in Yemen: Evaluation Study %A Lardi,Eman Abdullah %A Al Kuhlani,Sharaf Sharaf %A Al Amad,Mohammed Abdullah %A Al Serouri,Abdulwahed Abduljabar %A Khader,Yousef Saleh %+ Field Epidemiology Training Program, Ministry of Public Health and Population, AL-Hasaba, Mazda street, Sana'a, Yemen, 967 771866574, eman2020-2020@hotmail.com %K Rotavirus %K surveillance system %K evaluation, Yemen %D 2021 %7 8.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Rotavirus (RV) kills over 185,000 children <5 years every year and is responsible for over one-third of all child diarrheal deaths worldwide. The Rotavirus Surveillance System (RVSS) in Yemen was launched in 2007 at five sentinel sites to monitor the impact of the vaccine on RV morbidity and mortality. Objective: This study aimed to determine the usefulness of the RVSS, assess its performance, and identify the strengths and weaknesses of its implementation. Methods: The Centers for Disease Control and Prevention’s updated guidelines on evaluating a public health surveillance system were used to evaluate the RVSS. In this assessment, qualitative indicators, such as usefulness, flexibility, stability, simplicity, and acceptability, were assessed through in-depth interviews with stakeholders at the central level and semistructured questionnaires with the sentinel site coordinators. The indicators for quantitative attributes—sensitivity, positive predictive value (PPV), completeness, and timeliness—were assessed by reviewing the results of laboratory samples and a random sample of case report forms. The scores for the indicators were expressed as poor (<60%), average (60% to <80%), and good (≥80%). Results: The overall usefulness score of the RVSS was 73%, indicating an average rank. The RVSS was rated as having good flexibility (91%) and stability (81%), and average simplicity (77%) and acceptability (76%). In terms of quantitative attributes, the system was poor for sensitivity (16%), average for PPV (73%), and good for completeness (100%) and timeliness (100%). Conclusions: Although the system attributes were flexible, stable, capable of providing quality data, and performing timely data reporting, some attributes still needed improvements (eg, usefulness, simplicity, acceptability, and PPV). There is a need for a gradual replacement of donor funds with government funds to ensure sustainability. The RVSS in Yemen strongly requires a progressive increase in the number of sites in governorates and sensitivity enhancement. %M 34100759 %R 10.2196/27625 %U https://publichealth.jmir.org/2021/6/e27625 %U https://doi.org/10.2196/27625 %U http://www.ncbi.nlm.nih.gov/pubmed/34100759 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e27753 %T Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature %A O'Connell,James %A Abbas,Manzar %A Beecham,Sarah %A Buckley,Jim %A Chochlov,Muslim %A Fitzgerald,Brian %A Glynn,Liam %A Johnson,Kevin %A Laffey,John %A McNicholas,Bairbre %A Nuseibeh,Bashar %A O'Callaghan,Michael %A O'Keeffe,Ian %A Razzaq,Abdul %A Rekanar,Kaavya %A Richardson,Ita %A Simpkin,Andrew %A Storni,Cristiano %A Tsvyatkova,Damyanka %A Walsh,Jane %A Welsh,Thomas %A O'Keeffe,Derek %+ School of Medicine, National University of Ireland Galway, Clinical Sciences Institute, Costello Road, Galway, H91 V4AY, Ireland, 353 (0)91 495960, derek.okeeffe@nuigalway.ie %K digital contact tracing %K automated contact tracing %K COVID-19 %K SARS-CoV-2 %K mHealth %K mobile app %K app %K tracing %K monitoring %K surveillance %K review %K best practice %K design %D 2021 %7 7.6.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. Objective: This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. Methods: A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. Results: Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. Conclusions: In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature. %M 34003764 %R 10.2196/27753 %U https://mhealth.jmir.org/2021/6/e27753 %U https://doi.org/10.2196/27753 %U http://www.ncbi.nlm.nih.gov/pubmed/34003764 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e27917 %T Impact of Public Health and Social Measures on the COVID-19 Pandemic in the United States and Other Countries: Descriptive Analysis %A Zweig,Sophia Alison %A Zapf,Alexander John %A Xu,Hanmeng %A Li,Qingfeng %A Agarwal,Smisha %A Labrique,Alain Bernard %A Peters,David H %+ Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21202, United States, 1 9175749743, szweig3@jh.edu %K surveillance %K COVID-19 %K public health %K health policy %K global health %K policy %K epidemiology %K descriptive epidemiology %D 2021 %7 2.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The United States of America has the highest global number of COVID-19 cases and deaths, which may be due in part to delays and inconsistencies in implementing public health and social measures (PHSMs). Objective: In this descriptive analysis, we analyzed the epidemiological evidence for the impact of PHSMs on COVID-19 transmission in the United States and compared these data to those for 10 other countries of varying income levels, population sizes, and geographies. Methods: We compared PHSM implementation timing and stringency against COVID-19 daily case counts in the United States and against those in Canada, China, Ethiopia, Japan, Kazakhstan, New Zealand, Singapore, South Korea, Vietnam, and Zimbabwe from January 1 to November 25, 2020. We descriptively analyzed the impact of border closures, contact tracing, household confinement, mandated face masks, quarantine and isolation, school closures, limited gatherings, and states of emergency on COVID-19 case counts. We also compared the relationship between global socioeconomic indicators and national pandemic trajectories across the 11 countries. PHSMs and case count data were derived from various surveillance systems, including the Health Intervention Tracking for COVID-19 database, the World Health Organization PHSM database, and the European Centre for Disease Prevention and Control. Results: Implementing a specific package of 4 PHSMs (quarantine and isolation, school closures, household confinement, and the limiting of social gatherings) early and stringently was observed to coincide with lower case counts and transmission durations in Vietnam, Zimbabwe, New Zealand, South Korea, Ethiopia, and Kazakhstan. In contrast, the United States implemented few PHSMs stringently or early and did not use this successful package. Across the 11 countries, national income positively correlated (r=0.624) with cumulative COVID-19 incidence. Conclusions: Our findings suggest that early implementation, consistent execution, adequate duration, and high adherence to PHSMs represent key factors of reducing the spread of COVID-19. Although national income may be related to COVID-19 progression, a country’s wealth appears to be less important in controlling the pandemic and more important in taking rapid, centralized, and consistent public health action. %M 33975277 %R 10.2196/27917 %U https://publichealth.jmir.org/2021/6/e27917 %U https://doi.org/10.2196/27917 %U http://www.ncbi.nlm.nih.gov/pubmed/33975277 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e27590 %T Epidemiology of Diphtheria in Yemen, 2017-2018: Surveillance Data Analysis %A Moghalles,Suaad Ameen %A Aboasba,Basher Ahmed %A Alamad,Mohammed Abdullah %A Khader,Yousef Saleh %+ Yemen Field Epidemiology Training Programme, Ministry of Public Health and Population, Hadh Street, Sana'a, 00967, Yemen, 967 735 800 572, smughalles@gmail.com %K diphtheria %K epidemiology %K incidence %K case fatality rate %D 2021 %7 2.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: As a consequence of war and the collapse of the health system in Yemen, which prevented many people from accessing health facilities to obtain primary health care, vaccination coverage was affected, leading to a deadly diphtheria epidemic at the end of 2017. Objective: This study aimed to describe the epidemiology of diphtheria in Yemen and determine its incidence and case fatality rate. Methods: Data were obtained from the diphtheria surveillance program 2017-2018, using case definitions of the World Health Organization. A probable case was defined as a case involving a person having laryngitis, pharyngitis, or tonsillitis and an adherent membrane of the tonsils, pharynx, and/or nose. A confirmed case was defined as a probable case that was laboratory confirmed or linked epidemiologically to a laboratory-confirmed case. Data from the Central Statistical Organization was used to calculate the incidence per 100,000 population. A P value <.05 was considered significant. Results: A total of 2243 cases were reported during the period between July 2017 and August 2018. About 49% (1090/2243, 48.6%) of the cases were males. About 44% (978/2243, 43.6%) of the cases involved children aged 5 to 15 years. Respiratory tract infection was the predominant symptom (2044/2243, 91.1%), followed by pseudomembrane (1822/2243, 81.2%). Based on the vaccination status, the percentages of partially vaccinated, vaccinated, unvaccinated, and unknown status patients were 6.6% (148/2243), 30.8% (690/2243), 48.6% (10902243), and 14.0% (315/2243), respectively. The overall incidence of diphtheria was 8 per 100,000 population. The highest incidence was among the age group <15 years (11 per 100,000 population), and the lowest incidence was among the age group ≥15 years (5 per 100,000 population). The overall case fatality rate among all age groups was 5%, and it was higher (10%) in the age group <5 years. Five governorates that were difficult to access (Raymah, Abyan, Sa'ada, Lahj, and Al Jawf) had a very high case fatality rate (22%). Conclusions: Diphtheria affected a large number of people in Yemen in 2017-2018. The majority of patients were partially or not vaccinated. Children aged ≤15 years were more affected, with higher fatality among children aged <5 years. Five governorates that were difficult to access had a case fatality rate twice that of the World Health Organization estimate (5%-10%). To control the diphtheria epidemic in Yemen, it is recommended to increase routine vaccination coverage and booster immunizations, increase public health awareness toward diphtheria, and strengthen the surveillance system for early detection and immediate response. %M 34076583 %R 10.2196/27590 %U https://publichealth.jmir.org/2021/6/e27590 %U https://doi.org/10.2196/27590 %U http://www.ncbi.nlm.nih.gov/pubmed/34076583 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 6 %P e26784 %T Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach %A Lee,Hyojung %A Kim,Yeahwon %A Kim,Eunsu %A ‍Lee,Sunmi %+ Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, 17104, Republic of Korea, 82 312012409, sunmilee@khu.ac.kr %K COVID-19 %K transmission dynamics %K South Korea %K international travels %K imported and local transmission %K basic reproduction number %K effective reproduction number %K mitigation intervention strategies %K risk %K assessment %K transmission %K mitigation %K strategy %K travel %K mobility %K spread %K intervention %K diagnosis %K monitoring %K testing %D 2021 %7 1.6.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally. Objective: South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19. Methods: The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers. Results: The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R0 was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The Rt was estimated in South Korea and in both Seoul and Gyeonggi. Conclusions: A statistical model accounting for imported and locally transmitted cases was employed to estimate R0 and Rt. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea. %M 33819165 %R 10.2196/26784 %U https://publichealth.jmir.org/2021/6/e26784 %U https://doi.org/10.2196/26784 %U http://www.ncbi.nlm.nih.gov/pubmed/33819165 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e28594 %T An Overview of the Treatment Options Used for the Management of COVID-19 in Pakistan: Retrospective Observational Study %A Akhtar,Hashaam %A Akhtar,Samar %A Rahman,Fazal-Ul %A Afridi,Maham %A Khalid,Sundas %A Ali,Sabahat %A Akhtar,Nasim %A Khader,Yousef S %A Ahmad,Hamaad %A Khan,Muhammad Mujeeb %+ Yusra Institute of Pharmaceutical Sciences, Yusra Medical and Dental College, Zaraj Housing Society, Opposite DHA Phase 2 Gate III, Main GT Road, Islamabad, 44000, Pakistan, 92 515709447, hashaamakhtar@gmail.com %K COVID-19 %K antibiotics %K Pakistan %K multidrug resistant infections %K antibiotic resistance %K first wave %D 2021 %7 27.5.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Since the first reports of COVID-19 infection, the foremost requirement has been to identify a treatment regimen that not only fights the causative agent but also controls the associated complications of the infection. Due to the time-consuming process of drug discovery, physicians have used readily available drugs and therapies for treatment of infections to minimize the death toll. Objective: The aim of this study is to provide a snapshot analysis of the major drugs used in a cohort of 1562 Pakistani patients during the period from May to July 2020, when the first wave of COVID-19 peaked in Pakistan. Methods: A retrospective observational study was performed to provide an overview of the major drugs used in a cohort of 1562 patients with COVID-19 admitted to the four major tertiary-care hospitals in the Rawalpindi-Islamabad region of Pakistan during the peak of the first wave of COVID-19 in the country (May-July 2020). Results: Antibiotics were the most common choice out of all the therapies employed, and they were used as first line of treatment for COVID-19. Azithromycin was the most prescribed drug for treatment. No monthly trend was observed in the choice of antibiotics, and these drugs appeared to be a random but favored choice throughout the months of the study. It was also noted that even antibiotics used for multidrug resistant infections were prescribed irrespective of the severity or progression of the infection. The results of the analysis are alarming, as this approach may lead to antibiotic resistance and complications in immunocompromised patients with COVID-19. A total of 1562 patients (1064 male, 68.1%, and 498 female, 31.9%) with a mean age of 47.35 years (SD 17.03) were included in the study. The highest frequency of patient hospitalizations occurred in June (846/1562, 54.2%). Conclusions: Guidelines for a targeted treatment regime are needed to control related complications and to limit the misuse of antibiotics in the management of COVID-19. %M 33945498 %R 10.2196/28594 %U https://publichealth.jmir.org/2021/5/e28594 %U https://doi.org/10.2196/28594 %U http://www.ncbi.nlm.nih.gov/pubmed/33945498 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 5 %P e23305 %T Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study %A Jang,Beakcheol %A Kim,Inhwan %A Kim,Jong Wook %+ Department of Computer Science, Sangmyung Univerisity, 20, Hongjimun 2-gil, Jongno-gu, Seoul, 03016, Republic of Korea, 82 027817590, jkim@smu.ac.kr %K influenza %K training data extraction %K keyword %K sorting %K word embedding %K Pearson correlation coefficient %K long short-term memory %K surveillance %K infodemiology %K infoveillance %K model %D 2021 %7 25.5.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective: In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods: Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results: Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions: The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction. %M 34032577 %R 10.2196/23305 %U https://medinform.jmir.org/2021/5/e23305 %U https://doi.org/10.2196/23305 %U http://www.ncbi.nlm.nih.gov/pubmed/34032577 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e27806 %T A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study %A Yu,Cheng-Sheng %A Chang,Shy-Shin %A Chang,Tzu-Hao %A Wu,Jenny L %A Lin,Yu-Jiun %A Chien,Hsiung-Fei %A Chen,Ray-Jade %+ Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, No.250, Wuxing St.,, Taipei, 11031, Taiwan, 886 227372181 ext 3966, rayjchen@tmu.edu.tw %K COVID-19 %K artificial intelligence %K time series %K deep learning %K machine learning %K statistical analysis %K pandemic %K data visualization %D 2021 %7 20.5.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures. Objective: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. Methods: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. Results: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. Conclusions: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic. %M 33900932 %R 10.2196/27806 %U https://www.jmir.org/2021/5/e27806 %U https://doi.org/10.2196/27806 %U http://www.ncbi.nlm.nih.gov/pubmed/33900932 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e27609 %T Notes From the Field: The Combined Effects of Tocilizumab and Remdesivir in a Patient With Severe COVID-19 and Cytokine Release Syndrome %A Ali,Sabahat %A Khalid,Sundas %A Afridi,Maham %A Akhtar,Samar %A Khader,Yousef S %A Akhtar,Hashaam %+ Yusra Institute of Pharmaceutical Sciences, Zaraj Housing Society, Main GT Road, Islamabad, 44000, Pakistan, 92 3215272489, hashaamakhtar@gmail.com %K COVID-19 %K remdesivir %K treatment %K tocilizumab %D 2021 %7 19.5.2021 %9 Notes from the Field %J JMIR Public Health Surveill %G English %X SARS-CoV-2 is known to cause severe bilateral pneumonia and acute respiratory distress syndrome or COVID-19 in patients, which can be debilitating and even fatal. With no drugs or vaccines available yet, a wide range of treatment regimens used are being repurposed. The need of the hour is to analyze various currently available regimens and devise a treatment plan that is most effective for COVID-19. Here we describe the case of a 68-year-old man with hypertension and diabetes, exhibiting symptoms of cough and shortness of breath, who presented at the emergency department of our hospital. Chest computed tomography revealed bilateral ground glass opacities that were indicative of COVID-19, and a computed tomography score of 24 was indicative of severe pulmonary pneumonia. He tested positive for COVID-19. His treatment regimen included the use of convalescent plasma, oxygen therapy, steroids, high-dose antibiotics, broad-spectrum antiviral remdesivir, and anti–interleukin-6 monoclonal antibody (Tocilizumab) at various stages of the disease. Oxygen supplementation was required at the time of admission. The patient initially developed a cytokine release storm, and oxygen supplementation was initiated to manage his condition. Supportive care and multiple treatment regimens were used to successfully recover the patient’s health. With a rapid increase in number of confirmed cases worldwide, COVID-19 has become a major challenge to our health care system. With no available vaccines currently, the establishment of a combination of therapeutic drugs that effectively reduce disease progression is of utmost importance. %M 34009133 %R 10.2196/27609 %U https://publichealth.jmir.org/2021/5/e27609 %U https://doi.org/10.2196/27609 %U http://www.ncbi.nlm.nih.gov/pubmed/34009133 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e27638 %T Impact of the Protracted War in Yemen on the Acute Flaccid Paralysis Surveillance System: Retrospective Descriptive Analysis %A Al-Dubaiee,Riham %A AL Qassimi,Mutaher %A Al-Dar,Ahmed %A Al Serouri,Abdulwahed %A Khader,Yousef %+ Ministry of Public Health and Population, Al-Hasaba, Mazda street, Sana'a, Yemen, 967 776417333, rihamm10@yahoo.com %K acute flaccid paralysis %K surveillance indicators %K war %K Yemen %D 2021 %7 19.5.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Highly sensitive acute flaccid paralysis (AFP) surveillance, which includes immediate case investigation and specimen collection, is critical for achieving global polio eradication. In Yemen, the Acute Flaccid Paralysis Surveillance System (AFPSS) was launched in 1998 to achieve the polio eradication target. Although Yemen was certified as a polio-free country in 2009, the protracted war since 2015 has placed the country at risk for polio reemergence. Objective: The objectives of this analysis were to evaluate the performance of the Yemen AFPSS at both the national and governorate levels, and to assess the impact of the ongoing war on the performance. Methods: Retrospective descriptive analysis was performed on Yemen secondary AFP surveillance data for the years 2014 (before the war) and 2015-2017 (during the war). Data comprising all children <15 years old reported as having AFP were included in the analysis. AFP surveillance performance was evaluated using World Health Organization–specified AFP surveillance indicators. Results: At the national level, all indicators were met before and after the war except for “lab results received within ≤28 days,” which was unmet since the war erupted. Furthermore, the indicator “stool specimens arriving at a central level within ≤3 days” was unmet after the war but only in 2017. At the governorate level, although the indicators “adequacy” and “stool specimens arriving at the laboratory in good condition” were met before the war in all governorates, the former indicator was unmet in 9 (41%) governorates since the war erupted and the latter indicator was also unmet in 9 governorates (41%) but only in 2017. Conclusions: The findings show that some AFP surveillance indicators were negatively impacted by eruption of the war in Yemen due to closure of the Sana’a capital airport and postponement of sample shipment to the reference laboratory, which remained under long-term poor storage conditions. To ensure rapid detection of polio cases, improving specimen collection, storage, and transportation, together with proper and timely shipment of specimens to the reference laboratory should be considered. %M 34009132 %R 10.2196/27638 %U https://publichealth.jmir.org/2021/5/e27638 %U https://doi.org/10.2196/27638 %U http://www.ncbi.nlm.nih.gov/pubmed/34009132 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e27268 %T Morbidity and Mortality Associated with Typhoid Fever Among Hospitalized Patients in Hyderabad District, Pakistan, 2017-2018: Retrospective Record Review %A Fatima,Munaza %A Kumar,Santosh %A Hussain,Mudassar %A Memon,Naveed Masood %A Vighio,Anum %A Syed,Muhammad Asif %A Chaudhry,Ambreen %A Hussain,Zakir %A Baig,Zeeshan Iqbal %A Baig,Mirza Amir %A Asghar,Rana Jawad %A Ikram,Aamer %A Khader,Yousef %+ National Institute of Health Pakistan, Park Road, Chak Shehzad, Islamabad, 44000, Pakistan, 92 519255302, ambr.chaudhry@gmail.com %K antimicrobial resistance %K complications %K control drug resistance %K extensive drug resistance %K hospitalization %K Hyderabad %K ileal perforation %K medical records %K microbiological %K morbidity %K mortality %K Pakistan %K prevention %K typhoid %D 2021 %7 17.5.2021 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Hyderabad, Pakistan, was the first city to witness an outbreak of extensively drug resistant (XDR) typhoid fever. The outbreak strain is resistant to ampicillin, chloramphenicol, trimethoprim-sulfamethoxazole, fluoroquinolones, and third-generation cephalosporin, thus greatly limiting treatment options. However, despite over 5000 documented cases, information on mortality and morbidity has been limited. Objective: To address the existing knowledge gap, this study aimed to assess the morbidity and mortality associated with XDR and non-XDR Salmonella serovar Typhi infections in Pakistan. Methods: We reviewed the medical records of culture-confirmed typhoid cases in 5 hospitals in Hyderabad from October 1, 2016, to September 30, 2018. We recorded data on age, gender, onset of fever, physical examination, serological and microbiological test results, treatment before and during hospitalization, duration of hospitalization, complications, and deaths. Results: A total of 1452 culture-confirmed typhoid cases, including 947 (66%) XDR typhoid cases and 505 (34%) non-XDR typhoid cases, were identified. Overall, ≥1 complications were reported in 360 (38%) patients with XDR typhoid and 89 (18%) patients with non-XDR typhoid (P<.001). Ileal perforation was the most commonly reported complication in both patients with XDR typhoid (n=210, 23%) and patients with non-XDR typhoid (n=71, 14%) (P<.001). Overall, mortality was documented among 17 (1.8%) patients with XDR S Typhi infections and 3 (0.6%) patients with non-XDR S Typhi infections (P=.06). Conclusions: As this first XDR typhoid outbreak continues to spread, the increased duration of illness before hospitalization and increased rate of complications have important implications for clinical care and medical costs and heighten the importance of prevention and control measures. %M 33999000 %R 10.2196/27268 %U https://publichealth.jmir.org/2021/5/e27268 %U https://doi.org/10.2196/27268 %U http://www.ncbi.nlm.nih.gov/pubmed/33999000 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e27442 %T Cutaneous Leishmaniasis Outbreak Investigation in Hajjah Governorate, Yemen, in 2018: Case-Control Study %A Nassar,Abdulkareem Ali %A Abdelrazzaq,Mahmood Hasan %A Almahaqri,Ali Hamoud %A Al-Amad,Mohammed Abdullah %A Al Serouri,Abulwahed Abduljabbar %A Khader,Yousef Saleh %+ Sana'a City's Public Health and Population Office, Yemen Ministry of Public Health and Population, Al-Thawra District, Sana'a, Yemen, 967 777228977, abdulkareemnassar@gmail.com %K cutaneous leishmaniasis %K outbreak %K risk factors %K Yemen %K Field Epidemiology Training Program %D 2021 %7 14.5.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Cutaneous leishmaniasis (CL) is endemic in Yemen. About 4440 cases were reported in 2019. On July 23, 2018, a Hajjah governorate surveillance officer notified the Ministry of Public Health and Population about an increase in the number of CL cases in Bani-Oshb, Kuhlan district, Hajjah governorate. On July 24, 2018, Yemen Field Epidemiology Training Program sent a team to perform an investigation. Objective: We aimed to describe a CL outbreak in Hajjah governorate and determine its risk factors. Methods: A descriptive study and case-control study (1:1 ratio) were conducted. Cases included people who met the suspected or confirmed case definition of the World Health Organization and lived in Bani-Oshb subdistrict during the period from August 2017 to July 2018. Controls included people living for at least 1 year in Bani-Oshb without new or old skin lesions. Crude odds ratios (cORs) and adjusted odds ratios (aORs) with 95% CI were used to test the significance of associations. Results: We identified 30 CL cases. Among the 30 patients, 7 (23%) were younger than 5 years, 17 (57%) were 5 to 14 years, 17 (57%) were females, and 23 (77%) had one lesion. The attack rate was 7 per 1000 population in the age group <15 years and 1 per 1000 population in the age group ≥15 years. On bivariate analysis, the following factors were significantly associated with CL: female gender (cOR 5.2, 95% CI 1.7-16.5), malnutrition (cOR 5.2, 95% CI 1.7-16.5), not using a bed net (cOR 14.5, 95% CI 1.7-122.4), poor house lighting (cOR 6.4, 95% CI 2.1-19.7), poor house hygiene (cOR 11.2, 95% CI 3.1-40.7), poor sanitation (cOR 14.5, 95% CI 1.7-122.4), living in houses without window nets (cOR 5.2, 95% CI 1.3-21.2), plantation around the house (cOR 6.5, 95% CI 2.1-20.5), animal barn inside or close to the house (cOR 9.3, 95% CI 1.9-46.7), raising animals (cOR 8.1, 95% CI 1.6-40.7), and having animal dung in or near the house (cOR 6.8, 95% CI 1.7-27.7). The following risk factors remained significant on multivariate stepwise analysis: female gender (aOR 22.7, 95% CI 1.6-320.5), malnutrition (aOR 17.2, 95% CI 1.3-225.8), poor house hygiene (aOR 45.6, 95% CI 2.5-846.4), plantation around the house (aOR 43.8, 95% CI 1.9-1009.9), and raising animals (aOR 287.1, 95% CI 5.4-15205.6). Conclusions: CL was endemic in Hajjah governorate, and an increase in cases was confirmed. Many individual, housing, and animal related factors were shown to contribute to CL endemicity. Implementation of control measures directed toward altering the factors favoring contact among vectors, reservoirs, and susceptible humans is strongly recommended to control future outbreaks. %M 33988521 %R 10.2196/27442 %U https://publichealth.jmir.org/2021/5/e27442 %U https://doi.org/10.2196/27442 %U http://www.ncbi.nlm.nih.gov/pubmed/33988521 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 5 %P e24446 %T Reporting of Differences in Taste Between Branded and Unbranded Cigarettes by Smokers Blinded to Cigarette Branding: Within-Person, Randomized Crossover Study %A BinDhim,Nasser F %A Althumiri,Nora A %A Basyouni,Mada H %A Almubark,Rasha A %A Alkhamaali,Zaied %A Banjar,Weam %A Zamakhshary,Mohammed %A AlKattan,Khaled M %+ Sharik Association for Health Research, 4 Anas ibn Malik, Riyadh, 11778, Saudi Arabia, 966 505435544, nora@althumiri.net %K smoking %K plain packaging %K sensory %K Saudi Arabia %K tobacco %K virtual reality %K cigarettes %D 2021 %7 14.5.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Saudi Arabia implemented a plain tobacco packaging regulation, one of the World Health Organization’s recommended initiatives to help reduce smoking rates, in August 2019. A few weeks after implementation, a large number of smokers complained via various media channels, especially social media (eg, Twitter), that an extreme change in cigarette taste had occurred, frequency of coughing had increased, and for some, shortness of breath had led to hospitalization. Objective: The main objective is to determine whether smokers blinded to cigarette branding report differences in taste between branded and unbranded cigarettes. The secondary objective is to observe the frequency of immediate cough or shortness of breath. Methods: This study employed a within-person, randomized crossover design that recruited current smokers 18 years and older who were cleared upon physical assessment before the experiment. Participants received 6 sequences of different random exposures (3 puffs) to 3 plain-packaged cigarettes (2 from their favorite brand and 1 from another brand as a control) and 3 branded cigarettes (2 from the favorite brand and 1 from another brand as a control). Participants wore virtual reality goggles accompanied by special software to alter visual reality and gloves to alter the touch sensation. Results: This study recruited 18 participants, measured at 6 time points, to produce 108 experiments. Participants were not able to identify the correct type of cigarettes (plain or branded, estimate of fixed effect=−0.01, P=.79). Moreover, there were no differences in the ability of the participants to identify their favorite brand (t107=−0.63, mean 0.47, P=.53). In terms of immediate coughing, out of the 108 experiments, 1 episode of short coughing was observed, which was attributed to the branded cigarette, not the plain-packaged cigarette. Conclusions: After controlling the visual and touch sensations, participants were not able to differentiate between branded and plain-packaged cigarettes in terms of taste or inducing immediate shortness of breath or cough. Interestingly, participants were not able to identify their favorite brand. %M 33988511 %R 10.2196/24446 %U https://formative.jmir.org/2021/5/e24446 %U https://doi.org/10.2196/24446 %U http://www.ncbi.nlm.nih.gov/pubmed/33988511 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e22160 %T Burden of Cervical Cancer in the Eastern Mediterranean Region During the Years 2000 and 2017: Retrospective Data Analysis of the Global Burden of Disease Study %A Safaeian,Fereshteh %A Ghaemimood,Shidrokh %A El-Khatib,Ziad %A Enayati,Sahba %A Mirkazemi,Roksana %A Reeder,Bruce %+ Public Health Graduate Studies, The Bahá'í Institute for Higher Education, Tehran, Iran, 98 9388520250, shidrokh.ghaemi@gmail.com %K cervical cancer %K Eastern Mediterranean Region %K burden of disease %K cancer %K burden %K inequality %K mortality %K preventable disease %D 2021 %7 12.5.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Cervical cancer is a growing health concern, especially in resource-limited settings. Objective: The objective of this study was to assess the burden of cervical cancer mortality and disability-adjusted life years (DALYs) in the Eastern Mediterranean Region (EMR) and globally between the years 2000 and 2017 by using a pooled data analysis approach. Methods: We used an ecological approach at the country level. This included extracting data from publicly available databases and linking them together in the following 3 steps: (1) extraction of data from the Global Burden of Disease (GBD) study in the years 2000 and 2017, (2) categorization of EMR countries according to the World Bank gross domestic product per capita, and (3) linking age-specific population data from the Population Statistics Division of the United Nations (20-29 years, 30-49 years, and >50 years) and GBD’s data with gross national income per capita and globally extracted data, including cervical cancer mortality and DALY numbers and rates per country. The cervical cancer mortality rate was provided by the GBD study using the following formula: number of cervical cancer deaths × 100,000/female population in the respective age group. Results: The absolute number of deaths due to cervical cancer increased from the year 2000 (n=6326) to the year 2017 (n=8537) in the EMR; however, the mortality rate due to this disease decreased from the year 2000 (2.7 per 100,000) to the year 2017 (2.5 per 100,000). According to age-specific data, the age group ≥50 years showed the highest mortality rate in both EMR countries and globally, and the age group of 20-29 years showed the lowest mortality rate both globally and in the EMR countries. Further, the rates of cervical cancer DALYs in the EMR were lower compared to the global rates (2.7 vs 6.8 in 2000 and 2.5 vs 6.8 in 2017 for mortality rate per 100,000; 95.8 vs 222.2 in 2000 and 86.3 vs 211.8 in 2017 for DALY rate per 100,000; respectively). However, the relative difference in the number of DALYs due to cervical cancer between the year 2000 and year 2017 in the EMR was higher than that reported globally (34.9 vs 24.0 for the number of deaths and 23.5 vs 18.1 for the number of DALYs, respectively). Conclusions: We found an increase in the burden of cervical cancer in the EMR as per the data on the absolute number of deaths and DALYs. Further, we found that the health care system has an increased number of cases to deal with, despite the decrease in the absolute number of deaths and DALYs. Cervical cancer is preventable if human papilloma vaccination is taken and early screening is performed. Therefore, we recommend identifying effective vaccination programs and interventions to reduce the burden of this disease. %M 33978592 %R 10.2196/22160 %U https://publichealth.jmir.org/2021/5/e22160 %U https://doi.org/10.2196/22160 %U http://www.ncbi.nlm.nih.gov/pubmed/33978592 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e25753 %T SARS-CoV-2 Surveillance System in Canada: Longitudinal Trend Analysis %A Post,Lori %A Boctor,Michael J %A Issa,Tariq Z %A Moss,Charles B %A Murphy,Robert Leo %A Achenbach,Chad J %A Ison,Michael G %A Resnick,Danielle %A Singh,Lauren %A White,Janine %A Welch,Sarah B %A Oehmke,James F %+ Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, 420 E Superior, Chicago, IL, 60611, United States, 1 203 980 7107, lori.post@northwestern.edu %K global COVID surveillance %K COVID-19 %K COVID-21 %K new COVID strains %K Canada Public Health Surveillance %K Great COVID Shutdown %K Canadian COVID-19 %K surveillance metrics %K wave 2 Canada COVID-19 %K dynamic panel data %K generalized method of the moments %K Canadian econometrics %K Canada SARS-CoV-2 %K Canadian COVID-19 surveillance system %K Canadian COVID transmission speed %K Canadian COVID transmission acceleration %K COVID transmission deceleration %K COVID transmission jerk %K COVID 7-day lag %K Alberta %K British Columbia %K Manitoba %K New Brunswick %K Newfoundland and Labrador %K Northwest Territories %K Nova Scotia %K Nunavut %K Ontario %K Prince Edward Island %K Quebec %K Saskatchewan %K Yukon %D 2021 %7 10.5.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 global pandemic has disrupted structures and communities across the globe. Numerous regions of the world have had varying responses in their attempts to contain the spread of the virus. Factors such as public health policies, governance, and sociopolitical climate have led to differential levels of success at controlling the spread of SARS-CoV-2. Ultimately, a more advanced surveillance metric for COVID-19 transmission is necessary to help government systems and national leaders understand which responses have been effective and gauge where outbreaks occur. Objective: The goal of this study is to provide advanced COVID-19 surveillance metrics for Canada at the country, province, and territory level that account for shifts in the pandemic including speed, acceleration, jerk, and persistence. Enhanced surveillance identifies risks for explosive growth and regions that have controlled outbreaks successfully. Methods: Using a longitudinal trend analysis study design, we extracted 62 days of COVID-19 data from Canadian public health registries for 13 provinces and territories. We used an empirical difference equation to measure the daily number of cases in Canada as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results: We compare the week of February 7-13, 2021, with the week of February 14-20, 2021. Canada, as a whole, had a decrease in speed from 8.4 daily new cases per 100,000 population to 7.5 daily new cases per 100,000 population. The persistence of new cases during the week of February 14-20 reported 7.5 cases that are a result of COVID-19 transmissions 7 days earlier. The two most populous provinces of Ontario and Quebec both experienced decreases in speed from 7.9 and 11.5 daily new cases per 100,000 population for the week of February 7-13 to speeds of 6.9 and 9.3 for the week of February 14-20, respectively. Nunavut experienced a significant increase in speed during this time, from 3.3 daily new cases per 100,000 population to 10.9 daily new cases per 100,000 population. Conclusions: Canada excelled at COVID-19 control early on in the pandemic, especially during the first COVID-19 shutdown. The second wave at the end of 2020 resulted in a resurgence of the outbreak, which has since been controlled. Enhanced surveillance identifies outbreaks and where there is the potential for explosive growth, which informs proactive health policy. %M 33852410 %R 10.2196/25753 %U https://publichealth.jmir.org/2021/5/e25753 %U https://doi.org/10.2196/25753 %U http://www.ncbi.nlm.nih.gov/pubmed/33852410 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e18593 %T Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis %A Hswen,Yulin %A Zhang,Amanda %A Ventelou,Bruno %+ Department of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, University of California San Francisco, 490 Illinois St, San Francisco, CA, 94158, United States, 1 415 476 1000, yulin.hswen@ucsf.edu %K digital epidemiology %K Google queries %K asthma %K symptoms %K health information seeking %D 2021 %7 10.5.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Asthma affects over 330 million people worldwide. Timing of an asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms. Objective: This study evaluates the utility of the internet search query data for the identification of the onset of asthma symptoms. Methods: Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks –1 and –2 as exogenous variables were conducted to validate our correlation results. Results: Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04). Conclusions: Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset. %M 33970108 %R 10.2196/18593 %U https://publichealth.jmir.org/2021/5/e18593 %U https://doi.org/10.2196/18593 %U http://www.ncbi.nlm.nih.gov/pubmed/33970108 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e17240 %T Validation of 4D Components for Measuring Quality of the Public Health Data Collection Process: Elicitation Study %A Chen,Hong %A Yu,Ping %A Hailey,David %A Cui,Tingru %+ School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Northfields Ave, Wollongong, 2522, Australia, 61 2 42215412, ping@uow.edu.au %K data quality %K data collection %K HIV/AIDS %K public health informatics %K health information systems %K component validation %K expert elicitation %K public health %K health informatics %D 2021 %7 10.5.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Identification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice. Objective: This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework. Methods: Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework. Results: A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices. Conclusions: This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction. %M 33970112 %R 10.2196/17240 %U https://www.jmir.org/2021/5/e17240 %U https://doi.org/10.2196/17240 %U http://www.ncbi.nlm.nih.gov/pubmed/33970112 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 5 %P e27412 %T The Impact of Implementing the Egypt Pandemic Preparedness Plan for Acute Respiratory Infections in Combating the Early Stage of the COVID-19 Pandemic, February-July 2020: Viewpoint %A Abu El Sood,Hanaa %A Abu Kamer,Shimaa Ali %A Kamel,Reham %A Magdy,Hesham %A Osman,Fatma S %A Fahim,Manal %A Mohsen,Amira %A AbdelFatah,Mohamad %A Hassany,Mohamed %A Afifi,Salma %A Eid,Alaa %+ Department of Epidemiology and Surveillance, Preventive Sector, Ministry of Health and Population, 3 Magles Elshaab St, Cairo, , Egypt, 20 01282833078 ext 002, hanaaabuelsood@hotmail.com %K pandemic preparedness %K Egypt %K ARI %K epidemic mitigation %K COVID-19 %D 2021 %7 7.5.2021 %9 Viewpoint %J JMIR Public Health Surveill %G English %X This article briefly describes Egypt’s acute respiratory infection (ARI) epidemic preparedness and containment plan and illustrates the impact of implementation of the plan on combating the early stage of the COVID-19 epidemic in Egypt. Pillars of the plan include crisis management, enhancing surveillance systems and contact tracing, case and hospital management, raising community awareness, and quarantine and entry points. To identify the impact of the implementation of the plan on epidemic mitigation, a literature review was performed of studies published from Egypt in the early stage of the pandemic. In addition, data for patients with COVID-19 from February to July 2020 were obtained from the National Egyptian Surveillance system and studied to describe the situation in the early stage of the epidemic in Egypt. The lessons learned indicated that the single most important key to success in early-stage epidemic containment is the commitment of all partners to a predeveloped and agreed-upon preparedness plan. This information could be useful for other countries in the region and worldwide in mitigating future anticipated ARI epidemics and pandemics. Postepidemic evaluation is needed to better assess Egypt’s national response to the COVID-19 epidemic. %M 33830932 %R 10.2196/27412 %U https://publichealth.jmir.org/2021/5/e27412 %U https://doi.org/10.2196/27412 %U http://www.ncbi.nlm.nih.gov/pubmed/33830932 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e25695 %T Surveillance of the Second Wave of COVID-19 in Europe: Longitudinal Trend Analyses %A Post,Lori %A Culler,Kasen %A Moss,Charles B %A Murphy,Robert L %A Achenbach,Chad J %A Ison,Michael G %A Resnick,Danielle %A Singh,Lauren Nadya %A White,Janine %A Boctor,Michael J %A Welch,Sarah B %A Oehmke,James Francis %+ Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, 420 E Superior, Chicago, IL, 60611, United States, 1 203 980 7107, lori.post@northwestern.edu %K SARS-CoV-2 surveillance %K wave two %K second wave %K global COVID surveillance %K Europe Public Health Surveillance %K Europe COVID %K Europe surveillance metrics %K dynamic panel data %K generalized method of the moments %K Europe econometrics %K Europe SARS-CoV-2 %K Europe COVID surveillance system %K European COVID transmission speed %K European COVID transmission acceleration %K COVID transmission deceleration %K COVID transmission jerk %K COVID 7-day lag %K SARS-CoV-2 %K Arellano-Bond estimator %K GMM %K Albania %K Andorra %K Austria %K Belarus %K Belgium %K Bosnia and Herzegovina %K Bulgaria %K Croatia %K Czech Republic %K Denmark %K Estonia %K Finland %K France %K Germany %K Greece %K Greenland %K Hungary %K Iceland %K Ireland %K Isle of Man %K Italy %K Latvia %K Liechtenstein %K Lithuania %K Luxembourg %K Moldova %K Monaco %K Montenegro %K Netherlands %K Norway %K Poland %K Portugal %K Romania %K San Marino %K Serbia %K Slovakia %K Slovenia %K Spain %K Sweden %K Switzerland %K Ukraine %K United Kingdom %K Vatican City %D 2021 %7 28.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has severely impacted Europe, resulting in a high caseload and deaths that varied by country. The second wave of the COVID-19 pandemic has breached the borders of Europe. Public health surveillance is necessary to inform policy and guide leaders. Objective: This study aimed to provide advanced surveillance metrics for COVID-19 transmission that account for weekly shifts in the pandemic, speed, acceleration, jerk, and persistence, to better understand countries at risk for explosive growth and those that are managing the pandemic effectively. Methods: We performed a longitudinal trend analysis and extracted 62 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in Europe as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results: New COVID-19 cases slightly decreased from 158,741 (week 1, January 4-10, 2021) to 152,064 (week 2, January 11-17, 2021), and cumulative cases increased from 22,507,271 (week 1) to 23,890,761 (week 2), with a weekly increase of 1,383,490 between January 10 and January 17. France, Germany, Italy, Spain, and the United Kingdom had the largest 7-day moving averages for new cases during week 1. During week 2, the 7-day moving average for France and Spain increased. From week 1 to week 2, the speed decreased (37.72 to 33.02 per 100,000), acceleration decreased (0.39 to –0.16 per 100,000), and jerk increased (–1.30 to 1.37 per 100,000). Conclusions: The United Kingdom, Spain, and Portugal, in particular, are at risk for a rapid expansion in COVID-19 transmission. An examination of the European region suggests that there was a decrease in the COVID-19 caseload between January 4 and January 17, 2021. Unfortunately, the rates of jerk, which were negative for Europe at the beginning of the month, reversed course and became positive, despite decreases in speed and acceleration. Finally, the 7-day persistence rate was higher during week 2 than during week 1. These measures indicate that the second wave of the pandemic may be subsiding, but some countries remain at risk for new outbreaks and increased transmission in the absence of rapid policy responses. %M 33818391 %R 10.2196/25695 %U https://publichealth.jmir.org/2021/4/e25695 %U https://doi.org/10.2196/25695 %U http://www.ncbi.nlm.nih.gov/pubmed/33818391 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e27433 %T Coinfection With SARS-CoV-2 and Influenza A(H1N1) in a Patient Seen at an Influenza-like Illness Surveillance Site in Egypt: Case Report %A Fahim,Manal %A Ghonim,Hanaa Abu El Sood %A Roshdy,Wael H %A Naguib,Amel %A Elguindy,Nancy %A AbdelFatah,Mohamad %A Hassany,Mohamed %A Mohsen,Amira %A Afifi,Salma %A Eid,Alaa %+ Department of Surveillance and Epidemiology, Ministry of Health and Population, 3 Magles El Shab Street, Cairo, , Egypt, 20 01222598200 ext 202, fahimmanal@yahoo.com %K influenza-like Illness %K pandemic %K SARS-CoV-2 %K COVID-19 %K influenza %K virus %K case study %K Egypt %K flu %K coinfection %K infectious disease %K surveillance %K outcome %K demographic %D 2021 %7 28.4.2021 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Sentinel surveillance of influenza-like illness (ILI) in Egypt started in 2000 at 8 sentinel sites geographically distributed all over the country. In response to the COVID-19 pandemic, SARS-CoV-2 was added to the panel of viral testing by polymerase chain reaction for the first 2 patients with ILI seen at one of the sentinel sites. We report the first SARS-CoV-2 and influenza A(H1N1) virus co-infection with mild symptoms detected through routine ILI surveillance in Egypt. Objective: This report aims to describe how the case was identified and the demographic and clinical characteristics and outcomes of the patient. Methods: The case was identified by Central Public Health Laboratory staff, who contacted the ILI sentinel surveillance officer at the Ministry of Health. The case patient was contacted through a telephone call. Detailed information about the patient’s clinical picture, course of disease, and outcome was obtained. The contacts of the patient were investigated for acute respiratory symptoms, disease confirmation, and outcomes. Results: Among 510 specimens collected from patients with ILI symptoms from October 2019 to August 2020, 61 (12.0%) were COVID-19–positive and 29 (5.7%) tested positive for influenza, including 15 (51.7%) A(H1N1), 11 (38.0%) A(H3N2), and 3 (10.3%) influenza B specimens. A 21-year-old woman was confirmed to have SARS-CoV-2 and influenza A(H1N1) virus coinfection. She had a high fever of 40.2 °C and mild respiratory symptoms that resolved within 2 days with symptomatic treatment. All five of her family contacts had mild respiratory symptoms 2-3 days after exposure to the confirmed case, and their symptoms resolved without treatment or investigation. Conclusions: This case highlights the possible occurrence of SARS-CoV-2/influenza A(H1N1) coinfection in younger and healthy people, who may resolve the infection rapidly. We emphasize the usefulness of the surveillance system for detection of viral causative agents of ILI and recommend broadening of the testing panel, especially if it can guide case management. %M 33784634 %R 10.2196/27433 %U https://publichealth.jmir.org/2021/4/e27433 %U https://doi.org/10.2196/27433 %U http://www.ncbi.nlm.nih.gov/pubmed/33784634 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e26628 %T Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation %A Yeung,Arnold YS %A Roewer-Despres,Francois %A Rosella,Laura %A Rudzicz,Frank %+ Department of Computer Science, University of Toronto, 27 King's College Cir, Toronto, ON, M5S 3H7, Canada, 1 416 978 2011, arnoldyeung@cs.toronto.edu %K COVID-19 %K machine learning %K nonpharmaceutical interventions %K cultural dimensions %K random forest %K AdaBoost %K forecast %K informatics %K epidemiology %K artificial intelligence %D 2021 %7 23.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects. Objective: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth—the percentage change in total cumulative cases—across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms. Methods: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non–time series machine learning models in predicting confirmed infection growth. We used three validation methods—in-distribution, out-of-distribution, and country-based cross-validation—for the evaluation, each of which was applicable to a different use case of the models. Results: Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case. Conclusions: This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections. %M 33844636 %R 10.2196/26628 %U https://www.jmir.org/2021/4/e26628 %U https://doi.org/10.2196/26628 %U http://www.ncbi.nlm.nih.gov/pubmed/33844636 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e23806 %T Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study %A Henriksen,André %A Johannessen,Erlend %A Hartvigsen,Gunnar %A Grimsgaard,Sameline %A Hopstock,Laila Arnesdatter %+ Department of Community Medicine, UiT The Arctic University of Norway, Postboks 6050 langnes, Tromsø, 9037, Norway, 47 77645214, andre.henriksen@uit.no %K COVID-19 %K energy expenditure %K steps %K smart watch %K fitness tracker %K actigraphy %K public health %K lockdown %K SARS-CoV-2 %K pandemic %K wearables %D 2021 %7 23.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems. Objective: The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. Methods: We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests. Results: Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020. Conclusions: mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period. %M 33843598 %R 10.2196/23806 %U https://publichealth.jmir.org/2021/4/e23806 %U https://doi.org/10.2196/23806 %U http://www.ncbi.nlm.nih.gov/pubmed/33843598 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e26042 %T Impact of Firearm Surveillance on Gun Control Policy: Regression Discontinuity Analysis %A Post,Lori %A Mason,Maryann %A Singh,Lauren Nadya %A Wleklinski,Nicholas P %A Moss,Charles B %A Mohammad,Hassan %A Issa,Tariq Z %A Akhetuamhen,Adesuwa I %A Brandt,Cynthia A %A Welch,Sarah B %A Oehmke,James Francis %+ Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, 420 E Superior, Chicago, IL, 60611, United States, 1 203 980 7107, lori.post@northwestern.edu %K firearm surveillance %K assault weapons ban %K large-capacity magazines %K guns control policy %K mass shootings %K regression lines of discontinuity %D 2021 %7 22.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Public mass shootings are a significant public health problem that require ongoing systematic surveillance to test and inform policies that combat gun injuries. Although there is widespread agreement that something needs to be done to stop public mass shootings, opinions on exactly which policies that entails vary, such as the prohibition of assault weapons and large-capacity magazines. Objective: The aim of this study was to determine if the Federal Assault Weapons Ban (FAWB) (1994-2004) reduced the number of public mass shootings while it was in place. Methods: We extracted public mass shooting surveillance data from the Violence Project that matched our inclusion criteria of 4 or more fatalities in a public space during a single event. We performed regression discontinuity analysis, taking advantage of the imposition of the FAWB, which included a prohibition on large-capacity magazines in addition to assault weapons. We estimated a regression model of the 5-year moving average number of public mass shootings per year for the period of 1966 to 2019 controlling for population growth and homicides in general, introduced regression discontinuities in the intercept and a time trend for years coincident with the federal legislation (ie, 1994-2004), and also allowed for a differential effect of the homicide rate during this period. We introduced a second set of trend and intercept discontinuities for post-FAWB years to capture the effects of termination of the policy. We used the regression results to predict what would have happened from 1995 to 2019 had there been no FAWB and also to project what would have happened from 2005 onward had it remained in place. Results: The FAWB resulted in a significant decrease in public mass shootings, number of gun deaths, and number of gun injuries. We estimate that the FAWB prevented 11 public mass shootings during the decade the ban was in place. A continuation of the FAWB would have prevented 30 public mass shootings that killed 339 people and injured an additional 1139 people. Conclusions: This study demonstrates the utility of public health surveillance on gun violence. Surveillance informs policy on whether a ban on assault weapons and large-capacity magazines reduces public mass shootings. As society searches for effective policies to prevent the next mass shooting, we must consider the overwhelming evidence that bans on assault weapons and/or large-capacity magazines work. %M 33783360 %R 10.2196/26042 %U https://publichealth.jmir.org/2021/4/e26042 %U https://doi.org/10.2196/26042 %U http://www.ncbi.nlm.nih.gov/pubmed/33783360 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e20699 %T The Impact of COVID-19 Management Policies Tailored to Airborne SARS-CoV-2 Transmission: Policy Analysis %A Telles,Charles Roberto %A Roy,Archisman %A Ajmal,Mohammad Rehan %A Mustafa,Syed Khalid %A Ahmad,Mohammad Ayaz %A de la Serna,Juan Moises %A Frigo,Elisandro Pires %A Rosales,Manuel Hernández %+ Internal Control Center, Secretary of State for Education and Sport of Paraná, Av Água Verde, 2140, Curitiba, 80240-900, Brazil, 55 41996056321, charlestelles@seed.pr.gov.br %K social distancing policies %K COVID-19 %K airborne transmission %K convergence and stability properties %D 2021 %7 21.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Daily new COVID-19 cases from January to April 2020 demonstrate varying patterns of SARS-CoV-2 transmission across different geographical regions. Constant infection rates were observed in some countries, whereas China and South Korea had a very low number of daily new cases. In fact, China and South Korea successfully and quickly flattened their COVID-19 curve. To understand why this was the case, this paper investigated possible aerosol-forming patterns in the atmosphere and their relationship to the policy measures adopted by select countries. Objective: The main research objective was to compare the outcomes of policies adopted by countries between January and April 2020. Policies included physical distancing measures that in some cases were associated with mask use and city disinfection. We investigated whether the type of social distancing framework adopted by some countries (ie, without mask use and city disinfection) led to the continual dissemination of SARS-CoV-2 (daily new cases) in the community during the study period. Methods: We examined the policies used as a preventive framework for virus community transmission in some countries and compared them to the policies adopted by China and South Korea. Countries that used a policy of social distancing by 1-2 m were divided into two groups. The first group consisted of countries that implemented social distancing (1-2 m) only, and the second comprised China and South Korea, which implemented distancing with additional transmission/isolation measures using masks and city disinfection. Global daily case maps from Johns Hopkins University were used to provide time-series data for the analysis. Results: The results showed that virus transmission was reduced due to policies affecting SARS-CoV-2 propagation over time. Remarkably, China and South Korea obtained substantially better results than other countries at the beginning of the epidemic due to their adoption of social distancing (1-2 m) with the additional use of masks and sanitization (city disinfection). These measures proved to be effective due to the atmosphere carrier potential of SARS-CoV-2 transmission. Conclusions: Our findings confirm that social distancing by 1-2 m with mask use and city disinfection yields positive outcomes. These strategies should be incorporated into prevention and control policies and be adopted both globally and by individuals as a method to fight the COVID-19 pandemic. %M 33729168 %R 10.2196/20699 %U https://publichealth.jmir.org/2021/4/e20699 %U https://doi.org/10.2196/20699 %U http://www.ncbi.nlm.nih.gov/pubmed/33729168 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e26645 %T Evolving Epidemiological Characteristics of COVID-19 in Hong Kong From January to August 2020: Retrospective Study %A Kwok,Kin On %A Wei,Wan In %A Huang,Ying %A Kam,Kai Man %A Chan,Emily Ying Yang %A Riley,Steven %A Chan,Ho Hin Henry %A Hui,David Shu Cheong %A Wong,Samuel Yeung Shan %A Yeoh,Eng Kiong %+ JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Room 419, 4/F, School of Public Health Building, Prince of Wales Hospital, Shatin, NT, Hong Kong, China (Hong Kong), 852 22528405, kkokwok@cuhk.edu.hk %K SARS-CoV-2 %K COVID-19 %K evolving epidemiology %K containment delay %K serial interval %K Hong Kong %K epidemiology %K public health %K transmission %K China %K intervention %K case study %D 2021 %7 16.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: COVID-19 has plagued the globe, with multiple SARS-CoV-2 clusters hinting at its evolving epidemiology. Since the disease course is governed by important epidemiological parameters, including containment delays (time between symptom onset and mandatory isolation) and serial intervals (time between symptom onsets of infector-infectee pairs), understanding their temporal changes helps to guide interventions. Objective: This study aims to characterize the epidemiology of the first two epidemic waves of COVID-19 in Hong Kong by doing the following: (1) estimating the containment delays, serial intervals, effective reproductive number (Rt), and proportion of asymptomatic cases; (2) identifying factors associated with the temporal changes of the containment delays and serial intervals; and (3) depicting COVID-19 transmission by age assortativity and types of social settings. Methods: We retrieved the official case series and the Apple mobility data of Hong Kong from January-August 2020. The empirical containment delays and serial intervals were fitted to theoretical distributions, and factors associated with their temporal changes were quantified in terms of percentage contribution (the percentage change in the predicted outcome from multivariable regression models relative to a predefined comparator). Rt was estimated with the best fitted distribution for serial intervals. Results: The two epidemic waves were characterized by imported cases and clusters of local cases, respectively. Rt peaked at 2.39 (wave 1) and 3.04 (wave 2). The proportion of asymptomatic cases decreased from 34.9% (0-9 years) to 12.9% (≥80 years). Log-normal distribution best fitted the 1574 containment delays (mean 5.18 [SD 3.04] days) and the 558 serial intervals (17 negative; mean 4.74 [SD 4.24] days). Containment delays decreased with involvement in a cluster (percentage contribution: 10.08%-20.73%) and case detection in the public health care sector (percentage contribution: 27.56%, 95% CI 22.52%-32.33%). Serial intervals decreased over time (6.70 days in wave 1 versus 4.35 days in wave 2) and with tertiary transmission or beyond (percentage contribution: –50.75% to –17.31%), but were lengthened by mobility (percentage contribution: 0.83%). Transmission within the same age band was high (18.1%). Households (69.9%) and social settings (20.3%) were where transmission commonly occurred. Conclusions: First, the factors associated with reduced containment delays suggested government-enacted interventions were useful for achieving outbreak control and should be further encouraged. Second, the shorter serial intervals associated with the composite mobility index calls for empirical surveys to disentangle the role of different contact dimensions in disease transmission. Third, the presymptomatic transmission and asymptomatic cases underscore the importance of remaining vigilant about COVID-19. Fourth, the time-varying epidemiological parameters suggest the need to incorporate their temporal variations when depicting the epidemic trajectory. Fifth, the high proportion of transmission events occurring within the same age group supports the ban on gatherings outside of households, and underscores the need for residence-centered preventive measures. %M 33750740 %R 10.2196/26645 %U https://www.jmir.org/2021/4/e26645 %U https://doi.org/10.2196/26645 %U http://www.ncbi.nlm.nih.gov/pubmed/33750740 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e27045 %T Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis %A Zeng,Chengbo %A Zhang,Jiajia %A Li,Zhenlong %A Sun,Xiaowen %A Olatosi,Bankole %A Weissman,Sharon %A Li,Xiaoming %+ South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC, 29208, United States, 1 8034775298, czeng@email.sc.edu %K COVID-19 %K mobility %K incidence %K South Carolina %D 2021 %7 13.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective: The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results: Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions: Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences. %M 33784239 %R 10.2196/27045 %U https://www.jmir.org/2021/4/e27045 %U https://doi.org/10.2196/27045 %U http://www.ncbi.nlm.nih.gov/pubmed/33784239 %0 Journal Article %@ 2563-6316 %I JMIR Publications %V 2 %N 2 %P e27254 %T Mass Testing With Contact Tracing Compared to Test and Trace for the Effective Suppression of COVID-19 in the United Kingdom: Systematic Review %A Mbwogge,Mathew %+ London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom, 44 07424409211, mathew.ngime@alumni.lshtm.ac.uk %K COVID-19 %K SARS-CoV-2 %K test and trace %K universal testing %K mass testing %K contact tracing %K infection surveillance %K prevention and control %K review %D 2021 %7 12.4.2021 %9 Review %J JMIRx Med %G English %X Background: Making testing available to everyone and tracing contacts might be the gold standard to control COVID-19. Many countries including the United Kingdom have relied on the symptom-based test and trace strategy in bringing the COVID-19 pandemic under control. The effectiveness of a test and trace strategy based on symptoms has been questionable and has failed to meet testing and tracing needs. This is further exacerbated by it not being delivered at the point of care, leading to rising cases and deaths. Increases in COVID-19 cases and deaths in the United Kingdom despite performing the highest number of tests in Europe suggest that symptom-based testing and contact tracing might not be effective as a control strategy. An alternative strategy is making testing available to all. Objective: The primary objective of this review was to compare mass testing and contact tracing with the conventional test and trace method in the suppression of SARS-CoV-2 infections. The secondary objective was to determine the proportion of asymptomatic COVID-19 cases reported during mass testing interventions. Methods: Literature in English was searched from September through December 2020 in Google Scholar, ScienceDirect, Mendeley, and PubMed. Search terms included “mass testing,” “test and trace,” “contact tracing,” “COVID-19,” “SARS-CoV-2,” “effectiveness,” “asymptomatic,” “symptomatic,” “community screening,” “UK,” and “2020.” Search results were synthesized without meta-analysis using the direction of effect as the standardized metric and vote counting as the synthesis metric. A statistical synthesis was performed using Stata 14.2. Tabular and graphical methods were used to present findings. Results: The literature search yielded 286 articles from Google Scholar, 20 from ScienceDirect, 14 from Mendeley, 27 from PubMed, and 15 through manual search. A total of 35 articles were included in the review, with a sample size of nearly 1 million participants. We found a 76.9% (10/13, 95% CI 46.2%-95.0%; P=.09) majority vote in favor of the intervention under the primary objective. The overall proportion of asymptomatic cases among those who tested positive and in the tested sample populations under the secondary objective was 40.7% (1084/2661, 95% CI 38.9%-42.6%) and 0.0% (1084/9,942,878, 95% CI 0.0%-0.0%), respectively. Conclusions: There was low-level but promising evidence that mass testing and contact tracing could be more effective in bringing the virus under control and even more effective if combined with social distancing and face coverings. The conventional test and trace method should be superseded by decentralized and regular mass rapid testing and contact tracing, championed by general practitioner surgeries and low-cost community services. %M 33857269 %R 10.2196/27254 %U https://xmed.jmir.org/2021/2/e27254 %U https://doi.org/10.2196/27254 %U http://www.ncbi.nlm.nih.gov/pubmed/33857269 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 4 %P e24192 %T An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study %A Staffini,Alessio %A Svensson,Akiko Kishi %A Chung,Ung-Il %A Svensson,Thomas %+ Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan, 81 080 7058 1309, alessio.staffini@bocconialumni.it %K computational epidemiology %K COVID-19 %K SARS-CoV-2 %K agent-based modeling %K public health %K computational models %K modeling %K agent %K spread %K computation %K epidemiology %K policy %D 2021 %7 6.4.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations. Objective: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health. Methods: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection. Results: The 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths. Conclusions: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures. %M 33750735 %R 10.2196/24192 %U https://medinform.jmir.org/2021/4/e24192 %U https://doi.org/10.2196/24192 %U http://www.ncbi.nlm.nih.gov/pubmed/33750735 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e22880 %T The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis %A Asgari Mehrabadi,Milad %A Dutt,Nikil %A Rahmani,Amir M %+ Department of Electrical Engineering and Computer Science, University of California Irvine, Berk Hall, 1st Floor, Irvine, CA, 92617, United States, 1 949 506 8187, masgarim@uci.edu %K bars %K coronavirus %K COVID-19 %K deep learning %K infodemiology %K infoveillance %K Google Trends %K LSTM %K machine learning %K restaurants %D 2021 %7 6.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes. %M 33690143 %R 10.2196/22880 %U https://publichealth.jmir.org/2021/4/e22880 %U https://doi.org/10.2196/22880 %U http://www.ncbi.nlm.nih.gov/pubmed/33690143 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 3 %P e27317 %T Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis %A Chu,Amanda MY %A Chan,Jacky NL %A Tsang,Jenny TY %A Tiwari,Agnes %A So,Mike KP %+ Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China (Hong Kong), 852 23587726, immkpso@ust.hk %K air traffic %K coronavirus %K COVID-19 %K human mobility %K network analysis %K travel restrictions %D 2021 %7 29.3.2021 %9 Rapid Surveillance Report %J JMIR Public Health Surveill %G English %X Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future. %M 33711799 %R 10.2196/27317 %U https://publichealth.jmir.org/2021/3/e27317 %U https://doi.org/10.2196/27317 %U http://www.ncbi.nlm.nih.gov/pubmed/33711799 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e27232 %T Analyzing the Essential Attributes of Nationally Issued COVID-19 Contact Tracing Apps: Open-Source Intelligence Approach and Content Analysis %A Weiß,Jan-Patrick %A Esdar,Moritz %A Hübner,Ursula %+ Health Informatics Research Group, Faculty of Business Management and Social Sciences, University of Applied Sciences Osnabrueck, Caprivistraße 30a, Osnabrück, , Germany, 49 5419692012, u.huebner@hs-osnabrueck.de %K COVID-19 %K contact tracing %K app %K protocol %K privacy %K assessment %K review %K surveillance %K monitoring %K design %K framework %K feature %K usage %D 2021 %7 26.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide. Objective: Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps. This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information. Methods: We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features. These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach. Results: Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories: (1) background information (open-source code, public information, and collaborators); (2) purpose and workflow (secondary data use and warning process design); (3) technical information (protocol, tracing technology, exposure notification system, and interoperability); (4) privacy protection (the entity of trust and anonymity); and (5) availability and use (release date and the number of downloads). Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps’ technical makeup. Conclusions: We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries. %M 33724920 %R 10.2196/27232 %U https://mhealth.jmir.org/2021/3/e27232 %U https://doi.org/10.2196/27232 %U http://www.ncbi.nlm.nih.gov/pubmed/33724920 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 3 %P e25859 %T App Use and Usability of a Barcode-Based Digital Platform to Augment COVID-19 Contact Tracing: Postpilot Survey and Paradata Analysis %A Scherr,Thomas Foster %A DeSousa,Jenna Maria %A Moore,Carson Paige %A Hardcastle,Austin %A Wright,David Wilson %+ Department of Chemistry, Vanderbilt University, 1234 Stevenson Center Lane, Nashville, TN, 37212, United States, 1 615 322 5516, thomas.f.scherr@vanderbilt.edu %K contact tracing %K COVID-19 %K mobile health %K usability %K app %K usage %K tracking %K monitoring %K survey %K pilot %D 2021 %7 26.3.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has drastically changed life in the United States, as the country has recorded over 23 million cases and 383,000 deaths to date. In the leadup to widespread vaccine deployment, testing and surveillance are critical for detecting and stopping possible routes of transmission. Contact tracing has become an important surveillance measure to control COVID-19 in the United States, and mobile health interventions have found increased prominence in this space. Objective: The aim of this study was to investigate the use and usability of MyCOVIDKey, a mobile-based web app to assist COVID-19 contact tracing efforts, during the 6-week pilot period. Methods: A 6-week study was conducted on the Vanderbilt University campus in Nashville, Tennessee. The study participants, consisting primarily of graduate students, postdoctoral researchers, and faculty in the Chemistry Department at Vanderbilt University, were asked to use the MyCOVIDKey web app during the course of the study period. Paradata were collected as users engaged with the MyCOVIDKey web app. At the end of the study, all participants were asked to report on their user experience in a survey, and the results were analyzed in the context of the user paradata. Results: During the pilot period, 45 users enrolled in MyCOVIDKey. An analysis of their enrollment suggests that initial recruiting efforts were effective; however, participant recruitment and engagement efforts at the midpoint of the study were less effective. App use paralleled the number of users, indicating that incentives were useful for recruiting new users to sign up but did not result in users attempting to artificially inflate their use as a result of prize offers. Times to completion of key tasks were low, indicating that the main features of the app could be used quickly. Of the 45 users, 30 provided feedback through a postpilot survey, with 26 (58%) completing it in its entirety. The MyCOVIDKey app as a whole was rated 70.0 on the System Usability Scale, indicating that it performed above the accepted threshold for usability. When the key-in and self-assessment features were examined on their own, it was found that they individually crossed the same thresholds for acceptable usability but that the key-in feature had a higher margin for improvement. Conclusions: The MyCOVIDKey app was found overall to be a useful tool for COVID-19 contact tracing in a university setting. Most users suggested simple-to-implement improvements, such as replacing the web app framework with a native app format or changing the placement of the scanner within the app workflow. After these updates, this tool could be readily deployed and easily adapted to other settings across the country. The need for digital contact tracing tools is becoming increasingly apparent, particularly as COVID-19 case numbers continue to increase while more businesses begin to reopen. %M 33630745 %R 10.2196/25859 %U https://publichealth.jmir.org/2021/3/e25859 %U https://doi.org/10.2196/25859 %U http://www.ncbi.nlm.nih.gov/pubmed/33630745 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e25696 %T Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study %A Huang,Yingxiang %A Radenkovic,Dina %A Perez,Kevin %A Nadeau,Kari %A Verdin,Eric %A Furman,David %+ Buck Institute for Research on Aging, 8001 Redwood Blvd, Novato, CA, 94945, United States, 1 (415) 209 2000, DFurman@buckinstitute.org %K clinical informatics %K predictive modeling %K COVID-19 %K app %K model %K prediction %K symptom %K informatics %K age %K morbidity %K hospital %D 2021 %7 25.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China, and was subsequently recognized by the World Health Organization as an international public health emergency and declared a pandemic in March 2020. Since then, the disruptions caused by the COVID-19 pandemic have had an unparalleled effect on all aspects of life. Objective: With increasing total hospitalization and intensive care unit admissions, a better understanding of features related to patients with COVID-19 could help health care workers stratify patients based on the risk of developing a more severe case of COVID-19. Using predictive models, we strive to select the features that are most associated with more severe cases of COVID-19. Methods: Over 3 million participants reported their potential symptoms of COVID-19, along with their comorbidities and demographic information, on a smartphone-based app. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting. We then analyzed such features in relation to age and other demographics and their longitudinal trend. Results: The most predictive features found include fever, use of immunosuppressant medication, use of a mobility aid, shortness of breath, and severe fatigue. Such features are age-related, and some are disproportionally high in minority populations. Conclusions: Predictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require. This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations. %M 33621185 %R 10.2196/25696 %U https://www.jmir.org/2021/3/e25696 %U https://doi.org/10.2196/25696 %U http://www.ncbi.nlm.nih.gov/pubmed/33621185 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 3 %P e26719 %T Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation %A Peterson,Kelly S %A Lewis,Julia %A Patterson,Olga V %A Chapman,Alec B %A Denhalter,Daniel W %A Lye,Patricia A %A Stevens,Vanessa W %A Gamage,Shantini D %A Roselle,Gary A %A Wallace,Katherine S %A Jones,Makoto %+ VA Salt Lake City Health Care System, US Department of Veterans Affairs, 500 Foothill Dr, Salt Lake City, UT, 84148, United States, 1 801 582 1565, kelly.peterson2@va.gov %K natural language processing %K machine learning %K travel history %K COVID-19 %K Zika %K infectious disease surveillance %K surveillance applications %K biosurveillance %K electronic health record %D 2021 %7 24.3.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. Objective: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. Methods: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. Results: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases. %M 33759790 %R 10.2196/26719 %U https://publichealth.jmir.org/2021/3/e26719 %U https://doi.org/10.2196/26719 %U http://www.ncbi.nlm.nih.gov/pubmed/33759790 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 3 %P e21606 %T The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study %A Tran,Phoebe %A Tran,Lam %A Tran,Liem %+ Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, BGB 306, Knoxville, TN, United States, 1 865 974 6034, ltran1@utk.edu %K COVID-19 %K marginal effects %K mortality %K negative binomial model %K social distancing %D 2021 %7 18.3.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Previous studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths. Objective: This study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020). Methods: Using social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people. Results: We observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001). Conclusions: As stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country. %M 33497348 %R 10.2196/21606 %U https://publichealth.jmir.org/2021/3/e21606 %U https://doi.org/10.2196/21606 %U http://www.ncbi.nlm.nih.gov/pubmed/33497348 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e25726 %T Adoption of COVID-19 Contact Tracing Apps: A Balance Between Privacy and Effectiveness %A Seto,Emily %A Challa,Priyanka %A Ware,Patrick %+ Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Suite 425, Toronto, ON, M5T 3M6, Canada, 1 416 669 9295, emily.seto@utoronto.ca %K mobile apps %K COVID-19 %K contact tracing %K exposure notification %K privacy %K effectiveness %K app %K surveillance %K tracing %K transmission %K security %K digital health %D 2021 %7 4.3.2021 %9 Viewpoint %J J Med Internet Res %G English %X With the relative ubiquity of smartphones, contact tracing and exposure notification apps have been looked to as novel methods to help reduce the transmission of COVID-19. Many countries have created apps that lie across a spectrum from privacy-first approaches to those that have very few privacy measures. The level of privacy incorporated into an app is largely based on the societal norms and values of a particular country. Digital health technologies can be highly effective and preserve privacy at the same time, but in the case of contact tracing and exposure notification apps, there is a trade-off between increased privacy measures and the effectiveness of the app. In this article, examples from various countries are used to highlight how characteristics of contract tracing and exposure notification apps contribute to the perceived levels of privacy awarded to citizens and how this impacts an app’s effectiveness. We conclude that finding the right balance between privacy and effectiveness, while critical, is challenging because it is highly context-specific. %M 33617459 %R 10.2196/25726 %U https://www.jmir.org/2021/3/e25726 %U https://doi.org/10.2196/25726 %U http://www.ncbi.nlm.nih.gov/pubmed/33617459 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 3 %P e26550 %T Digital Interventions to Support Population Mental Health in Canada During the COVID-19 Pandemic: Rapid Review %A Strudwick,Gillian %A Sockalingam,Sanjeev %A Kassam,Iman %A Sequeira,Lydia %A Bonato,Sarah %A Youssef,Alaa %A Mehta,Rohan %A Green,Nadia %A Agic,Branka %A Soklaridis,Sophie %A Impey,Danielle %A Wiljer,David %A Crawford,Allison %+ Centre for Addiction and Mental Health, 1001 Queen St W, Toronto, ON, M6J 1H4, Canada, 1 416 535 8501, gillian.strudwick@camh.ca %K digital health %K psychiatry %K mental health %K informatics %K pandemic %K COVID-19 %K telemedicine %K eHealth %K public health %K virtual care %K mobile apps %K population health %D 2021 %7 2.3.2021 %9 Review %J JMIR Ment Health %G English %X Background: The COVID-19 pandemic has resulted in a number of negative health related consequences, including impacts on mental health. More than 22% of Canadians reported that they had felt depressed in the last week, in response to a December 2020 national survey. Given the need to physically distance during the pandemic, and the increase in demand for mental health services, digital interventions that support mental health and wellness may be beneficial. Objective: The purpose of this research was to identify digital interventions that could be used to support the mental health of the Canadian general population during the COVID-19 pandemic. The objectives were to identify (1) the populations these interventions were developed for, inclusive of exploring areas of equity such as socioeconomic status, sex/gender, race/ethnicity and culture, and relevance to Indigenous peoples and communities; (2) the effect of the interventions; and (3) any barriers or facilitators to the use of the intervention. Methods: This study was completed using a Cochrane Rapid Review methodology. A search of Embase, PsycInfo, Medline, and Web of Science, along with Google, Million Short, and popular mobile app libraries, was conducted. Two screeners were involved in applying inclusion criteria using Covidence software. Academic articles and mobile apps identified were screened using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields resource, the American Psychiatric Association App Evaluation Framework, and the Mental Health Commission of Canada’s guidance on app assessment and selection. Results: A total of 31 mobile apps and 114 web-based resources (eg, telemedicine, virtual peer support groups, discussion forums, etc) that could be used to support the mental health of the Canadian population during the pandemic were identified. These resources have been listed on a publicly available website along with search tags that may help an individual make a suitable selection. Variability exists in the populations that the interventions were developed for, and little assessment has been done with regard to areas of equity. The effect of the interventions was not reported for all those identified in this synthesis; however, for those that did report the effect, it was shown that they were effective in the context that they were used. A number of barriers and facilitators to using these interventions were identified, such as access, cost, and connectivity. Conclusions: A number of digital interventions that could support population mental health in Canada during the global COVID-19 pandemic were identified, indicating that individuals have several options to choose from. These interventions vary in their purpose, approach, design, cost, and targeted user group. While some research and digital interventions addressed equity-related considerations, more research and focused attention should be given to this area. %M 33650985 %R 10.2196/26550 %U https://mental.jmir.org/2021/3/e26550 %U https://doi.org/10.2196/26550 %U http://www.ncbi.nlm.nih.gov/pubmed/33650985 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e24322 %T Health Apps for Combating COVID-19: Descriptive Review and Taxonomy %A Almalki,Manal %A Giannicchi,Anna %+ Department of Health Informatics, Faculty of Public Health and Tropical Medicine, Jazan University, Gizan, Jazan, 82726, Saudi Arabia, 966 597636563, manal_almalki@hotmail.com %K app %K COVID-19 %K corona %K self-care %K personal tracking %K review %K mHealth %K track %K surveillance %K awareness %K exposure %K consumer health informatics %D 2021 %7 2.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile phone apps have been leveraged to combat the spread of COVID-19. However, little is known about these technologies’ characteristics, technical features, and various applications in health care when responding to this public health crisis. The lack of understanding has led developers and governments to make poor choices about apps’ designs, which resulted in creating less useful apps that are overall less appealing to consumers due to their technical flaws. Objective: This review aims to identify, analyze, and categorize health apps related to COVID-19 that are currently available for consumers in app stores; in particular, it focuses on exploring their key technical features and classifying the purposes that these apps were designed to serve. Methods: A review of health apps was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The Apple Store and Google Play were searched between April 20 and September 11, 2020. An app was included if it was dedicated for this disease and was listed under the health and medical categories in these app stores. The descriptions of these apps were extracted from the apps’ web pages and thematically analyzed via open coding to identify both their key technical features and overall purpose. The characteristics of the included apps were summarized and presented with descriptive statistics. Results: Of the 298 health apps that were initially retrieved, 115 met the inclusion criteria. A total of 29 technical features were found in our sample of apps, which were then categorized into five key purposes of apps related to COVID-19. A total of 77 (67%) apps were developed by governments or national authorities and for the purpose of promoting users to track their personal health (9/29, 31%). Other purposes included raising awareness on how to combat COVID-19 (8/29, 27%), managing exposure to COVID-19 (6/29, 20%), monitoring health by health care professionals (5/29, 17%), and conducting research studies (1/29, 3.5%). Conclusions: This study provides an overview and taxonomy of the health apps currently available in the market to combat COVID-19 based on their differences in basic technical features and purpose. As most of the apps were provided by governments or national authorities, it indicates the essential role these apps have as tools in public health crisis management. By involving most of the population in self-tracking their personal health and providing them with the technology to self-assess, the role of these apps is deemed to be a key driver for a participatory approach to curtail the spread of COVID-19. Further effort is required from researchers to evaluate these apps’ effectiveness and from governmental organizations to increase public awareness of these digital solutions. %M 33626017 %R 10.2196/24322 %U https://mhealth.jmir.org/2021/3/e24322 %U https://doi.org/10.2196/24322 %U http://www.ncbi.nlm.nih.gov/pubmed/33626017 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e25682 %T Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards %A Ivanković,Damir %A Barbazza,Erica %A Bos,Véronique %A Brito Fernandes,Óscar %A Jamieson Gilmore,Kendall %A Jansen,Tessa %A Kara,Pinar %A Larrain,Nicolas %A Lu,Shan %A Meza-Torres,Bernardo %A Mulyanto,Joko %A Poldrugovac,Mircha %A Rotar,Alexandru %A Wang,Sophie %A Willmington,Claire %A Yang,Yuanhang %A Yelgezekova,Zhamin %A Allin,Sara %A Klazinga,Niek %A Kringos,Dionne %+ Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, Netherlands, 31 981801700, d.ivankovic@amsterdamumc.nl %K COVID-19 %K pandemic %K internet %K performance measures %K public reporting of health care data %K public health %K surveillance %K health information management %K dashboard %K accessibility %K online tool %K communication %K feature %K expert %D 2021 %7 24.2.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. Objective: The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (“why”), content and data (“what”), and analyses and displays (“how” they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. Methods: We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. Results: A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are “close to home”; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. Conclusions: COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified. %M 33577467 %R 10.2196/25682 %U https://www.jmir.org/2021/2/e25682 %U https://doi.org/10.2196/25682 %U http://www.ncbi.nlm.nih.gov/pubmed/33577467 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 2 %P e23870 %T Evaluating Closures of Fresh Fruit and Vegetable Vendors During the COVID-19 Pandemic: Methodology and Preliminary Results Using Omnidirectional Street View Imagery %A Ali,Shahmir H %A Imbruce,Valerie M %A Russo,Rienna G %A Kaplan,Samuel %A Stevenson,Kaye %A Mezzacca,Tamar Adjoian %A Foster,Victoria %A Radee,Ashley %A Chong,Stella %A Tsui,Felice %A Kranick,Julie %A Yi,Stella S %+ Department of Population Health, NYU Grossman School of Medicine, 8th Floor, Room 8-13, 180 Madison Avenue, New York, NY, United States, 1 646 501 3477, Stella.Yi@nyulangone.org %K built environment %K Google Street View %K food retail environment %K COVID-19 %K geographic surveillance %K food %K longitudinal %K supply chain %K economy %K demand %K service %K vendor %K surveillance %D 2021 %7 18.2.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The COVID-19 pandemic has significantly disrupted the food retail environment. However, its impact on fresh fruit and vegetable vendors remains unclear; these are often smaller, more community centered, and may lack the financial infrastructure to withstand supply and demand changes induced by such crises. Objective: This study documents the methodology used to assess fresh fruit and vegetable vendor closures in New York City (NYC) following the start of the COVID-19 pandemic by using Google Street View, the new Apple Look Around database, and in-person checks. Methods: In total, 6 NYC neighborhoods (in Manhattan and Brooklyn) were selected for analysis; these included two socioeconomically advantaged neighborhoods (Upper East Side, Park Slope), two socioeconomically disadvantaged neighborhoods (East Harlem, Brownsville), and two Chinese ethnic neighborhoods (Chinatown, Sunset Park). For each neighborhood, Google Street View was used to virtually walk down each street and identify vendors (stores, storefronts, street vendors, or wholesalers) that were open and active in 2019 (ie, both produce and vendor personnel were present at a location). Past vendor surveillance (when available) was used to guide these virtual walks. Each identified vendor was geotagged as a Google Maps pinpoint that research assistants then physically visited. Using the “notes” feature of Google Maps as a data collection tool, notes were made on which of three categories best described each vendor: (1) open, (2) open with a more limited setup (eg, certain sections of the vendor unit that were open and active in 2019 were missing or closed during in-person checks), or (3) closed/absent. Results: Of the 135 open vendors identified in 2019 imagery data, 35% (n=47) were absent/closed and 10% (n=13) were open with more limited setups following the beginning of the COVID-19 pandemic. When comparing boroughs, 35% (28/80) of vendors in Manhattan were absent/closed, as were 35% (19/55) of vendors in Brooklyn. Although Google Street View was able to provide 2019 street view imagery data for most neighborhoods, Apple Look Around was required for 2019 imagery data for some areas of Park Slope. Past surveillance data helped to identify 3 additional established vendors in Chinatown that had been missed in street view imagery. The Google Maps “notes” feature was used by multiple research assistants simultaneously to rapidly collect observational data on mobile devices. Conclusions: The methodology employed enabled the identification of closures in the fresh fruit and vegetable retail environment and can be used to assess closures in other contexts. The use of past baseline surveillance data to aid vendor identification was valuable for identifying vendors that may have been absent or visually obstructed in the street view imagery data. Data collection using Google Maps likewise has the potential to enhance the efficiency of fieldwork in future studies. %M 33539310 %R 10.2196/23870 %U http://formative.jmir.org/2021/2/e23870/ %U https://doi.org/10.2196/23870 %U http://www.ncbi.nlm.nih.gov/pubmed/33539310 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 2 %P e25037 %T Fostering Engagement With Health and Housing Innovation: Development of Participant Personas in a Social Housing Cohort %A Williams,Andrew James %A Menneer,Tamaryn %A Sidana,Mansi %A Walker,Tim %A Maguire,Kath %A Mueller,Markus %A Paterson,Cheryl %A Leyshon,Michael %A Leyshon,Catherine %A Seymour,Emma %A Howard,Zoë %A Bland,Emma %A Morrissey,Karyn %A Taylor,Timothy J %+ School of Medicine, University of St Andrews, Room 219, Medical and Biological Sciences Building, North Haugh, St Andrews, Fife, KY16 9TF, United Kingdom, 44 7906019236, ajw45@st-andrews.ac.uk %K user-centered design %K community %K social network analysis %K United Kingdom %K mobile phone %D 2021 %7 16.2.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Personas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale. Objective: This study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community. Methods: The project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health–related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes. Results: The Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact. Conclusions: Through the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public. %M 33591284 %R 10.2196/25037 %U http://publichealth.jmir.org/2021/2/e25037/ %U https://doi.org/10.2196/25037 %U http://www.ncbi.nlm.nih.gov/pubmed/33591284 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 2 %P e24204 %T The Global Infectious Diseases Epidemic Information Monitoring System: Development and Usability Study of an Effective Tool for Travel Health Management in China %A Gu,Dayong %A He,Jianan %A Sun,Jie %A Shi,Xin %A Ye,Ying %A Zhang,Zishuai %A Wang,Xiangjun %A Su,Qun %A Yu,Wenjin %A Yuan,Xiaopeng %A Dong,Ruiling %+ Shenzhen International Travel Health Care Center (Shenzhen Customs District Port Outpatient Clinics), Shenzhen Customs District, Room 406, Shenzhen Kouan Hospital, Huanggang Kouan Shenghuo Qu, Futian District, Shenzhen, China, 86 15019457579, 1047194259@qq.com %K infectious disease %K epidemic information %K travel health %K global %K surveillance %D 2021 %7 16.2.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Obtaining comprehensive epidemic information for specific global infectious diseases is crucial to travel health. However, different infectious disease information websites may have different purposes, which may lead to misunderstanding by travelers and travel health staff when making accurate epidemic control and management decisions. Objective: The objective of this study was to develop a Global Infectious Diseases Epidemic Information Monitoring System (GIDEIMS) in order to provide comprehensive and timely global epidemic information. Methods: Distributed web crawler and cloud agent acceleration technologies were used to automatically collect epidemic information about more than 200 infectious diseases from 26 established epidemic websites and Baidu News. Natural language processing and in-depth learning technologies have been utilized to intelligently process epidemic information collected in 28 languages. Currently, the GIDEIMS presents world epidemic information using a geographical map, including date, disease name, reported cases in different countries, and the epidemic situation in China. In order to make a practical assessment of the GIDEIMS, we compared infectious disease data collected from the GIDEIMS and other websites on July 16, 2019. Results: Compared with the Global Incident Map and Outbreak News Today, the GIDEIMS provided more comprehensive information on human infectious diseases. The GIDEIMS is currently used in the Health Quarantine Department of Shenzhen Customs District (Shenzhen, China) and was recommended to the Health Quarantine Administrative Department of the General Administration of Customs (China) and travel health–related departments. Conclusions: The GIDEIMS is one of the most intelligent tools that contributes to safeguarding the health of travelers, controlling infectious disease epidemics, and effectively managing public health in China. %M 33591286 %R 10.2196/24204 %U http://publichealth.jmir.org/2021/2/e24204/ %U https://doi.org/10.2196/24204 %U http://www.ncbi.nlm.nih.gov/pubmed/33591286 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 2 %P e20335 %T Evaluating Apple Inc Mobility Trend Data Related to the COVID-19 Outbreak in Japan: Statistical Analysis %A Kurita,Junko %A Sugishita,Yoshiyuki %A Sugawara,Tamie %A Ohkusa,Yasushi %+ Department of Nursing, Tokiwa University, 1-430-1 Miwa, Mito, Ibraki, 3108585, Japan, 81 29 232 2511, kuritaj@tokiwa.ac.jp %K peak %K COVID-19 %K effective reproduction number %K mobility trend data %K Apple %K countermeasure %D 2021 %7 15.2.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In Japan, as a countermeasure against the COVID-19 outbreak, both the national and local governments issued voluntary restrictions against going out from residences at the end of March 2020 in preference to the lockdowns instituted in European and North American countries. The effect of such measures can be studied with mobility data, such as data which is generated by counting the number of requests made to Apple Maps for directions in select countries/regions, sub-regions, and cities. Objective: We investigate the associations of mobility data provided by Apple Inc and an estimate an an effective reproduction number R(t). Methods: We regressed R(t) on a polynomial function of daily Apple data, estimated using the whole period, and analyzed subperiods delimited by March 10, 2020. Results: In the estimation results, R(t) was 1.72 when voluntary restrictions against going out ceased and mobility reverted to a normal level. However, the critical level of reducing R(t) to <1 was obtained at 89.3% of normal mobility. Conclusions: We demonstrated that Apple mobility data are useful for short-term prediction of R(t). The results indicate that the number of trips should decrease by 10% until herd immunity is achieved and that higher voluntary restrictions against going out might not be necessary for avoiding a re-emergence of the outbreak. %M 33481755 %R 10.2196/20335 %U http://publichealth.jmir.org/2021/2/e20335/ %U https://doi.org/10.2196/20335 %U http://www.ncbi.nlm.nih.gov/pubmed/33481755 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 2 %P e26406 %T Central Auditory Tests to Track Cognitive Function in People With HIV: Longitudinal Cohort Study %A Niemczak,Christopher %A Fellows,Abigail %A Lichtenstein,Jonathan %A White-Schwoch,Travis %A Magohe,Albert %A Gui,Jiang %A Wilbur,Jed %A Clavier,Odile %A Massawe,Enica %A Moshi,Ndeserua %A Boivin,Michael %A Kraus,Nina %A Buckey,Jay %+ Geisel School of Medicine at Dartmouth, Dartmouth College, One Medical Center Drive, Lebanon, NH, 03755, United States, 1 603 650 6012, christopher.e.niemczak@dartmouth.edu %K HIV %K central auditory function %K auditory perception %K cognitive dysfunction %K testing %K cognition %K cognitive function %K neurocognitive deficit %K longitudinal %K auditory %K nervous system %K screening %K monitoring %K surveillance %D 2021 %7 9.2.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The development of neurocognitive deficits in people infected with HIV is a significant public health problem. Previous cross-sectional studies have shown that performance on central auditory tests (CATs) correlates with cognitive test results in those with HIV, but no longitudinal data exist for confirmation. We have been performing longitudinal assessments of central auditory and cognitive function on a cohort of HIV-positive and HIV-negative individuals in Dar es Salaam, Tanzania to understand how the central auditory system could be used to study and track the progress of central nervous system dysfunction. Objective: The goal of the project was to determine if CATs can track the trajectory of cognitive function over time in people diagnosed with HIV. Methods: Tests of peripheral and central auditory function as well as cognitive performance were performed on 382 individuals over the course of 3.5 years. Visits were scheduled every 6 months. CATs included tests of auditory temporal processing (gap detection) and speech perception in noise (Hearing in Noise Test and Triple Digit Test). Cognitive tests included the Montreal Cognitive Assessment (MoCA), Test of Variables of Attention (TOVA), and subtests from the Cogstate battery. HIV-positive subjects were divided into groups based on their CAT results at their final visit (bottom 20%, top 20%, middle 60%). Primary analyses focused on the comparison between HIV-positive individuals that performed worse on CATs (bottom 20%) and the overall HIV-positive group (middle 60%). Data were analyzed using linear mixed-effect models with time as the main fixed effect. Results: The group with the worst (bottom 20%) CAT performance showed a difference in trajectory for the MoCA (P=.003), TOVA (P<.048), and Cogstate (P<.046) over the course of the study period compared to the overall HIV-positive group. A battery of three CATs showed a significant difference in cognitive trajectory over a relatively short study period of 3.5 years independent of age (bottom 20% vs HIV-positive group). Conclusions: The results of this study support the ability for CATs to track cognitive function over time, suggesting that central auditory processing can provide a window into central nervous system performance. CATs can be simple to perform, and are relatively insensitive to education and socioeconomic status because they only require repeating sentences, numbers, or detecting gaps in noise. These tests could potentially provide a time-efficient, low-cost method to screen for and monitor cognitive decline in patients with HIV, making them a useful surveillance tool for this major public health problem. %M 33470933 %R 10.2196/26406 %U http://formative.jmir.org/2021/2/e26406/ %U https://doi.org/10.2196/26406 %U http://www.ncbi.nlm.nih.gov/pubmed/33470933 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 2 %P e22980 %T Conducting Virtual, Synchronous Focus Groups Among Black Sexual Minority Men: Qualitative Study %A Dangerfield II,Derek T %A Wylie,Charleen %A Anderson,Janeane N %+ Johns Hopkins School of Nursing, 855 N Wolfe St, Baltimore, MD, 21205, United States, 1 4109553757, ddanger2@jhu.edu %K engagement %K recruitment %K sexual health %K telehealth %D 2021 %7 8.2.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Focus groups are useful to support HIV prevention research among US subpopulations, such as Black gay, Black bisexual, and other Black sexual minority men (BSMM). Virtual synchronous focus groups provide an electronic means to obtain qualitative data and are convenient to implement; however, the protocols and acceptability for conducting virtual synchronous focus groups in HIV prevention research among BSMM are lacking. Objective: This paper describes the protocols and acceptability of conducting virtual synchronous focus groups in HIV prevention research among BSMM Methods: Data for this study came from 8 virtual synchronous focus groups examined in 2 studies of HIV-negative BSMM in US cities, stratified by age (N=39): 2 groups of BSMM ages 18-24 years, 5 groups of BSMM ages 25-34 years, and 1 group of BSMM 35 years and older. Virtual synchronous focus groups were conducted via Zoom, and participants were asked to complete an electronic satisfaction survey distributed to their email via Qualtrics. Results: The age of participants ranged from 18 to 44 years (mean 28.3, SD 6.0). All participants “strongly agreed” or “agreed” that they were satisfied participating in an online focus group. Only 17% (5/30) preferred providing written informed consent versus oral consent. Regarding privacy, most (30/30,100%) reported “strongly agree” or “agree” that their information was safe to share with other participants in the group. Additionally, 97% (29/30) reported being satisfied with the incentive. Conclusions: Conducting virtual synchronous focus groups in HIV prevention research among BSMM is feasible. However, thorough oral informed consent with multiple opportunities for questions, culturally relevant facilitation procedures, and appropriate incentives are needed for optimal focus group participation. %M 33427671 %R 10.2196/22980 %U http://publichealth.jmir.org/2021/2/e22980/ %U https://doi.org/10.2196/22980 %U http://www.ncbi.nlm.nih.gov/pubmed/33427671 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 1 %P e24207 %T Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach %A Vaid,Akhil %A Jaladanki,Suraj K %A Xu,Jie %A Teng,Shelly %A Kumar,Arvind %A Lee,Samuel %A Somani,Sulaiman %A Paranjpe,Ishan %A De Freitas,Jessica K %A Wanyan,Tingyi %A Johnson,Kipp W %A Bicak,Mesude %A Klang,Eyal %A Kwon,Young Joon %A Costa,Anthony %A Zhao,Shan %A Miotto,Riccardo %A Charney,Alexander W %A Böttinger,Erwin %A Fayad,Zahi A %A Nadkarni,Girish N %A Wang,Fei %A Glicksberg,Benjamin S %+ The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Avenue, 14th Floor, New York, NY, 10065, United States, 1 (212) 731 7078, benjamin.glicksberg@mssm.edu %K federated learning %K COVID-19 %K machine learning %K electronic health records %D 2021 %7 27.1.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy. %M 33400679 %R 10.2196/24207 %U http://medinform.jmir.org/2021/1/e24207/ %U https://doi.org/10.2196/24207 %U http://www.ncbi.nlm.nih.gov/pubmed/33400679 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e24591 %T Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study %A Garnier,Romain %A Benetka,Jan R %A Kraemer,John %A Bansal,Shweta %+ Department of Biology, Georgetown University, Reiss Science Building, 37th and O Streets, Washington, DC, 20057, United States, 1 2026879256, sb753@georgetown.edu %K COVID-19 %K SARS-CoV-2 %K disease ecology %K nonpharmaceutical interventions %K mobility data %K economic %K disparity %K social distancing %K equity %K access %K socioeconomic %K infectious disease %K mobility %D 2021 %7 22.1.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. Objective: We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. Methods: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. Results: We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. Conclusions: Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts. %M 33351774 %R 10.2196/24591 %U http://www.jmir.org/2021/1/e24591/ %U https://doi.org/10.2196/24591 %U http://www.ncbi.nlm.nih.gov/pubmed/33351774 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e21382 %T Data Quality and Cost-effectiveness Analyses of Electronic and Paper-Based Interviewer-Administered Public Health Surveys: Systematic Review %A Zeleke,Atinkut Alamirrew %A Naziyok,Tolga %A Fritz,Fleur %A Christianson,Lara %A Röhrig,Rainer %+ Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstraße 1-2, Greifswald, D-17487, Germany, 49 3834 86 7548, atinkut.zeleke@uni-greifswald.de %K electronic data collection %K demographic and health survey %K tablet computer %K smartphone %K mobile phone %D 2021 %7 22.1.2021 %9 Review %J J Med Internet Res %G English %X Background: A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system. Objective: This systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods. Methods: We conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening. Results: Our search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems. Conclusions: Although evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction. International Registered Report Identifier (IRRID): RR2-10.2196/10678 %M 33480859 %R 10.2196/21382 %U http://www.jmir.org/2021/1/e21382/ %U https://doi.org/10.2196/21382 %U http://www.ncbi.nlm.nih.gov/pubmed/33480859 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e17691 %T Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review %A Fischer,Florian %A Kleen,Sina %+ Institut of Public Health, Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany, 49 751 501 9441, florian.fischer1@charite.de %K apps %K questionnaire %K survey %K epidemiology %K healthcare %D 2021 %7 22.1.2021 %9 Review %J J Med Internet Res %G English %X Background: The broad availability of smartphones and the number of health apps in app stores have risen in recent years. Health apps have benefits for individuals (eg, the ability to monitor one’s health) as well as for researchers (eg, the ability to collect data in population-based, clinical, and observational studies). Although the number of health apps on the global app market is huge and the associated potential seems to be great, app-based questionnaires for collecting patient-related data have not played an important role in epidemiological studies so far. Objective: This study aims to provide an overview of studies that have collected patient data using an app-based approach, with a particular focus on longitudinal studies. This literature review describes the current extent to which smartphones have been used for collecting (patient) data for research purposes, and the potential benefits and challenges associated with this approach. Methods: We conducted a scoping review of studies that used data collection via apps. PubMed was used to identify studies describing the use of smartphone app questionnaires for collecting data over time. Overall, 17 articles were included in the summary. Results: Based on the results of this scoping review, there are only a few studies that integrate smartphone apps into data-collection approaches. Studies dealing with the collection of health-related data via smartphone apps have mainly been developed with regard to psychosomatic, neurodegenerative, respiratory, and cardiovascular diseases, as well as malign neoplasm. Among the identified studies, the duration of data collection ranged from 4 weeks to 12 months, and the participants’ mean ages ranged from 7 to 69 years. Potential can be seen for real-time information transfer, fast data synchronization (which saves time and increases effectivity), and the possibility of tracking responses longitudinally. Furthermore, smartphone-based data-collection techniques might prevent biases, such as reminder bias or mistakes occurring during manual data transfers. In chronic diseases, real-time communication with physicians and early detection of symptoms enables rapid modifications in disease management. Conclusions: The results indicate that using mobile technologies can help to overcome challenges linked with data collection in epidemiological research. However, further feasibility studies need to be conducted in the near future to test the applicability and acceptance of these mobile apps for epidemiological research in various subpopulations. %M 33480850 %R 10.2196/17691 %U http://www.jmir.org/2021/1/e17691/ %U https://doi.org/10.2196/17691 %U http://www.ncbi.nlm.nih.gov/pubmed/33480850 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 4 %P e15524 %T An Interactive Text Message Survey as a Novel Assessment for Bedtime Routines in Public Health Research: Observational Study %A Kitsaras,George %A Goodwin,Michaela %A Allan,Julia %A Kelly,Michael %A Pretty,Iain %+ University of Manchester, Dental Health Unit, Williams House Manchester Science Park, Manchester, M15 6SE, United Kingdom, 44 01612261211, georgios.kitsaras@manchester.ac.uk %K digital technologies %K mobile health %K child %K well-being %K development %K assessment %K bedtime routines %K P4 health care %K text survey %D 2020 %7 21.12.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional research approaches, especially questionnaires and paper-based assessments, limit in-depth understanding of the fluid dynamic processes associated with child well-being and development. This includes bedtime routine activities such as toothbrushing and reading a book before bed. The increase in innovative digital technologies alongside greater use and familiarity among the public creates unique opportunities to use these technical developments in research. Objective: This study aimed to (1) examine the best way of assessing bedtime routines in families and develop an automated, interactive, text message survey assessment delivered directly to participants’ mobile phones and (2) test the assessment within a predominately deprived sociodemographic sample to explore retention, uptake, feedback, and effectiveness. Methods: A public and patient involvement project showed clear preference for interactive text surveys regarding bedtime routines. The developed interactive text survey included questions on bedtime routine activities and was delivered for seven consecutive nights to participating parents’ mobile phones. A total of 200 parents participated. Apart from the completion of the text survey, feedback was provided by participants, and data on response, completion, and retention rates were captured. Results: There was a high retention rate (185/200, 92.5%), and the response rate was high (160/185, 86.5%). In total, 114 participants provided anonymized feedback. Only a small percentage (5/114, 4.4%) of participants reported problems associated with completing the assessment. The majority (99/114, 86.8%) of participants enjoyed their participation in the study, with an average satisfaction score of 4.6 out of 5. Conclusions: This study demonstrated the potential of deploying SMS text message–based surveys to capture and quantify real-time information on recurrent dynamic processes in public health research. Changes and adaptations based on recommendations are crucial next steps in further exploring the diagnostic and potential intervention properties of text survey and text messaging approaches. %M 33346734 %R 10.2196/15524 %U http://publichealth.jmir.org/2020/4/e15524/ %U https://doi.org/10.2196/15524 %U http://www.ncbi.nlm.nih.gov/pubmed/33346734 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 4 %P e20579 %T Barriers to Creating Scalable Business Models for Digital Health Innovation in Public Systems: Qualitative Case Study %A Kelley,Leah Taylor %A Fujioka,Jamie %A Liang,Kyle %A Cooper,Madeline %A Jamieson,Trevor %A Desveaux,Laura %+ Institute for Health System Solutions and Virtual Care, Women's College Hospital, 76 Grenville Street, Toronto, ON, M5S 1B2, Canada, 1 4163236400 ext 4302, laura.desveaux@wchospital.ca %K digital technologies %K telemedicine %K innovation diffusion %K health policy %K evaluation study %K reimbursement %K incentive %K mobile phone %D 2020 %7 10.12.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Health systems are increasingly looking toward the private sector to provide digital solutions to address health care demands. Innovation in digital health is largely driven by small- and medium-sized enterprises (SMEs), yet these companies experience significant barriers to entry, especially in public health systems. Complex and fragmented care models, alongside a myriad of relevant stakeholders (eg, purchasers, providers, and producers of health care products), make developing value propositions for digital solutions highly challenging. Objective: This study aims to identify areas for health system improvement to promote the integration of innovative digital health technologies developed by SMEs. Methods: This paper qualitatively analyzes a series of case studies to identify health system barriers faced by SMEs developing digital health technologies in Canada and proposed solutions to encourage a more innovative ecosystem. The Women’s College Hospital Institute for Health System Solutions and Virtual Care established a consultation program for SMEs to help them increase their innovation capacity and take their ideas to market. The consultation involved the SME filling out an onboarding form and review of this information by an expert advisory committee using guided considerations, leading to a recommendation report provided to the SME. This paper reports on the characteristics of 25 SMEs who completed the program and qualitatively analyzed their recommendation reports to identify common barriers to digital health innovation. Results: A total of 2 central themes were identified, each with 3 subthemes. First, a common barrier to system integration was the lack of formal evaluation, with SMEs having limited resources and opportunities to conduct such an evaluation. Second, the health system’s current structure does not create incentives for clinicians to use digital technologies, which threatens the sustainability of SMEs’ business models. SMEs faced significant challenges in engaging users and payers from the public system due to perverse economic incentives. Physicians are compensated by in-person visits, which actively works against the goals of many digital health solutions of keeping patients out of clinics and hospitals. Conclusions: There is a significant disconnect between the economic incentives that drive clinical behaviors and the use of digital technologies that would benefit patients’ well-being. To encourage the use of digital health technologies, publicly funded health systems need to dedicate funding for the evaluation of digital solutions and streamlined pathways for clinical integration. %M 33300882 %R 10.2196/20579 %U http://publichealth.jmir.org/2020/4/e20579/ %U https://doi.org/10.2196/20579 %U http://www.ncbi.nlm.nih.gov/pubmed/33300882 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 12 %P e23170 %T COVID-19 Mobile Apps: A Systematic Review of the Literature %A Kondylakis,Haridimos %A Katehakis,Dimitrios G %A Kouroubali,Angelina %A Logothetidis,Fokion %A Triantafyllidis,Andreas %A Kalamaras,Ilias %A Votis,Konstantinos %A Tzovaras,Dimitrios %+ Computational Biomedicine Laboratory, Foundation for Research and Technology - Hellas–Institute of Computer Science, N. Plastira 100, Heraklion, 70013, Greece, 30 2810391449, kondylak@ics.forth.gr %K mobile apps %K systematic survey %K COVID-19 %K mobile health %K eHealth %D 2020 %7 9.12.2020 %9 Review %J J Med Internet Res %G English %X Background: A vast amount of mobile apps have been developed during the past few months in an attempt to “flatten the curve” of the increasing number of COVID-19 cases. Objective: This systematic review aims to shed light into studies found in the scientific literature that have used and evaluated mobile apps for the prevention, management, treatment, or follow-up of COVID-19. Methods: We searched the bibliographic databases Global Literature on Coronavirus Disease, PubMed, and Scopus to identify papers focusing on mobile apps for COVID-19 that show evidence of their real-life use and have been developed involving clinical professionals in their design or validation. Results: Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making, rapidly offering effective and usable tools for managing the COVID-19 pandemic. Conclusions: Mobile apps are considered to be a valuable tool for citizens, health professionals, and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden on hospitals, providing access to credible information, tracking the symptoms and mental health of individuals, and discovering new predictors. %M 33197234 %R 10.2196/23170 %U http://www.jmir.org/2020/12/e23170/ %U https://doi.org/10.2196/23170 %U http://www.ncbi.nlm.nih.gov/pubmed/33197234 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 4 %P e18950 %T Outcomes of the Deployment of the Auto-Visual Acute Flaccid Paralysis Detection and Reporting (AVADAR) System for Strengthening Polio Surveillance in Africa From 2017 to 2018: Evaluation Study %A Ticha,Johnson Muluh %A Akpan,Godwin Ubong %A Paige,Lara MF %A Senouci,Kamel %A Stein,Andrew %A Briand,Patrick %A Tuma,Jude %A Oyaole,Daniel Rasheed %A Ngofa,Reuben %A Maleghemi,Sylvester %A Touray,Kebba %A Salihu,Abdullahi Ahmed %A Diallo,Mamadou %A Tegegne,Sisay Gashu %A Bello,Isah Mohammed %A Idris,Umar Kabo %A Maduka,Omosivie %A Manengu,Casimir %A Shuaib,Faisal %A Galway,Michael %A Mkanda,Pascal %+ World Health Organization Regional Office for Africa, Cite Du Djoue, Brazzaville, 500101, Congo, 242 055736476, akpang@who.int %K Auto-Visual Acute Flaccid Paralysis Detection and Reporting %K surveillance %K informants %K acute flaccid paralysis %K smartphones %K polio %D 2020 %7 2.12.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: As we move toward a polio-free world, the challenge for the polio program is to create an unrelenting focus on smaller areas where the virus is still present, where children are being repeatedly missed, where immunity levels are low, and where surveillance is weak. Objective: This article aimed to describe a possible solution to address weak surveillance systems and document the outcomes of the deployment of the Auto-Visual Acute Flaccid Paralysis Detection and Reporting (AVADAR) project. Methods: This intervention was implemented in 99 targeted high-risk districts with concerns for silent polio circulation from eight countries in Africa between August 1, 2017, and July 31, 2018. A total of 6954 persons (5390 community informants and 1564 health workers) were trained and equipped with a smartphone on which the AVADAR app was configured to allow community informants to send alerts on suspected acute flaccid paralysis (AFP) and allow health worker to use electronic checklists for investigation of such alerts. The AVADAR and Open Data Kit ONA servers were at the center of the entire process. A dashboard system and coordination teams for monitoring and supervision were put in place at all levels. Results: Overall, 96.44% (24,142/25,032) of potential AFP case alerts were investigated by surveillance personnel, yielding 1414 true AFP cases. This number (n=1414) reported through AVADAR was higher than the 238 AFP cases expected during the study period in the AVADAR districts and the 491 true AFP cases reported by the traditional surveillance system. A total of 203 out of the 1414 true AFP cases reported were from special population settings, such as refugee camps and insecure areas. There was an improvement in reporting in silent health areas in all the countries using the AVADAR system. Finally, there were 23,473 reports for other diseases, such as measles, diarrhea, and cerebrospinal meningitis, using the AVADAR platform. Conclusions: This article demonstrates the added value of AVADAR to rapidly improve surveillance sensitivity. AVADAR is capable of supporting countries to improve surveillance sensitivity within a short interval before and beyond polio-free certification. %M 33263550 %R 10.2196/18950 %U http://publichealth.jmir.org/2020/4/e18950/ %U https://doi.org/10.2196/18950 %U http://www.ncbi.nlm.nih.gov/pubmed/33263550 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 4 %P e21939 %T Leveraging a Cloud-Based Critical Care Registry for COVID-19 Pandemic Surveillance and Research in Low- and Middle-Income Countries %A , %A Hashmi,Madiha %A Beane,Abi %A Murthy,Srinivas %A Dondorp,Arjen M %A Haniffa,Rashan %+ Collaboration for Research, Improvement and Training in Critical Care in Asia, Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand, 66 07841131252, abi@nicslk.com %K critical care %K registry %K informatics %K COVID-19 %K severe acute respiratory infection %K pandemic %K surveillance %K cloud-based %K research %K low-and-middle-income countries %D 2020 %7 23.11.2020 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The COVID-19 pandemic has revealed limitations in real-time surveillance needed for responsive health care action in low- and middle-income countries (LMICs). The Pakistan Registry for Intensive CarE (PRICE) was adapted to enable International Severe Acute Respiratory and emerging Infections Consortium (ISARIC)–compliant real-time reporting of severe acute respiratory infection (SARI). The cloud-based common data model and standardized nomenclature of the registry platform ensure interoperability of data and reporting between regional and global stakeholders. Inbuilt analytics enable stakeholders to visualize individual and aggregate epidemiological, clinical, and operational data in real time. The PRICE system operates in 5 of 7 administrative regions of Pakistan. The same platform supports acute and critical care registries in eleven countries in South Asia and sub-Saharan Africa. ISARIC-compliant SARI reporting was successfully implemented by leveraging the existing PRICE infrastructure in all 49 member intensive care units (ICUs), enabling clinicians, operational leads, and established stakeholders with responsibilities for coordinating the pandemic response to access real-time information on suspected and confirmed COVID-19 cases (N=592 as of May 2020) via secure registry portals. ICU occupancy rates, use of ICU resources, mechanical ventilation, renal replacement therapy, and ICU outcomes were reported through registry dashboards. This information has facilitated coordination of critical care resources, health care worker training, and discussions on treatment strategies. The PRICE network is now being recruited to international multicenter clinical trials regarding COVID-19 management, leveraging the registry platform. Systematic and standardized reporting of SARI is feasible in LMICs. Existing registry platforms can be adapted for pandemic research, surveillance, and resource planning. %M 33147162 %R 10.2196/21939 %U http://publichealth.jmir.org/2020/4/e21939/ %U https://doi.org/10.2196/21939 %U http://www.ncbi.nlm.nih.gov/pubmed/33147162 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 4 %P e21434 %T COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology %A de Lusignan,Simon %A Liyanage,Harshana %A McGagh,Dylan %A Jani,Bhautesh Dinesh %A Bauwens,Jorgen %A Byford,Rachel %A Evans,Dai %A Fahey,Tom %A Greenhalgh,Trisha %A Jones,Nicholas %A Mair,Frances S %A Okusi,Cecilia %A Parimalanathan,Vaishnavi %A Pell,Jill P %A Sherlock,Julian %A Tamburis,Oscar %A Tripathy,Manasa %A Ferreira,Filipa %A Williams,John %A Hobbs,F D Richard %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Rd, Oxford, OX2 6GG, United Kingdom, 44 1865617283, simon.delusignan@phc.ox.ac.uk %K COVID-19 %K medical informatics %K sentinel surveillance %D 2020 %7 17.11.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. Objective: This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. Methods: We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. Results: Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). Conclusions: The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential. %M 33112762 %R 10.2196/21434 %U http://publichealth.jmir.org/2020/4/e21434/ %U https://doi.org/10.2196/21434 %U http://www.ncbi.nlm.nih.gov/pubmed/33112762 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e21209 %T Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance %A Jalali,Niloofar %A Sahu,Kirti Sundar %A Oetomo,Arlene %A Morita,Plinio Pelegrini %+ School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 31372, plinio.morita@uwaterloo.ca %K public health %K IoT %K anomaly detection %K behavioral monitoring %K deep learning %K variational autoencoder %K LSTM %D 2020 %7 13.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective: The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods: From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results: The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions: This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment. %M 33185562 %R 10.2196/21209 %U http://mhealth.jmir.org/2020/11/e21209/ %U https://doi.org/10.2196/21209 %U http://www.ncbi.nlm.nih.gov/pubmed/33185562 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e22594 %T Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review %A Wirth,Felix Nikolaus %A Johns,Marco %A Meurers,Thierry %A Prasser,Fabian %+ Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany, 49 15773326360, felix-nikolaus.wirth@charite.de %K pandemic %K epidemic %K infectious disease management %K mobile apps %K automated digital contact tracing %K mobility tracking %K outbreak detection %K location-based risk assessment %K public health %K informatics %K app %K infectious disease %K COVID-19 %K review %D 2020 %7 10.11.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. Objective: Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented. Methods: We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions. Results: We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases. Conclusions: We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy). %M 33074833 %R 10.2196/22594 %U http://mhealth.jmir.org/2020/11/e22594/ %U https://doi.org/10.2196/22594 %U http://www.ncbi.nlm.nih.gov/pubmed/33074833 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e21955 %T Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study %A Oehmke,James Francis %A Moss,Charles B %A Singh,Lauren Nadya %A Oehmke,Theresa Bristol %A Post,Lori Ann %+ Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, 420 E Superior St, Chicago, IL, , United States, 1 203 980 7107, lori.post@northwestern.edu %K COVID-19 %K models %K surveillance %K reopening America %K contagion %K metrics %K surveillance %K health policy %K public health %D 2020 %7 5.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R0 and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. Objective: This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. Methods: Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. Results: The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. Conclusions: Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily. %M 32924962 %R 10.2196/21955 %U https://www.jmir.org/2020/10/e21955 %U https://doi.org/10.2196/21955 %U http://www.ncbi.nlm.nih.gov/pubmed/32924962 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e17969 %T Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample %A Huang,Dina %A Huang,Yuru %A Khanna,Sahil %A Dwivedi,Pallavi %A Slopen,Natalie %A Green,Kerry M %A He,Xin %A Puett,Robin %A Nguyen,Quynh %+ Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, 4200 Valley Drive, College Park, MD, 20742, United States, 1 6107879386, dinahuang26@gmail.com %K neighborhood study %K cardiometabolic outcomes %K Twitter %D 2020 %7 18.8.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. Objective: This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. Methods: We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter’s Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. Results: Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI –1.10 to –0.20) and 0.85 kg/m2 (95% CI –1.48 to –0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively. Conclusions: Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention. %M 32808935 %R 10.2196/17969 %U http://publichealth.jmir.org/2020/3/e17969/ %U https://doi.org/10.2196/17969 %U http://www.ncbi.nlm.nih.gov/pubmed/32808935 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e19399 %T Rapid Deployment of a Free, Privacy-Assured COVID-19 Symptom Tracker for Public Safety During Reopening: System Development and Feasibility Study %A Kassaye,Seble G %A Spence,Amanda Blair %A Lau,Edwin %A Bridgeland,David M %A Cederholm,John %A Dimolitsas,Spiros %A Smart,JC %+ Department of Medicine, Georgetown University, 2115 Wisconsin Ave, Washington, DC, 20007, United States, 1 2026878114, sgk23@georgetown.edu %K COVID-19 %K SARS-CoV-2 %K home isolation %K quarantine %K symptom monitoring %K information systems %K privacy %K contact tracing %K virus %K transmission %K public health %K eHealth %D 2020 %7 13.8.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of cases of coronavirus disease (COVID-19) in the United States has exponentially increased. Identifying and monitoring individuals with COVID-19 and individuals who have been exposed to the disease is critical to prevent transmission. Traditional contact tracing mechanisms are not structured on the scale needed to address this pandemic. As businesses reopen, institutions and agencies not traditionally engaged in disease prevention are being tasked with ensuring public safety. Systems to support organizations facing these new challenges are critically needed. Most currently available symptom trackers use a direct-to-consumer approach and use personal identifiers, which raises privacy concerns. Objective: Our aim was to develop a monitoring and reporting system for COVID-19 to support institutions conducting monitoring activities without compromising privacy. Methods: Our multidisciplinary team designed a symptom tracking system after consultation with experts. The system was designed in the Georgetown University AvesTerra knowledge management environment, which supports data integration and synthesis to identify actionable events and maintain privacy. We conducted a beta test for functionality among consenting Georgetown University medical students. Results: The symptom tracker system was designed based on guiding principles developed during peer consultations. Institutions are provided access to the system through an efficient onboarding process that uses clickwrap technology to document agreement to limited terms of use to rapidly enable free access. Institutions provide their constituents with a unique identifier to enter data through a web-based user interface to collect vetted symptoms as well as clinical and epidemiologic data. The website also provides individuals with educational information through links to the COVID-19 prevention recommendations from the US Centers for Disease Control and Prevention. Safety features include instructions for people with new or worsening symptoms to seek care. No personal identifiers are collected in the system. The reporter mechanism safeguards data access so that institutions can only access their own data, and it provides institutions with on-demand access to the data entered by their constituents, organized in summary reports that highlight actionable data. Development of the system began on March 15, 2020, and it was launched on March 20, 2020. In the beta test, 48 Georgetown University School of Medicine students or their social contacts entered data into the system from March 31 to April 5, 2020. One of the 48 users (2%) reported active COVID-19 infection and had no symptoms by the end of the monitoring period. No other participants reported symptoms. Only data with the unique entity identifier for our beta test were generated in our summary reports. Conclusions: This system harnesses insights into privacy and data sharing to avoid regulatory and legal hurdles to rapid adaption by entities tasked with maintaining public safety. Our pilot study demonstrated feasibility and ease of use. Refinements based on feedback from early adapters included release of a Spanish language version. These systems provide technological advances to complement the traditional contact tracing and digital tracing applications being implemented to limit SARS-CoV-2 transmission during reopening. %M 32788148 %R 10.2196/19399 %U http://publichealth.jmir.org/2020/3/e19399/ %U https://doi.org/10.2196/19399 %U http://www.ncbi.nlm.nih.gov/pubmed/32788148 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e17451 %T The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study %A Lee,Edmund WJ %A Bekalu,Mesfin Awoke %A McCloud,Rachel %A Vallone,Donna %A Arya,Monisha %A Osgood,Nathaniel %A Li,Xiaoyan %A Minsky,Sara %A Viswanath,Kasisomayajula %+ Dana-Farber Cancer Institute, 375 Longwood Avenue, Boston, MA, 02215, United States, 1 6178587988, Edmund_Lee@dfci.harvard.edu %K mobile health %K mobile phone %K tobacco use %K big data %K spatial analysis %K data science %D 2020 %7 7.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: People from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States—when used alongside data sources on TRO locations—apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the locations of TROs as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health promotion organizations to better reach underserved communities in their tobacco control efforts. Objective: This study aimed to demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts. Methods: Data for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data. Results: Participants reported encountering protobacco messages mostly from store signs and displays and antitobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown. Conclusions: Smartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment. %M 32673252 %R 10.2196/17451 %U https://www.jmir.org/2020/7/e17451 %U https://doi.org/10.2196/17451 %U http://www.ncbi.nlm.nih.gov/pubmed/32673252 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e19902 %T Health Observation App for COVID-19 Symptom Tracking Integrated With Personal Health Records: Proof of Concept and Practical Use Study %A Yamamoto,Keiichi %A Takahashi,Tsubasa %A Urasaki,Miwa %A Nagayasu,Yoichi %A Shimamoto,Tomonari %A Tateyama,Yukiko %A Matsuzaki,Keiichi %A Kobayashi,Daisuke %A Kubo,Satoshi %A Mito,Shigeyuki %A Abe,Tatsuya %A Matsuura,Hideo %A Iwami,Taku %+ Information Technology Center, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan, 81 73 441 0901, kyamamo@wakayama-med.ac.jp %K public health informatics %K public health administration %K emerging infectious disease %K preventive medicine %K mobile apps %K contact tracing %D 2020 %7 6.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As a counter-cluster measure to prevent the spread of the infectious novel coronavirus disease (COVID-19), an efficient system for health observation outside the hospital is urgently required. Personal health records (PHRs) are suitable for the daily management of physical conditions. Importantly, there are no major differences between the items collected by daily health observation via PHR and the observation of items related to COVID-19. Until now, observations related to COVID-19 have been performed exclusively based on disease-specific items. Therefore, we hypothesize that PHRs would be suitable as a symptom-tracking tool for COVID-19. To this end, we integrated health observation items specific to COVID-19 with an existing PHR-based app. Objective: This study is conducted as a proof-of-concept study in a real-world setting to develop a PHR-based COVID-19 symptom-tracking app and to demonstrate the practical use of health observations for COVID-19 using a smartphone or tablet app integrated with PHRs. Methods: We applied the PHR-based health observation app within an active epidemiological investigation conducted by Wakayama City Public Health Center. At the public health center, a list is made of individuals who have been in close contact with known infected cases (health observers). Email addresses are used by the app when a health observer sends data to the public health center. Each health observer downloads the app and installs it on their smartphone. Self-observed health data are entered daily into the app. These data are then sent via the app by email at a designated time. Localized epidemiological officers can visualize the collected data using a spreadsheet macro and, thus, monitor the health condition of all health observers. Results: We used the app as part of an active epidemiological investigation executed at a public health center. During the investigation, 72 close contacts were discovered. Among them, 57 had adopted the use of the health observation app. Before the introduction of the app, all health observers would have been interviewed by telephone, a slow process that took four epidemiological officers more than 2 hours. After the introduction of the app, a single epidemiological officer can carry out health observations. The app was distributed for free beginning in early March, and by mid-May, it had been used by more than 20,280 users and 400 facilities and organizations across Japan. Currently, health observation of COVID-19 is socially recognized and has become one of the requirements for resuming social activities. Conclusions: Health observation by PHRs for the purpose of improving health management can also be effectively applied as a measure against large-scale infectious diseases. Individual habits of improving awareness of personal health and the use of PHRs for daily health management are powerful armaments against the rapid spread of infectious diseases. Ultimately, similar actions may help to prevent the spread of COVID-19. %M 32568728 %R 10.2196/19902 %U https://mhealth.jmir.org/2020/7/e19902 %U https://doi.org/10.2196/19902 %U http://www.ncbi.nlm.nih.gov/pubmed/32568728 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e14337 %T Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset %A Caldwell,Wendy K %A Fairchild,Geoffrey %A Del Valle,Sara Y %+ X Computational Physics Division, Los Alamos National Laboratory, P.O. Box 1663, Mail Stop T086, Los Alamos, NM, 87545, United States, 1 5056678593, wkcaldwell@lanl.gov %K influenza %K surveillance %K infoveillance %K infodemiology %K projections and predictions %K internet %K data sources %D 2020 %7 3.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. Objective: This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. Methods: We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data. Results: Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level. Conclusions: These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream. %M 32437327 %R 10.2196/14337 %U https://www.jmir.org/2020/7/e14337 %U https://doi.org/10.2196/14337 %U http://www.ncbi.nlm.nih.gov/pubmed/32437327 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e19855 %T Epidemiological Observations on the Association Between Anosmia and COVID-19 Infection: Analysis of Data From a Self-Assessment Web Application %A Denis,Fabrice %A Galmiche,Simon %A Dinh,Aurélien %A Fontanet,Arnaud %A Scherpereel,Arnaud %A Benezit,Francois %A Lescure,François-Xavier %+ Inter-regional Cancer Institut Jean Bernard, 9, rue Beauverger, Le Mans, 72100, France, 33 243475810, f.denis@cjb72.org %K COVID-19 %K anosmia %K epidemiological surveillance %K self-assessment %K web application %K outbreak %K symptoms %K self-assessment %K surveillance %K epidemiology %D 2020 %7 11.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: We developed a self-assessment and participatory surveillance web application for coronavirus disease (COVID-19), which was launched in France in March 2020. Objective: Our objective was to determine if self-reported symptoms could help monitor the dynamics of the COVID-19 outbreak in France. Methods: Users were asked questions about underlying conditions, sociodemographic status, zip code, and COVID-19 symptoms. Depending on the symptoms reported and the presence of coexisting disorders, users were told to either stay at home, contact a general practitioner (GP), or call an emergency phone number. Data regarding COVID-19–related hospitalizations were retrieved from the Ministry of Health. Results: As of March 29, 2020, the application was opened 4,126,789 times; 3,799,535 electronic questionnaires were filled out; and 2,477,174 users had at least one symptom. In total, 34.8% (n=1,322,361) reported no symptoms. The remaining users were directed to self-monitoring (n=858,878, 22.6%), GP visit or teleconsultation (n=1,033,922, 27.2%), or an emergency phone call (n=584,374, 15.4%). Emergency warning signs were reported by 39.1% of participants with anosmia, a loss of the sense of smell (n=127,586) versus 22.7% of participants without anosmia (n=1,597,289). Anosmia and fever and/or cough were correlated with hospitalizations for COVID-19 (Spearman correlation coefficients=0.87 and 0.82, respectively; P<.001 for both). Conclusions: This study suggests that anosmia may be strongly associated with COVID-19 and its severity. Despite a lack of medical assessment and virological confirmation, self-checking application data could be a relevant tool to monitor outbreak trends. Trial Registration: ClinicalTrials.gov NCT04331171; https://clinicaltrials.gov/ct2/show/NCT04331171 %M 32496206 %R 10.2196/19855 %U http://www.jmir.org/2020/6/e19855/ %U https://doi.org/10.2196/19855 %U http://www.ncbi.nlm.nih.gov/pubmed/32496206 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e15917 %T Comparing Methods for Record Linkage for Public Health Action: Matching Algorithm Validation Study %A Avoundjian,Tigran %A Dombrowski,Julia C %A Golden,Matthew R %A Hughes,James P %A Guthrie,Brandon L %A Baseman,Janet %A Sadinle,Mauricio %+ Department of Epidemiology, School of Public Health, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, United States, 1 5431065, tavoun@uw.edu %K medical record linkage %K public health surveillance %K public health practice %K data management %D 2020 %7 30.4.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Many public health departments use record linkage between surveillance data and external data sources to inform public health interventions. However, little guidance is available to inform these activities, and many health departments rely on deterministic algorithms that may miss many true matches. In the context of public health action, these missed matches lead to missed opportunities to deliver interventions and may exacerbate existing health inequities. Objective: This study aimed to compare the performance of record linkage algorithms commonly used in public health practice. Methods: We compared five deterministic (exact, Stenger, Ocampo 1, Ocampo 2, and Bosh) and two probabilistic record linkage algorithms (fastLink and beta record linkage [BRL]) using simulations and a real-world scenario. We simulated pairs of datasets with varying numbers of errors per record and the number of matching records between the two datasets (ie, overlap). We matched the datasets using each algorithm and calculated their recall (ie, sensitivity, the proportion of true matches identified by the algorithm) and precision (ie, positive predictive value, the proportion of matches identified by the algorithm that were true matches). We estimated the average computation time by performing a match with each algorithm 20 times while varying the size of the datasets being matched. In a real-world scenario, HIV and sexually transmitted disease surveillance data from King County, Washington, were matched to identify people living with HIV who had a syphilis diagnosis in 2017. We calculated the recall and precision of each algorithm compared with a composite standard based on the agreement in matching decisions across all the algorithms and manual review. Results: In simulations, BRL and fastLink maintained a high recall at nearly all data quality levels, while being comparable with deterministic algorithms in terms of precision. Deterministic algorithms typically failed to identify matches in scenarios with low data quality. All the deterministic algorithms had a shorter average computation time than the probabilistic algorithms. BRL had the slowest overall computation time (14 min when both datasets contained 2000 records). In the real-world scenario, BRL had the lowest trade-off between recall (309/309, 100.0%) and precision (309/312, 99.0%). Conclusions: Probabilistic record linkage algorithms maximize the number of true matches identified, reducing gaps in the coverage of interventions and maximizing the reach of public health action. %M 32352389 %R 10.2196/15917 %U http://publichealth.jmir.org/2020/2/e15917/ %U https://doi.org/10.2196/15917 %U http://www.ncbi.nlm.nih.gov/pubmed/32352389 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 1 %P e14627 %T Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care: Qualitative Content Analysis %A Reukers,Daphne F M %A Marbus,Sierk D %A Smit,Hella %A Schneeberger,Peter %A Donker,Gé %A van der Hoek,Wim %A van Gageldonk-Lafeber,Arianne B %+ Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Postbus 1, 3720 BA, Bilthoven, , Netherlands, 31 302743419, daphne.reukers@rivm.nl %K influenza %K severe acute respiratory infections %K SARI %K surveillance %K media reports %K news articles %K hospital care %D 2020 %7 4.3.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Netherlands, like most European countries, has a robust influenza surveillance system in primary care. However, there is a lack of real-time nationally representative data on hospital admissions for complications of influenza. Anecdotal information about hospital capacity problems during influenza epidemics can, therefore, not be substantiated. Objective: The aim of this study was to assess whether media reports could provide relevant information for estimating the impact of influenza on hospital capacity, in the absence of hospital surveillance data. Methods: Dutch news articles on influenza in hospitals during the influenza season (week 40 of 2017 until week 20 of 2018) were searched in a Web-based media monitoring program (Coosto). Trends in the number of weekly articles were compared with trends in 5 different influenza surveillance systems. A content analysis was performed on a selection of news articles, and information on the hospital, department, problem, and preventive or response measures was collected. Results: The trend in weekly news articles correlated significantly with the trends in all 5 surveillance systems, including severe acute respiratory infections (SARI) surveillance. However, the peak in all 5 surveillance systems preceded the peak in news articles. Content analysis showed hospitals (N=69) had major capacity problems (46/69, 67%), resulting in admission stops (9/46, 20%), postponement of nonurgent surgical procedures (29/46, 63%), or both (8/46, 17%). Only few hospitals reported the use of point-of-care testing (5/69, 7%) or a separate influenza ward (3/69, 4%) to accelerate clinical management, but most resorted to ad hoc crisis management (34/69, 49%). Conclusions: Media reports showed that the 2017/2018 influenza epidemic caused serious problems in hospitals throughout the country. However, because of the time lag in media reporting, it is not a suitable alternative for near real-time SARI surveillance. A robust SARI surveillance program is important to inform decision making. %M 32130197 %R 10.2196/14627 %U http://publichealth.jmir.org/2020/1/e14627/ %U https://doi.org/10.2196/14627 %U http://www.ncbi.nlm.nih.gov/pubmed/32130197 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 2 %P e15727 %T Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review %A Lee,Yejin %A Raviglione,Mario C %A Flahault,Antoine %+ Institute of Global Health, Faculty of Medicine, University of Geneva, 9 Chemin des Mines, Geneva, 1202, Switzerland, 41 765383087, Ye-Jin.Lee@etu.unige.ch %K tuberculosis %K mHealth %K eHealth %K medical informatics %D 2020 %7 13.2.2020 %9 Review %J J Med Internet Res %G English %X Background: Tuberculosis (TB) is the leading cause of death from a single infectious agent, with around 1.5 million deaths reported in 2018, and is a major contributor to suffering worldwide, with an estimated 10 million new cases every year. In the context of the World Health Organization’s End TB strategy and the quest for digital innovations, there is a need to understand what is happening around the world regarding research into the use of digital technology for better TB care and control. Objective: The purpose of this scoping review was to summarize the state of research on the use of digital technology to enhance TB care and control. This study provides an overview of publications covering this subject and answers 3 main questions: (1) to what extent has the issue been addressed in the scientific literature between January 2016 and March 2019, (2) which countries have been investing in research in this field, and (3) what digital technologies were used? Methods: A Web-based search was conducted on PubMed and Web of Science. Studies that describe the use of digital technology with specific reference to keywords such as TB, digital health, eHealth, and mHealth were included. Data from selected studies were synthesized into 4 functions using narrative and graphical methods. Such digital health interventions were categorized based on 2 classifications, one by function and the other by targeted user. Results: A total of 145 relevant studies were identified out of the 1005 published between January 2016 and March 2019. Overall, 72.4% (105/145) of the research focused on patient care and 20.7% (30/145) on surveillance and monitoring. Other programmatic functions 4.8% (7/145) and electronic learning 2.1% (3/145) were less frequently studied. Most digital health technologies used for patient care included primarily diagnostic 59.4% (63/106) and treatment adherence tools 40.6% (43/106). On the basis of the second type of classification, 107 studies targeted health care providers (107/145, 73.8%), 20 studies targeted clients (20/145, 13.8%), 17 dealt with data services (17/145, 11.7%), and 1 study was on the health system or resource management. The first authors’ affiliations were mainly from 3 countries: the United States (30/145 studies, 20.7%), China (20/145 studies, 13.8%), and India (17/145 studies, 11.7%). The researchers from the United States conducted their research both domestically and abroad, whereas researchers from China and India conducted all studies domestically. Conclusions: The majority of research conducted between January 2016 and March 2019 on digital interventions for TB focused on diagnostic tools and treatment adherence technologies, such as video-observed therapy and SMS. Only a few studies addressed interventions for data services and health system or resource management. %M 32053111 %R 10.2196/15727 %U https://www.jmir.org/2020/2/e15727 %U https://doi.org/10.2196/15727 %U http://www.ncbi.nlm.nih.gov/pubmed/32053111 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 1 %P e13347 %T Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study %A Memon,Shahan Ali %A Razak,Saquib %A Weber,Ingmar %+ Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States, 1 4123785380, samemon@cs.cmu.edu %K noncommunicable diseases %K lifestyle disease surveillance %K infodemiology %K infoveillance %K Google Trends %K Web search %K nowcasting %K public health %K digital epidemiology %D 2020 %7 27.1.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: As the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines. Objective: The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States. Methods: For each of the target variables (diabetes, obesity, and exercise), prevalence rates were collected. After a rigorous keyword selection process, data from Google Trends were collected. These data were denormalized to form spatio-temporal indices. L1-regularized regression models were trained to predict prevalence rates from denormalized Google Trends indices. Models were tested on a held-out set and compared against baselines from the literature as well as a trivial last year equals this year baseline. A similar analysis was done using a multivariate spatio-temporal model where the previous year’s prevalence was included as a covariate. This model was modified to create a time-lagged regression analysis framework. Finally, a hierarchical time-lagged multivariate spatio-temporal model was created to account for subnational trends in the data. The model trained on US data was, then, applied in a transfer learning framework to Canada. Results: In the US context, our proposed models beat the performances of the prior work, as well as the trivial baselines. In terms of the mean absolute error (MAE), the best of our proposed models yields 24% improvement (0.72-0.55; P<.001) for diabetes; 18% improvement (1.20-0.99; P=.001) for obesity, and 34% improvement (2.89-1.95; P<.001) for exercise. Our proposed across-country transfer learning framework also shows promising results with an average Spearman and Pearson correlation of 0.70 for diabetes and 0.90 and 0.91 for obesity, respectively. Conclusions: Although our proposed models beat the baselines, we find the modeling of lifestyle diseases to be a challenging problem, one that requires an abundance of data as well as creative modeling strategies. In doing so, this study shows a low-to-moderate validity of Google Trends in the context of lifestyle disease surveillance, even when applying novel corrective approaches, including a proposed denormalization scheme. We envision qualitative analyses to be a more practical use of Google Trends in the context of lifestyle disease surveillance. For the quantitative analyses, the highest utility of using Google Trends is in the context of transfer learning where low-resource countries could benefit from high-resource countries by using proxy models. %M 32012050 %R 10.2196/13347 %U http://www.jmir.org/2020/1/e13347/ %U https://doi.org/10.2196/13347 %U http://www.ncbi.nlm.nih.gov/pubmed/32012050 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 1 %P e12503 %T The Role of Campus Data in Representing Depression Among College Students: Exploratory Research %A Mei,Guang %A Xu,Weisheng %A Li,Li %A Zhao,Zhen %A Li,Hao %A Liu,Wenqing %A Jiao,Yueming %+ Department of Control Science and Engineering, College of Electronic and Information Engineering, Tongji University, Zhixin Bldg, 6th Fl, 4800 Caoan Rd, Shanghai, China, 86 18916087269, lili@tongji.edu.cn %K depression %K mental health %K behavior analysis %D 2020 %7 27.1.2020 %9 Original Paper %J JMIR Ment Health %G English %X Background: Depression is a predominant feature of many psychological problems leading to extreme behaviors and, in some cases, suicide. Campus information systems keep detailed and reliable student behavioral data; however, whether these data can reflect depression and we know the differences in behavior between depressive and nondepressive students are still research problems. Objective: The purpose of this paper is to investigate the behavioral patterns of depressed students by using multisource campus data and exploring the link between behavioral preferences and depressive symptoms. The campus data described in this paper include basic personal information, academic performance, poverty subsidy, consumption habit, daily routine, library behavior, and meal habit, totaling 121 features. Methods: To identify potentially depressive students, we developed an online questionnaire system based on a standard psychometric instrument, the Zung Self-Rating Depression Scale (SDS). To explore the differences in behavior of depressive and nondepressive students, the Mann-Whitney U test was applied. In order to investigate the behavioral features of different depressive symptoms, factor analysis was used to divide the questionnaire items into different symptom groups and then correlation analysis was employed to study the extrinsic characteristics of each depressive symptom. Results: The correlation between these factors and the features were computed. The results indicated that there were 25 features correlated with either 4 factors or SDS score. The statistical results indicated that depressive students were more likely to fail exams, have poor meal habits, have increased night activities and decreased morning activities, and engage less in social activities (eg, avoiding meal times with friends). Correlation analysis showed that the somatic factor 2 (F4) was negatively correlated with the number of library visits (r=–.179, P<.001), and, compared with other factors, had the greatest impact on students’ daily schedule, eating and social habits. The biggest influencing factor to poor academic performance was cognitive factor F1, and its score was found to be significantly positively correlated with fail rate (r=.185, P=.02). Conclusions: The results presented in this study indicate that campus data can reflect depression and its symptoms. By collecting a large amount of questionnaire data and combining machine learning algorithms, it is possible to realize an identification method of depression and depressive symptoms based on campus data. %M 32012070 %R 10.2196/12503 %U http://mental.jmir.org/2020/1/e12503/ %U https://doi.org/10.2196/12503 %U http://www.ncbi.nlm.nih.gov/pubmed/32012070 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 1 %P e15645 %T The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance %A Prieto,José Tomás %A Scott,Kenneth %A McEwen,Dean %A Podewils,Laura J %A Al-Tayyib,Alia %A Robinson,James %A Edwards,David %A Foldy,Seth %A Shlay,Judith C %A Davidson,Arthur J %+ Division of Scientific Education and Professional Development, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, United States, 1 3036024487, josetomasprieto@gmail.com %K naloxone %K emergency medical services %K natural language processing %K heroin %K substance-related disorders %K opioid crisis %K artificial intelligence %D 2020 %7 3.1.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. Objective: This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. Methods: First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. Results: In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). Conclusions: A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities. %M 31899451 %R 10.2196/15645 %U https://www.jmir.org/2020/1/e15645 %U https://doi.org/10.2196/15645 %U http://www.ncbi.nlm.nih.gov/pubmed/31899451 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e12016 %T Enhanced Safety Surveillance of Influenza Vaccines in General Practice, Winter 2015-16: Feasibility Study %A de Lusignan,Simon %A Correa,Ana %A Dos Santos,Gaël %A Meyer,Nadia %A Haguinet,François %A Webb,Rebecca %A McGee,Christopher %A Byford,Rachel %A Yonova,Ivelina %A Pathirannehelage,Sameera %A Ferreira,Filipa Matos %A Jones,Simon %+ University of Surrey, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom, 44 01483 68 ext 3089, s.lusignan@surrey.ac.uk %K vaccines %K safety management %K medical records systems, computerized %K drug-related side effects and adverse reactions %K influenza, human %K influenza vaccines %K general practice %K England %D 2019 %7 14.11.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The European Medicines Agency (EMA) requires vaccine manufacturers to conduct enhanced real-time surveillance of seasonal influenza vaccination. The EMA has specified a list of adverse events of interest to be monitored. The EMA sets out 3 different ways to conduct such surveillance: (1) active surveillance, (2) enhanced passive surveillance, or (3) electronic health record data mining (EHR-DM). English general practice (GP) is a suitable setting to implement enhanced passive surveillance and EHR-DM. Objective: This study aimed to test the feasibility of conducting enhanced passive surveillance in GP using the yellow card scheme (adverse events of interest reporting cards) to determine if it has any advantages over EHR-DM alone. Methods: A total of 9 GPs in England participated, of which 3 tested the feasibility of enhanced passive surveillance and the other 6 EHR-DM alone. The 3 that tested EPS provided patients with yellow (adverse events) cards for patients to report any adverse events. Data were extracted from all 9 GPs’ EHRs between weeks 35 and 49 (08/24/2015 to 12/06/2015), the main period of influenza vaccination. We conducted weekly analysis and end-of-study analyses. Results: Our GPs were largely distributed across England with a registered population of 81,040. In the week 49 report, 15,863/81,040 people (19.57% of the registered practice population) were vaccinated. In the EPS practices, staff managed to hand out the cards to 61.25% (4150/6776) of the vaccinees, and of these cards, 1.98% (82/4150) were returned to the GP offices. Adverse events of interests were reported by 113 /7223 people (1.56%) in the enhanced passive surveillance practices, compared with 322/8640 people (3.73%) in the EHR-DM practices. Conclusions: Overall, we demonstrated that GPs EHR-DM was an appropriate method of enhanced surveillance. However, the use of yellow cards, in enhanced passive surveillance practices, did not enhance the collection of adverse events of interests as demonstrated in this study. Their return rate was poor, data entry from them was not straightforward, and there were issues with data reconciliation. We concluded that customized cards prespecifying the EMA’s adverse events of interests, combined with EHR-DM, were needed to maximize data collection. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2016-015469 %M 31724955 %R 10.2196/12016 %U http://publichealth.jmir.org/2019/4/e12016/ %U https://doi.org/10.2196/12016 %U http://www.ncbi.nlm.nih.gov/pubmed/31724955 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e13593 %T Using Web-Based Pin-Drop Maps to Capture Activity Spaces Among Young Adults Who Use Drugs in Rural Areas: Cross-Sectional Survey %A Cooper,Hannah Luke Fenimore %A Crawford,Natalie D %A Haardörfer,Regine %A Prood,Nadya %A Jones-Harrell,Carla %A Ibragimov,Umedjon %A Ballard,April M %A Young,April M %+ College of Public Health, University of Kentucky, 111 Washington Avenue, Office 211C, Lexington, KY, 40536, United States, 1 859 218 2090, april.young@uky.edu %K rural %K substance use disorder %K Web-based data collection %K geospatial methods %K risk environment %K activity spaces %D 2019 %7 18.10.2019 %9 Review %J JMIR Public Health Surveill %G English %X Background: Epicenters of harmful drug use are expanding to US rural areas, with rural young adults bearing a disproportionate burden. A large body of work suggests that place characteristics (eg, spatial access to health services) shape vulnerability to drug-related harms among urban residents. Research on the role of place characteristics in shaping these harms among rural residents is nascent, as are methods of gathering place-based data. Objective: We (1) analyzed whether young rural adults who used drugs answered self-administered Web-based mapping items about locations where they engaged in risk behaviors and (2) determined the precision of mapped locations. Methods: Eligible individuals had to report recently using opioids to get high; be aged between 18 and 35 years; and live in the 5-county rural Appalachian Kentucky study area. We used targeted outreach and peer-referral methods to recruit participants. The survey asked participants to drop a pin in interactive maps to mark where they completed the survey, and where they had slept most; used drugs most; and had sex most in the past 6 months. Precision was assessed by (1) determining whether mapped locations were within 100 m of a structure and (2) calculating the Euclidean distance between the pin-drop home location and the street address where participants reported sleeping most often. Measures of central tendency and dispersion were calculated for all variables; distributions of missingness for mapping items and for the Euclidean distance variable were explored across participant characteristics. Results: Of the 151 participants, 88.7% (134/151) completed all mapping items, and ≥92.1% (>139/151) dropped a pin at each of the 4 locations queried. Missingness did not vary across most participant characteristics, except that lower percentages of full-time workers and peer-recruited participants mapped some locations. Two-thirds of the pin-drop sex and drug use locations were less than 100 m from a structure, as were 92.1% (139/151) of pin-drop home locations. The median distance between the pin-drop and street-address home locations was 2.0 miles (25th percentile=0.8 miles; 75th percentile=5.5 miles); distances were shorter for high-school graduates, staff-recruited participants, and participants reporting no technical difficulties completing the survey. Conclusions: Missingness for mapping items was low and unlikely to introduce bias, given that it varied across few participant characteristics. Precision results were mixed. In a rural study area of 1378 square miles, most pin-drop home addresses were near a structure; it is unsurprising that fewer drug and sex locations were near structures because most participants reported engaging in these activities outside at times. The error in pin-drop home locations, however, might be too large for some purposes. We offer several recommendations to strengthen future research, including gathering metadata on the extent to which participants zoom in on each map and recruiting participants via trusted staff. %M 31628787 %R 10.2196/13593 %U https://publichealth.jmir.org/2019/4/e13593 %U https://doi.org/10.2196/13593 %U http://www.ncbi.nlm.nih.gov/pubmed/31628787 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 3 %P e11555 %T Developing a Digital Solution for Dengue Through Epihack: Qualitative Evaluation Study of a Five-Day Health Hackathon in Sri Lanka %A Panchapakesan,Chitra %A Sheldenkar,Anita %A Wimalaratne,Prasad %A Wijayamuni,Ruwan %A Lwin,May Oo %+ Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore,, Singapore, 65 9446 6036, chitra002@e.ntu.edu.sg %K Epihack %K civic engagement %K dengue %K digital epidemiology %K participatory surveillance %K participatory epidemiology %K participatory design %K workshop %D 2019 %7 29.08.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Dengue is a mosquito-borne viral disease that has increasingly affected Sri Lanka in recent years. To address this issue, dengue surveillance through increasingly prevalent digital surveillance applications has been suggested for use by health authorities and the general public. Epihack Sri Lanka was a 5-day hackathon event organized to develop a digital dengue surveillance tool. Objective: The goal of the research was to examine the effectiveness of a collaborative hackathon that brought together information technology (IT) and health experts from around the globe to develop a solution to the dengue pandemic in Sri Lanka. Methods: Ethnographic observation and qualitative informal interviews were conducted with 58 attendees from 11 countries over the 5-day Epihack to identify the main factors that influence a collaborative hackathon. Interviews were transcribed and coded based on grounded theory. Results: Three major themes were identified during the Epihack Sri Lanka event: engagement, communication, and current disease environment. Unlike other hackathons, Epihack had no winners or prizes and was collaborative rather than competitive, which worked well in formulating a variety of ideas and bringing together volunteers with a sense of civic duty to improve public health. Having health and IT experts work together concurrently was received positively and considered highly beneficial to the development of the product. Participants were overall very satisfied with the event, although they thought it could have been longer. Communication issues and cultural differences were observed but continued to decrease as the event progressed. This was found to be extremely important to the efficiency of the event, which highlighted the benefit of team-bonding exercises. Bringing expert knowledge and examples of systems from around the world benefited the creation of new ideas. However, developing a system that can adapt and cater to the local disease environment is important in successfully developing the concepts. Conclusions: Epihack Sri Lanka was successful in bringing together health and IT experts to develop a digital solution for dengue surveillance. The collaborative format achieved a variety of fruitful ideas and may lead to more hackathons working in this way in the future. Good communication, participant engagement, and stakeholder interest with adaptation of ideas to complement the current environment are vital to achieve the goals of the event. %M 31469074 %R 10.2196/11555 %U http://formative.jmir.org/2019/3/e11555/ %U https://doi.org/10.2196/11555 %U http://www.ncbi.nlm.nih.gov/pubmed/31469074 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e12245 %T Factors Influencing User Engagement of Health Information Disseminated by Chinese Provincial Centers for Disease Control and Prevention on WeChat: Observational Study %A Zhang,Yan %A Xia,Tingsong %A Huang,Lingfeng %A Yin,Mingjuan %A Sun,Mingwei %A Huang,Jingxiao %A Ni,Yu %A Ni,Jindong %+ Guangdong Medical University, No. 1 Xincheng Road, Songshan Lake, Dongguan,, China, 86 769 2289 6570, nijd-gw@gdmu.edu.cn %K WeChat %K WeChat official accounts %K user engagement %K CDC %K health education %D 2019 %7 27.06.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Social media is currently becoming a new channel for information acquisition and exchange. In China, with the growing popularity of WeChat and WeChat official accounts (WOAs), health promotion agencies have an opportunity to use them for successful information distribution and diffusion online. Objective: We aimed to identify features of articles pushed by WOAs of Chinese provincial Centers for Disease Control and Prevention (CDC) that are associated with user engagement. Methods: We searched and subscribed to 28 WOAs of provincial CDCs. Data for this study consisted of WeChat articles on these WOAs between January 1, 2017 and December 31, 2017. We developed a features frame containing title type, article content, article type, communication skills, number of marketing elements, and article length for each article and coded the data quantitatively using a coding scheme that assigned numeric values to article features. We examined the descriptive characteristics of articles for every WOA and generated descriptive statistics for six article features. The amount of reading and liking was converted into the level of reading and liking by the 75% position. Two-category univariate logistic regression and multivariable logistic regression were conducted to explore associations between the features of the articles and user engagement, operationalized as reading level and liking level. Results: All provincial CDC WOAs provided a total of 5976 articles in 2017. Shanghai CDC articles attracted the most user engagement, and Ningxia CDC articles attracted the least. For all articles, the median reading was 551.5 and the median liking was 10. Multivariable logistic regression analysis revealed that article content, article type, communication skills, number of marketing elements, and article length were associated with reading level and liking level. However, title type was only associated with liking level. Conclusions: How social media can be used to best achieve health information dissemination and public health outcomes is a topic of much discussion and study in the public health community. Given the lack of related studies based on WeChat or official accounts, we conducted this study and found that article content, article type, communication skills, number of marketing elements, article length, and title type were associated with user engagement. Our study may provide public health and community leaders with insight into the diffusion of important health topics of concern. %M 31250833 %R 10.2196/12245 %U http://mhealth.jmir.org/2019/6/e12245/ %U https://doi.org/10.2196/12245 %U http://www.ncbi.nlm.nih.gov/pubmed/31250833 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 5 %P e12273 %T Participatory Disease Surveillance Systems: Ethical Framework %A Geneviève,Lester Darryl %A Martani,Andrea %A Wangmo,Tenzin %A Paolotti,Daniela %A Koppeschaar,Carl %A Kjelsø,Charlotte %A Guerrisi,Caroline %A Hirsch,Marco %A Woolley-Meza,Olivia %A Lukowicz,Paul %A Flahault,Antoine %A Elger,Bernice Simone %+ Institute for Biomedical Ethics, University of Basel, Bernoullistrasse 28, Basel, 4056, Switzerland, 41 61 207 17 82, lester.genevieve@unibas.ch %K ethics %K research %K influenza, human %K smartphone %K public health surveillance %D 2019 %7 23.05.2019 %9 Viewpoint %J J Med Internet Res %G English %X Advances in information technology are changing public health at an unprecedented rate. Participatory surveillance systems are contributing to public health by actively engaging digital (eg, Web-based) communities of volunteer citizens to report symptoms and other pertinent information on public health threats and also by empowering individuals to promptly respond to them. However, this digital model raises ethical issues on top of those inherent in traditional forms of public health surveillance. Research ethics are undergoing significant changes in the digital era where not only participants’ physical and psychological well-being but also the protection of their sensitive data have to be considered. In this paper, the digital platform of Influenzanet is used as a case study to illustrate those ethical challenges posed to participatory surveillance systems using digital platforms and mobile apps. These ethical challenges include the implementation of electronic consent, the protection of participants’ privacy, the promotion of justice, and the need for interdisciplinary capacity building of research ethics committees. On the basis of our analysis, we propose a framework to regulate and strengthen ethical approaches in the field of digital public health surveillance. %M 31124466 %R 10.2196/12273 %U https://www.jmir.org/2019/5/e12273/ %U https://doi.org/10.2196/12273 %U http://www.ncbi.nlm.nih.gov/pubmed/31124466 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 5 %P e13498 %T Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques %A Brenas,Jon Hael %A Shin,Eun Kyong %A Shaban-Nejad,Arash %+ Department of Pediatrics, Oak Ridge National Laboratory Center for Biomedical Informatics, University of Tennessee Health Science Center, 50 N Dunlap Street, R492, Memphis, TN, 38103, United States, 1 901 287 5836, ashabann@uthsc.edu %K ontologies %K mental health surveillance %K adverse childhood experiences %K semantics %K computational psychiatry %D 2019 %7 21.05.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. Objective: To better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs’ surveillance and research. Methods: We use advanced knowledge representation and semantic Web tools and techniques to implement the ontology. The current implementation of the ontology is expressed in the description logic ALCRIQ(D), a sublogic of Web Ontology Language (OWL 2). Results: The ACEs Ontology has been implemented and made available to the mental health community and the public via the BioPortal repository. Moreover, multiple use-case scenarios have been introduced to showcase and evaluate the usability of the ontology in action. The ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals and predicting potential negative outcomes that they might encounter to the prevention of ACEs in a population and designing interventions and policies. Conclusions: The ACEs Ontology provides a uniform and reusable semantic network and an integrated knowledge structure for mental health practitioners and researchers to improve ACEs’ surveillance and evaluation. %M 31115344 %R 10.2196/13498 %U http://mental.jmir.org/2019/5/e13498/ %U https://doi.org/10.2196/13498 %U http://www.ncbi.nlm.nih.gov/pubmed/31115344 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e11263 %T A Software Tool Aimed at Automating the Generation, Distribution, and Assessment of Social Media Messages for Health Promotion and Education Research %A Reuter,Katja %A MacLennan,Alicia %A Le,NamQuyen %A Unger,Jennifer B %A Kaiser,Elsi M %A Angyan,Praveen %+ Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Institute for Health Promotion & Disease Prevention Research, University of Southern California, 3rd Floor, MC 9239, 2001 N Soto Street, Los Angeles, CA, 90032, United States, 1 3234422046, katja.reuter@usc.edu %K algorithm %K automation %K digital %K Facebook %K health communication %K health promotion %K Instagram %K internet %K online %K smoking %K social network %K social media %K tobacco %K Twitter %D 2019 %7 07.05.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Social media offers promise for communicating the risks and health effects of harmful products and behaviors to larger and hard-to-reach segments of the population. Nearly 70% of US adults use some social media. However, rigorous research across different social media is vital to establish successful evidence-based health communication strategies that meet the requirements of the evolving digital landscape and the needs of diverse populations. Objective: The aim of this study was to expand and test a software tool (Trial Promoter) to support health promotion and education research by automating aspects of the generation, distribution, and assessment of large numbers of social media health messages and user comments. Methods: The tool supports 6 functions (1) data import, (2) message generation deploying randomization techniques, (3) message distribution, (4) import and analysis of message comments, (5) collection and display of message performance data, and (6) reporting based on a predetermined data dictionary. The tool was built using 3 open-source software products: PostgreSQL, Ruby on Rails, and Semantic UI. To test the tool’s utility and reliability, we developed parameterized message templates (N=102) based upon 2 government-sponsored health education campaigns, extracted images from these campaigns and a free stock photo platform (N=315), and topic-related hashtags (N=4) from Twitter. We conducted a functional correctness analysis of the generated social media messages to assess the algorithm’s ability to produce the expected output for each input. We defined 100% correctness as use of the message template text and substitution of 3 message parameters (ie, image, hashtag, and destination URL) without any error. The percent correct was calculated to determine the probability with which the tool generates accurate messages. Results: The tool generated, distributed, and assessed 1275 social media health messages over 85 days (April 19 to July 12, 2017). It correctly used the message template text and substituted the message parameters 100% (1275/1275) of the time as verified by human reviewers and a custom algorithm using text search and attribute-matching techniques. Conclusions: A software tool can effectively support the generation, distribution, and assessment of hundreds of health promotion messages and user comments across different social media with the highest degree of functional correctness and minimal human interaction. The tool has the potential to support social media–enabled health promotion research and practice: first, by enabling the assessment of large numbers of messages to develop evidence-based health communication, and second, by providing public health organizations with a tool to increase their output of health education messages and manage user comments. We call on readers to use and develop the tool and to contribute to evidence-based communication methods in the digital age. %M 31066708 %R 10.2196/11263 %U http://publichealth.jmir.org/2019/2/e11263/ %U https://doi.org/10.2196/11263 %U http://www.ncbi.nlm.nih.gov/pubmed/31066708 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 2 %P e11342 %T Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings %A Concannon,David %A Herbst,Kobus %A Manley,Ed %+ Centre for Advanced Spatial Analysis, University College London, Gower Street, London,, United Kingdom, 44 02031083884, ed.manley@ucl.ac.uk %K data visualization %K data dashboards %K health and demographic surveillance %K sub-Saharan Africa %K treatment as prevention %K clinical trials %K demographics %K real-time %K data literacy %D 2019 %7 04.04.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Population surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information. Objectives: This paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context. Methods: The framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards. Results: The evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use. Conclusions: Overall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context. %M 30946016 %R 10.2196/11342 %U https://formative.jmir.org/2019/2/e11342/ %U https://doi.org/10.2196/11342 %U http://www.ncbi.nlm.nih.gov/pubmed/30946016 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e11666 %T An Automated Text-Messaging Platform for Enhanced Retention and Data Collection in a Longitudinal Birth Cohort: Cohort Management Platform Analysis %A Barry,Caroline M %A Sabhlok,Aditi %A Saba,Victoria C %A Majors,Alesha D %A Schechter,Julia C %A Levine,Erica L %A Streicher,Martin %A Bennett,Gary G %A Kollins,Scott H %A Fuemmeler,Bernard F %+ Cancer Prevention and Control, Department of Health Behavior and Policy, Virginia Commonwealth University, PO Box 980149, 830 E Main St, Richmond, VA, 23219, United States, 1 8048288892, bernard.fuemmeler@vcuhealth.org %K data collection %K longitudinal studies %K mobile health %K text messaging %D 2019 %7 02.04.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional methods for recruiting and maintaining contact with participants in cohort studies include print-based correspondence, which can be unidirectional, labor intensive, and slow. Leveraging technology can substantially enhance communication, maintain engagement of study participants in cohort studies, and facilitate data collection on a range of outcomes. Objective: This paper provides an overview of the development process and design of a cohort management platform (CMP) used in the Newborn Epigenetic STudy (NEST), a large longitudinal birth cohort study. Methods: The platform uses short message service (SMS) text messaging to facilitate interactive communication with participants; it also semiautomatically performs many recruitment and retention procedures typically completed by research assistants over the course of multiple study follow-up visits. Results: Since February 2016, 302 participants have consented to enrollment in the platform and 162 have enrolled with active engagement in the system. Daily reminders are being used to help improve adherence to the study’s accelerometer wear protocol. At the time of this report, 213 participants in our follow-up study who were also registered to use the CMP were eligible for the accelerometer protocol. Preliminary data show that texters (138/213, 64.8%), when compared to nontexters (75/213, 35.2%), had significantly longer average accelerometer-wearing hours (165.6 hours, SD 56.5, vs 145.3 hours, SD 58.5, P=.01) when instructed to wear the devices for 1 full week. Conclusions: This platform can serve as a model for enhancing communication and engagement with longitudinal study cohorts, especially those involved in studies assessing environmental exposures. %M 30938689 %R 10.2196/11666 %U https://publichealth.jmir.org/2019/2/e11666/ %U https://doi.org/10.2196/11666 %U http://www.ncbi.nlm.nih.gov/pubmed/30938689 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e11477 %T Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance %A Kogan,Nicole E %A Bolon,Isabelle %A Ray,Nicolas %A Alcoba,Gabriel %A Fernandez-Marquez,Jose L %A Müller,Martin M %A Mohanty,Sharada P %A Ruiz de Castañeda,Rafael %+ Institute of Global Health, Faculty of Medicine, University of Geneva, Campus Biotech, Chemin des Mines 9, Geneva, 1202, Switzerland, 41 223790951, rafael.ruizdecastaneda@unige.ch %K epidemiology %K maps %K foodborne diseases %K social networking %K travel %K agriculture %D 2019 %7 01.04.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Wet markets are markets selling fresh meat and produce. Wet markets are critical for food security and sustainable development in their respective regions. Due to their cultural significance, they attract numerous visitors and consequently generate tourist-geared information on the Web (ie, on social networks such as TripAdvisor). These data can be used to create a novel, international wet market inventory to support epidemiological surveillance and control in such settings, which are often associated with negative health outcomes. Objective: Using social network data, we aimed to assess the level of wet markets’ touristic importance on the Web, produce the first distribution map of wet markets of touristic interest, and identify common diseases facing visitors in these settings. Methods: A Google search was performed on 31 food market–related keywords, with the first 150 results for each keyword evaluated based on their relevance to tourism. Of all these queries, wet market had the highest number of tourism-related Google Search results; among these, TripAdvisor was the most frequently-occurring travel information aggregator, prompting its selection as the data source for this study. A Web scraping tool (ParseHub) was used to extract wet market names, locations, and reviews from TripAdvisor. The latter were searched for disease-related content, which enabled assignment of GeoSentinel diagnosis codes to each. This syndromic categorization was overlaid onto a mapping of wet market locations. Regional prevalence of the most commonly occurring symptom group - food poisoning - was then determined (ie, by dividing the number of wet markets per continent with more than or equal to 1 review containing this syndrome by the total number of wet markets on that continent with syndromic information). Results: Of the 1090 hits on TripAdvisor for wet market, 36.06% (393/1090) conformed to the query’s definition; wet markets were heterogeneously distributed: Asia concentrated 62.6% (246/393) of them, Europe 19.3% (76/393), North America 7.9% (31/393), Oceania 5.1% (20/393), Africa 3.1% (12/393), and South America 2.0% (8/393). Syndromic information was available for 14.5% (57/393) of wet markets. The most frequently occurring syndrome among visitors to these wet markets was food poisoning, accounting for 54% (51/95) of diagnoses. Cases of this syndrome were identified in 56% (22/39) of wet markets with syndromic information in Asia, 71% (5/7) in Europe, and 71% (5/7) in North America. All wet markets in South America and Oceania reported food poisoning cases, but the number of reviews with syndromic information was very limited in these regions (n=2). Conclusions: The map produced illustrates the potential role of touristically relevant social network data to support global epidemiological surveillance. This includes the possibility to approximate the global distribution of wet markets and to identify diseases (ie, food poisoning) that are most prevalent in such settings. %M 30932867 %R 10.2196/11477 %U https://publichealth.jmir.org/2019/2/e11477/ %U https://doi.org/10.2196/11477 %U http://www.ncbi.nlm.nih.gov/pubmed/30932867 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 1 %P e12032 %T Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks %A Velappan,Nileena %A Daughton,Ashlynn Rae %A Fairchild,Geoffrey %A Rosenberger,William Earl %A Generous,Nicholas %A Chitanvis,Maneesha Elizabeth %A Altherr,Forest Michael %A Castro,Lauren A %A Priedhorsky,Reid %A Abeyta,Esteban Luis %A Naranjo,Leslie A %A Hollander,Attelia Dawn %A Vuyisich,Grace %A Lillo,Antonietta Maria %A Cloyd,Emily Kathryn %A Vaidya,Ashvini Rajendra %A Deshpande,Alina %+ Los Alamos National Laboratory, TA-43, bldg1, MS - M888, Los Alamos, NM, 87545, United States, 1 505 667 9938, deshpande_a@lanl.gov %K epidemiology %K infectious diseases %K algorithm %K public health informatics %K web browser %D 2019 %7 25.02.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak. %M 30801254 %R 10.2196/12032 %U http://publichealth.jmir.org/2019/1/e12032/ %U https://doi.org/10.2196/12032 %U http://www.ncbi.nlm.nih.gov/pubmed/30801254 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 2 %P e12783 %T Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations %A Wakamiya,Shoko %A Morita,Mizuki %A Kano,Yoshinobu %A Ohkuma,Tomoko %A Aramaki,Eiji %+ Institute for Research Initiatives, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, 630-0192, Japan, 81 743 72 6053, socialcomputing-office@is.naist.jp %K text mining %K social media %K machine learning %K natural language processing %K artificial intelligence %K surveillance %K infodemiology %K infoveillance %D 2019 %7 20.02.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media–based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient’s symptom and those that do not. Objective: This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP. Methods: In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. Results: The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively. Conclusions: This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications. %M 30785407 %R 10.2196/12783 %U http://www.jmir.org/2019/2/e12783/ %U https://doi.org/10.2196/12783 %U http://www.ncbi.nlm.nih.gov/pubmed/30785407 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e10995 %T Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation %A Zeleke,Atinkut Alamirrew %A Worku,Abebaw Gebeyehu %A Demissie,Adina %A Otto-Sobotka,Fabian %A Wilken,Marc %A Lipprandt,Myriam %A Tilahun,Binyam %A Röhrig,Rainer %+ Division of Medical Informatics, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Building V04-1-133, Ammerländer Heerstraße 140, Oldenburg, 26129, Germany, 49 1732587251, atinkut.alamirrew.zeleke@uni-oldenburg.de %K public health %K maternal health %K surveillance %K survey %K data collection %K data quality %K tablet computer %K mHealth %K Ethiopia %D 2019 %7 11.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. Objective: This study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. Methods: A randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit–based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. Results: From the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. Conclusions: EDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site. %M 30741642 %R 10.2196/10995 %U http://mhealth.jmir.org/2019/2/e10995/ %U https://doi.org/10.2196/10995 %U http://www.ncbi.nlm.nih.gov/pubmed/30741642 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 1 %P e11357 %T Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment %A Talaei-Khoei,Amir %A Wilson,James M %A Kazemi,Seyed-Farzan %+ Department of Information Systems, University of Nevada Reno, Ansari Business Building, 1664 N Virginia Street, Room 314F, Reno, NV, 89557, United States, 1 7754407005, atalaeikhoei@unr.edu %K autocorrelation %K disease counts %K prediction %K public health surveillance %K time-series analysis %D 2019 %7 15.01.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. Objective: This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. Methods: The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. Results: Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. Conclusions: The use of change-point analysis with autocorrelation provides the best and most practical period of measurement. %M 30664479 %R 10.2196/11357 %U http://publichealth.jmir.org/2019/1/e11357/ %U https://doi.org/10.2196/11357 %U http://www.ncbi.nlm.nih.gov/pubmed/30664479 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e10436 %T Where No Universal Health Care Identifier Exists: Comparison and Determination of the Utility of Score-Based Persons Matching Algorithms Using Demographic Data %A Waruru,Anthony %A Natukunda,Agnes %A Nyagah,Lilly M %A Kellogg,Timothy A %A Zielinski-Gutierrez,Emily %A Waruiru,Wanjiru %A Masamaro,Kenneth %A Harklerode,Richelle %A Odhiambo,Jacob %A Manders,Eric-Jan %A Young,Peter W %+ Division of Global HIV and TB, Centers for Disease Control and Prevention, PO Box 606, Nairobi, 00621, Kenya, 254 722200179, awaruru@cdc.gov %K deterministic matching %K score-based matching %K HIV case-based surveillance %K unique case identification %K universal health care identifier %D 2018 %7 13.12.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A universal health care identifier (UHID) facilitates the development of longitudinal medical records in health care settings where follow up and tracking of persons across health care sectors are needed. HIV case-based surveillance (CBS) entails longitudinal follow up of HIV cases from diagnosis, linkage to care and treatment, and is recommended for second generation HIV surveillance. In the absence of a UHID, records matching, linking, and deduplication may be done using score-based persons matching algorithms. We present a stepwise process of score-based persons matching algorithms based on demographic data to improve HIV CBS and other longitudinal data systems. Objective: The aim of this study is to compare deterministic and score-based persons matching algorithms in records linkage and matching using demographic data in settings without a UHID. Methods: We used HIV CBS pilot data from 124 facilities in 2 high HIV-burden counties (Siaya and Kisumu) in western Kenya. For efficient processing, data were grouped into 3 scenarios within (1) HIV testing services (HTS), (2) HTS-care, and (3) within care. In deterministic matching, we directly compared identifiers and pseudo-identifiers from medical records to determine matches. We used R stringdist package for Jaro, Jaro-Winkler score-based matching and Levenshtein, and Damerau-Levenshtein string edit distance calculation methods. For the Jaro-Winkler method, we used a penalty (р)=0.1 and applied 4 weights (ω) to Levenshtein and Damerau-Levenshtein: deletion ω=0.8, insertion ω=0.8, substitutions ω=1, and transposition ω=0.5. Results: We abstracted 12,157 cases of which 4073/12,157 (33.5%) were from HTS, 1091/12,157 (9.0%) from HTS-care, and 6993/12,157 (57.5%) within care. Using the deterministic process 435/12,157 (3.6%) duplicate records were identified, yielding 96.4% (11,722/12,157) unique cases. Overall, of the score-based methods, Jaro-Winkler yielded the most duplicate records (686/12,157, 5.6%) while Jaro yielded the least duplicates (546/12,157, 4.5%), and Levenshtein and Damerau-Levenshtein yielded 4.6% (563/12,157) duplicates. Specifically, duplicate records yielded by method were: (1) Jaro 5.7% (234/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.4% (308/6993) within care, (2) Jaro-Winkler 7.4% (302/4073) within HTS, 0.5% (6/1091) in HTS-care, and 5.4% (378/6993) within care, (3) Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care, and (4) Damerau-Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care. Conclusions: Without deduplication, over reporting occurs across the care and treatment cascade. Jaro-Winkler score-based matching performed the best in identifying matches. A pragmatic estimate of duplicates in health care settings can provide a corrective factor for modeled estimates, for targeting and program planning. We propose that even without a UHID, standard national deduplication and persons-matching algorithm that utilizes demographic data would improve accuracy in monitoring HIV care clinical cascades. %M 30545805 %R 10.2196/10436 %U http://publichealth.jmir.org/2018/4/e10436/ %U https://doi.org/10.2196/10436 %U http://www.ncbi.nlm.nih.gov/pubmed/30545805 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 1 %N 1 %P e11425 %T Nursing Unit Communication During a US Public Health Emergency: Natural Experiment %A Benham-Hutchins,Marge %A Carley,Kathleen M %A Brewer,Barbara B %A Effken,Judith A %A Reminga,Jeffrey %+ School of Nursing, University of Texas at Austin, 1710 Red River, Mail code: D0100, Austin, TX, 78712, United States, 1 512 475 6351, mbenhamhutchins@austin.utexas.edu %K social network analysis %K nursing unit communication %K Ebola virus disease %K public health emergency %K natural experiment %K nursing %D 2018 %7 06.12.2018 %9 Original Paper %J JMIR Nursing %G English %X Background: In the second half of 2014, the first case of Ebola virus disease (EVD) was diagnosed in the United States. During this time period, we were collecting data for the Measuring Network Stability and Fit (NetFIT) longitudinal study, which used social network analysis (SNA) to study relationships between nursing staff communication patterns and patient outcomes. One of the data collection sites was a few blocks away from where the initial EVD diagnosis was made. The EVD public health emergency during the NetFIT data collection time period resulted in the occurrence of a natural experiment. Objective: The objectives of the NetFIT study were to examine the structure of nursing unit decision-making and information-sharing networks, identify a parsimonious set of network metrics that can be used to measure the longitudinal stability of these networks, examine the relationship between the contextual features of a unit and network metrics, and identify relationships between key network measures and nursing-sensitive patient-safety and quality outcomes. This paper reports on unit communication and outcome changes that occurred during the EVD natural disaster time period on the 10 hospital units that had data collected before, during, and after the crisis period. Methods: For the NetFIT study, data were collected from nursing staff working on 25 patient care units, in three hospitals, and at four data collection points over a 7-month period: Baseline, Month 1, Month 4, and Month 7. Data collection was staggered by hospital and unit. To evaluate the influence of this public health emergency on nursing unit outcomes and communication characteristics, this paper focuses on a subsample of 10 units from two hospitals where data were collected before, during, and after the EVD crisis period. No data were collected from Hospital B during the crisis period. Network data from individual staff were aggregated to the nursing unit level to create 24-hour networks and three unit-level safety outcome measures—fall rate, medication errors, and hospital-acquired pressure ulcers—were collected. Results: This analysis includes 40 data collection points and 608 staff members who completed questionnaires. Participants (N=608) included registered nurses (431, 70.9%), licensed vocational nurses (3, 0.5%), patient care technicians (133, 21.9%), unit clerks (28, 4.6%), and monitor watchers (13, 2.1%). Changes in SNA metrics associated with communication (ie, average distance, diffusion, and density) were noted in units that had changes in patient safety outcome measures. Conclusions: Units in the hospital site in the same city as the EVD case exhibited multiple changes in patient outcomes, network communication metrics, and response rates. Future research using SNA to examine the influence of public health emergencies on hospital communication networks and relationships to patient outcomes is warranted. %M 34345768 %R 10.2196/11425 %U https://nursing.jmir.org/2018/1/e11425/ %U https://doi.org/10.2196/11425 %U http://www.ncbi.nlm.nih.gov/pubmed/34345768 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e10150 %T #Healthy Selfies: Exploration of Health Topics on Instagram %A Muralidhara,Sachin %A Paul,Michael J %+ Department of Information Science, University of Colorado Boulder, 315 UCB, Boulder, CO, 80309, United States, 1 303 735 7581, mpaul@colorado.edu %K social media %K Instagram %K image sharing %K topic modeling %K computer vision %K public health %D 2018 %7 29.06.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity. Objective: This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research. Methods: The study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool. Results: We identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet (8,293/96,426; 8.6% of posts) and exercise (7,328/96,426; 7.6% of posts). Conclusions: A large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter. %M 29959106 %R 10.2196/10150 %U http://publichealth.jmir.org/2018/2/e10150/ %U https://doi.org/10.2196/10150 %U http://www.ncbi.nlm.nih.gov/pubmed/29959106 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e42 %T Improving Visualization of Female Breast Cancer Survival Estimates: Analysis Using Interactive Mapping Reports %A Ben Ramadan,Awatef Ahmed %A Jackson-Thompson,Jeannette %A Schmaltz,Chester Lee %+ Department of Mathematics, Science and Informatics, Penfield College, Mercer University, AACC Building Suite 475 Office 479, 3001 Mercer University Dr, Atlanta, GA, 30341, United States, 1 (678) 547 6240, benramadan_aa@mercer.edu %K survival %K female breast cancer %K Missouri %K cancer registry %D 2018 %7 03.05.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Missouri Cancer Registry collects population-based cancer incidence data on Missouri residents diagnosed with reportable malignant neoplasms. The Missouri Cancer Registry wanted to produce data that would be of interest to lawmakers as well as public health officials at the legislative district level on breast cancer, the most common non-skin cancer among females. Objective: The aim was to measure and interactively visualize survival data of female breast cancer cases in the Missouri Cancer Registry. Methods: Female breast cancer data were linked to Missouri death records and the Social Security Death Index. Unlinked female breast cancer cases were crossmatched to the National Death Index. Female breast cancer cases in subcounty senate districts were geocoded using TIGER/Line shapefiles to identify their district. A database was created and analyzed in SEER*Stat. Senatorial district maps were created using US Census Bureau’s cartographic boundary files. The results were loaded with the cartographic data into InstantAtlas software to produce interactive mapping reports. Results: Female breast cancer survival profiles of 5-year cause-specific survival percentages and 95% confidence intervals, displayed in tables and interactive maps, were created for all 34 senatorial districts. The maps visualized survival data by age, race, stage, and grade at diagnosis for the period from 2004 through 2010. Conclusions: Linking cancer registry data to the National Death Index database improved accuracy of female breast cancer survival data in Missouri and this could positively impact cancer research and policy. The created survival mapping report could be very informative and usable by public health professionals, policy makers, at-risk women, and the public. %M 29724710 %R 10.2196/publichealth.8163 %U http://publichealth.jmir.org/2018/2/e42/ %U https://doi.org/10.2196/publichealth.8163 %U http://www.ncbi.nlm.nih.gov/pubmed/29724710 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e48 %T Studying Cannabis Use Behaviors With Facebook and Web Surveys: Methods and Insights %A Borodovsky,Jacob T %A Marsch,Lisa A %A Budney,Alan J %+ The Center for Technology and Behavioral Health, Dartmouth Geisel School of Medicine, 46 Centerra Parkway, Lebanon, NH, 03766, United States, 1 603 646 7004, jacob.borodovsky@gmail.com %K epidemiology %K cross-sectional studies %K sampling studies %K social media %K data collection %K cannabis %K surveys %D 2018 %7 02.05.2018 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The rapid and wide-reaching expansion of internet access and digital technologies offers epidemiologists numerous opportunities to study health behaviors. One particularly promising new data collection strategy is the use of Facebook’s advertising platform in conjunction with Web-based surveys. Our research team at the Center for Technology and Behavioral Health has used this quick and cost-efficient method to recruit large samples and address unique scientific questions related to cannabis use. In conducting this research, we have gleaned several insights for using this sampling method effectively and have begun to document the characteristics of the resulting data. We believe this information could be useful to other researchers attempting to study cannabis use or, potentially, other health behaviors. The first aim of this paper is to describe case examples of procedures for using Facebook as a survey sampling method for studying cannabis use. We then present several distinctive features of the data produced using this method. Finally, we discuss the utility of this sampling method for addressing specific types of epidemiological research questions. Overall, we believe that sampling with Facebook advertisements and Web surveys is best conceptualized as a targeted, nonprobability-based method for oversampling cannabis users across the United States. %M 29720366 %R 10.2196/publichealth.9408 %U http://publichealth.jmir.org/2018/2/e48/ %U https://doi.org/10.2196/publichealth.9408 %U http://www.ncbi.nlm.nih.gov/pubmed/29720366 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e40 %T Estimating the Risk of Influenza-Like Illness Transmission Through Social Contacts: Web-Based Participatory Cohort Study %A Chan,Ta-Chien %A Hu,Tsuey-Hwa %A Hwang,Jing-Shiang %+ Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Taipei City, 115, Taiwan, 886 2 2783 5611, hwang@sinica.edu.tw %K flu transmission %K social networks %K contact diary %K diet %K exercise %K sleep quality %D 2018 %7 09.04.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Epidemiological studies on influenza have focused mostly on enhancing vaccination coverage or promoting personal hygiene behavior. Few studies have investigated potential effects of personal health behaviors and social contacts on the risk of getting influenza-like illness (ILI). Objective: Taking advantage of an online participatory cohort, this study aimed to estimate the increased risk of getting ILI after contact with infected persons and examine how personal health behaviors, weather, and air pollution affect the probability of getting ILI. Methods: A Web-based platform was designed for participants to record daily health behaviors and social contacts during the influenza season of October 1, 2015 to March 31, 2016, in Taiwan. Data on sleep, diet, physical activity, self-reported ILI, and contact with infected persons were retrieved from the diaries. Measurements of weather and air pollutants were used for calculating environmental exposure levels for the participants. We fitted a mixed-effects logistic regression model to the daily measurements of the diary keepers to estimate the effects of these variables on the risk of getting ILI. Results: During the influenza season, 160 participants provided 14,317 health diaries and recorded 124,222 face-to-face contacts. The model estimated odds ratio of getting ILI was 1.87 (95% CI 1.40-2.50) when a person had contact with others having ILI in the previous 3 days. Longer duration of physical exercise and eating more fruits, beans, and dairy products were associated with lower risk of getting ILI. However, staying up late was linked to an elevated risk of getting ILI. Higher variation of ambient temperature and worse air quality were associated with increased risk of developing ILI. Conclusions: Developing a healthier lifestyle, avoiding contact with persons having ILI symptoms, and staying alert with respect to temperature changes and air quality can reduce the risk of getting ILI. %M 29631987 %R 10.2196/publichealth.8874 %U http://publichealth.jmir.org/2018/2/e40/ %U https://doi.org/10.2196/publichealth.8874 %U http://www.ncbi.nlm.nih.gov/pubmed/29631987 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e31 %T The SMART Study, a Mobile Health and Citizen Science Methodological Platform for Active Living Surveillance, Integrated Knowledge Translation, and Policy Interventions: Longitudinal Study %A Katapally,Tarun Reddy %A Bhawra,Jasmin %A Leatherdale,Scott T %A Ferguson,Leah %A Longo,Justin %A Rainham,Daniel %A Larouche,Richard %A Osgood,Nathaniel %+ Johnson Shoyama Graduate School of Public Policy, University of Regina, 110-2 Research Drive, Innovation Place, Regina, SK, S4S7H9, Canada, 1 3065854544, tarun.katapally@uregina.ca %K exercise %K sedentary lifestyle %K smartphone %K ecological momentary assessments %K epidemiological monitoring %K translational medical research %K health policy %D 2018 %7 27.03.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Physical inactivity is the fourth leading cause of death worldwide, costing approximately US $67.5 billion per year to health care systems. To curb the physical inactivity pandemic, it is time to move beyond traditional approaches and engage citizens by repurposing sedentary behavior (SB)–enabling ubiquitous tools (eg, smartphones). Objective: The primary objective of the Saskatchewan, let’s move and map our activity (SMART) Study was to develop a mobile and citizen science methodological platform for active living surveillance, knowledge translation, and policy interventions. This methodology paper enumerates the SMART Study platform’s conceptualization, design, implementation, data collection procedures, analytical strategies, and potential for informing policy interventions. Methods: This longitudinal investigation was designed to engage participants (ie, citizen scientists) in Regina and Saskatoon, Saskatchewan, Canada, in four different seasons across 3 years. In spring 2017, pilot data collection was conducted, where 317 adult citizen scientists (≥18 years) were recruited in person and online. Citizen scientists used a custom-built smartphone app, Ethica (Ethica Data Services Inc), for 8 consecutive days to provide a complex series of objective and subjective data. Citizen scientists answered a succession of validated surveys that were assigned different smartphone triggering mechanisms (eg, user-triggered and schedule-triggered). The validated surveys captured physical activity (PA), SB, motivation, perception of outdoor and indoor environment, and eudaimonic well-being. Ecological momentary assessments were employed on each day to capture not only PA but also physical and social contexts along with barriers and facilitators of PA, as relayed by citizen scientists using geo-coded pictures and audio files. To obtain a comprehensive objective picture of participant location, motion, and compliance, 6 types of sensor-based (eg, global positioning system and accelerometer) data were surveilled for 8 days. Initial descriptive analyses were conducted using geo-coded photographs and audio files. Results: Pictures and audio files (ie, community voices) showed that the barriers and facilitators of active living included intrinsic or extrinsic motivations, social contexts, and outdoor or indoor environment, with pets and favorable urban design featuring as the predominant facilitators, and work-related screen time proving to be the primary barrier. Conclusions: The preliminary pilot results show the flexibility of the SMART Study surveillance platform in identifying and addressing limitations based on empirical evidence. The results also show the successful implementation of a platform that engages participants to catalyze policy interventions. Although SMART Study is currently geared toward surveillance, using the same platform, active living interventions could be remotely implemented. SMART Study is the first mobile, citizen science surveillance platform utilizing a rigorous, longitudinal, and mixed-methods investigation to temporally capture behavioral data for knowledge translation and policy interventions. %M 29588267 %R 10.2196/publichealth.8953 %U http://publichealth.jmir.org/2018/1/e31/ %U https://doi.org/10.2196/publichealth.8953 %U http://www.ncbi.nlm.nih.gov/pubmed/29588267 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e23 %T Defining Care Patterns and Outcomes Among Persons Living with HIV in Washington, DC: Linkage of Clinical Cohort and Surveillance Data %A Castel,Amanda D %A Terzian,Arpi %A Opoku,Jenevieve %A Happ,Lindsey Powers %A Younes,Naji %A Kharfen,Michael %A Greenberg,Alan %A , %+ Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, The George Washington University, 950 New Hampshire Ave NW, 5th Floor, Washington, DC, 20052, United States, 1 202 994 8325, acastel@gwu.edu %K HIV/AIDS %K health information technology %K surveillance %K retention %K viral suppression %K antiretroviral therapy %D 2018 %7 16.03.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Triangulation of data from multiple sources such as clinical cohort and surveillance data can help improve our ability to describe care patterns, service utilization, comorbidities, and ultimately measure and monitor clinical outcomes among persons living with HIV infection. Objectives: The objective of this study was to determine whether linkage of clinical cohort data and routinely collected HIV surveillance data would enhance the completeness and accuracy of each database and improve the understanding of care patterns and clinical outcomes. Methods: We linked data from the District of Columbia (DC) Cohort, a large HIV observational clinical cohort, with Washington, DC, Department of Health (DOH) surveillance data between January 2011 and June 2015. We determined percent concordance between select variables in the pre- and postlinked databases using kappa test statistics. We compared retention in care (RIC), viral suppression (VS), sexually transmitted diseases (STDs), and non-HIV comorbid conditions (eg, hypertension) and compared HIV clinic visit patterns determined using the prelinked database (DC Cohort) versus the postlinked database (DC Cohort + DOH) using chi-square testing. Additionally, we compared sociodemographic characteristics, RIC, and VS among participants receiving HIV care at ≥3 sites versus <3 sites using chi-square testing. Results: Of the 6054 DC Cohort participants, 5521 (91.19%) were included in the postlinked database and enrolled at a single DC Cohort site. The majority of the participants was male, black, and had men who have sex with men (MSM) as their HIV risk factor. In the postlinked database, 619 STD diagnoses previously unknown to the DC Cohort were identified. Additionally, the proportion of participants with RIC was higher compared with the prelinked database (59.83%, 2678/4476 vs 64.95%, 2907/4476; P<.001) and the proportion with VS was lower (87.85%, 2277/2592 vs 85.15%, 2391/2808; P<.001). Almost a quarter of participants (23.06%, 1279/5521) were identified as receiving HIV care at ≥2 sites (postlinked database). The participants using ≥3 care sites were more likely to achieve RIC (80.7%, 234/290 vs 62.61%, 2197/3509) but less likely to achieve VS (72.3%, 154/213 vs 89.51%, 1869/2088). The participants using ≥3 care sites were more likely to have unstable housing (15.1%, 64/424 vs 8.96%, 380/4242), public insurance (86.1%, 365/424 vs 57.57%, 2442/4242), comorbid conditions (eg, hypertension) (37.7%, 160/424 vs 22.98%, 975/4242), and have acquired immunodeficiency syndrome (77.8%, 330/424 vs 61.20%, 2596/4242) (all P<.001). Conclusions: Linking surveillance and clinical data resulted in the improved completeness of each database and a larger volume of available data to evaluate HIV outcomes, allowing for refinement of HIV care continuum estimates. The postlinked database also highlighted important differences between participants who sought HIV care at multiple clinical sites. Our findings suggest that combined datasets can enhance evaluation of HIV-related outcomes across an entire metropolitan area. Future research will evaluate how to best utilize this information to improve outcomes in addition to monitoring them. %M 29549065 %R 10.2196/publichealth.9221 %U http://publichealth.jmir.org/2018/1/e23/ %U https://doi.org/10.2196/publichealth.9221 %U http://www.ncbi.nlm.nih.gov/pubmed/29549065 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 3 %P e71 %T Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study %A Wenham,Clare %A Gray,Eleanor R %A Keane,Candice E %A Donati,Matthew %A Paolotti,Daniela %A Pebody,Richard %A Fragaszy,Ellen %A McKendry,Rachel A %A Edmunds,W John %+ Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom, 44 207 955 ext 6592, c.wenham@lse.ac.uk %K influenza %K influenza-like illness %K surveillance %K online %K cohort study %K virological confirmation %D 2018 %7 01.03.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK’s largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza. Objective: We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory. Methods: Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing. Results: A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35%). Conclusions: This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity. %M 29496658 %R 10.2196/jmir.9084 %U http://www.jmir.org/2018/3/e71/ %U https://doi.org/10.2196/jmir.9084 %U http://www.ncbi.nlm.nih.gov/pubmed/29496658 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 1 %P e12 %T Potential Application of Digitally Linked Tuberculosis Diagnostics for Real-Time Surveillance of Drug-Resistant Tuberculosis Transmission: Validation and Analysis of Test Results %A Ng,Kamela Charmaine %A Meehan,Conor Joseph %A Torrea,Gabriela %A Goeminne,Léonie %A Diels,Maren %A Rigouts,Leen %A de Jong,Bouke Catherine %A André,Emmanuel %+ Mycobacteriology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Nationalestraat 155, Antwerp, 2000, Belgium, 32 (0) 33455345, kng@itg.be %K tuberculosis %K drug resistance %K rifampicin-resistant tuberculosis %K rapid diagnostic tests %K Xpert MTB/RIF %K Genotype MTBDRplus v2.0 %K Genoscholar NTM + MDRTB II %K RDT probe reactions %K rpoB mutations %K validation and analysis %K real-time detection %D 2018 %7 27.02.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: Tuberculosis (TB) is the highest-mortality infectious disease in the world and the main cause of death related to antimicrobial resistance, yet its surveillance is still paper-based. Rifampicin-resistant TB (RR-TB) is an urgent public health crisis. The World Health Organization has, since 2010, endorsed a series of rapid diagnostic tests (RDTs) that enable rapid detection of drug-resistant strains and produce large volumes of data. In parallel, most high-burden countries have adopted connectivity solutions that allow linking of diagnostics, real-time capture, and shared repository of these test results. However, these connected diagnostics and readily available test results are not used to their full capacity, as we have yet to capitalize on fully understanding the relationship between test results and specific rpoB mutations to elucidate its potential application to real-time surveillance. Objective: We aimed to validate and analyze RDT data in detail, and propose the potential use of connected diagnostics and associated test results for real-time evaluation of RR-TB transmission. Methods: We selected 107 RR-TB strains harboring 34 unique rpoB mutations, including 30 within the rifampicin resistance–determining region (RRDR), from the Belgian Coordinated Collections of Microorganisms, Antwerp, Belgium. We subjected these strains to Xpert MTB/RIF, GenoType MTBDRplus v2.0, and Genoscholar NTM + MDRTB II, the results of which were validated against the strains’ available rpoB gene sequences. We determined the reproducibility of the results, analyzed and visualized the probe reactions, and proposed these for potential use in evaluating transmission. Results: The RDT probe reactions detected most RRDR mutations tested, although we found a few critical discrepancies between observed results and manufacturers’ claims. Based on published frequencies of probe reactions and RRDR mutations, we found specific probe reactions with high potential use in transmission studies: Xpert MTB/RIF probes A, Bdelayed, C, and Edelayed; Genotype MTBDRplus v2.0 WT2, WT5, and WT6; and Genoscholar NTM + MDRTB II S1 and S3. Inspection of probe reactions of disputed mutations may potentially resolve discordance between genotypic and phenotypic test results. Conclusions: We propose a novel approach for potential real-time detection of RR-TB transmission through fully using digitally linked TB diagnostics and shared repository of test results. To our knowledge, this is the first pragmatic and scalable work in response to the consensus of world-renowned TB experts in 2016 on the potential of diagnostic connectivity to accelerate efforts to eliminate TB. This is evidenced by the ability of our proposed approach to facilitate comparison of probe reactions between different RDTs used in the same setting. Integrating this proposed approach as a plug-in module to a connectivity platform will increase usefulness of connected TB diagnostics for RR-TB outbreak detection through real-time investigation of suspected RR-TB transmission cases based on epidemiologic linking. %M 29487047 %R 10.2196/medinform.9309 %U http://medinform.jmir.org/2018/1/e12/ %U https://doi.org/10.2196/medinform.9309 %U http://www.ncbi.nlm.nih.gov/pubmed/29487047 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e17 %T Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study %A Turner,Anne M %A Dew,Kristin N %A Desai,Loma %A Martin,Nathalie %A Kirchhoff,Katrin %+ Northwest Center for Public Health Practice, Department of Health Services, University of Washington, Suite 400, 1107 NE 45th Street, Seattle, WA, 98105, United States, 1 206 491 1489, amturner@uw.edu %K public health informatics %K public health %K natural language processing %K machine translation %K Chinese language %K health promotion %K public health departments %K consumer health %K limited English proficiency %K health literacy %D 2015 %7 17.11.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Chinese is the second most common language spoken by limited English proficiency individuals in the United States, yet there are few public health materials available in Chinese. Previous studies have indicated that use of machine translation plus postediting by bilingual translators generated quality translations in a lower time and at a lower cost than human translations. Objective: The purpose of this study was to investigate the feasibility of using machine translation (MT) tools (eg, Google Translate) followed by human postediting (PE) to produce quality Chinese translations of public health materials. Methods: From state and national public health websites, we collected 60 health promotion documents that had been translated from English to Chinese through human translation. The English version of the documents were then translated to Chinese using Google Translate. The MTs were analyzed for translation errors. A subset of the MT documents was postedited by native Chinese speakers with health backgrounds. Postediting time was measured. Postedited versions were then blindly compared against human translations by bilingual native Chinese quality raters. Results: The most common machine translation errors were errors of word sense (40%) and word order (22%). Posteditors corrected the MTs at a rate of approximately 41 characters per minute. Raters, blinded to the source of translation, consistently selected the human translation over the MT+PE. Initial investigation to determine the reasons for the lower quality of MT+PE indicate that poor MT quality, lack of posteditor expertise, and insufficient posteditor instructions can be barriers to producing quality Chinese translations. Conclusions: Our results revealed problems with using MT tools plus human postediting for translating public health materials from English to Chinese. Additional work is needed to improve MT and to carefully design postediting processes before the MT+PE approach can be used routinely in public health practice for a variety of language pairs. %M 27227135 %R 10.2196/publichealth.4779 %U http://publichealth.jmir.org/2015/2/e17/ %U https://doi.org/10.2196/publichealth.4779 %U http://www.ncbi.nlm.nih.gov/pubmed/27227135