%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e20924 %T Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation %A Oehmke,James Francis %A Oehmke,Theresa B %A Singh,Lauren Nadya %A Post,Lori Ann %+ Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, 420 E. Superior St, Chicago, IL, 60611, United States, 1 203 980 7107, lori.post@northwestern.edu %K COVID-19 %K models %K surveillance %K COVID-19 surveillance system %K dynamic panel data %K infectious disease modeling %K reopening America %K COVID-19 guidelines %K COVID-19 health policy %D 2020 %7 22.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective: The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods: Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results: A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. Conclusions: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites. %M 32915762 %R 10.2196/20924 %U http://www.jmir.org/2020/9/e20924/ %U https://doi.org/10.2196/20924 %U http://www.ncbi.nlm.nih.gov/pubmed/32915762 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e21653 %T Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers %A Soriano,Joan B %A Fernández,Esteve %A de Astorza,Álvaro %A Pérez de Llano,Luis A %A Fernández-Villar,Alberto %A Carnicer-Pont,Dolors %A Alcázar-Navarrete,Bernardino %A García,Arturo %A Morales,Aurelio %A Lobo,María %A Maroto,Marcos %A Ferreras,Eloy %A Soriano,Cecilia %A Del Rio-Bermudez,Carlos %A Vega-Piris,Lorena %A Basagaña,Xavier %A Muncunill,Josep %A Cosio,Borja G %A Lumbreras,Sara %A Catalina,Carlos %A Alzaga,José María %A Gómez Quilón,David %A Valdivia,Carlos Alberto %A de Lara,Celia %A Ancochea,Julio %+ Hospital Universitario La Princesa, Universidad Autónoma de Madrid, Planta 6, Servicio de Neumología, Diego de León 62, Madrid, 28006, Spain, +34 915202200, jbsoriano2@gmail.com %K app %K COVID-19 %K coronavirus %K e-medicine %K monitoring %K symptoms %K surveillance %D 2020 %7 21.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Hospital workers have been the most frequently and severely affected professional group during the COVID-19 pandemic, and have a big impact on transmission. In this context, innovative tools are required to measure the symptoms compatible with COVID-19, the spread of infection, and testing capabilities within hospitals in real time. Objective: We aimed to develop and test an effective and user-friendly tool to identify and track symptoms compatible with COVID-19 in hospital workers. Methods: We developed and pilot tested Hospital Epidemics Tracker (HEpiTracker), a newly designed app to track the spread of COVID-19 among hospital workers. Hospital staff in 9 hospital centers across 5 Spanish regions (Andalusia, Balearics, Catalonia, Galicia, and Madrid) were invited to download the app on their phones and to register their daily body temperature, COVID-19–compatible symptoms, and general health score, as well as any polymerase chain reaction and serological test results. Results: A total of 477 hospital staff participated in the study between April 8 and June 2, 2020. Of note, both health-related (n=329) and non–health-related (n=148) professionals participated in the study; over two-thirds of participants (68.8%) were health workers (43.4% physicians and 25.4% nurses), while the proportion of non–health-related workers by center ranged from 40% to 85%. Most participants were female (n=323, 67.5%), with a mean age of 45.4 years (SD 10.6). Regarding smoking habits, 13.0% and 34.2% of participants were current or former smokers, respectively. The daily reporting of symptoms was highly variable across participating hospitals; although we observed a decline in adherence after an initial participation peak in some hospitals, other sites were characterized by low participation rates throughout the study period. Conclusions: HEpiTracker is an already available tool to monitor COVID-19 and other infectious diseases in hospital workers. This tool has already been tested in real conditions. HEpiTracker is available in Spanish, Portuguese, and English. It has the potential to become a customized asset to be used in future COVID-19 pandemic waves and other environments. Trial Registration: ClinicalTrials.gov NCT04326400; https://clinicaltrials.gov/ct2/show/NCT04326400 %M 32845852 %R 10.2196/21653 %U http://publichealth.jmir.org/2020/3/e21653/ %U https://doi.org/10.2196/21653 %U http://www.ncbi.nlm.nih.gov/pubmed/32845852 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e21152 %T Prediction of the Transition From Subexponential to the Exponential Transmission of SARS-CoV-2 in Chennai, India: Epidemic Nowcasting %A Krishnamurthy,Kamalanand %A Ambikapathy,Bakiya %A Kumar,Ashwani %A Britto,Lourduraj De %+ Vector Control Research Centre, Indian Council for Medical Research, Indra Nagar, Puducherry, 605006, India, 91 4132272841, rljbritto@gmail.com %K COVID-19 %K epidemic %K mathematical modeling %K probabilistic models %K public transport %K exponential transmission %D 2020 %7 18.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Several countries adopted lockdown to slowdown the exponential transmission of the coronavirus disease (COVID-19) epidemic. Disease transmission models and the epidemic forecasts at the national level steer the policy to implement appropriate intervention strategies and budgeting. However, it is critical to design a data-driven reliable model for nowcasting for smaller populations, in particular metro cities. Objective: The aim of this study is to analyze the transition of the epidemic from subexponential to exponential transmission in the Chennai metro zone and to analyze the probability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) secondary infections while availing the public transport systems in the city. Methods: A single geographical zone “Chennai-Metro-Merge” was constructed by combining Chennai District with three bordering districts. Subexponential and exponential models were developed to analyze and predict the progression of the COVID-19 epidemic. Probabilistic models were applied to assess the probability of secondary infections while availing public transport after the release of the lockdown. Results: The model predicted that transition from subexponential to exponential transmission occurs around the eighth week after the reporting of a cluster of cases. The probability of secondary infections with a single index case in an enclosure of the city bus, the suburban train general coach, and the ladies coach was found to be 0.192, 0.074, and 0.114, respectively. Conclusions: Nowcasting at the early stage of the epidemic predicts the probable time point of the exponential transmission and alerts the public health system. After the lockdown release, public transportation will be the major source of SARS-CoV-2 transmission in metro cities, and appropriate strategies based on nowcasting are needed. %M 32609621 %R 10.2196/21152 %U https://publichealth.jmir.org/2020/3/e21152 %U https://doi.org/10.2196/21152 %U http://www.ncbi.nlm.nih.gov/pubmed/32609621 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e18281 %T Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering %A Adnan,Mehnaz %A Gao,Xiaoying %A Bai,Xiaohan %A Newbern,Elizabeth %A Sherwood,Jill %A Jones,Nicholas %A Baker,Michael %A Wood,Tim %A Gao,Wei %+ Institute of Environmental Science and Research, Kenepuru Science Centre, Porirua, 5022, New Zealand, 64 274044941, mehnaz.adnan@esr.cri.nz %K Campylobacter %K disease outbreaks %K forecasting %K spatio-temporal analysis %D 2020 %7 17.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective: The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods: We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results: Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions: Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice. %M 32940617 %R 10.2196/18281 %U http://publichealth.jmir.org/2020/3/e18281/ %U https://doi.org/10.2196/18281 %U http://www.ncbi.nlm.nih.gov/pubmed/32940617 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e17242 %T An Online Influenza Surveillance System for Primary Care Workers in Switzerland: Observational Prospective Pilot Study %A Martin,Sébastien %A Maeder,Muriel Nirina %A Gonçalves,Ana Rita %A Pedrazzini,Baptiste %A Perdrix,Jean %A Rochat,Carine %A Senn,Nicolas %A Mueller,Yolanda %+ Center for Primary Care and Public Health (Unisanté), Lausanne, Department of Family Medicine, University of Lausanne, Rue du Bugnon 44, Lausanne, 1011, Switzerland, 41 21 314 60 63, sebastien.martin@unisante.ch %K influenza %K surveillance system %K primary care %K online %K nosocomial %K transmission %D 2020 %7 10.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: A better understanding of the influenza epidemiology among primary care workers could guide future recommendations to prevent transmission in primary care practices. Therefore, we designed a pilot study to assess the feasibility of using a work-based online influenza surveillance system among primary care workers. Such an approach is of particular relevance in the context of the coronavirus disease (COVID-19) pandemic, as its findings could apply to other infectious diseases with similar mechanisms of transmission. Objective: This study aims to determine the feasibility of using a work-based online influenza surveillance system for primary care workers in Switzerland. Methods: Physicians and staff of one walk-in clinic and two selected primary care practices were enrolled in this observational prospective pilot study during the 2017-2018 influenza season. They were invited to record symptoms of influenza-like illness in a weekly online survey sent by email and to self-collect a nasopharyngeal swab in case any symptoms were recorded. Samples were tested by real-time polymerase chain reaction for influenza A, influenza B, and a panel of respiratory pathogens. Results: Among 67 eligible staff members, 58% (n=39) consented to the study and 53% (n=36) provided data. From the time all participants were included, the weekly survey response rate stayed close to 100% until the end of the study. Of 79 symptomatic episodes (mean 2.2 episodes per participant), 10 episodes in 7 participants fitted the definition of an influenza-like illness case (attack rate: 7/36, 19%). One swab tested positive for influenza A H1N1 (attack rate: 3%, 95% CI 0%-18%). Swabbing was considered relatively easy. Conclusions: A work-based online influenza surveillance system is feasible for use among primary care workers. This promising methodology could be broadly used in future studies to improve the understanding of influenza epidemiology and other diseases such as COVID-19. This could prove to be highly useful in primary care settings and guide future recommendations to prevent transmission. A larger study will also help to assess asymptomatic infections. %M 32909955 %R 10.2196/17242 %U http://publichealth.jmir.org/2020/3/e17242/ %U https://doi.org/10.2196/17242 %U http://www.ncbi.nlm.nih.gov/pubmed/32909955 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e21956 %T App-Based Tracking of Self-Reported COVID-19 Symptoms: Analysis of Questionnaire Data %A Zens,Martin %A Brammertz,Arne %A Herpich,Juliane %A Südkamp,Norbert %A Hinterseer,Martin %+ Department of Medicine, Kliniken Ostallgaeu-Kaufbeuren, Stadtbleiche 1, Fuessen, 87629, Germany, 49 8362 500 726, martin.zens@me.com %K COVID-19 %K self-reporting %K symptom %K tracking %K app %K surveillance %K distribution %K digital tool %K screening %D 2020 %7 9.9.2020 %9 Short Paper %J J Med Internet Res %G English %X Background: COVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. Objective: To determine the distribution pattern of COVID-19 symptoms as well as possible unreported symptoms, we created an app-based self-reporting tool. Methods: The COVID-19 Symptom Tracker is an app-based daily self-reporting tool. Between April 8 and May 15, 2020, a total of 22,327 individuals installed this app on their mobile device. An initial questionnaire asked for demographic information (age, gender, postal code) and past medical history comprising relevant chronic diseases. The participants were reminded daily to report whether they were experiencing any symptoms and if they had been tested for SARS-CoV-2 infection. Participants who sought health care services were asked additional questions regarding diagnostics and treatment. Participation was open to all adults (≥18 years). The study was completely anonymous. Results: In total, 11,829 (52.98%) participants completed the symptom questionnaire at least once. Of these, 291 (2.46%) participants stated that they had undergone an RT-PCR (reverse transcription-polymerase chain reaction) test for SARS-CoV-2; 65 (0.55%) reported a positive test result and 226 (1.91%) a negative one. The mean number of reported symptoms among untested participants was 0.81 (SD 1.85). Participants with a positive test result had, on average, 5.63 symptoms (SD 2.82). The most significant risk factors were diabetes (odds ratio [OR] 8.95, 95% CI 3.30-22.37) and chronic heart disease (OR 2.85, 95% CI 1.43-5.69). We identified chills, fever, loss of smell, nausea and vomiting, and shortness of breath as the top five strongest predictors for a COVID-19 infection. The odds ratio for loss of smell was 3.13 (95% CI 1.76-5.58). Nausea and vomiting (OR 2.84, 95% CI 1.61-5.00) had been reported as an uncommon symptom previously; however, our data suggest a significant predictive value. Conclusions: Self-reported symptom tracking helps to identify novel symptoms of COVID-19 and to estimate the predictive value of certain symptoms. This aids in the development of reliable screening tools. Clinical screening with a high pretest probability allows for the rapid identification of infections and the cost-effective use of testing resources. Based on our results, we suggest that loss of smell and taste be considered cardinal symptoms; we also stress that diabetes is a risk factor for a highly symptomatic course of COVID-19 infection. %M 32791493 %R 10.2196/21956 %U http://www.jmir.org/2020/9/e21956/ %U https://doi.org/10.2196/21956 %U http://www.ncbi.nlm.nih.gov/pubmed/32791493 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e22321 %T A QR Code–Based Contact Tracing Framework for Sustainable Containment of COVID-19: Evaluation of an Approach to Assist the Return to Normal Activity %A Nakamoto,Ichiro %A Wang,Sheng %A Guo,Yan %A Zhuang,Weiqing %+ School of Internet Economics and Business, Fujian University of Technology, 999 Dongsanhuang Road, JinAn District, Fuzhou , China, 86 132 550 66365, fw107@foxmail.com %K COVID-19 %K coronavirus %K symptom-based %K quick response %K eHealth %K digital health %K telesurveillance %K pandemic %K epidemic %K interoperability %D 2020 %7 7.9.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X We discuss a pandemic management framework using symptom-based quick response (QR) codes to contain the spread of COVID-19. In this approach, symptom-based QR health codes are issued by public health authorities. The codes do not retrieve the location data of the users; instead, two different colors are displayed to differentiate the health status of individuals. The QR codes are officially regarded as electronic certificates of individuals’ health status, and can be used for contact tracing, exposure risk self-triage, self-update of health status, health care appointments, and contact-free psychiatric consultations. This approach can be effectively deployed as a uniform platform interconnecting a variety of responders (eg, individuals, institutions, and public authorities) who are affected by the pandemic, which minimizes the errors of manual operation and the costs of fragmented coordination. At the same time, this approach enhances the promptness, interoperability, credibility, and traceability of containment measures. The proposed approach not only provides a supplemental mechanism for manual control measures but also addresses the partial failures of pandemic management tools in the abovementioned facets. The QR tool has been formally deployed in Fujian, a province located in southeast China that has a population of nearly 40 million people. All individuals aged ≥3 years were officially requested to present their QR code during daily public activities, such as when using public transportation systems, working at institutions, and entering or exiting schools. The deployment of this approach has achieved sizeable containment effects and played remarkable roles in shifting the negative gross domestic product (–6.8%) to a positive value by July 2020. The number of cumulative patients with COVID-19 in this setting was confined to 363, of whom 361 had recovered (recovery rate 99.4%) as of July 12, 2020. A simulation showed that if only partial measures of the framework were followed, the number of cumulative cases of COVID-19 could potentially increase ten-fold. This approach can serve as a reliable solution to counteract the emergency of a public health crisis; as a routine tool to enhance the level of public health; to accelerate the recovery of social activities; to assist decision making for policy makers; and as a sustainable measure that enables scalability. %M 32841151 %R 10.2196/22321 %U http://mhealth.jmir.org/2020/9/e22321/ %U https://doi.org/10.2196/22321 %U http://www.ncbi.nlm.nih.gov/pubmed/32841151 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e19891 %T Practical and Ethical Concerns in Implementing Enhanced Surveillance Methods to Improve Continuity of HIV Care: Qualitative Expert Stakeholder Study %A Buchbinder,Mara %A Blue,Colleen %A Rennie,Stuart %A Juengst,Eric %A Brinkley-Rubinstein,Lauren %A Rosen,David L %+ Department of Social Medicine, Center for Bioethics, University of North Carolina at Chapel Hill, 333 S Columbia Street, MacNider 341A CB 7240, Chapel Hill, NC, 27599, United States, 1 9198436881, mara_buchbinder@med.unc.edu %K HIV surveillance %K retention in HIV care %K qualitative research %K public health ethics %D 2020 %7 4.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Retention in HIV care is critical to maintaining viral suppression and preventing further transmission, yet less than 50% of people living with HIV in the United States are engaged in care. All US states have a funding mandate to implement Data-to-Care (D2C) programs, which use surveillance data (eg, laboratory, Medicaid billing) to identify out-of-care HIV-positive persons and relink them to treatment. Objective: The purpose of this qualitative study was to identify and describe practical and ethical considerations that arise in planning for and implementing D2C. Methods: Via purposive sampling, we recruited 43 expert stakeholders—including ethicists, privacy experts, researchers, public health personnel, HIV medical providers, legal experts, and community advocates—to participate in audio-recorded semistructured interviews to share their perspectives on D2C. Interview transcripts were analyzed across a priori and inductively derived thematic categories. Results: Stakeholders reported practical and ethical concerns in seven key domains: permission and consent, government assistance versus overreach, privacy and confidentiality, stigma, HIV exceptionalism, criminalization, and data integrity and sharing. Conclusions: Participants expressed a great deal of support for D2C, yet also stressed the role of public trust and transparency in addressing the practical and ethical concerns they identified. %M 32886069 %R 10.2196/19891 %U http://publichealth.jmir.org/2020/3/e19891/ %U https://doi.org/10.2196/19891 %U http://www.ncbi.nlm.nih.gov/pubmed/32886069 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e12842 %T An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study %A Sambaturu,Prathyush %A Bhattacharya,Parantapa %A Chen,Jiangzhuo %A Lewis,Bryan %A Marathe,Madhav %A Venkatramanan,Srinivasan %A Vullikanti,Anil %+ University of Virginia, Biocomplexity Institute and Initiative, 995 Research Park Boulevard, Charlottesville, VA, 22911, United States, 1 540 577 3102, vsakumar@virginia.edu %K epidemic data analysis %K summarization %K spatio-temporal patterns %K transactional data mining %D 2020 %7 4.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective: Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods: We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results: We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions: Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives. %M 32701458 %R 10.2196/12842 %U http://publichealth.jmir.org/2020/3/e12842/ %U https://doi.org/10.2196/12842 %U http://www.ncbi.nlm.nih.gov/pubmed/32701458 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e15409 %T Contributing to Elimination of Cross-Border Malaria Through a Standardized Solution for Case Surveillance, Data Sharing, and Data Interpretation: Development of a Cross-Border Monitoring System %A Saldanha,Raphael %A Mosnier,Émilie %A Barcellos,Christovam %A Carbunar,Aurel %A Charron,Christophe %A Desconnets,Jean-Christophe %A Guarmit,Basma %A Gomes,Margarete Do Socorro Mendonça %A Mandon,Théophile %A Mendes,Anapaula Martins %A Peiter,Paulo César %A Musset,Lise %A Sanna,Alice %A Van Gastel,Benoît %A Roux,Emmanuel %+ Laboratório de Informação em Saúde, Instituto de Comunicação e Informação Científica e Tecnológica em Saúde, Fundação Oswaldo Cruz, Avenida Brasil, 4365, Manguinhos, Rio de Janeiro, 21040-360, Brazil, 55 2138653242, christovam.barcellos@fiocruz.br %K cross-border malaria %K surveillance %K data interoperability %K data visualization %K French Guiana %K Brazil %D 2020 %7 1.9.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Cross-border malaria is a significant obstacle to achieving malaria control and elimination worldwide. Objective: This study aimed to build a cross-border surveillance system that can make comparable and qualified data available to all parties involved in malaria control between French Guiana and Brazil. Methods: Data reconciliation rules based on expert knowledge were defined and applied to the heterogeneous data provided by the existing malaria surveillance systems of both countries. Visualization dashboards were designed to facilitate progressive data exploration, analysis, and interpretation. Dedicated advanced open source and robust software solutions were chosen to facilitate solution sharing and reuse. Results: A database gathering the harmonized data on cross-border malaria epidemiology is updated monthly with new individual malaria cases from both countries. Online dashboards permit a progressive and user-friendly visualization of raw data and epidemiological indicators, in the form of time series, maps, and data quality indexes. The monitoring system was shown to be able to identify changes in time series that are related to control actions, as well as differentiated changes according to space and to population subgroups. Conclusions: This cross-border monitoring tool could help produce new scientific evidence on cross-border malaria dynamics, implementing cross-border cooperation for malaria control and elimination, and can be quickly adapted to other cross-border contexts. %M 32663141 %R 10.2196/15409 %U http://publichealth.jmir.org/2020/3/e15409/ %U https://doi.org/10.2196/15409 %U http://www.ncbi.nlm.nih.gov/pubmed/32663141 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e21413 %T COVID-19 Mortality Underreporting in Brazil: Analysis of Data From Government Internet Portals %A Veiga e Silva,Lena %A de Andrade Abi Harb,Maria Da Penha %A Teixeira Barbosa dos Santos,Aurea Milene %A de Mattos Teixeira,Carlos André %A Macedo Gomes,Vitor Hugo %A Silva Cardoso,Evelin Helena %A S da Silva,Marcelino %A Vijaykumar,N L %A Venâncio Carvalho,Solon %A Ponce de Leon Ferreira de Carvalho,André %A Lisboa Frances,Carlos Renato %+ Federal University of Pará, R Augusto Corrêa, 01, Guamá, Belém, 66073-040, Brazil, 55 91 3201 7634, lenaveiga@gmail.com %K Brazil %K COVID-19 %K mortality %K underreporting %K respiratory system diseases %K public health %K pandemic %K time series %K forecasting %D 2020 %7 18.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: In Brazil, a substantial number of coronavirus disease (COVID-19) cases and deaths have been reported. It has become the second most affected country worldwide, as of June 9, 2020. Official Brazilian government sources present contradictory data on the impact of the disease; thus, it is possible that the actual number of infected individuals and deaths in Brazil is far larger than those officially reported. It is very likely that the actual spread of the disease has been underestimated. Objective: This study investigates the underreporting of cases and deaths related to COVID-19 in the most affected cities in Brazil, based on public data available from official Brazilian government internet portals, to identify the actual impact of the pandemic. Methods: We used data from historical deaths due to respiratory problems and other natural causes from two public portals: DATASUS (Department of Informatics of the Unified Healthcare System) (2010-2018) and the Brazilian Transparency Portal of Civil Registry (2019-2020). These data were used to build time-series models (modular regressions) to predict the expected mortality patterns for 2020. The forecasts were used to estimate the possible number of deaths that were incorrectly registered during the pandemic and posted on government internet portals in the most affected cities in the country. Results: Our model found a significant difference between the real and expected values. The number of deaths due to severe acute respiratory syndrome (SARS) was considerably higher in all cities, with increases between 493% and 5820%. This sudden increase may be associated with errors in reporting. An average underreporting of 40.68% (range 25.9%-62.7%) is estimated for COVID-19–related deaths. Conclusions: The significant rates of underreporting of deaths analyzed in our study demonstrate that officially released numbers are much lower than actual numbers, making it impossible for the authorities to implement a more effective pandemic response. Based on analyses carried out using different fatality rates, it can be inferred that Brazil’s epidemic is worsening, and the actual number of infectees could already be between 1 to 5.4 million. %M 32730219 %R 10.2196/21413 %U http://www.jmir.org/2020/8/e21413/ %U https://doi.org/10.2196/21413 %U http://www.ncbi.nlm.nih.gov/pubmed/32730219 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e20285 %T Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models %A Liu,Dianbo %A Clemente,Leonardo %A Poirier,Canelle %A Ding,Xiyu %A Chinazzi,Matteo %A Davis,Jessica %A Vespignani,Alessandro %A Santillana,Mauricio %+ Computational Health Informatics Program, Boston Children’s Hospital, 300 Longwood Avenue, Landmark 5th Floor East, Boston, MA, 02215, United States, 1 (617) 919 1795, msantill@g.harvard.edu %K COVID-19 %K coronavirus %K digital epidemiology %K modeling %K modeling disease outbreaks %K emerging outbreak %K machine learning %K precision public health %K machine learning in public health %K forecasting %K digital data %K mechanistic model %K hybrid simulation %K hybrid model %K simulation %D 2020 %7 17.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. Objective: We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Methods: Our method uses the following as inputs: (a) official health reports, (b) COVID-19–related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Results: Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Conclusions: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention. %M 32730217 %R 10.2196/20285 %U http://www.jmir.org/2020/8/e20285/ %U https://doi.org/10.2196/20285 %U http://www.ncbi.nlm.nih.gov/pubmed/32730217 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e18912 %T A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism %A Woldaregay,Ashenafi Zebene %A Launonen,Ilkka Kalervo %A Albers,David %A Igual,Jorge %A Årsand,Eirik %A Hartvigsen,Gunnar %+ Department of Computer Science, University of Tromsø – The Arctic University of Norway, Hansine Hansens veg 54, Science building Realfagbygget, office A124, Tromsø, Norway, 47 46359333, ashenafi.z.woldaregay@uit.no %K type 1 diabetes %K self-recorded health data %K infection detection %K decision support techniques %K outbreak detection system %K syndromic surveillance %D 2020 %7 12.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model. Objective: This study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio. Methods: Three groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required. Results: The one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time. Conclusions: We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale. %M 32784179 %R 10.2196/18912 %U https://www.jmir.org/2020/8/e18912 %U https://doi.org/10.2196/18912 %U http://www.ncbi.nlm.nih.gov/pubmed/32784179 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e15394 %T Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study %A Cheng,Hao-Yuan %A Wu,Yu-Chun %A Lin,Min-Hau %A Liu,Yu-Lun %A Tsai,Yue-Yang %A Wu,Jo-Hua %A Pan,Ke-Han %A Ke,Chih-Jung %A Chen,Chiu-Mei %A Liu,Ding-Ping %A Lin,I-Feng %A Chuang,Jen-Hsiang %+ Taiwan Centers for Disease Control, 9F, No. 6, Linsen S. Road, Zhong-zheng District, Taipei, 100, Taiwan, 886 2 2395 9825, jhchuang@cdc.gov.tw %K influenza %K Influenza-like illness %K forecasting %K machine learning %K artificial intelligence %K epidemic forecasting %K surveillance %D 2020 %7 5.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. Objective: We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. Methods: Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. Results: All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). Conclusions: This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making. %M 32755888 %R 10.2196/15394 %U https://www.jmir.org/2020/8/e15394 %U https://doi.org/10.2196/15394 %U http://www.ncbi.nlm.nih.gov/pubmed/32755888 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e19483 %T Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns %A Cousins,Henry C %A Cousins,Clara C %A Harris,Alon %A Pasquale,Louis R %+ Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, One Gustave L Levy Place, Box 1183, New York, NY, 10029, United States, 1 212 241 6752, louis.pasquale@mssm.edu %K epidemiology %K infoveillance %K COVID-19 %K internet activity %K Google Trends %K infectious disease %K surveillance %K public health %D 2020 %7 30.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. Objective: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. Methods: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Results: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Conclusions: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity. %M 32692691 %R 10.2196/19483 %U http://www.jmir.org/2020/7/e19483/ %U https://doi.org/10.2196/19483 %U http://www.ncbi.nlm.nih.gov/pubmed/32692691 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e17340 %T Surveillance of Noncommunicable Disease Epidemic Through the Integrated Noncommunicable Disease Collaborative Management System: Feasibility Pilot Study Conducted in the City of Ningbo, China %A Li,Sixuan %A Zhang,Liang %A Liu,Shiwei %A Hubbard,Richard %A Li,Hui %+ Division of Chronic and Noncommunicable Disease Control and Prevention, Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo, 315010, China, 86 0574 87274542, lihui4329@163.com %K surveillance %K noncommunicable diseases %K regional health information platform %K electronic health records. %D 2020 %7 23.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Noncommunicable diseases (NCDs) have become the main public health concern worldwide. With rapid economic development and changes in lifestyles, the burden of NCDs in China is increasing dramatically every year. Monitoring is a critical measure for NCDs control and prevention. However, because of the lack of regional representativeness, unsatisfactory data quality, and inefficient data sharing and utilization, the existing surveillance systems and surveys in China cannot track the status and transition of NCDs epidemic. Objective: To efficaciously track NCDs epidemic in China, this pilot program conducted in Ningbo city by the Chinese Center for Disease Control and Prevention (CDC) aimed to develop an innovative model for NCDs surveillance and management: the integrated noncommunicable disease collaborative management system (NCDCMS). Methods: This Ningbo model was designed and developed through a 3-level (county/district, municipal, and provincial levels) direct reporting system based on the regional health information platform. The uniform data standards and interface specifications were established to connect different platforms and conduct data exchanges. The performance of the system was evaluated based on the 9 attributes of surveillance system evaluation framework recommended by the US CDC. Results: NCDCMS allows automatic NCDs data exchanging and sharing via a 3-level public health data exchange platform in China. It currently covers 201 medical institutions throughout the city. Compared with previous systems, automatic popping up of the report card, automatic patient information extraction, and real-time data exchange process have highly improved the simplicity and timeliness of the system. The data quality meets the requirements to monitor the incidence trend of NCDs accurately, and the comprehensive data types obtained from the database (ie, directly from the 3-level platform on the data warehouse) also provide a useful information to conduct scientific studies. So far, 98.1% (201/205) of medical institutions across Ningbo having been involved in data exchanges with the model. Evaluations of the system performance showed that NCDCMS has high levels of simplicity, data quality, acceptability, representativeness, and timeliness. Conclusions: NCDCMS completely reshaped the process of NCD surveillance reporting and had unique advantages, which include reducing the work burden of different stakeholders by data sharing and exchange, eliminating unnecessary redundancies, reducing the amount of underreporting, and structuring population-based cohorts. The Ningbo model will be gradually promoted elsewhere following this success of the pilot project, and is expected to be a milestone in NCDs surveillance, control, and prevention in China. %M 32706706 %R 10.2196/17340 %U http://www.jmir.org/2020/7/e17340/ %U https://doi.org/10.2196/17340 %U http://www.ncbi.nlm.nih.gov/pubmed/32706706 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e21163 %T COVIDApp as an Innovative Strategy for the Management and Follow-Up of COVID-19 Cases in Long-Term Care Facilities in Catalonia: Implementation Study %A Echeverría,Patricia %A Mas Bergas,Miquel Angel %A Puig,Jordi %A Isnard,Mar %A Massot,Mireia %A Vedia,Cristina %A Peiró,Ricardo %A Ordorica,Yolanda %A Pablo,Sara %A Ulldemolins,María %A Iruela,Mercé %A Balart,Dolors %A Ruiz,José María %A Herms,Jordi %A Clotet Sala,Bonaventura %A Negredo,Eugenia %+ Infectious Diseases Department, Hospital Universitari Germans Trias i Pujol, Carretera de Canyet s/n, Badalona, Spain, 34 934978887 ext 3417, pecheverria@flsida.org %K COVID-19 %K mobile health %K app %K COVIDApp %K long-term care facilities %K institutionalized individuals %K mHealth %K elderly %K long-term %K care %K public health %K management %K surveillance %D 2020 %7 17.7.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The coronavirus disease (COVID-19) pandemic has caused an unprecedented worldwide public health crisis that requires new management approaches. COVIDApp is a mobile app that was adapted for the management of institutionalized individuals in long-term care facilities. Objective: The aim of this paper is to report the implementation of this innovative tool for the management of long-term care facility residents as a high-risk population, specifically for early identification and self-isolation of suspected cases, remote monitoring of mild cases, and real-time monitoring of the progression of the infection. Methods: COVIDApp was implemented in 196 care centers in collaboration with 64 primary care teams. The following parameters of COVID-19 were reported daily: signs/symptoms; diagnosis by reverse transcriptase–polymerase chain reaction; absence of symptoms for ≥14 days; total deaths; and number of health care workers isolated with suspected COVID-19. The number of at-risk centers was also described. Results: Data were recorded from 10,347 institutionalized individuals and up to 4000 health care workers between April 1 and 30, 2020. A rapid increase in suspected cases was seen until day 6 but decreased during the last two weeks (from 1084 to 282 cases). The number of confirmed cases increased from 419 (day 6) to 1293 (day 22) and remained stable during the last week. Of the 10,347 institutionalized individuals, 5,090 (49,2%) remained asymptomatic for ≥14 days. A total of 854/10,347 deaths (8.3%) were reported; 383 of these deaths (44.8%) were suspected/confirmed cases. The number of isolated health care workers remained high over the 30 days, while the number of suspected cases decreased during the last 2 weeks. The number of high-risk long-term care facilities decreased from 19/196 (9.5%) to 3/196 (1.5%). Conclusions: COVIDApp can help clinicians rapidly detect and remotely monitor suspected and confirmed cases of COVID-19 among institutionalized individuals, thus limiting the risk of spreading the virus. The platform shows the progression of infection in real time and can aid in designing new monitoring strategies. %M 32629425 %R 10.2196/21163 %U http://publichealth.jmir.org/2020/3/e21163/ %U https://doi.org/10.2196/21163 %U http://www.ncbi.nlm.nih.gov/pubmed/32629425 %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 %@ 2369-2960 %I JMIR Publications %V 6 %N 3 %P e19773 %T The Oxford Royal College of General Practitioners Clinical Informatics Digital Hub: Protocol to Develop Extended COVID-19 Surveillance and Trial Platforms %A de Lusignan,Simon %A Jones,Nicholas %A Dorward,Jienchi %A Byford,Rachel %A Liyanage,Harshana %A Briggs,John %A Ferreira,Filipa %A Akinyemi,Oluwafunmi %A Amirthalingam,Gayatri %A Bates,Chris %A Lopez Bernal,Jamie %A Dabrera,Gavin %A Eavis,Alex %A Elliot,Alex J %A Feher,Michael %A Krajenbrink,Else %A Hoang,Uy %A Howsam,Gary %A Leach,Jonathan %A Okusi,Cecilia %A Nicholson,Brian %A Nieri,Philip %A Sherlock,Julian %A Smith,Gillian %A Thomas,Mark %A Thomas,Nicholas %A Tripathy,Manasa %A Victor,William %A Williams,John %A Wood,Ian %A Zambon,Maria %A Parry,John %A O’Hanlon,Shaun %A Joy,Mark %A Butler,Chris %A Marshall,Martin %A Hobbs,FD Richard %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Woodstock Road, Oxford, OX26GG, United Kingdom, 44 1865617283, simon.delusignan@phc.ox.ac.uk %K primary health care %K general practice %K medical record systems, computerized %K sentinel surveillance %K public health surveillance %K clinical trials as a topic %K adaptive clinical trials %K severe acute respiratory syndrome coronavirus 2 %K COVID-19 %D 2020 %7 2.7.2020 %9 Protocol %J JMIR Public Health Surveill %G English %X Background: Routinely recorded primary care data have been used for many years by sentinel networks for surveillance. More recently, real world data have been used for a wider range of research projects to support rapid, inexpensive clinical trials. Because the partial national lockdown in the United Kingdom due to the coronavirus disease (COVID-19) pandemic has resulted in decreasing community disease incidence, much larger numbers of general practices are needed to deliver effective COVID-19 surveillance and contribute to in-pandemic clinical trials. Objective: The aim of this protocol is to describe the rapid design and development of the Oxford Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID) and its first two platforms. The Surveillance Platform will provide extended primary care surveillance, while the Trials Platform is a streamlined clinical trials platform that will be integrated into routine primary care practice. Methods: We will apply the FAIR (Findable, Accessible, Interoperable, and Reusable) metadata principles to a new, integrated digital health hub that will extract routinely collected general practice electronic health data for use in clinical trials and provide enhanced communicable disease surveillance. The hub will be findable through membership in Health Data Research UK and European metadata repositories. Accessibility through an online application system will provide access to study-ready data sets or developed custom data sets. Interoperability will be facilitated by fixed linkage to other key sources such as Hospital Episodes Statistics and the Office of National Statistics using pseudonymized data. All semantic descriptors (ie, ontologies) and code used for analysis will be made available to accelerate analyses. We will also make data available using common data models, starting with the US Food and Drug Administration Sentinel and Observational Medical Outcomes Partnership approaches, to facilitate international studies. The Surveillance Platform will provide access to data for health protection and promotion work as authorized through agreements between Oxford, the Royal College of General Practitioners, and Public Health England. All studies using the Trials Platform will go through appropriate ethical and other regulatory approval processes. Results: The hub will be a bottom-up, professionally led network that will provide benefits for member practices, our health service, and the population served. Data will only be used for SQUIRE (surveillance, quality improvement, research, and education) purposes. We have already received positive responses from practices, and the number of practices in the network has doubled to over 1150 since February 2020. COVID-19 surveillance has resulted in tripling of the number of virology sites to 293 (target 300), which has aided the collection of the largest ever weekly total of surveillance swabs in the United Kingdom as well as over 3000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology samples. Practices are recruiting to the PRINCIPLE (Platform Randomised trial of INterventions against COVID-19 In older PeopLE) trial, and these participants will be followed up through ORCHID. These initial outputs demonstrate the feasibility of ORCHID to provide an extended national digital health hub. Conclusions: ORCHID will provide equitable and innovative use of big data through a professionally led national primary care network and the application of FAIR principles. The secure data hub will host routinely collected general practice data linked to other key health care repositories for clinical trials and support enhanced in situ surveillance without always requiring large volume data extracts. ORCHID will support rapid data extraction, analysis, and dissemination with the aim of improving future research and development in general practice to positively impact patient care. International Registered Report Identifier (IRRID): DERR1-10.2196/19773 %M 32484782 %R 10.2196/19773 %U https://publichealth.jmir.org/2020/3/e19773 %U https://doi.org/10.2196/19773 %U http://www.ncbi.nlm.nih.gov/pubmed/32484782 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e19731 %T Suitability and Sufficiency of Telehealth Clinician-Observed, Participant-Collected Samples for SARS-CoV-2 Testing: The iCollect Cohort Pilot Study %A Guest,Jodie L %A Sullivan,Patrick S %A Valentine-Graves,Mariah %A Valencia,Rachel %A Adam,Elizabeth %A Luisi,Nicole %A Nakano,Mariko %A Guarner,Jeannette %A del Rio,Carlos %A Sailey,Charles %A Goedecke,Zoe %A Siegler,Aaron J %A Sanchez,Travis H %+ Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA, 30322, United States, 1 4046802450, Jodie.Guest@emory.edu %K COVID-19 %K testing %K home testing %K telehealth %K pilot study %K diagnostic %K diagnosis %D 2020 %7 25.6.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic calls for expanded opportunities for testing, including novel testing strategies such as home-collected specimens. Objective: We aimed to understand whether oropharyngeal swab (OPS), saliva, and dried blood spot (DBS) specimens collected by participants at home and mailed to a laboratory were sufficient for use in diagnostic and serology tests of SARS-CoV-2. Methods: Eligible participants consented online and were mailed a participant-collection kit to support collection of three specimens for SARS-CoV-2 testing: saliva, OPS, and DBS. Participants performed the specimen collection procedures during a telehealth video appointment while clinical observers watched and documented the suitability of the collection. The biological sufficiency of the specimens for detection of SARS-CoV-2 by reverse transcriptase–polymerase chain reaction and serology testing was assessed by laboratorians using visual inspection and quantification of the nucleic acid contents of the samples by ribonuclease P (RNase P) measurements. Results: Of the enrolled participants,153/159 (96.2%) returned their kits, which were included in this analysis. All these participants attended their video appointments. Clinical observers assessed that of the samples collected, 147/153 (96.1%) of the saliva samples, 146/151 (96.7%) of the oropharyngeal samples, and 135/145 (93.1%) of the DBS samples were of sufficient quality for submission for laboratory testing; 100% of the OPS samples and 98% of the saliva samples had cycle threshold values for RNase P <30, indicating that the samples contained sufficient nucleic acid for RNA-PCR testing for SARS-CoV-2. Conclusions: These pilot data indicate that most participant-collected OPS, saliva, and DBS specimens are suitable and sufficient for testing for SARS-CoV-2 RNA and serology. Clinical observers rated the collection of specimens as suitable for testing, and visual and quantitative laboratory assessment indicated that the specimens were biologically sufficient. These data support the utility of participant-collected and mailed-in specimens for SARS-CoV-2 testing. International Registered Report Identifier (IRRID): RR2-10.2196/19054 %M 32479412 %R 10.2196/19731 %U http://publichealth.jmir.org/2020/2/e19731/ %U https://doi.org/10.2196/19731 %U http://www.ncbi.nlm.nih.gov/pubmed/32479412 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e16757 %T Privacy-Preserving Record Linkage of Deidentified Records Within a Public Health Surveillance System: Evaluation Study %A Nguyen,Long %A Stoové,Mark %A Boyle,Douglas %A Callander,Denton %A McManus,Hamish %A Asselin,Jason %A Guy,Rebecca %A Donovan,Basil %A Hellard,Margaret %A El-Hayek,Carol %+ Burnet Institute, 85 Commercial Rd, Melbourne, 3004, Australia, 61 468689589, long.nguyen@burnet.edu.au %K medical record linkage %K public health surveillance %K sentinel surveillance %K sensitivity and specificity %K data linkage %K confidentiality %K evaluation studies as a topic %D 2020 %7 24.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS) was established to monitor national testing and test outcomes for blood-borne viruses (BBVs) and sexually transmissible infections (STIs) in key populations. ACCESS extracts deidentified data from sentinel health services that include general practice, sexual health, and infectious disease clinics, as well as public and private laboratories that conduct a large volume of BBV/STI testing. An important attribute of ACCESS is the ability to accurately link individual-level records within and between the participating sites, as this enables the system to produce reliable epidemiological measures. Objective: The aim of this study was to evaluate the use of GRHANITE software in ACCESS to extract and link deidentified data from participating clinics and laboratories. GRHANITE generates irreversible hashed linkage keys based on patient-identifying data captured in the patient electronic medical records (EMRs) at the site. The algorithms to produce the data linkage keys use probabilistic linkage principles to account for variability and completeness of the underlying patient identifiers, producing up to four linkage key types per EMR. Errors in the linkage process can arise from imperfect or missing identifiers, impacting the system’s integrity. Therefore, it is important to evaluate the quality of the linkages created and evaluate the outcome of the linkage for ongoing public health surveillance. Methods: Although ACCESS data are deidentified, we created two gold-standard datasets where the true match status could be confirmed in order to compare against record linkage results arising from different approaches of the GRHANITE Linkage Tool. We reported sensitivity, specificity, and positive and negative predictive values where possible and estimated specificity by comparing a history of HIV and hepatitis C antibody results for linked EMRs. Results: Sensitivity ranged from 96% to 100%, and specificity was 100% when applying the GRHANITE Linkage Tool to a small gold-standard dataset of 3700 clinical medical records. Medical records in this dataset contained a very high level of data completeness by having the name, date of birth, post code, and Medicare number available for use in record linkage. In a larger gold-standard dataset containing 86,538 medical records across clinics and pathology services, with a lower level of data completeness, sensitivity ranged from 94% to 95% and estimated specificity ranged from 91% to 99% in 4 of the 6 different record linkage approaches. Conclusions: This study’s findings suggest that the GRHANITE Linkage Tool can be used to link deidentified patient records accurately and can be confidently used for public health surveillance in systems such as ACCESS. %M 32579128 %R 10.2196/16757 %U https://www.jmir.org/2020/6/e16757 %U https://doi.org/10.2196/16757 %U http://www.ncbi.nlm.nih.gov/pubmed/32579128 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e19284 %T Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers %A Fagherazzi,Guy %A Goetzinger,Catherine %A Rashid,Mohammed Ally %A Aguayo,Gloria A %A Huiart,Laetitia %+ Luxembourg Institute of Health, 1 A-B Rue Thomas Edison, Strassen, 1445, Luxembourg, 352 33669396334, guy.fagherazzi@lih.lu %K coronavirus %K COVID-19 %K digital health %K eHealth %K digital technology %K health care %K surveillance %K communication %K review %K epidemiology %K infodemiology %K public health %D 2020 %7 16.6.2020 %9 Viewpoint %J J Med Internet Res %G English %X The coronavirus disease (COVID-19) pandemic has created an urgent need for coordinated mechanisms to respond to the outbreak across health sectors, and digital health solutions have been identified as promising approaches to address this challenge. This editorial discusses the current situation regarding digital health solutions to fight COVID-19 as well as the challenges and ethical hurdles to broad and long-term implementation of these solutions. To decrease the risk of infection, telemedicine has been used as a successful health care model in both emergency and primary care. Official communication plans should promote facile and diverse channels to inform people about the pandemic and to avoid rumors and reduce threats to public health. Social media platforms such as Twitter and Google Trends analyses are highly beneficial to model pandemic trends as well as to monitor the evolution of patients’ symptoms or public reaction to the pandemic over time. However, acceptability of digital solutions may face challenges due to potential conflicts with users’ cultural, moral, and religious backgrounds. Digital tools can provide collective public health benefits; however, they may be intrusive and can erode individual freedoms or leave vulnerable populations behind. The COVID-19 pandemic has demonstrated the strong potential of various digital health solutions that have been tested during the crisis. More concerted measures should be implemented to ensure that future digital health initiatives will have a greater impact on the epidemic and meet the most strategic needs to ease the life of people who are at the forefront of the crisis. %M 32501804 %R 10.2196/19284 %U http://www.jmir.org/2020/6/e19284/ %U https://doi.org/10.2196/19284 %U http://www.ncbi.nlm.nih.gov/pubmed/32501804 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e19938 %T Information Technology–Based Management of Clinically Healthy COVID-19 Patients: Lessons From a Living and Treatment Support Center Operated by Seoul National University Hospital %A Bae,Ye Seul %A Kim,Kyung Hwan %A Choi,Sae Won %A Ko,Taehoon %A Jeong,Chang Wook %A Cho,BeLong %A Kim,Min Sun %A Kang,EunKyo %+ Office of Hospital Information, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2 2072 7600, kkh726@snu.ac.kr %K COVID-19 %K clinical informatics %K mobile app %K telemedicine %K hospital information system %K app %K health information technology %D 2020 %7 12.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: South Korea took preemptive action against coronavirus disease (COVID-19) by implementing extensive testing, thorough epidemiological investigation, strict social distancing, and rapid treatment of patients according to disease severity. The Korean government entrusted large-scale hospitals with the operation of living and treatment support centers (LTSCs) for the management for clinically healthy COVID-19 patients. Objective: The aim of this paper is to introduce our experience implementing information and communications technology (ICT)-based remote patient management systems at a COVID-19 LTSC. Methods: We adopted new electronic health record templates, hospital information system (HIS) dashboards, cloud-based medical image sharing, a mobile app, and smart vital sign monitoring devices. Results: Enhancements were made to the HIS to assist in the workflow and care of patients in the LTSC. A dashboard was created for the medical staff to view the vital signs and symptoms of all patients. Patients used a mobile app to consult with their physician or nurse, answer questionnaires, and input self-measured vital signs; the results were uploaded to the hospital information system in real time. Cloud-based image sharing enabled interoperability between medical institutions. Korea’s strategy of aggressive mitigation has “flattened the curve” of the rate of infection. A multidisciplinary approach was integral to develop systems supporting patient care and management at the living and treatment support center as quickly as possible. Conclusions: Faced with a novel infectious disease, we describe the implementation and experience of applying an ICT-based patient management system in the LTSC affiliated with Seoul National University Hospital. ICT-based tools and applications are increasingly important in health care, and we hope that our experience will provide insight into future technology-based infectious disease responses. %M 32490843 %R 10.2196/19938 %U http://www.jmir.org/2020/6/e19938/ %U https://doi.org/10.2196/19938 %U http://www.ncbi.nlm.nih.gov/pubmed/32490843 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e17009 %T Antimicrobial Resistance of Neisseria Gonorrhoeae in a Newly Implemented Surveillance Program in Uganda: Surveillance Report %A Workneh,Meklit %A Hamill,Matthew M %A Kakooza,Francis %A Mande,Emmanuel %A Wagner,Jessica %A Mbabazi,Olive %A Mugasha,Rodney %A Kajumbula,Henry %A Walwema,Richard %A Zenilman,Jonathan %A Musinguzi,Patrick %A Kyambadde,Peter %A Lamorde,Mohammed %A Manabe,Yukari C %+ Division of Infectious Diseases, Johns Hopkins School of Medicine, 5200 Eastern Avenue, Baltimore, MD, 21224, United States, 1 410 550 9080, mhamill6@jhu.edu %K gonorrhea %K antimicrobial resistance %K surveillance %K Uganda %K STD %K STI %K sexually transmitted %K Neisseria Gonorrhoeae %K antibiotic resistance, EGASP %D 2020 %7 10.6.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Neisseria gonorrhoeae (commonly known as gonorrhea) has developed resistance to all first-line therapy in Southeast Asia. East Africa has historically had absent or rudimentary gonorrhea surveillance programs and, while the existence of antimicrobial-resistant gonorrhea is recognized, the extent of its resistance is largely unknown. In 2016, the World Health Organization’s Enhanced Gonococcal Antimicrobial Surveillance Program (EGASP) was initiated in Uganda to monitor resistance trends. Objective: This study characterizes gonorrhea and antibiotic resistance in a large surveillance program of men with urethral discharge syndrome from Kampala, Uganda. Methods: Men attending sentinel clinics with urethritis provided demographic information, behavior data, and a urethral swab in line with the World Health Organization’s EGASP protocols for culture, identification, and antibiotic-sensitivity testing using 2 methods—disk diffusion (Kirby-Bauer test) and Etest (BioMérieux Inc). A subset of samples underwent detailed antimicrobial resistance testing. Results: Of 639 samples collected from September 2016 to February 2018, 400 (62.6%) were culture-positive though 414 (64.8%) had microscopic evidence of gonorrhea. The mean age of the men from whom the samples were collected was 26.9 (SD 9.6) years and 7.2% (46/639) reported having HIV. There was high-level resistance to ciprofloxacin, tetracycline, and penicillin (greater than 90%) by Kirby-Bauer disk diffusion and 2.1% (4/188) had reduced azithromycin sensitivity by Etest. Of the early isolates that underwent detailed characterization, 60.3% (70/116) were culture-positive, 94% (66/69) isolates were either ciprofloxacin-resistant or ciprofloxacin-intermediate by Etest, 96% (65/68) were azithromycin-sensitive, and 96% (66/69) were gentamicin-sensitive. Resistance profiles were comparable between methods except for ceftriaxone (disk diffusion: 68/69, 99%; Etest: 67/69, 97%) and for gentamicin (disk diffusion: 2/8, 25%; Etest: 66/69, 96%) sensitivity. Conclusions: This is the first report from a systematic gonorrhea surveillance program in Uganda. Findings demonstrated resistance or increased minimum inhibitory concentration to all key antigonococcal antibiotics. There was evidence of poor antibiotic stewardship, near-universal resistance to several antibiotics, and emerging resistance to others. Individuals in the population sampled were at exceptionally high risk of STI and HIV infection requiring intervention. Ongoing surveillance efforts to develop interventions to curtail antimicrobial-resistant gonorrhea are needed. %M 32519969 %R 10.2196/17009 %U http://publichealth.jmir.org/2020/2/e17009/ %U https://doi.org/10.2196/17009 %U http://www.ncbi.nlm.nih.gov/pubmed/32519969 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e17446 %T Active Surveillance of Adverse Events Following Human Papillomavirus Vaccination: Feasibility Pilot Study Based on the Regional Health Care Information Platform in the City of Ningbo, China %A Liu,Zhike %A Zhang,Liang %A Yang,Yu %A Meng,Ruogu %A Fang,Ting %A Dong,Ying %A Li,Ning %A Xu,Guozhang %A Zhan,Siyan %+ Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Haidian District, Beijing, 100191, China, 86 13641084052, siyan-zhan@bjmu.edu.cn %K safety %K HPV %K human papillomavirus %K vaccine %K active surveillance %D 2020 %7 1.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Comprehensive safety data for vaccines from post-licensure surveillance, especially active surveillance, could guide administrations and individuals to make reasonable decisions on vaccination. Therefore, we designed a pilot study to assess the capability of a regional health care information platform to actively monitor the safety of a newly licensed vaccine. Objective: This study aimed to conduct active surveillance of human papillomavirus (HPV) vaccine safety based on this information platform. Methods: In 2017, one of China’s most mature information platforms with superior data linkage was selected. A structured questionnaire and open-ended interview guidelines were developed to investigate the feasibility of active surveillance following HPV vaccination using the regional health care information platform in Ningbo. The questionnaire was sent to participants via email, and a face-to-face interview was conducted to confirm details or resolve discrepancies. Results: Five databases that could be considered essential to active surveillance of vaccine safety were integrated into the platform starting in 2015. Except for residents' health records, which had a coverage rate of 87%, the data sources covered more than 95% of the records that were documented in Ningbo. All the data could be inherently linked using the national identity card. There were 19,328 women who received the HPV vaccine, and 37,988 doses were administered in 2017 and 2018. Women aged 30-40 years accounted for the largest proportion. Quadrivalent vaccination accounted for 73.1% of total vaccination, a much higher proportion than that of bivalent vaccination. Of the first doses, 60 (60/19,328, 0.31%) occurred outside Ningbo. There were no missing data for vaccination-relevant variables, such as identity card, vaccine name, vaccination doses, vaccination date, and manufacturer. ICD-10 coding could be used to identify 9,180 cases using a predefined list of the outcomes of interest, and 1.88% of these cases were missing the identity card. During the 90 days following HPV vaccination, 4 incident cases were found through the linked vaccination history and electronic medical records. The combined incident rate of rheumatoid arthritis, optic neuritis, and Henoch-Schonlein purpura was 8.84/100,000 doses of bivalent HPV, and the incidence rate of rheumatoid arthritis was 3.75/100,000 doses of quadrivalent HPV. Conclusions: This study presents an available approach to initiate an active surveillance system for adverse events following HPV vaccination, based on a regional health care information platform in China. An extended observation period or the inclusion of additional functional sites is warranted to conduct future hypothesis-generating and hypothesis-confirming studies for vaccine safety concerns. %M 32234696 %R 10.2196/17446 %U https://www.jmir.org/2020/6/e17446 %U https://doi.org/10.2196/17446 %U http://www.ncbi.nlm.nih.gov/pubmed/32234696 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e10877 %T Injuries Reported by Selected Health Facilities During the Arbaeenia Mass Gathering at Babel Governorate, Iraq, 2014: Retrospective Records Analysis %A Chitheer,Abdulaal %A Lami,Faris %A Radhi,Ahmed %A Arbaji,Ali %+ Department of Community and Family Medicine, College of Medicine, University of Baghdad, Baghdad College of Medicine, Baghdad, 00964, Iraq, 964 7901402692, farislami@gmail.com %K mass gathering %K Injury %K Karbala, Iraq %D 2020 %7 28.5.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Arbaeenia is the largest religious mass gathering in Iraq. The conditions associated with mass gatherings result in high rates of injury. There have been no prior studies on injuries during the Arbaeenia mass gathering. Objective: This study describes the injuries observed during the Arbaeenia mass gathering in Babel Governorate in Iraq between November 24 and December 14, 2014. Methods: The study was conducted in Babel Governorate at the emergency departments of six public hospitals and two major temporary medical units that were located along the three roads connecting the Middle and Southern Iraqi governorates. We used the Iraq Injury Surveillance System modified form to collect information on injured patients treated in the selected facilities. Data on fatal injuries was obtained from the coroner’s office. The following data were collected from the patients: demographics, outcome of injury, place and time of occurrence, mode of evacuation and medical care before arriving at the hospital, duration of travel from place of occurrence to hospital, disposition of non-fatal injury, cause and mode of injury, and whether the injury occurred in connection with the Arbaeenia mass gathering. Results: Information was collected on 1564 injury cases, of which 73 were fatal. About half of the reported nonfatal injuries, 687/1404 (48.9%), and a quarter of fatalities, 18/73 (25%) were related to the Arbaeenia mass gathering (P<.001). Most of the reported injuries were unintentional, 1341/1404 (95.51%), occurred on the street, 864/1323 (65.6%), occurred during the daytime 1103/1174 (93.95 %). Most of those injured were evacuated by means other than ambulance 1107/1206 (91.79%) and did not receive pre-hospital medical care 788/1163 (67.7%). Minor injuries 400/1546 (25.9%) and traffic accidents 394/1546 (25.5%) were the most common types of injuries, followed by falls 270/1546 (17.5%). Among fatal injuries, traffic accidents 38/73 (52%) and violence 18/73 (25%) were the leading causes of death. Mass gathering injuries were more likely to occur among individuals aged 21-40 years (odds ratio [OR] 3.5; 95% CI 2.7-4.5) and >41 years (OR 7.6; 95% CI 5.4-10.6) versus those <21 years; more likely to be unintentional than assault (OR 5.3; 95% CI 1.8-15.5); more likely to happen on the street versus at home (OR 37.7; 95% CI 22.4-63.6); less likely to happen at night than during the day (OR 0.2; 95% CI 0.1-0.4); and less likely to result in hospital admission (OR 0.5; 95% CI 0.3-0.7). Conclusions: The study shows that most injuries were minor, unintentional, and nonfatal, and most people with injuries had limited access to ambulance transportation and did not require hospitalization. %M 32463371 %R 10.2196/10877 %U http://publichealth.jmir.org/2020/2/e10877/ %U https://doi.org/10.2196/10877 %U http://www.ncbi.nlm.nih.gov/pubmed/32463371 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 5 %P e18664 %T Optimization of Precontrol Methods and Analysis of a Dynamic Model for Brucellosis: Model Development and Validation %A Huang,Yihao %A Li,Mingtao %+ College of Mathematics, Shanxi University of Technology, 79, Yingze West St, Taiyuan, 030024, China, 86 13403459876, mingtaoli@sohu.com %K brucellosis %K dynamic model %K protective measures %K precontrol methods %D 2020 %7 27.5.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Brucella is a gram-negative, nonmotile bacterium without a capsule. The infection scope of Brucella is wide. The major source of infection is mammals such as cattle, sheep, goats, pigs, and dogs. Currently, human beings do not transmit Brucella to each other. When humans eat Brucella-contaminated food or contact animals or animal secretions and excretions infected with Brucella, they may develop brucellosis. Although brucellosis does not originate in humans, its diagnosis and cure are very difficult; thus, it has a huge impact on humans. Even with the rapid development of medical science, brucellosis is still a major problem for Chinese people. Currently, the number of patients with brucellosis in China is 100,000 per year. In addition, due to the ongoing improvement in the living standards of Chinese people, the demand for meat products has gradually increased, and increased meat transactions have greatly promoted the spread of brucellosis. Therefore, many researchers are concerned with investigating the transmission of Brucella as well as the diagnosis and treatment of brucellosis. Mathematical models have become an important tool for the study of infectious diseases. Mathematical models can reflect the spread of infectious diseases and be used to study the effect of different inhibition methods on infectious diseases. The effect of control measures to obtain effective suppression can provide theoretical support for the suppression of infectious diseases. Therefore, it is the objective of this study to build a suitable mathematical model for brucellosis infection. Objective: We aimed to study the optimized precontrol methods of brucellosis using a dynamic threshold–based microcomputer model and to provide critical theoretical support for the prevention and control of brucellosis. Methods: By studying the transmission characteristics of Brucella and building a Brucella transmission model, the precontrol methods were designed and presented to the key populations (Brucella-susceptible populations). We investigated the utilization of protective tools by the key populations before and after precontrol methods. Results: An improvement in the amount of glove-wearing was evident and significant (P<.001), increasing from 51.01% before the precontrol methods to 66.22% after the precontrol methods, an increase of 15.21%. However, the amount of hat-wearing did not improve significantly (P=.95). Hat-wearing among the key populations increased from 57.3% before the precontrol methods to 58.6% after the precontrol methods, an increase of 1.3%. Conclusions: By demonstrating the optimized precontrol methods for a brucellosis model built on a dynamic threshold–based microcomputer model, this study provides theoretical support for the suppression of Brucella and the improved usage of protective measures by key populations. %M 32459180 %R 10.2196/18664 %U https://medinform.jmir.org/2020/5/e18664 %U https://doi.org/10.2196/18664 %U http://www.ncbi.nlm.nih.gov/pubmed/32459180 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e11512 %T Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development %A Yeng,Prosper Kandabongee %A Woldaregay,Ashenafi Zebene %A Solvoll,Terje %A Hartvigsen,Gunnar %+ Department of Computer Science, University of Tromsø, The Arctic University of Norway, NTNU Gjøvik Teknologiveien 22, Gjøvik, 2815, Norway, 47 96992743, prosper.yeng@gmail.com %K sentinel surveillance %K space-time clustering %K aberration detection %D 2020 %7 26.5.2020 %9 Review %J JMIR Public Health Surveill %G English %X Background: The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective: Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods: We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results: The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions: The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time. %M 32357126 %R 10.2196/11512 %U http://publichealth.jmir.org/2020/2/e11512/ %U https://doi.org/10.2196/11512 %U http://www.ncbi.nlm.nih.gov/pubmed/32357126 %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 2 %P e15860 %T The Surveillance Outbreak Response Management and Analysis System (SORMAS): Digital Health Global Goods Maturity Assessment %A Tom-Aba,Daniel %A Silenou,Bernard Chawo %A Doerrbecker,Juliane %A Fourie,Carl %A Leitner,Carl %A Wahnschaffe,Martin %A Strysewske,Maté %A Arinze,Chinedu Chukwujekwu %A Krause,Gerard %+ Helmholtz Centre for Infection Research, Department of Epidemiology, Inhoffenstrasse 7, Braunschweig, 38124, Germany, 49 01739136081, daniel.tom-aba@helmholtz-hzi.de %K mHealth %K eHealth %K contact tracing %K case management %K epidemiology %K Ebola Virus Disease %K West Africa %K infectious diseases %D 2020 %7 29.4.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Digital health is a dynamic field that has been generating a large number of tools; many of these tools do not have the level of maturity required to function in a sustainable model. It is in this context that the concept of global goods maturity is gaining importance. Digital Square developed a global good maturity model (GGMM) for digital health tools, which engages the digital health community to identify areas of investment for global goods. The Surveillance Outbreak Response Management and Analysis System (SORMAS) is an open-source mobile and web application software that we developed to enable health workers to notify health departments about new cases of epidemic-prone diseases, detect outbreaks, and simultaneously manage outbreak response. Objective: The objective of this study was to evaluate the maturity of SORMAS using Digital Square’s GGMM and to describe the applicability of the GGMM on the use case of SORMAS and identify opportunities for system improvements. Methods: We evaluated SORMAS using the GGMM version 1.0 indicators to measure its development. SORMAS was scored based on all the GGMM indicator scores. We described how we used the GGMM to guide the development of SORMAS during the study period. GGMM contains 15 subindicators grouped into the following core indicators: (1) global utility, (2) community support, and (3) software maturity. Results: The assessment of SORMAS through the GGMM from November 2017 to October 2019 resulted in full completion of all subscores (10/30, (33%) in 2017; 21/30, (70%) in 2018; and 30/30, (100%) in 2019). SORMAS reached the full score of the GGMM for digital health software tools by accomplishing all 10 points for each of the 3 indicators on global utility, community support, and software maturity. Conclusions: To our knowledge, SORMAS is the first electronic health tool for disease surveillance, and also the first outbreak response management tool, that has achieved a 100% score. Although some conceptual changes would allow for further improvements to the system, the GGMM already has a robust supportive effect on developing software toward global goods maturity. %M 32347809 %R 10.2196/15860 %U https://publichealth.jmir.org/2020/2/e15860 %U https://doi.org/10.2196/15860 %U http://www.ncbi.nlm.nih.gov/pubmed/32347809 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e14986 %T Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study %A Daughton,Ashlynn R %A Chunara,Rumi %A Paul,Michael J %+ Analytics, Intelligence and Technology, Los Alamos National Laboratory, Mail Stop F608, Los Alamos, NM, 87545, United States, 1 505 667 7000, adaughton@lanl.gov %K social media %K infodemiology %K influenza, human %K selection bias %K bias %K logistic models %D 2020 %7 24.4.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective: This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods: This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Results: Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions: To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data. %M 32329741 %R 10.2196/14986 %U http://publichealth.jmir.org/2020/2/e14986/ %U https://doi.org/10.2196/14986 %U http://www.ncbi.nlm.nih.gov/pubmed/32329741 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 4 %P e18948 %T Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China %A Gong,Mengchun %A Liu,Li %A Sun,Xin %A Yang,Yue %A Wang,Shuang %A Zhu,Hong %+ Nanfang Hospital, Southern Medical University, North 1838, Guangzhou Ave, Baiyun District, Guangzhou, China, 86 130 7687 721, zhnfyy@yeah.net %K COVID-19 %K cloud system %K syndromic surveillance %K clinical decision support %K stakeholders involvement %K pandemic %K medical informatics %D 2020 %7 22.4.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective: The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. Methods: The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. Results: HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. Conclusions: The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated. %M 32287040 %R 10.2196/18948 %U http://www.jmir.org/2020/4/e18948/ %U https://doi.org/10.2196/18948 %U http://www.ncbi.nlm.nih.gov/pubmed/32287040 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 2 %P e16119 %T Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study %A Leal Neto,Onicio %A Cruz,Oswaldo %A Albuquerque,Jones %A Nacarato de Sousa,Mariana %A Smolinski,Mark %A Pessoa Cesse,Eduarda Ângela %A Libel,Marlo %A Vieira de Souza,Wayner %+ University of Zurich, Schönberggasse 1, Room SOF-H10, Zurich, , Switzerland, 41 44 634 55 50, onicio@gmail.com %K participatory surveillance %K epidemiology %K infectious diseases %K pandemics %K health innovation %K digital disease detection %K disease surveillance %K mobile phone %D 2020 %7 7.4.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: With the evolution of digital media, areas such as public health are adding new platforms to complement traditional systems of epidemiological surveillance. Participatory surveillance and digital epidemiology have become innovative tools for the construction of epidemiological landscapes with citizens’ participation, improving traditional sources of information. Strategies such as these promote the timely detection of warning signs for outbreaks and epidemics in the region. Objective: This study aims to describe the participatory surveillance platform Guardians of Health, which was used in a project conducted during the 2016 Olympic and Paralympic Games in Rio de Janeiro, Brazil, and officially used by the Brazilian Ministry of Health for the monitoring of outbreaks and epidemics. Methods: This is a descriptive study carried out using secondary data from Guardians of Health available in a public digital repository. Based on syndromic signals, the information subsidy for decision making by policy makers and health managers becomes more dynamic and assertive. This type of information source can be used as an early route to understand the epidemiological scenario. Results: The main result of this research was demonstrating the use of the participatory surveillance platform as an additional source of information for the epidemiological surveillance performed in Brazil during a mass gathering. The platform Guardians of Health had 7848 users who generated 12,746 reports about their health status. Among these reports, the following were identified: 161 users with diarrheal syndrome, 68 users with respiratory syndrome, and 145 users with rash syndrome. Conclusions: It is hoped that epidemiological surveillance professionals, researchers, managers, and workers become aware of, and allow themselves to use, new tools that improve information management for decision making and knowledge production. This way, we may follow the path for a more intelligent, efficient, and pragmatic disease control system. %M 32254042 %R 10.2196/16119 %U https://publichealth.jmir.org/2020/2/e16119 %U https://doi.org/10.2196/16119 %U http://www.ncbi.nlm.nih.gov/pubmed/32254042 %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 %@ 2561-326X %I JMIR Publications %V 4 %N 1 %P e16424 %T An Integrated mHealth App for Dengue Reporting and Mapping, Health Communication, and Behavior Modification: Development and Assessment of Mozzify %A Herbuela,Von Ralph Dane Marquez %A Karita,Tomonori %A Francisco,Micanaldo Ernesto %A Watanabe,Kozo %+ Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama, Japan, 81 89-927-9847, watanabe_kozo@cee.ehime-u.ac.jp %K dengue fever %K mHealth %K real-time surveillance %K health communication %K behavior modification %D 2020 %7 8.1.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: For the last 10 years, mobile phones have provided the global health community with innovative and cost-effective strategies to address the challenges in the prevention and management of dengue fever. Objective: The aim is to introduce and describe the design and development process of Mozzify, an integrated mobile health (mHealth) app that features real-time dengue fever case reporting and mapping system, health communication (real-time worldwide news and chat forum/timeline, within-app educational videos, links to local and international health agency websites, interactive signs and symptoms checker, and a hospital directions system), and behavior modification (reminders alert program on the preventive practices against dengue fever). We also aim to assess Mozzify in terms of engagement and information-sharing abilities, functionality, aesthetics, subjective quality, and perceived impact. Methods: The main goals of the Mozzify app were to increase awareness, improve knowledge, and change attitudes about dengue fever, health care-seeking behavior, and intention-to-change behavior on preventive practices for dengue fever among users. It was assessed using the Mobile Application Rating Scale (MARS) among 50 purposively sampled individuals: public health experts (n=5), environment and health-related researchers (n=23), and nonclinical (end users) participants (n=22). Results: High acceptability and excellent satisfaction ratings (mean scores ≥4.0 out of 5) based on the MARS subscales indicate that the app has excellent user design, functionality, usability, engagement, and information among public health experts, environment and health-related researchers, and end users. The app’s subjective quality (recommending the app to other people and the app’s overall star rating), and specific quality (increase awareness, improve knowledge, and change attitudes about dengue fever; health care-seeking behavior; and intention-to-change behavior on preventive practices for dengue fever) also obtained excellent satisfaction ratings from the participants. Some issues and suggestions were raised during the focus group and individual discussions regarding the availability of the app for Android devices, language options limitations, provision of predictive surveillance, and inclusion of other mosquito-borne diseases. Conclusions: Mozzify may be a promising integrated strategic health intervention system for dengue fever case reporting and mapping; increase awareness, improve knowledge, and change attitude about dengue fever; and disseminating and sharing information on dengue fever among the general population and health experts. It also can be an effective aid in the successful translation of knowledge on preventive measures against dengue fever to practice. %M 31913128 %R 10.2196/16424 %U https://formative.jmir.org/2020/1/e16424 %U https://doi.org/10.2196/16424 %U http://www.ncbi.nlm.nih.gov/pubmed/31913128 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e14413 %T The Acute Flaccid Paralysis (AFP) Surveillance System in Yemen, 2010-2015: Descriptive Study Based on Secondary Data Analysis %A Almoayed,Khaled Abdullah %A Bin Break,Ali %A Al-Qassimi,Mutahar %A Assabri,Ali %A Khader,Yousef %+ Yemen Field Epidemiology Training Program, Ministry of Public Health and Population, Alhasabah Area, Althawrah District, Sana'a, Yemen, 967 771707060, Khaled.moayed@yahoo.com %K evaluation %K acute %K flaccid %K paralysis %K surveillance %K Yemen %D 2019 %7 6.12.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Acute flaccid paralysis (AFP) surveillance is an essential strategy for poliovirus eradication. Objective: This study aimed to evaluate the performance of the AFP surveillance system in Yemen from 2010 to 2015, identify components that require strengthening, and compare the indicators by year and governorates. Methods: This descriptive study was based on secondary analysis of AFP surveillance data reported during 2010-2015 from all Yemeni governorates. The World Health Organization (WHO) minimum performance standards were used to evaluate the performance of the AFP surveillance system. Results: A total of 3019 AFP cases were reported between January 2010 and December 2015. At the national level, AFP surveillance achieved WHO targets throughout the evaluating period for the nonpolio AFP rate of cases per 100,000 members of the population younger than 15 years of age, proportion of AFP cases reported within 7 days, proportion of AFP cases investigated within 48 hours of notification, proportion of AFP cases with two adequate stool specimens, and proportion of stool specimens from which nonpolio enterovirus was isolated. However, the proportion of specimens that arrived at the central level within 3 days of the first sample collection and the proportion of stool specimens with results sent from the reference laboratory within 28 days of receipt did not reach targets in 2011 and 2015, respectively. Conclusions: The AFP surveillance system in Yemen has met most of the WHO indicator levels. Nevertheless, the evaluation showed areas of weakness regarding the arrival of specimens at the central level within 3 days of the first sample collection and delays in processing of the results and submitting feedback by the laboratory. Therefore, there is a need to strengthen the follow-up of specimens submitted to the laboratory. %M 31808749 %R 10.2196/14413 %U http://publichealth.jmir.org/2019/4/e14413/ %U https://doi.org/10.2196/14413 %U http://www.ncbi.nlm.nih.gov/pubmed/31808749 %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 %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14276 %T The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study %A Kim,Myeongchan %A Yune,Sehyo %A Chang,Seyun %A Jung,Yuseob %A Sa,Soon Ok %A Han,Hyun Wook %+ Department of Biomedical Informatics, Graduate School of Medicine, CHA University, Pangyo-ro 335, Bundang-gu, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea, 82 31 881 7109, stepano7@gmail.com %K data collection %K detecting epidemics %K mobile app %K health care app %K influenza epidemics %K influenza in children %D 2019 %7 17.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. Objective: This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. Methods: Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). Results: During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. Conclusions: The Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network. %M 31625946 %R 10.2196/14276 %U https://mhealth.jmir.org/2019/10/e14276 %U https://doi.org/10.2196/14276 %U http://www.ncbi.nlm.nih.gov/pubmed/31625946 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e10920 %T Syndromic Surveillance of Communicable Diseases in Mobile Clinics During the Arbaeenia Mass Gathering in Wassit Governorate, Iraq, in 2014: Cross-Sectional Study %A Lami,Faris %A Asi,Wejdan %A Khistawi,Adnan %A Jawad,Iman %+ Department of Community and Family Medicine, College of Medicine, University of Baghdad, Bab Al Muadham, Rusafa, Baghdad, 00964, Iraq, 964 7901402692, farislami@yahoo.com %K Arbaeenia %K mass gathering %K syndromic surveillance %K communicable diseases %K Iraq %D 2019 %7 7.10.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Arbaeenia is the largest religious mass gathering organized annually in Karbala city, Iraq, and is attended by 8-14 million people. Outbreaks of communicable diseases are a significant risk due to overcrowding and potential food and water contamination. Syndromic surveillance is often used for rapid detection and response to disease outbreaks. Objective: This study was conducted to identify the main communicable diseases syndromes among pilgrims during the Arbaeenia mass gathering in Wassit governorate, Iraq, in 2014. Methods: This cross-sectional study was conducted in the 40 mobile clinics established within Wassit governorates along the road to Karbala during the Arbaeenia mass gathering. Six communicable disease syndromes were selected: acute watery diarrhea, bloody diarrhea, fever and cough, vomiting with or without diarrhea, fever and bleeding tendency, and fever and rash. A simple questionnaire was used to directly gather basic demographics and the syndromic diagnosis from the attendees. Results: A total of 87,865 patients attended the clinics during the 10-day period, with an average of 219 patients/clinic/day. Approximately 5% (3999) of the attendees had communicable diseases syndromes: of these, 1693 (42%) had fever and cough, 1144 (29%) had acute diarrhea, 1062 (27%) presented with vomiting with/without diarrhea, and 100 (2%) had bloody diarrhea. The distribution of the syndromes did not vary by age or gender. Stool specimen cultures for Vibrio cholerae performed for 120 patients with acute diarrhea were all negative. Conclusions: Syndromic surveillance was useful in determining the main communicable diseases encountered during the mass gathering. Expansion of this surveillance to other governorates and the use of mobile technology can help in timely detection and response to communicable disease outbreaks. %M 31593544 %R 10.2196/10920 %U https://publichealth.jmir.org/2019/4/e10920 %U https://doi.org/10.2196/10920 %U http://www.ncbi.nlm.nih.gov/pubmed/31593544 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e14510 %T Real-Time Surveillance of Infectious Diseases and Other Health Conditions During Iraq’s Arbaeenia Mass Gathering: Cross-Sectional Study %A Lami,Faris %A Hameed,Inam %A Jewad,Abdul Wahhab %A Khader,Yousef %A Amiri,Mirwais %+ Department of Community and Family Medicine, College of Medicine, University of Baghdad, Bab Al Muadham, Resafa, Baghdad, 00964, Iraq, 964 7901402692, farislami@gmail.com %K mass gathering %K Arbaeenia %K surveillance %K Iraq %D 2019 %7 4.10.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The most common religious mass gatherings in the Middle East are the Hajj at Mecca in Saudi Arabia, which occurs annually, and the Arbaeenia in Karbala. The importance of developing public health surveillance systems for mass gatherings has been previously emphasized in other reports. Objective: This study aimed to describe the common illnesses and health conditions affecting people during the Arbaeenia mass gathering in Iraq in 2016. Methods: A total of 60 data collectors took part in the field data collection over a period of 11 days, from November 12, 2016 to November 22, 2016. Data were collected from 20 health outlets along the major route from Najaf to Karbala (10 health facilities in each governorate). Two digital forms, the Health Facility Survey and the Case Survey, were used for data collection. Results: A total of 41,689 patients (33.3% female and 66.7% male) visited the 20 health care facilities over a period of 11 days from November 12, 2016 to November 22, 2016. More than three quarters of patients (77.5%; n=32,309) were between 20-59 years of age, more than half of patients were mainly from Iraq (56.5%; n=23,554), and about 38.9% (n=16,217) were from Iran. Patients in this study visited these health care facilities and presented with one or more conditions. Of a total 41,689 patients, 58.5% (n=24,398) had acute or infectious conditions and symptoms, 33.1% (n=13,799) had chronic conditions, 23.9% (n=9974) had traumas or injuries, 28.2% (n=11,762) had joint pain related to walking long distances, and 0.3% (n=133) had chronic dermatologic conditions. Conclusions: The Arbaeenia mass gathering in 2016 exerted a high burden on the Iraqi health care system. Therefore, efforts must be made both before and during the event to ensure preparedness, proper management, and control of different conditions. %M 31588905 %R 10.2196/14510 %U https://publichealth.jmir.org/2019/4/e14510 %U https://doi.org/10.2196/14510 %U http://www.ncbi.nlm.nih.gov/pubmed/31588905 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 4 %P e13403 %T Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation %A Baltrusaitis,Kristin %A Vespignani,Alessandro %A Rosenfeld,Roni %A Gray,Josh %A Raymond,Dorrie %A Santillana,Mauricio %+ Computational Health Informatics Program, Boston Children’s Hospital, 1 Autumn St, Boston, MA, 02215, United States, 1 617 599 5460, msantill@fas.harvard.edu %K digital disease surveillance %K influenza %K surveillance %K participatory syndromic surveillance %K disease modeling %D 2019 %7 14.9.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear. Objective: The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources—CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org—to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas. Methods: We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care–seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care–seeking percentages and baselines for each surveillance data source. Results: We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines. Conclusions: Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models. %M 31579019 %R 10.2196/13403 %U https://publichealth.jmir.org/2019/4/e13403 %U https://doi.org/10.2196/13403 %U http://www.ncbi.nlm.nih.gov/pubmed/31579019 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 9 %P e13941 %T A Fully Integrated Real-Time Detection, Diagnosis, and Control of Community Diarrheal Disease Clusters and Outbreaks (the INTEGRATE Project): Protocol for an Enhanced Surveillance System %A McIntyre,Kirsty Marie %A Bolton,Frederick J %A Christley,Rob M %A Cleary,Paul %A Deja,Elizabeth %A Durie,Ann E %A Diggle,Peter J %A Hughes,Dyfrig A %A de Lusignan,Simon %A Orton,Lois %A Radford,Alan D %A Elliot,Alex J %A Smith,Gillian E %A Snape,Darlene A %A Stanistreet,Debbi %A Vivancos,Roberto %A Winstanley,Craig %A O’Brien,Sarah J %+ Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Chester High Road, Neston, CH64 7TE, United Kingdom, 44 1519758307, k.m.mcintyre@liverpool.ac.uk %K gastrointestinal diseases %K syndromic surveillance %K microbiology %K diarrhea %K vomiting %D 2019 %7 26.9.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Diarrheal disease, which affects 1 in 4 people in the United Kingdom annually, is the most common cause of outbreaks in community and health care settings. Traditional surveillance methods tend to detect point-source outbreaks of diarrhea and vomiting; they are less effective at identifying low-level and intermittent food supply contamination. Furthermore, it can take up to 9 weeks for infections to be confirmed, reducing slow-burn outbreak recognition, potentially impacting hundreds or thousands of people over wide geographical areas. There is a need to address fundamental problems in traditional diarrheal disease surveillance because of underreporting and subsequent unconfirmed infection by patients and general practitioners (GPs); varying submission practices and selective testing of samples in laboratories; limitations in traditional microbiological diagnostics, meaning that the timeliness of sample testing and etiology of most cases remains unknown; and poorly integrated human and animal surveillance systems, meaning that identification of zoonoses is delayed or missed. Objective: This study aims to detect anomalous patterns in the incidence of gastrointestinal disease in the (human) community; to target sampling; to test traditional diagnostic methods against rapid, modern, and sensitive molecular and genomic microbiology methods that identify and characterize responsible pathogens rapidly and more completely; and to determine the cost-effectiveness of rapid, modern, sensitive molecular and genomic microbiology methods. Methods: Syndromic surveillance will be used to aid identification of anomalous patterns in microbiological events based on temporal associations, demographic similarities among patients and animals, and changes in trends in acute gastroenteritis cases using a point process statistical model. Stool samples will be obtained from patients’ consulting GPs, to improve the timeliness of cluster detection and characterize the pathogens responsible, allowing health protection professionals to investigate and control outbreaks quickly, limiting their size and impact. The cost-effectiveness of the proposed system will be examined using formal cost-utility analysis to inform decisions on national implementation. Results: The project commenced on April 1, 2013. Favorable approval was obtained from the Research Ethics Committee on June 15, 2015, and the first patient was recruited on October 13, 2015, with 1407 patients recruited and samples processed using traditional laboratory techniques as of March 2017. Conclusions: The overall aim of this study is to create a new One Health paradigm for detecting and investigating diarrhea and vomiting in the community in near-real time, shifting from passive human surveillance and management of laboratory-confirmed infection toward an integrated, interdisciplinary enhanced surveillance system including management of people with symptoms. International Registered Report Identifier (IRRID): DERR1-10.2196/13941 %M 31573952 %R 10.2196/13941 %U https://www.researchprotocols.org/2019/9/e13941 %U https://doi.org/10.2196/13941 %U http://www.ncbi.nlm.nih.gov/pubmed/31573952 %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 %@ 2561-326X %I JMIR Publications %V 3 %N 3 %P e13621 %T Redesigning a Sentinel Surveillance System for Collecting and Disseminating Near Real-Time Agricultural Injury Reports: System Usability Study %A Weichelt,Bryan %A Heimonen,Tomi %A Gorucu,Serap %A Redmond,Emily %A Vechinski,Josef %A Pflughoeft,Kurt %A Bendixsen,Casper %A Salzwedel,Marsha %A Scott,Erika %A Namkoong,Kang %A Purschwitz,Mark %A Rautiainen,Risto %A Murphy,Dennis J %+ National Farm Medicine Center, Marshfield Clinic Research Institute, 1000 N Oak Ave, Marshfield, WI, 54449, United States, 1 7152217276, weichelt.bryan@marshfieldresearch.org %K agriculture %K risk %K wounds and injuries %K safety %K farms %K news %K newspaper article %D 2019 %7 02.08.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Injury data and reports provide valuable information for both public and private organizations to guide programming, policy, and prevention, but in the increasingly complex and dangerous industry of US agriculture, the injury surveillance needed to produce this data is lacking. To address the gap, AgInjuryNews was established in 2015. The system includes fatal and nonfatal injury cases derived from publicly available reports, including occupational and nonoccupational injuries, occurring in the agricultural, forestry, and fishing (AFF) industry. Objective: The study aimed to develop a stakeholder-engaged redesign of the interactive, up-to-date, and publicly available dataset of US AFF injury and fatality reports. Methods: Instructor-led heuristic evaluations within a 15-student undergraduate course, data from 8 student participants of laboratory-based usability testing and 2016 and 2017 AgInjuryNews-registered user surveys, coupled with input from the National Steering Committee informed the development priorities for 2018. An interdisciplinary team employed an agile methodology of 2-week sprints developing in ASP.NET and Structured Query Language to deliver an intuitive frontend and a flexible, yet structured, backend, including a case report input form for capturing more than 50 data points on each injury report. Results: AgInjuryNews produced 17,714 page views from 43 countries in 2018 captured via Google Analytics, whereas 623 injury reports were coded and loaded, totaling more than 31,000 data points. Newly designed features include customizable email alerts, an interactive map, and expanded search and filter options. User groups such as the Bureau of Labor Statistics and the Agricultural Safety and Health Council of America have endorsed the system within their networks. News media have cited or referenced the system in national outlets such as the New York Times, Politico, and the Washington Post. Conclusions: The new system’s features, functions, and improved data granularity have sparked innovative lines of research and increased collaborative interest domestically and abroad. It is anticipated that this nontraditional sentinel surveillance system and its dataset will continue to serve many purposes for public and private agricultural safety and health stakeholders in the years to come.  %M 31376278 %R 10.2196/13621 %U http://formative.jmir.org/2019/3/e13621/ %U https://doi.org/10.2196/13621 %U http://www.ncbi.nlm.nih.gov/pubmed/31376278 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e12383 %T Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study %A Alessa,Ali %A Faezipour,Miad %+ Department of Computer Science and Engineering, University of Bridgeport, 221 University Avenue, Bridgeport, CT, 06604, United States, 1 203 576 4702, mfaezipo@bridgeport.edu %K FastText %K influenza %K machine learning %K social networking site %K text classification %D 2019 %7 23.6.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% . Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs. %R 10.2196/12383 %U http://publichealth.jmir.org/2019/2/e12383/ %U https://doi.org/10.2196/12383 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e13086 %T New York City HIV Care Continuum Dashboards: Using Surveillance Data to Improve HIV Care Among People Living With HIV in New York City %A Braunstein,Sarah L %A Coeytaux,Karen %A Sabharwal,Charulata J %A Xia,Qiang %A Robbins,Rebekkah S %A Obeng,Beverly %A Daskalakis,Demetre C %+ HIV Epidemiology and Field Services Program, Bureau of HIV Prevention and Control, New York City Department of Health and Mental Hygiene, 42-09 28th Street, Long Island City, NY, 11101, United States, 1 347 396 7760, sbraunstein@health.nyc.gov %K HIV %K surveillance %K quality of care %K best practices %D 2019 %7 19.06.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: HIV surveillance data can be used to improve patient outcomes. Objective: This study aimed to describe and present findings from the HIV care continuum dashboards (CCDs) initiative, which uses surveillance data to quantify and track outcomes for HIV patients at major clinical institutions in New York City. Methods: HIV surveillance data collected since 2011 were used to provide high-volume New York City clinical facilities with their performance on two key outcomes: linkage to care (LTC), among patients newly diagnosed with HIV and viral load suppression (VLS), among patients in HIV care. Results: The initiative included 21 facilities covering 33.78% (1135/3360) of new HIV diagnoses and 46.34% (28,405/61,298) of patients in HIV care in New York City in 2011 and was extended to a total of 47 sites covering 44.23% (1008/2279) of new diagnoses and 69.59% (43,897/63,083) of New York City patients in care in 2016. Since feedback of outcomes to providers began, aggregate LTC has improved by 1 percentage point and VLS by 16 percentage points. Conclusions: Disseminating information on key facility–level HIV outcomes promotes collaboration between public health and the clinical community to end the HIV epidemic. Similar initiatives can be adopted by other jurisdictions with mature surveillance systems and supportive laws and policies. %M 31219053 %R 10.2196/13086 %U http://publichealth.jmir.org/2019/2/e13086/ %U https://doi.org/10.2196/13086 %U http://www.ncbi.nlm.nih.gov/pubmed/31219053 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 1 %P e11465 %T A Syndrome-Based Surveillance System for Infectious Diseases Among Asylum Seekers in Austrian Reception Centers, 2015-2018: Analysis of Reported Data %A El-Khatib,Ziad %A Taus,Karin %A Richter,Lukas %A Allerberger,Franz %A Schmid,Daniela %+ Department of Surveillance and Infectious Disease Epidemiology, Institute of Medical Microbiology and Hygiene, Austrian Agency for Health and Food Safety, Währingerstraße 25a, Vienna, 1096, Austria, 43 50 555 37304, daniela.schmid@ages.at %K Austria %K refugee health %K asylum seekers %K syndrome surveillance system %K mass health monitoring %K refugees %K population surveillance %K public health surveillance %K epidemiological monitoring %D 2019 %7 27.02.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Austria has been among the main European countries hosting incoming asylum seekers since 2015. Consequently, there was an urgent need to predict any public health threats associated with the arriving asylum seekers. The Department of Surveillance and Infectious Disease Epidemiology at the Austrian Agency for Health and Food Safety (AGES) was mandated to implement a national syndrome-based surveillance system in the 7 reception centers by the Austrian Ministry of Interior and Ministry of Health. Objective: We aimed to analyze the occurrence and spread of infectious diseases among asylum seekers using data reported by reception centers through the syndrome-based surveillance system from September 2015 through February 2018. Methods: We deployed a daily data collection system for 13 syndromes: rash with fever; rash without fever; acute upper respiratory tract infection; acute lower respiratory tract infection; meningitis or encephalitis; fever and bleeding; nonbloody gastroenteritis or watery diarrhea; bloody diarrhea; acute jaundice; skin, soft tissue, or bone abnormalities; acute flaccid paralysis; high fever with no other signs; and unexplained death. General practitioners, the first professionals to consult for health problems at reception centers in Austria, sent the tally sheets on identified syndromes daily to the AGES. Results: We identified a total of 2914 cases, presenting 8 of the 13 syndromes. A total of 405 signals were triggered, and 6.4% (26/405) of them generated alerts. Suspected acute upper respiratory tract infection (1470/2914, 50.45% of cases), rash without fever (1174/2914, 40.29% of cases), suspected acute lower respiratory tract infection (159/2914, 5.46% of cases), watery diarrhea (73/2914, 2.51% of cases), and skin, soft tissue, or bone abnormalities (32/2914, 1.10% of cases) were the top 5 syndromes. Conclusions: The cooperation of the AGES with reception center health care staff, supported by the 2 involved ministries, was shown to be useful for syndromic surveillance of infectious diseases among asylum seekers. None of the identified alerts escalated to an outbreak. %M 30810535 %R 10.2196/11465 %U http://publichealth.jmir.org/2019/1/e11465/ %U https://doi.org/10.2196/11465 %U http://www.ncbi.nlm.nih.gov/pubmed/30810535 %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 e12341 %T An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation %A Yang,Cheng-Yi %A Chen,Ray-Jade %A Chou,Wan-Lin %A Lee,Yuarn-Jang %A Lo,Yu-Sheng %+ Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, 886 939588576, Loyusen@tmu.edu.tw %K influenza %K epidemics %K influenza surveillance %K electronic disease surveillance %K electronic medical records %K electronic health records %K public health %D 2019 %7 01.02.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. Objective: We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods: First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results: Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions: Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations. %M 30707099 %R 10.2196/12341 %U http://www.jmir.org/2019/2/e12341/ %U https://doi.org/10.2196/12341 %U http://www.ncbi.nlm.nih.gov/pubmed/30707099 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e10978 %T Design, Development, and Evaluation of an Injury Surveillance App for Cricket: Protocol and Qualitative Study %A Soomro,Najeebullah %A Chhaya,Meraj %A Soomro,Mariam %A Asif,Naukhez %A Saurman,Emily %A Lyle,David %A Sanders,Ross %+ Broken Hill University Department of Rural Health, University of Sydney, Corrindah Court, Broken Hill, 2880, Australia, 61 880801282, naj.soomro@sydney.edu.au %K cricket %K injury surveillance %K mobile app %K mobile phone %K TeamDoc %K mHealth %D 2019 %7 22.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Injury surveillance and workload monitoring are important aspects of professional sports, including cricket. However, at the community level, there is a dearth of accessible and intelligent surveillance tools. Mobile apps are an accessible tool for monitoring cricket-related injuries at all levels. Objective: The objective of this paper is to share the novel methods associated with the development of the free TeamDoc app and provide evidence from an evaluation of the user experience and perception of the app regarding its functionality, utility, and design. Methods: TeamDoc mobile app for Android and Apple smartphones was developed using 3 languages: C++, Qt Modeling Language, and JavaScript. For the server-side connectivity, Hypertext Preprocessor (PHP) was used as it is a commonly used cross-platform language. PHP includes components that interact with popular database management systems, allowing for secure interaction with databases on a server level. The app was evaluated by administrating a modified user version of the Mobile App Rating Scale (uMARS; maximum score: 5). Results: TeamDoc is the first complementary, standalone mobile app that records cricket injuries through a smartphone. It can also record cricketing workloads, which is a known risk factor for injury. The app can be used without the need for supplementary computer devices for synchronization. The uMARS scores showed user satisfaction (overall mean score 3.6 [SD 0.5]), which demonstrates its acceptability by cricketers. Conclusions: Electronic injury surveillance systems have been shown to improve data collection during competitive sports. Therefore, TeamDoc may assist in improving injury reporting and may also act as a monitoring system for coaching staff to adjust individual training workloads. The methods described in this paper provide a template for researchers to develop similar apps for other sports. %M 30668516 %R 10.2196/10978 %U http://mhealth.jmir.org/2019/1/e10978/ %U https://doi.org/10.2196/10978 %U http://www.ncbi.nlm.nih.gov/pubmed/30668516 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 5 %N 1 %P e10447 %T Self-Care Behaviors of Ovarian Cancer Patients Before Their Diagnosis: Proof-of-Concept Study %A Flanagan,James M %A Skrobanski,Hanna %A Shi,Xin %A Hirst,Yasemin %+ Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, United Kingdom, 44 20 75942127, j.flanagan@imperial.ac.uk %K cancer %K early diagnosis %K proof of concept %K focus group %K acceptability %K data linkage %K cancer surveillance %D 2019 %7 17.01.2019 %9 Original Paper %J JMIR Cancer %G English %X Background: Longer patient intervals can lead to more late-stage cancer diagnoses and higher mortality rates. Individuals may delay presenting to primary care with red flag symptoms and instead turn to the internet to seek information, purchase over-the-counter medication, and change their diet or exercise habits. With advancements in machine learning, there is the potential to explore this complex relationship between a patient’s symptom appraisal and their first consultation at primary care through linkage of existing datasets (eg, health, commercial, and online). Objective: Here, we aimed to explore feasibility and acceptability of symptom appraisal using commercial- and health-data linkages for cancer symptom surveillance. Methods: A proof-of-concept study was developed to assess the general public’s acceptability of commercial- and health-data linkages for cancer symptom surveillance using a qualitative focus group study. We also investigated self-care behaviors of ovarian cancer patients using high-street retailer data, pre- and postdiagnosis. Results: Using a high-street retailer’s data, 1118 purchases—from April 2013 to July 2017—by 11 ovarian cancer patients and one healthy individual were analyzed. There was a unique presence of purchases for pain and indigestion medication prior to cancer diagnosis, which could signal disease in a larger sample. Qualitative findings suggest that the public are willing to consent to commercial- and health-data linkages as long as their data are safeguarded and users of this data are transparent about their purposes. Conclusions: Cancer symptom surveillance using commercial data is feasible and was found to be acceptable. To test efficacy of cancer surveillance using commercial data, larger studies are needed with links to individual electronic health records. %M 30664464 %R 10.2196/10447 %U https://cancer.jmir.org/2019/1/e10447/ %U https://doi.org/10.2196/10447 %U http://www.ncbi.nlm.nih.gov/pubmed/30664464 %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 e11361 %T Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study %A Poirier,Canelle %A Lavenu,Audrey %A Bertaud,Valérie %A Campillo-Gimenez,Boris %A Chazard,Emmanuel %A Cuggia,Marc %A Bouzillé,Guillaume %+ Laboratoire Traitement du Signal et de l'Image, Université de Rennes 1, 2 rue Henri Le Guilloux, Rennes, 35033, France, 33 667857225, canelle.poirier@outlook.fr %K electronic health records %K big data %K infodemiology %K infoveillance %K influenza %K machine learning %K Sentinelles network %D 2018 %7 21.12.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users’ activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models—random forest, elastic net, and support vector machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model. Conclusions: We found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable. %M 30578212 %R 10.2196/11361 %U http://publichealth.jmir.org/2018/4/e11361/ %U https://doi.org/10.2196/11361 %U http://www.ncbi.nlm.nih.gov/pubmed/30578212 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e69 %T Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study %A Delespierre,Tiba %A Josseran,Loic %+ Equipe de recherche (HANDIReSP), UFR des Sciences de la Santé Simone Veil, Université de Versailles Saint-Quentin-en-Yvelines et Université Paris-Saclay, 2 Avenue de la Source de la Bièvre, Montigny-le-Bretonneux, 78180, France, 33 658376503, tiba.baroukh@gmail.com %K Centers for Disease Control and Prevention %K nursing homes %K syndromic surveillance %K pattern recognition %K Delphi technique %K sentinel surveillance %D 2018 %7 13.12.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: New nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort. Objective: This study aimed to build and assess a national ecological NH PH surveillance system (SS). Methods: Using a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines. Results: By extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016). Conclusions: This study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats. %M 30545816 %R 10.2196/publichealth.9022 %U http://publichealth.jmir.org/2018/4/e69/ %U https://doi.org/10.2196/publichealth.9022 %U http://www.ncbi.nlm.nih.gov/pubmed/30545816 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 12 %P e11072 %T Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study %A Safi,Sabur %A Thiessen,Thomas %A Schmailzl,Kurt JG %+ Medical School Brandenburg, digilog, Center for Connected Health Care UG, Fehrbelliner Straße 38, Neuruppin, 16816, Germany, 49 40 3612264 9828, sabur.safi@gmx.de %K innovation diffusion %K information and technology communication %K telematics infrastructure %K health care innovation %D 2018 %7 04.12.2018 %9 Original Paper %J JMIR Res Protoc %G English %X Background: This study discusses the acceptance of new medical technologies in health care settings and resistance to these technologies from hospitals, doctors’ surgical centers, electronic health (eHealth) centers, and related institutions. We suggest a novel method of identifying factors that influence the acceptance of, and resistance to, new technologies by medical staff and patients. Objective: The objective of this study was to determine and evaluate the factors that influence acceptance and resistance to achieve a successful implementation of new technologies. Methods: The target group was patients residing in Brandenburg and major stakeholders in the local health care structure, for instance, medical institutions and medical professionals. The process relies on 3 models: the technology acceptance model, the unified technology acceptance and use of technology model, and the theory of technical innovation diffusion. Qualitative methodology was employed in this study, and an exploratory design was adopted to gain new insights into a poorly understood phenomenon in the German context. This enabled the researcher to take a flexible approach toward exploring a wide range of secondary data and to choose a different approach when unexpected information emerged. Content analysis was used to identify and interpret the data, and the researcher assured that the meaning associated with the information has concurred with that of the original source. Results: This study confirmed that adoption of new technologies in health care depended on individual opinions of the factors relating to them. Some medical professionals believed that technology would interfere with their ability to make independent diagnoses and their relationships with patients. Doctors also feared that technology was a means of management control. In contrast, other medical staff welcomed technology because it provided them with more opportunities to interact with patients and their carers. Generally, patients were more enthusiastic about technology than medical professionals and health care managers because it allowed them to have greater autonomy in selecting health care options. The need for all groups to be involved in the development of the new health care approach was an important outcome, otherwise resistance to it was likely to be greater. In other words, the strategy for change management was the indicator of success or failure. Therefore, following our analysis, a number of practical precepts emerged that could facilitate user acceptance of digital solutions and innovative medical technologies. Conclusions: The acceptance of digital solutions and innovative medical technology by patients and professionals relies on understanding their anxieties and feelings of insecurity. The process will take time because individuals accept change at different rates. Hence, the development of an extensive user community to fully and successfully implement eHealth is less likely in the short term; however, this should not prevent the push for changes in health care technology. %M 30514693 %R 10.2196/11072 %U http://www.researchprotocols.org/2018/12/e11072/ %U https://doi.org/10.2196/11072 %U http://www.ncbi.nlm.nih.gov/pubmed/30514693 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e11354 %T Conurbation, Urban, and Rural Living as Determinants of Allergies and Infectious Diseases: Royal College of General Practitioners Research and Surveillance Centre Annual Report 2016-2017 %A de Lusignan,Simon %A McGee,Christopher %A Webb,Rebecca %A Joy,Mark %A Byford,Rachel %A Yonova,Ivelina %A Hriskova,Mariya %A Matos Ferreira,Filipa %A Elliot,Alex J %A Smith,Gillian %A Rafi,Imran %+ Department of Clinical and Experimental Medicine, University of Surrey, Leggett Building, Daphne Jackson Road, Guildford,, United Kingdom, 44 01483 6848002, s.lusignan@surrey.ac.uk %K population surveillance %K respiratory tract infections %K conjunctivitis, allergic %K asthma %K urinary tract infections %K gastroenteritis %K healthcare disparities %K socioeconomic factors %K social determinants of health %K medical records systems, computerized %K data collection %K records as topic %K primary health care %K general practice %K infectious diseases %D 2018 %7 26.11.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Living in a conurbation, urban, or rural environment is an important determinant of health. For example, conurbation and rural living is associated with increased respiratory and allergic conditions, whereas a farm or rural upbringing has been shown to be a protective factor against this. Objective: The objective of the study was to assess differences in general practice presentations of allergic and infectious disease in those exposed to conurbation or urban living compared with rural environments. Methods: The population was a nationally representative sample of 175 English general practices covering a population of over 1.6 million patients registered with sentinel network general practices. General practice presentation rates per 100,000 population were reported for allergic rhinitis, asthma, and infectious conditions grouped into upper and lower respiratory tract infections, urinary tract infection, and acute gastroenteritis by the UK Office for National Statistics urban-rural category. We used multivariate logistic regression adjusting for age, sex, ethnicity, deprivation, comorbidities, and smoking status, reporting odds ratios (ORs) with 95% CIs. Results: For allergic rhinitis, the OR was 1.13 (95% CI 1.04-1.23; P=.003) for urban and 1.29 (95% CI 1.19-1.41; P<.001) for conurbation compared with rural dwellers. Conurbation living was associated with a lower OR for both asthma (OR 0.70, 95% CI 0.67-0.73; P<.001) and lower respiratory tract infections (OR 0.94, 95% CI 0.90-0.98; P=.005). Compared with rural dwellers, the OR for upper respiratory tract infection was greater in urban (OR 1.06, 95% CI 1.03-1.08; P<.001) but no different in conurbation dwellers (OR 1.00, 95% CI 0.97-1.03; P=.93). Acute gastroenteritis followed the same pattern: the OR was 1.13 (95% CI 1.01-1.25; P=.03) for urban dwellers and 1.04 (95% CI 0.93-1.17; P=.46) for conurbation dwellers. The OR for urinary tract infection was lower for urban dwellers (OR 0.94, 95% CI 0.89-0.99; P=.02) but higher in conurbation dwellers (OR 1.06, 95% CI 1.00-1.13; P=.04). Conclusions: Those living in conurbations or urban areas were more likely to consult a general practice for allergic rhinitis and upper respiratory tract infection. Both conurbation and rural living were associated with an increased risk of urinary tract infection. Living in rural areas was associated with an increased risk of asthma and lower respiratory tract infections. The data suggest that living environment may affect rates of consultations for certain conditions. Longitudinal analyses of these data would be useful in providing insights into important determinants. %M 30478022 %R 10.2196/11354 %U http://publichealth.jmir.org/2018/4/e11354/ %U https://doi.org/10.2196/11354 %U http://www.ncbi.nlm.nih.gov/pubmed/30478022 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 11 %P e10497 %T Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application %A Leroy,Gondy %A Gu,Yang %A Pettygrove,Sydney %A Galindo,Maureen K %A Arora,Ananyaa %A Kurzius-Spencer,Margaret %+ University of Arizona, 1130 E Helen Street, McClelland Hall, Tucson, AZ, 85721, United States, 1 5206214106, gondyleroy@email.arizona.edu %K parser %K natural language processing %K complex entity extraction %K Autism Spectrum Disorder %K DSM %K electronic health records %K decision tree %K machine learning %D 2018 %7 07.11.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. Objective: Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. Methods: We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. Results: We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. Conclusions: Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets. %M 30404767 %R 10.2196/10497 %U https://www.jmir.org/2018/11/e10497/ %U https://doi.org/10.2196/10497 %U http://www.ncbi.nlm.nih.gov/pubmed/30404767 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e68 %T Assessing the Concepts and Designs of 58 Mobile Apps for the Management of the 2014-2015 West Africa Ebola Outbreak: Systematic Review %A Tom-Aba,Daniel %A Nguku,Patrick Mboya %A Arinze,Chinedu Chukwujekwu %A Krause,Gerard %+ Department of Epidemiology, Helmholtz Centre for Infection Research (HZI), Inhoffen Strasse 7, Braunschweig, 38124, Germany, 49 01739136081, daniel.tom-aba@helmholtz-hzi.de %K case management %K contact tracing %K Ebola virus disease %K eHealth %K mHealth %K systematic review %K West Africa %D 2018 %7 29.10.2018 %9 Review %J JMIR Public Health Surveill %G English %X Background: The use of mobile phone information technology (IT) in the health sector has received much attention especially during the 2014-2015 Ebola virus disease (EVD) outbreak. mHealth can be attributed to a major improvement in EVD control, but there lacks an overview of what kinds of tools were available and used based on the functionalities they offer. Objective: We aimed to conduct a systematic review of mHealth tools in the context of the recent EVD outbreak to identify the most promising approaches and guide further mHealth developments for infectious disease control. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched for all reports on mHealth tools developed in the context of the 2014-2015 EVD outbreak published between January 1, 2014 and December 31, 2015 on Google Scholar, MEDLINE, CAB Abstracts (Global Health), POPLINE, and Web of Science in any language using the search strategy: (“outbreak” OR “epidemic”) AND (“mobile phone” OR “smartphone” OR “smart phone” OR “mobile phone” OR “tablet” OR “mHealth”) AND (“Ebola” OR ”EVD” OR “VHF” OR “Ebola virus disease” OR “viral hemorrhagic fever”) AND (“2014” OR “2015”). The relevant publications were selected by 2 independent reviewers who applied a standardized data extraction form on the tools’ functionalities. Results: We identified 1220 publications through the search strategy, of which 6.31% (77/1220) were original publications reporting on 58 specific mHealth tools in the context of the EVD outbreak. Of these, 62% (34/55) offered functionalities for surveillance, 22% (10/45) for case management, 18% (7/38) for contact tracing, and 6% (3/51) for laboratory data management. Only 3 tools, namely Community Care, Sense Ebola Followup, and Surveillance and Outbreak Response Management and Analysis System supported all four of these functionalities. Conclusions: Among the 58 identified tools related to EVD management in 2014 and 2015, only 3 appeared to contain all 4 key functionalities relevant for the response to EVD outbreaks and may be most promising for further development. %M 30373727 %R 10.2196/publichealth.9015 %U http://publichealth.jmir.org/2018/4/e68/ %U https://doi.org/10.2196/publichealth.9015 %U http://www.ncbi.nlm.nih.gov/pubmed/30373727 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 4 %P e66 %T Establishing a Demographic, Development and Environmental Geospatial Surveillance Platform in India: Planning and Implementation %A Dixit,Shikha %A Arora,Narendra K %A Rahman,Atiqur %A Howard,Natasha J %A Singh,Rakesh K %A Vaswani,Mayur %A Das,Manoja K %A Ahmed,Faruqueuddin %A Mathur,Prashant %A Tandon,Nikhil %A Dasgupta,Rajib %A Chaturvedi,Sanjay %A Jethwaney,Jaishri %A Dalpath,Suresh %A Prashad,Rajendra %A Kumar,Rakesh %A Gupta,Rakesh %A Dube,Laurette %A Daniel,Mark %+ Research, Epidemiology, The INCLEN Trust International, F-1/5, Okhla Industrial Area, Phase-1, New Delhi, 110025, India, 91 11 47730000 ext 109, nkarora@inclentrust.org %K geospatial surveillance %K health and nonhealth data harmonization %K spatial epidemiology %K participatory GIS %K caste, socioeconomic transition %K ground truthing %K built environment %D 2018 %7 05.10.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Inadequate administrative health data, suboptimal public health infrastructure, rapid and unplanned urbanization, environmental degradation, and poor penetration of information technology make the tracking of health and well-being of populations and their social determinants in the developing countries challenging. Technology-integrated comprehensive surveillance platforms have the potential to overcome these gaps. Objective: This paper provides methodological insights into establishing a geographic information system (GIS)-integrated, comprehensive surveillance platform in rural North India, a resource-constrained setting. Methods: The International Clinical Epidemiology Network Trust International established a comprehensive SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in rural Palwal, a district in Haryana, North India. The surveillance platform evolved by adopting four major steps: (1) site preparation, (2) data construction, (3) data quality assurance, and (4) data update and maintenance system. Arc GIS 10.3 and QGIS 2.14 software were employed for geospatial data construction. Surveillance data architecture was built upon the geospatial land parcel datasets. Dedicated software (SOMAARTH-1) was developed for handling high volume of longitudinal datasets. The built infrastructure data pertaining to land use, water bodies, roads, railways, community trails, landmarks, water, sanitation and food environment, weather and air quality, and demographic characteristics were constructed in a relational manner. Results: The comprehensive surveillance platform encompassed a population of 0.2 million individuals residing in 51 villages over a land mass of 251.7 sq km having 32,662 households and 19,260 nonresidential features (cattle shed, shops, health, education, banking, religious institutions, etc). All land parcels were assigned georeferenced location identification numbers to enable space and time monitoring. Subdivision of villages into sectors helped identify socially homogenous community clusters (418/676, 61.8%, sectors). Water and hygiene parameters of the whole area were mapped on the GIS platform and quantified. Risk of physical exposure to harmful environment (poor water and sanitation indicators) was significantly associated with the caste of individual household (P=.001), and the path was mediated through the socioeconomic status and density of waste spots (liquid and solid) of the sector in which these households were located. Ground-truthing for ascertaining the land parcel level accuracies, community involvement in mapping exercise, and identification of small habitations not recorded in the administrative data were key learnings. Conclusions: The SOMAARTH DDESS experience allowed us to document and explore dynamic relationships, associations, and pathways across multiple levels of the system (ie, individual, household, neighborhood, and village) through a geospatial interface. This could be used for characterization and monitoring of a wide range of proximal and distal determinants of health. %M 30291101 %R 10.2196/publichealth.9749 %U https://publichealth.jmir.org/2018/4/e66/ %U https://doi.org/10.2196/publichealth.9749 %U http://www.ncbi.nlm.nih.gov/pubmed/30291101 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e62 %T Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study %A Hamp,Auntre D %A Doshi,Rupali K %A Lum,Garret R %A Allston,Adam %+ HIV/AIDS, Hepatitis, STD, Tuberculosis Administration, District of Columbia Department of Health, 899 N Capital Street, NE, Fourth Floor, Washington, DC, 20002, United States, 1 202 487 3013, auntre.hamp@georgetown.edu %K HIV %K surveillance %K data sharing %K public health %K cross-jurisdictional %D 2018 %7 13.08.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Accurate HIV surveillance data are essential to monitor trends to help end the HIV epidemic. Owing to strict policies around data security and confidentiality, HIV surveillance data have not been routinely shared across jurisdictions except a biannual case-by-case review process to identify and remove duplicate cases (Routine Interstate Duplicate Review, RIDR). HIV surveillance estimates for the District of Columbia (DC) are complicated by migration and care seeking throughout the metropolitan area, which includes Maryland and Virginia. To address gaps in HIV surveillance data, health departments of DC, Maryland, and Virginia have established HIV surveillance data sharing agreements. Although the Black Box (a privacy data integration tool external to the health departments) facilitates the secure exchange of data between DC, Maryland, and Virginia, its previous iterations were limited by the frequency and scope of information exchanged. The health departments of DC, Maryland, and Virginia engaged in data sharing to further improve HIV surveillance estimates. Objective: This study assessed the impact of cross-jurisdictional data sharing on the estimation of people living with HIV in DC and reduction of cases in the RIDR process. Methods: Data sharing agreements established in 2014 allowed for the exchange of HIV case information (eg, current residential address) and laboratory information (eg, test types, result dates, and results) from the enhanced HIV/AIDS Reporting System (eHARS). Regular data exchanges began in 2017. The participating jurisdictions transferred data (via secure file transfer protocol) for individuals having a residential address in a partnering jurisdiction at the time of HIV diagnosis or evidence of receiving HIV-related services at a facility located in a partnering jurisdiction. The DC Department of Health compared the data received to DC eHARS and imported updated data that matched existing cases. Evaluation of changes in current residential address and HIV prevalence was conducted by comparing data before and after HIV surveillance data exchanges. Results: After the HIV surveillance data exchange, an average of 396 fewer cases were estimated to be living in DC each year from 2012 to 2016. Among cases with a residential status change, 66.4% (1316/1982) had relocated to Maryland and 19.8% (392/1982) to Virginia; majority of these had relocated to counties bordering DC. Relocation in and out of DC differed by mode of transmission, race and ethnicity, age group, and gender. After data exchange, the volume of HIV cases needing RIDR decreased by 74% for DC-Maryland and 81% for DC-Virginia. Conclusions: HIV surveillance data exchange between the public health departments of DC, Maryland, and Virginia reduced the number of cases misclassified as DC residents and reduced the number of cases needing RIDR. Continued data exchanges will enhance the ability of DC Department of Health to monitor the local HIV epidemic. %M 30104182 %R 10.2196/publichealth.9800 %U http://publichealth.jmir.org/2018/3/e62/ %U https://doi.org/10.2196/publichealth.9800 %U http://www.ncbi.nlm.nih.gov/pubmed/30104182 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 8 %P e10886 %T Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System %A Wang,Mei-Hua %A Chen,Han-Kun %A Hsu,Min-Huei %A Wang,Hui-Chi %A Yeh,Yu-Ting %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing Street, Taipei,, Taiwan, 886 22490088 ext 8901, yuting@tmu.edu.tw %K laboratory autoreporting system %K HALR %K electronic medical records %D 2018 %7 08.08.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. Results: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. Conclusions: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases. %M 30089608 %R 10.2196/10886 %U http://www.jmir.org/2018/8/e10886/ %U https://doi.org/10.2196/10886 %U http://www.ncbi.nlm.nih.gov/pubmed/30089608 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e11203 %T Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study %A Nsabimana,Alain Placide %A Uzabakiriho,Bernard %A Kagabo,Daniel M %A Nduwayo,Jerome %A Fu,Qinyouen %A Eng,Allison %A Hughes,Joshua %A Sia,Samuel K %+ Junco Labs, 423 W 127th Street, Ground Floor, New York, NY,, United States, 1 518 880 9667, samuelsia@juncolabs.com %K HIV surveillance %K smartphones %K mobile phones %K geospatial data %D 2018 %7 07.08.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Precise measurements of HIV incidences at community level can help mount a more effective public health response, but the most reliable methods currently require labor-intensive population surveys. Novel mobile phone technologies are being tested for adherence to medical appointments and antiretroviral therapy, but using them to track HIV test results with automatically generated geospatial coordinates has not been widely tested. Objective: We customized a portable reader for interpreting the results of HIV lateral flow tests and developed a mobile phone app to track HIV test results in urban and rural locations in Rwanda. The objective was to assess the feasibility of this technology to collect front line HIV test results in real time and with geospatial context to help measure HIV incidences and improve epidemiological surveillance. Methods: Twenty health care workers used the technology to track the test results of 2190 patients across 3 hospital sites (2 urban sites in Kigali and a rural site in the Western Province of Rwanda). Mobile phones for less than US $70 each were used. The mobile phone app to record HIV test results could take place without internet connectivity with uploading of results to the cloud taking place later with internet. Results: A total of 91.51% (2004/2190) of HIV test results could be tracked in real time on an online dashboard with geographical resolution down to street level. Out of the 20 health care workers, 14 (70%) would recommend the lateral flow reader, and 100% would recommend the mobile phone app. Conclusions: Smartphones have the potential to simplify the input of HIV test results with geospatial context and in real time to improve public health surveillance of HIV. %M 30087088 %R 10.2196/11203 %U http://publichealth.jmir.org/2018/3/e11203/ %U https://doi.org/10.2196/11203 %U http://www.ncbi.nlm.nih.gov/pubmed/30087088 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e59 %T Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology %A Meyers,Lindsay %A Ginocchio,Christine C %A Faucett,Aimie N %A Nolte,Frederick S %A Gesteland,Per H %A Leber,Amy %A Janowiak,Diane %A Donovan,Virginia %A Dien Bard,Jennifer %A Spitzer,Silvia %A Stellrecht,Kathleen A %A Salimnia,Hossein %A Selvarangan,Rangaraj %A Juretschko,Stefan %A Daly,Judy A %A Wallentine,Jeremy C %A Lindsey,Kristy %A Moore,Franklin %A Reed,Sharon L %A Aguero-Rosenfeld,Maria %A Fey,Paul D %A Storch,Gregory A %A Melnick,Steve J %A Robinson,Christine C %A Meredith,Jennifer F %A Cook,Camille V %A Nelson,Robert K %A Jones,Jay D %A Scarpino,Samuel V %A Althouse,Benjamin M %A Ririe,Kirk M %A Malin,Bradley A %A Poritz,Mark A %+ BioFire Diagnostics, 515 Colorow Drive, Salt Lake City, UT, 84108, United States, 1 8017366354 ext 365, lindsay.meyers@biofiredx.com %K epidemiology %K patients %K privacy %K communicable disease %K internet %K pathology, molecular %D 2018 %7 06.07.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. Objective: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. Methods: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. Results: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. Conclusions: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks. %M 29980501 %R 10.2196/publichealth.9876 %U http://publichealth.jmir.org/2018/3/e59/ %U https://doi.org/10.2196/publichealth.9876 %U http://www.ncbi.nlm.nih.gov/pubmed/29980501 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e10218 %T A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability %A Al Manir,Mohammad Sadnan %A Brenas,Jon Haël %A Baker,Christopher JO %A Shaban-Nejad,Arash %+ Oak Ridge National Laboratory Center for for Biomedical Informatics, Department of Pediatrics, The University of Tennessee Health Science Center, 50 N Dunlap Street, R492, Memphis, TN, 38103, United States, 1 901 287 5836, ashabann@uthsc.edu %K malaria surveillance %K global health %K interoperability %K change management %K Web services %K population health intelligence %D 2018 %7 15.06.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: According to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions. Objective: We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities. Methods: We used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services. Results: We developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime. Conclusions: We introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art. %M 29907554 %R 10.2196/10218 %U http://publichealth.jmir.org/2018/2/e10218/ %U https://doi.org/10.2196/10218 %U http://www.ncbi.nlm.nih.gov/pubmed/29907554 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 2 %P e36 %T Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore %A Hardjojo,Antony %A Gunachandran,Arunan %A Pang,Long %A Abdullah,Mohammed Ridzwan Bin %A Wah,Win %A Chong,Joash Wen Chen %A Goh,Ee Hui %A Teo,Sok Huang %A Lim,Gilbert %A Lee,Mong Li %A Hsu,Wynne %A Lee,Vernon %A Chen,Mark I-Cheng %A Wong,Franco %A Phang,Jonathan Siung King %+ Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, MD1, #10-01, 12 Science Drive 2, Singapore, 117549, Singapore, 65 65165781, mark_chen@nuhs.edu.sg %K natural language processing %K communicable diseases %K epidemiology %K surveillance %K syndromic surveillance %K electronic health records %D 2018 %7 11.6.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. Objective: The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. Methods: CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. Results: The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. Conclusions: We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations. %M 29907560 %R 10.2196/medinform.8204 %U http://medinform.jmir.org/2018/2/e36/ %U https://doi.org/10.2196/medinform.8204 %U http://www.ncbi.nlm.nih.gov/pubmed/29907560 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e136 %T A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study %A Fujibayashi,Kazutoshi %A Takahashi,Hiromizu %A Tanei,Mika %A Uehara,Yuki %A Yokokawa,Hirohide %A Naito,Toshio %+ Department of General Medicine, School of Medicine, Juntendo University, 3-1-3, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3 5802 1190, kfujiba@juntendo.ac.jp %K influenza %K epidemiology %K pandemics %K internet %K participatory surveillance %K participatory epidemiology %D 2018 %7 06.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Influenza infections can spread rapidly, and influenza outbreaks are a major public health concern worldwide. Early detection of signs of an influenza pandemic is important to prevent global outbreaks. Development of information and communications technologies for influenza surveillance, including participatory surveillance systems involving lay users, has recently increased. Many of these systems can estimate influenza activity faster than the conventional influenza surveillance systems. Unfortunately, few of these influenza-tracking systems are available in Japan. Objective: This study aimed to evaluate the flu-tracking ability of Flu-Report, a new influenza-tracking mobile phone app that uses a self-administered questionnaire for the early detection of influenza activity. Methods: Flu-Report was used to collect influenza-related information (ie, dates on which influenza infections were diagnosed) from November 2016 to March 2017. Participants were adult volunteers from throughout Japan, who also provided information about their cohabiting family members. The utility of Flu-Report was evaluated by comparison with the conventional influenza surveillance information and basic information from an existing large-scale influenza-tracking system (an automatic surveillance system based on electronic records of prescription drug purchases). Results: Information was obtained through Flu-Report for approximately 10,094 volunteers. In total, 2134 participants were aged <20 years, 6958 were aged 20-59 years, and 1002 were aged ≥60 years. Between November 2016 and March 2017, 347 participants reported they had influenza or an influenza-like illness in the 2016 season. Flu-Report-derived influenza infection time series data displayed a good correlation with basic information obtained from the existing influenza surveillance system (rho, ρ=.65, P=.001). However, the influenza morbidity ratio for our participants was approximately 25% of the mean influenza morbidity ratio for the Japanese population. The Flu-Report influenza morbidity ratio was 5.06% (108/2134) among those aged <20 years, 3.16% (220/6958) among those aged 20-59 years, and 0.59% (6/1002) among those aged ≥60 years. In contrast, influenza morbidity ratios for Japanese individuals aged <20 years, 20-59 years, and ≥60 years were recently estimated at 31.97% to 37.90%, 8.16% to 9.07%, and 2.71% to 4.39%, respectively. Conclusions: Flu-Report supports easy access to near real-time information about influenza activity via the accumulation of self-administered questionnaires. However, Flu-Report users may be influenced by selection bias, which is a common issue associated with surveillance using information and communications technologies. Despite this, Flu-Report has the potential to provide basic data that could help detect influenza outbreaks. %R 10.2196/mhealth.9834 %U http://mhealth.jmir.org/2018/6/e136/ %U https://doi.org/10.2196/mhealth.9834 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e51 %T Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach %A Bollegala,Danushka %A Maskell,Simon %A Sloane,Richard %A Hajne,Joanna %A Pirmohamed,Munir %+ Department of Computer Science, University of Liverpool, Ashton Street,, Liverpool, L693BX, United Kingdom, 44 1517954283, danushka.bollegala@liverpool.ac.uk %K machine learning %K ADR detection %K causality %K lexical patterns %K causality detection %K support vector machines %D 2018 %7 09.05.2018 %9 Proposal %J JMIR Public Health Surveill %G English %X Background: Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. Objective: This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. Methods: To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Results: Our proposed method obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. Conclusions: By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction–related events. %M 29743155 %R 10.2196/publichealth.8214 %U http://publichealth.jmir.org/2018/2/e51/ %U https://doi.org/10.2196/publichealth.8214 %U http://www.ncbi.nlm.nih.gov/pubmed/29743155 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e52 %T The Acceptability and Feasibility of Implementing a Bio-Behavioral Enhanced Surveillance Tool for Sexually Transmitted Infections in England: Mixed-Methods Study %A Wayal,Sonali %A Reid,David %A Blomquist,Paula B %A Weatherburn,Peter %A Mercer,Catherine H %A Hughes,Gwenda %+ Centre for Population Research in Sexual Health and HIV, Institute for Global Health, University College London, 3rd Floor, Mortimer Market Centre, London, WC1E6JB, United Kingdom, 44 02031082064, s.wayal@ucl.ac.uk %K public health surveillance %K sexually transmitted diseases %K feasibility studies %K electronic health records %K Web-based survey %D 2018 %7 04.05.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Sexually transmitted infection (STI) surveillance is vital for tracking the scale and pattern of epidemics; however, it often lacks data on the underlying drivers of STIs. Objective: This study aimed to assess the acceptability and feasibility of implementing a bio-behavioral enhanced surveillance tool, comprising a self-administered Web-based survey among sexual health clinic attendees, as well as linking this to their electronic health records (EHR) held in England’s national STI surveillance system. Methods: Staff from 19 purposively selected sexual health clinics across England and men who have sex with men and black Caribbeans, because of high STI burden among these groups, were interviewed to assess the acceptability of the proposed bio-behavioral enhanced surveillance tool. Subsequently, sexual health clinic staff invited all attendees to complete a Web-based survey on drivers of STI risk using a study tablet or participants’ own digital device. They recorded the number of attendees invited and participants’ clinic numbers, which were used to link survey data to the EHR. Participants’ online consent was obtained, separately for survey participation and linkage. In postimplementation phase, sexual health clinic staff were reinterviewed to assess the feasibility of implementing the bio-behavioral enhanced surveillance tool. Acceptability and feasibility of implementing the bio-behavioral enhanced surveillance tool were assessed by analyzing these qualitative and quantitative data. Results: Prior to implementation of the bio-behavioral enhanced surveillance tool, sexual health clinic staff and attendees emphasized the importance of free internet/Wi-Fi access, confidentiality, and anonymity for increasing the acceptability of the bio-behavioral enhanced surveillance tool among attendees. Implementation of the bio-behavioral enhanced surveillance tool across sexual health clinics varied considerably and was influenced by sexual health clinics’ culture of prioritization of research and innovation and availability of resources for implementing the surveys. Of the 7367 attendees invited, 85.28% (6283) agreed to participate. Of these, 72.97% (4585/6283) consented to participate in the survey, and 70.62% (4437/6283) were eligible and completed it. Of these, 91.19% (4046/4437) consented to EHR linkage, which did not differ by age or gender but was higher among gay/bisexual men than heterosexual men (95.50%, 722/756 vs 88.31%, 1073/1215; P<.003) and lower among black Caribbeans than white participants (87.25%, 568/651 vs 93.89%, 2181/2323; P<.002). Linkage was achieved for 88.88% (3596/4046) of consenting participants. Conclusions: Implementing a bio-behavioral enhanced surveillance tool in sexual health clinics was feasible and acceptable to staff and groups at STI risk; however, ensuring participants’ confidentiality and anonymity and availability of resources is vital. Bio-behavioral enhanced surveillance tools could enable timely collection of detailed behavioral data for effective commissioning of sexual health services. %M 29728348 %R 10.2196/publichealth.9010 %U http://publichealth.jmir.org/2018/2/e52/ %U https://doi.org/10.2196/publichealth.9010 %U http://www.ncbi.nlm.nih.gov/pubmed/29728348 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e29 %T Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning %A Munkhdalai,Tsendsuren %A Liu,Feifan %A Yu,Hong %+ Department of Computer Science, University of Massachusetts Lowell, 1 University Ave, Lowell, MA, 01854, United States, 1 9789343620, hong_yu@uml.edu %K medical informatics applications %K drug-related side effects and adverse reactions %K neural networks %K natural language processing %K electronic health records %D 2018 %7 25.04.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data. Objective: To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations. Methods: We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types. Results: Our results show that the SVM model achieved the best average F1-score of 89.1% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72%) as well as the rule induction baseline system (F1-score of 7.47%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35%. Conclusions: It shows that classical learning models (SVM) remains advantageous over deep learning models (RNN variants) for clinical relation identification, especially for long-distance intersentential relations. However, RNNs demonstrate a great potential of significant improvement if more training data become available. Our work is an important step toward mining EHRs to improve the efficacy of drug safety surveillance. Most importantly, the annotated data used in this study will be made publicly available, which will further promote drug safety research in the community. %M 29695376 %R 10.2196/publichealth.9361 %U http://publichealth.jmir.org/2018/2/e29/ %U https://doi.org/10.2196/publichealth.9361 %U http://www.ncbi.nlm.nih.gov/pubmed/29695376 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e3 %T Sustainable Monitoring and Surveillance Systems to Improve HIV Programs: Review %A Low-Beer,Daniel %A Mahy,Mary %A Renaud,Francoise %A Calleja,Txema %+ World Health Organization, 20 avenue Appia, Geneva, 1211, Switzerland, 41 795090693, daniellowbeer@gmail.com %K surveillance %K HIV %K development monitoring %K evaluation %D 2018 %7 24.04.2018 %9 Viewpoint %J JMIR Public Health Surveill %G English %X HIV programs have provided a major impetus for investments in surveillance data, with 5-10% of HIV program budgets recommended to support data. However there are questions concerning the sustainability of these investments. The Sustainable Development Goals have consolidated health into one goal and communicable diseases into one target (Target 3.3). Sustainable Development Goals now introduce targets focused specifically on data (Targets 17.18 and 17.19). Data are seen as one of the three systemic issues (in Goal 17) for implementing Sustainable Development Goals, alongside policies and partnerships. This paper reviews the surveillance priorities in the context of the Sustainable Development Goals and highlights the shift from periodic measurement towards sustainable disaggregated, real-time, case, and patient data, which are used routinely to improve programs. Finally, the key directions in developing person-centered monitoring systems are assessed with country examples. The directions contribute to the Sustainable Development Goal focus on people-centered development applied to data. %M 29691202 %R 10.2196/publichealth.8173 %U http://publichealth.jmir.org/2018/2/e3/ %U https://doi.org/10.2196/publichealth.8173 %U http://www.ncbi.nlm.nih.gov/pubmed/29691202 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 4 %P e142 %T Identifying Unmet Treatment Needs for Patients With Osteoporotic Fracture: Feasibility Study for an Electronic Clinical Surveillance System %A Lin,Fong-Ci %A Wang,Chen-Yu %A Shang,Rung Ji %A Hsiao,Fei-Yuan %A Lin,Mei-Shu %A Hung,Kuan-Yu %A Wang,Jui %A Lin,Zhen-Fang %A Lai,Feipei %A Shen,Li-Jiuan %A Huang,Chih-Fen %+ Department of Pharmacy, National Taiwan University Hospital, 33 Linsen S. Rd, Taipei, 10050, Taiwan, 886 223562162, cfhuang1023@ntu.edu.tw %K information systems %K public health surveillance %K osteoporotic fractures %K pharmacovigilance %K guideline adherence %D 2018 %7 24.04.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Traditional clinical surveillance relied on the results from clinical trials and observational studies of administrative databases. However, these studies not only required many valuable resources but also faced a very long time lag. Objective: This study aimed to illustrate a practical application of the National Taiwan University Hospital Clinical Surveillance System (NCSS) in the identification of patients with an osteoporotic fracture and to provide a high reusability infrastructure for longitudinal clinical data. Methods: The NCSS integrates electronic medical records in the National Taiwan University Hospital (NTUH) with a data warehouse and is equipped with a user-friendly interface. The NCSS was developed using professional insight from multidisciplinary experts, including clinical practitioners, epidemiologists, and biomedical engineers. The practical example identifying the unmet treatment needs for patients encountering major osteoporotic fractures described herein was mainly achieved by adopting the computerized workflow in the NCSS. Results: We developed the infrastructure of the NCSS, including an integrated data warehouse and an automatic surveillance workflow. By applying the NCSS, we efficiently identified 2193 patients who were newly diagnosed with a hip or vertebral fracture between 2010 and 2014 at NTUH. By adopting the filter function, we identified 1808 (1808/2193, 82.44%) patients who continued their follow-up at NTUH, and 464 (464/2193, 21.16%) patients who were prescribed anti-osteoporosis medications, within 3 and 12 months post the index date of their fracture, respectively. Conclusions: The NCSS systems can integrate the workflow of cohort identification to accelerate the survey process of clinically relevant problems and provide decision support in the daily practice of clinical physicians, thereby making the benefit of evidence-based medicine a reality. %M 29691201 %R 10.2196/jmir.9477 %U http://www.jmir.org/2018/4/e142/ %U https://doi.org/10.2196/jmir.9477 %U http://www.ncbi.nlm.nih.gov/pubmed/29691201 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e36 %T Strengthening Routine Data Systems to Track the HIV Epidemic and Guide the Response in Sub-Saharan Africa %A Rice,Brian %A Boulle,Andrew %A Baral,Stefan %A Egger,Matthias %A Mee,Paul %A Fearon,Elizabeth %A Reniers,Georges %A Todd,Jim %A Schwarcz,Sandra %A Weir,Sharon %A Rutherford,George %A Hargreaves,James %+ Social and Environmental Health Research, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, United Kingdom, 44 (0)2079272567, brian.rice@lshtm.ac.uk %K HIV %K data %K systems %K surveillance %K sub-Saharan Africa %K testing %K treatment %K prevention %K cascade %K monitoring %K quality assessment %K clinical %K program %D 2018 %7 03.04.2018 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The global HIV response has entered a new phase with the recommendation of treating all persons living with HIV with antiretroviral therapy, and with the goals of reducing new infections and AIDS-related deaths to fewer than 500,000 by 2020. This new phase has intensive data requirements that will need to utilize routine data collected through service delivery platforms to monitor progress toward these goals. With a focus on sub-Saharan African, we present the following priorities to improve the demand, supply, and use of routine HIV data: (1) strengthening patient-level HIV data systems that support continuity of clinical care and document sentinel events; (2) leveraging data from HIV testing programs; (3) using targeting data collection in communities and among clients; and (4) building capacity and promoting a culture of HIV data quality assessment and use. When fully leveraged, routine data can efficiently provide timely information at a local level to inform action, as well as provide information at scale with wide geographic coverage to strengthen estimation efforts. %M 29615387 %R 10.2196/publichealth.9344 %U http://publichealth.jmir.org/2018/2/e36/ %U https://doi.org/10.2196/publichealth.9344 %U http://www.ncbi.nlm.nih.gov/pubmed/29615387 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e25 %T A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System %A Yano,Terdsak %A Phornwisetsirikun,Somphorn %A Susumpow,Patipat %A Visrutaratna,Surasing %A Chanachai,Karoon %A Phetra,Polawat %A Chaisowwong,Warangkhana %A Trakarnsirinont,Pairat %A Hemwan,Phonpat %A Kaewpinta,Boontuan %A Singhapreecha,Charuk %A Kreausukon,Khwanchai %A Charoenpanyanet,Arisara  %A Robert,Chongchit Sripun %A Robert,Lamar %A Rodtian,Pranee %A Mahasing,Suteerat %A Laiya,Ekkachai %A Pattamakaew,Sakulrat %A Tankitiyanon,Taweesart %A Sansamur,Chalutwan %A Srikitjakarn,Lertrak %+ Faculty of Veterinary Medicine, Chiang Mai University, Chonlapratan Road, Maehia, Muang, Chiang Mai, 50100, Thailand, 66 871850280, lertrak.s@gmail.com %K community-owned disease surveillance system %K PODD %K mobile app %K one health %K participatory approach %K backyard chicken %K pandemic prevention %D 2018 %7 21.03.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves. Objective: The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks. Methods: The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers. Results: LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled. Conclusions: By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues. %M 29563079 %R 10.2196/publichealth.7375 %U http://publichealth.jmir.org/2018/1/e25/ %U https://doi.org/10.2196/publichealth.7375 %U http://www.ncbi.nlm.nih.gov/pubmed/29563079 %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 4 %N 1 %P e17 %T Near-Real-Time Surveillance of Illnesses Related to Shellfish Consumption in British Columbia: Analysis of Poison Center Data %A Wan,Victoria %A McIntyre,Lorraine %A Kent,Debra %A Leong,Dennis %A Henderson,Sarah B %+ Environmental Health Services, British Columbia Centre for Disease Control, LL0073, 655 12th Avenue West, Vancouver, BC, V5Z4R4, Canada, 1 6047072400 ext 273721, victoria.wan@bccdc.ca %K poison control centers %K public health surveillance %K shellfish poisoning %K norovirus %K Vibrio parahaemolyticus %D 2018 %7 23.02.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Data from poison centers have the potential to be valuable for public health surveillance of long-term trends, short-term aberrations from those trends, and poisonings occurring in near-real-time. This information can enable long-term prevention via programs and policies and short-term control via immediate public health response. Over the past decade, there has been an increasing use of poison control data for surveillance in the United States, Europe, and New Zealand, but this resource still remains widely underused. Objective: The British Columbia (BC) Drug and Poison Information Centre (DPIC) is one of five such services in Canada, and it is the only one nested within a public health agency. This study aimed to demonstrate how DPIC data are used for routine public health surveillance in near-real-time using the case study of its alerting system for illness related to consumption of shellfish (ASIRCS). Methods: Every hour, a connection is opened between the WBM software Visual Dotlab Enterprise, which holds the DPIC database, and the R statistical computing environment. This platform is used to extract, clean, and merge all necessary raw data tables into a single data file. ASIRCS automatically and retrospectively scans a 24-hour window within the data file for new cases related to illnesses from shellfish consumption. Detected cases are queried using a list of attributes: the caller location, exposure type, reasons for the exposure, and a list of keywords searched in the clinical notes. The alert generates a report that is tailored to the needs of food safety specialists, who then assess and respond to detected cases. Results: The ASIRCS system alerted on 79 cases between January 2015 and December 2016, and retrospective analysis found 11 cases that were missed. All cases were reviewed by food safety specialists, and 58% (46/79) were referred to designated regional health authority contacts for follow-up. Of the 42% (33/79) cases that were not referred to health authorities, some were missing follow-up information, some were triggered by allergies to shellfish, and some were triggered by shellfish-related keywords appearing in the case notes for nonshellfish-related cases. Improvements were made between 2015 and 2016 to reduce the number of cases with missing follow-up information. Conclusions: The surveillance capacity is evident within poison control data as shown from the novel use of DPIC data for identifying illnesses related to shellfish consumption in BC. The further development of surveillance programs could improve and enhance response to public health emergencies related to acute illnesses, chronic diseases, and environmental exposures. %M 29475825 %R 10.2196/publichealth.8944 %U http://publichealth.jmir.org/2018/1/e17/ %U https://doi.org/10.2196/publichealth.8944 %U http://www.ncbi.nlm.nih.gov/pubmed/29475825 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e18 %T Know Your Epidemic, Strengthen Your Response: Developing a New HIV Surveillance Architecture to Guide HIV Resource Allocation and Target Decisions %A Rice,Brian %A Sanchez,Travis %A Baral,Stefan %A Mee,Paul %A Sabin,Keith %A Garcia-Calleja,Jesus M %A Hargreaves,James %+ Department of Social and Environmental Health Research, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London,, United Kingdom, 44 2079272567, brian.rice@lshtm.ac.uk %K HIV %K data %K systems %K surveillance %K testing %K treatment %K prevention %K monitoring %K key populations %D 2018 %7 14.02.2018 %9 Editorial %J JMIR Public Health Surveill %G English %X To guide HIV prevention and treatment activities up to 2020, we need to generate and make better use of high quality HIV surveillance data. To highlight our surveillance needs, a special collection of papers in JMIR Public Health and Surveillance has been released under the title “Improving Global and National Responses to the HIV Epidemic Through High Quality HIV Surveillance Data.” We provide a summary of these papers and highlight methods for developing a new HIV surveillance architecture. %M 29444766 %R 10.2196/publichealth.9386 %U http://publichealth.jmir.org/2018/1/e18/ %U https://doi.org/10.2196/publichealth.9386 %U http://www.ncbi.nlm.nih.gov/pubmed/29444766 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e94 %T A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance %A Karimuribo,Esron Daniel %A Mutagahywa,Eric %A Sindato,Calvin %A Mboera,Leonard %A Mwabukusi,Mpoki %A Kariuki Njenga,M %A Teesdale,Scott %A Olsen,Jennifer %A Rweyemamu,Mark %+ Department of Veterinary Medicine and Public Health, College of Veterinary Medicine and Biomedical Sciences, Sokoine University of Agriculture, PO Box 3021 Chuo Kikuu, Morogoro, Tanzania, United Republic Of Tanzania, 255 695760, ekarimu@yahoo.co.uk %K public health %K surveillance systems %K epidemics %K outbreak %K technology %D 2017 %7 18.12.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: We describe the development and initial achievements of a participatory disease surveillance system that relies on mobile technology to promote Community Level One Health Security (CLOHS) in Africa. Objective: The objective of this system, Enhancing Community-Based Disease Outbreak Detection and Response in East and Southern Africa (DODRES), is to empower community-based human and animal health reporters with training and information and communication technology (ICT)–based solutions to contribute to disease detection and response, thereby complementing strategies to improve the efficiency of infectious disease surveillance at national, regional, and global levels. In this study, we refer to techno-health as the application of ICT-based solutions to enhance early detection, timely reporting, and prompt response to health events in human and animal populations. Methods: An EpiHack, involving human and animal health experts as well as ICT programmers, was held in Tanzania in 2014 to identify major challenges facing early detection, timely reporting, and prompt response to disease events. This was followed by a project inception workshop in 2015, which brought together key stakeholders, including policy makers and community representatives, to refine the objectives and implementation plan of the DODRES project. The digital ICT tools were developed and packaged together as the AfyaData app to support One Health disease surveillance. Community health reporters (CHRs) and officials from animal and human health sectors in Morogoro and Ngorongoro districts in Tanzania were trained to use the AfyaData app. The AfyaData supports near- to real-time data collection and submission at both community and health facility levels as well as the provision of feedback to reporters. The functionality of the One Health Knowledge Repository (OHKR) app has been integrated into the AfyaData app to provide health information on case definitions of diseases of humans and animals and to synthesize advice that can be transmitted to CHRs with next step response activities or interventions. Additionally, a WhatsApp social group was made to serve as a platform to sustain interactions between community members, local government officials, and DODRES team members. Results: Within the first 5 months (August-December 2016) of AfyaData tool deployment, a total of 1915 clinical cases in livestock (1816) and humans (99) were reported in Morogoro (83) and Ngorongoro (1832) districts. Conclusions: These initial results suggest that the DODRES community-level model creates an opportunity for One Health engagement of people in their own communities in the detection of infectious human and animal disease threats. Participatory approaches supported by digital and mobile technologies should be promoted for early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels. %M 29254916 %R 10.2196/publichealth.7373 %U http://publichealth.jmir.org/2017/4/e94/ %U https://doi.org/10.2196/publichealth.7373 %U http://www.ncbi.nlm.nih.gov/pubmed/29254916 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e90 %T Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends %A Samaras,Loukas %A García-Barriocanal,Elena %A Sicilia,Miguel-Angel %+ Computer Science Department, University of Alcalá, Polytechnic Building, Ctra. Barcelona Km. 33.6, Alcalá de Henares (Madrid), 28871, Spain, 34 6974706531, lsamaras@ath.forthnet.gr %K Google Trends %K influenza %K Web, syndromic surveillance %K statistical correlation %K forecast %K ARIMA %D 2017 %7 20.11.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: An extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak. Objective: The aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy. Methods: We used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy. Results: The results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population. Conclusions: We can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy. %M 29158208 %R 10.2196/publichealth.8015 %U http://publichealth.jmir.org/2017/4/e90/ %U https://doi.org/10.2196/publichealth.8015 %U http://www.ncbi.nlm.nih.gov/pubmed/29158208 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e87 %T Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala %A Prieto,José Tomás %A Jara,Jorge H %A Alvis,Juan Pablo %A Furlan,Luis R %A Murray,Christian Travis %A Garcia,Judith %A Benghozi,Pierre-Jean %A Kaydos-Daniels,Susan Cornelia %+ Center for Health Studies, Universidad del Valle de Guatemala, 18 Av. 11-95, Zona 15, Vista Hermosa III, Guatemala City, 01015, Guatemala, +1 4044216455, josetomasprieto@gmail.com %K crowdsourcing %K human flu %K influenza %K grippe %K mHealth %K texting %K mobile apps %K short message service %K text message %K developing countries %D 2017 %7 14.11.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system. Objective: The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI. Methods: We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system. Results: Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017). Conclusions: Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people’s fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions. %M 29138128 %R 10.2196/publichealth.8610 %U http://publichealth.jmir.org/2017/4/e87/ %U https://doi.org/10.2196/publichealth.8610 %U http://www.ncbi.nlm.nih.gov/pubmed/29138128 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e370 %T Subregional Nowcasts of Seasonal Influenza Using Search Trends %A Kandula,Sasikiran %A Hsu,Daniel %A Shaman,Jeffrey %+ Department of Environmental Health Sciences, Columbia University, ARB Building, 11th Floor, 722 West 168th Street, New York, NY, 10032, United States, 1 2123053590, sk3542@cumc.columbia.edu %K human influenza %K classification and regression trees %K nowcasts %K infodemiology %K infoveillance %K surveillance %D 2017 %7 06.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Limiting the adverse effects of seasonal influenza outbreaks at state or city level requires close monitoring of localized outbreaks and reliable forecasts of their progression. Whereas forecasting models for influenza or influenza-like illness (ILI) are becoming increasingly available, their applicability to localized outbreaks is limited by the nonavailability of real-time observations of the current outbreak state at local scales. Surveillance data collected by various health departments are widely accepted as the reference standard for estimating the state of outbreaks, and in the absence of surveillance data, nowcast proxies built using Web-based activities such as search engine queries, tweets, and access of health-related webpages can be useful. Nowcast estimates of state and municipal ILI were previously published by Google Flu Trends (GFT); however, validations of these estimates were seldom reported. Objective: The aim of this study was to develop and validate models to nowcast ILI at subregional geographic scales. Methods: We built nowcast models based on autoregressive (autoregressive integrated moving average; ARIMA) and supervised regression methods (Random forests) at the US state level using regional weighted ILI and Web-based search activity derived from Google's Extended Trends application programming interface. We validated the performance of these methods using actual surveillance data for the 50 states across six seasons. We also built state-level nowcast models using state-level estimates of ILI and compared the accuracy of these estimates with the estimates of the regional models extrapolated to the state level and with the nowcast estimates published by GFT. Results: Models built using regional ILI extrapolated to state level had a median correlation of 0.84 (interquartile range: 0.74-0.91) and a median root mean square error (RMSE) of 1.01 (IQR: 0.74-1.50), with noticeable variability across seasons and by state population size. Model forms that hypothesize the availability of timely state-level surveillance data show significantly lower errors of 0.83 (0.55-0.23). Compared with GFT, the latter model forms have lower errors but also lower correlation. Conclusions: These results suggest that the proposed methods may be an alternative to the discontinued GFT and that further improvements in the quality of subregional nowcasts may require increased access to more finely resolved surveillance data. %M 29109069 %R 10.2196/jmir.7486 %U http://www.jmir.org/2017/11/e370/ %U https://doi.org/10.2196/jmir.7486 %U http://www.ncbi.nlm.nih.gov/pubmed/29109069 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e67 %T A Smart Card-Based Electronic School Absenteeism System for Influenza-Like Illness Surveillance in Hong Kong: Design, Implementation, and Feasibility Assessment %A Ip,Dennis KM %A Lau,Eric HY %A So,Hau Chi %A Xiao,Jingyi %A Lam,Chi Kin %A Fang,Vicky J %A Tam,Yat Hung %A Leung,Gabriel M %A Cowling,Benjamin J %+ WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 1/F, Patrick Manson Building (North Wing), 7 Sassoon Road, Hong Kong,, China (Hong Kong), 852 39176712, dkmip@hku.hk %K influenza %K public health surveillance %K school health %K absenteeism %K smart cards %D 2017 %7 06.10.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: School-aged children have the highest incidence of respiratory virus infections each year, and transmission of respiratory viruses such as influenza virus can be a major concern in school settings. School absenteeism data have been employed as a component of influenza surveillance systems in some locations. Data timeliness and system acceptance remain as key determinants affecting the usefulness of a prospective surveillance system. Objective: The aim of this study was to assess the feasibility of implementing an electronic school absenteeism surveillance system using smart card–based technology for influenza-like illness (ILI) surveillance among a representative network of local primary and secondary schools in Hong Kong. Methods: We designed and implemented a surveillance system according to the Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER). We employed an existing smart card–based education and school administration platform for data capture, customized the user interface, and used additional back end systems built for other downstream surveillance steps. We invited local schools to participate and collected absenteeism data by the implemented system. We compared temporal trend of the absenteeism data with data from existing community sentinel and laboratory surveillance data. Results: We designed and implemented an ILI surveillance system utilizing smart card–based attendance tracking approach for data capture. We implemented the surveillance system in a total of 107 schools (including 66 primary schools and 41 secondary schools), covering a total of 75,052 children. The system successfully captured information on absences for 2 consecutive academic years (2012-2013 and 2013-2014). The absenteeism data we collected from the system reflected ILI activity in the community, with an upsurge in disease activity detected up to 1 to 2 weeks preceding other existing surveillance systems. Conclusions: We designed and implemented a novel smart card technology–based school absenteeism surveillance system. Our study demonstrated the feasibility of building a large-scale surveillance system riding on a routinely adopted data collection approach and the use of simple system enhancement to minimize workload implication and enhance system acceptability. Data from this system have potential value in supplementing existing sentinel influenza surveillance for situational awareness of influenza activity in the community. %M 28986338 %R 10.2196/publichealth.6810 %U http://publichealth.jmir.org/2017/4/e67/ %U https://doi.org/10.2196/publichealth.6810 %U http://www.ncbi.nlm.nih.gov/pubmed/28986338 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e65 %T Lessons From the Implementation of Mo-Buzz, a Mobile Pandemic Surveillance System for Dengue %A Lwin,May Oo %A Jayasundar,Karthikayen %A Sheldenkar,Anita %A Wijayamuni,Ruwan %A Wimalaratne,Prasad %A Ernst,Kacey C %A Foo,Schubert %+ Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore, 637718, Singapore, 65 67906669, tmaylwin@ntu.edu.sg %K pandemics %K dengue %K health communication %K telemedicine %K epidemiology %K participatory surveillance %K participatory epidemiology %D 2017 %7 02.10.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Approximately 128 countries and 3.9 billion people are at risk of dengue infection. Incidence of dengue has increased over the past decades, becoming a growing public health concern for countries with populations that are increasingly susceptible to this vector-borne disease, such as Sri Lanka. Almost 55,150 dengue cases were reported in Sri Lanka in 2016, with more than 30.40% of cases (n=16,767) originating from Colombo, which struggles with an outdated manual paper-based dengue outbreak management system. Community education and outreach about dengue are also executed using paper-based media channels such as pamphlets and brochures. Yet, Sri Lanka is one of the countries with the most affordable rates of mobile services in the world, with penetration rates higher than most developing countries. Objectives: To combat the issues of an exhausted dengue management system and to make use of new technology, in 2015, a mobile participatory system for dengue surveillance called Mo-Buzz was developed and launched in Colombo, Sri Lanka. This paper describes the system’s components and uptake, along with other similar disease surveillance systems. Methods: We developed Mo-Buzz and tested its feasibility for dengue. Two versions of the app were developed. The first was for use by public health inspectors (PHIs) to digitize form filling and recording of site visit information, and track dengue outbreaks on a real-time dengue hotspot map using the global positioning system technology. The system also provides updated dengue infographics and educational materials for the PHIs to educate the general public. The second version of Mo-Buzz was created for use by the general public. This system uses dynamic mapping to help educate and inform the general public about potential outbreak regions and allow them to report dengue symptoms and post pictures of potential dengue mosquito–breeding sites, which are automatically sent to the health authorities. Targeted alerts can be sent to users depending on their geographical location. Results: We assessed the usage and the usability of the app and its impact on overall dengue transmission in Colombo. Initial uptake of Mo-Buzz for PHIs was low; however, after more training and incentivizing of usage, the uptake of the app in PHIs increased from less than 10% (n=3) to 76% (n=38). The general public user evaluation feedback was fruitful in providing improvements to the app, and at present, a number of solutions are being reviewed as viable options to boost user uptake. Conclusions: From our Mo-Buzz study, we have learned that initial acceptance of such systems can be slow but eventually positive. Mobile and social media interventions, such as Mo-Buzz, are poised to play a greater role in shaping risk perceptions and managing seasonal and sporadic outbreaks of infectious diseases in Asia and around the world. %M 28970191 %R 10.2196/publichealth.7376 %U https://publichealth.jmir.org/2017/4/e65/ %U https://doi.org/10.2196/publichealth.7376 %U http://www.ncbi.nlm.nih.gov/pubmed/28970191 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e57 %T Evaluation of Sampling Recommendations From the Influenza Virologic Surveillance Right Size Roadmap for Idaho %A Rosenthal,Mariana %A Anderson,Katey %A Tengelsen,Leslie %A Carter,Kris %A Hahn,Christine %A Ball,Christopher %+ Centers for Disease Control and Prevention, Idaho Department of Health and Welfare, 450 W. State Street, 4th Floor, Boise, ID, 83720, United States, 1 619 808 3992, mariana.rosenthal@doh.wa.gov %K influenza %K sample size %K public health surveillance %D 2017 %7 24.08.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Right Size Roadmap was developed by the Association of Public Health Laboratories and the Centers for Disease Control and Prevention to improve influenza virologic surveillance efficiency. Guidelines were provided to state health departments regarding representativeness and statistical estimates of specimen numbers needed for seasonal influenza situational awareness, rare or novel influenza virus detection, and rare or novel influenza virus investigation. Objective: The aim of this study was to compare Roadmap sampling recommendations with Idaho’s influenza virologic surveillance to determine implementation feasibility. Methods: We calculated the proportion of medically attended influenza-like illness (MA-ILI) from Idaho’s influenza-like illness surveillance among outpatients during October 2008 to May 2014, applied data to Roadmap-provided sample size calculators, and compared calculations with actual numbers of specimens tested for influenza by the Idaho Bureau of Laboratories (IBL). We assessed representativeness among patients’ tested specimens to census estimates by age, sex, and health district residence. Results: Among outpatients surveilled, Idaho’s mean annual proportion of MA-ILI was 2.30% (20,834/905,818) during a 5-year period. Thus, according to Roadmap recommendations, Idaho needs to collect 128 specimens from MA-ILI patients/week for situational awareness, 1496 influenza-positive specimens/week for detection of a rare or novel influenza virus at 0.2% prevalence, and after detection, 478 specimens/week to confirm true prevalence is ≤2% of influenza-positive samples. The mean number of respiratory specimens Idaho tested for influenza/week, excluding the 2009-2010 influenza season, ranged from 6 to 24. Various influenza virus types and subtypes were collected and specimen submission sources were representative in terms of geographic distribution, patient age range and sex, and disease severity. Conclusions: Insufficient numbers of respiratory specimens are submitted to IBL for influenza laboratory testing. Increased specimen submission would facilitate meeting Roadmap sample size recommendations. %M 28838883 %R 10.2196/publichealth.6648 %U http://publichealth.jmir.org/2017/3/e57/ %U https://doi.org/10.2196/publichealth.6648 %U http://www.ncbi.nlm.nih.gov/pubmed/28838883 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 7 %P e138 %T Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic %A Nasrinpour,Hamid Reza %A Reimer,Alexander A %A Friesen,Marcia R %A McLeod,Robert D %+ University of Manitoba, Electrical and Computer Engineering Department, E1-450 EITC, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada, 1 204 474 7360, Hamid.Nasrinpour@UManitoba.CA %K AnyLogic %K shapefiles %K ArcMap %K West Nile Virus %K land cover %K bird roosts %K bird home range %K Manitoba %D 2017 %7 17.07.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: West Nile Virus (WNV) was first isolated in 1937. Since the 1950s, many outbreaks have occurred in various countries. The first appearance of infected birds in Manitoba, Canada was in 2002. Objective: This paper describes the data preparation phase of setting up a geographic information system (GIS) simulation environment for WNV Agent-Based Modelling in Manitoba. Methods: The main technology used in this protocol is based on AnyLogic and ArcGIS software. A diverse variety of topics and techniques regarding the data collection phase are presented, as modelling WNV has many disparate attributes, including landscape and weather impacts on mosquito population dynamics and birds’ roosting locations, population count, and movement patterns. Results: Different maps were combined to create a grid land cover map of Manitoba, Canada in a shapefile format compatible with AnyLogic, in order to modulate mosquito parameters. A significant amount of data regarding 152 bird species, along with their population estimates and locations in Manitoba, were gathered and assembled. Municipality shapefile maps were converted to built-in AnyLogic GIS regions for better compatibility with census data and initial placement of human agents. Accessing shapefiles and their databases in AnyLogic are also discussed. Conclusions: AnyLogic simulation software in combination with Esri ArcGIS provides a powerful toolbox for developers and modellers to simulate almost any GIS-based environment or process. This research should be useful to others working on a variety of mosquito-borne diseases (eg, Zika, dengue, and chikungunya) by demonstrating the importance of data relating to Manitoba and/or introducing procedures to compile such data. %M 28716770 %R 10.2196/resprot.6213 %U http://www.researchprotocols.org/2017/7/e138/ %U https://doi.org/10.2196/resprot.6213 %U http://www.ncbi.nlm.nih.gov/pubmed/28716770 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e44 %T Feasibility of Establishing HIV Case-Based Surveillance to Measure Progress Along the Health Sector Cascade: Situational Assessments in Tanzania, South Africa, and Kenya %A Harklerode,Richelle %A Schwarcz,Sandra %A Hargreaves,James %A Boulle,Andrew %A Todd,Jim %A Xueref,Serge %A Rice,Brian %+ University of California San Francisco, Global Health Sciences, 550 16th Street, Third Floor, San Francisco, CA, 94158, United States, 1 415 476 5766, richelle.harklerode@ucsf.edu %K HIV %K surveillance %K case reports %K continuum of care %K epidemiologic surveillance %D 2017 %7 10.07.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: To track the HIV epidemic and responses to it, the World Health Organization recommends 10 global indicators to collect information along the HIV care cascade. Patient diagnosis and medical record data, harnessed through case-based surveillance (CBS), can be used to measure 8 of these. While many high burden countries have well-established systems for monitoring patients on HIV treatment, few have formally adopted CBS. Objective: In response to the need for improved strategic HIV information and to facilitate the development of CBS in resource-limited countries, we aimed to conduct situational assessments of existing data collection systems in Tanzania, South Africa, and Kenya. Methods: We developed a standardized protocol and a modularized data collection tool to be adapted for the particular focus of the assessments within each country. The three countries were selected based on their stage of readiness for CBS. The assessment included three parts: a desk review of relevant materials on HIV surveillance and program monitoring, stakeholder meetings, and site visits. Results: In all three countries, routine HIV program monitoring is conducted, and information on new HIV diagnoses and persons accessing HIV care and treatment services is collected. Key findings from the assessments included substantial stakeholder support for the development of CBS, significant challenges in linking data within and between systems, data quality, the ability to obtain data from multiple sources, and information technology infrastructure. Viral load testing capacity varied by country, and vital registry data were not routinely linked to health systems to update medical records. Conclusions: Our findings support the development of CBS systems to systematically capture routinely collected health data to measure and monitor HIV epidemics and guide responses. Although there were wide variations in the systems examined, some of the current program and patient monitoring systems can be adapted to function effectively for CBS, especially if supported by an improved patient registration system with shared unique health identifiers. %M 28694240 %R 10.2196/publichealth.7610 %U http://publichealth.jmir.org/2017/3/e44/ %U https://doi.org/10.2196/publichealth.7610 %U http://www.ncbi.nlm.nih.gov/pubmed/28694240 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e42 %T A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports %A Quade,Patrick %A Nsoesie,Elaine Okanyene %+ Iwaspoisoned.com, 322 W 52nd St #633, New York, NY, 10101, United States, 1 9179034815, patrick@dinesafe.org %K foodborne illness surveillance %K crowdsourced surveillance %K foodborne diseases %K infectious diseases %K outbreaks %K food poisoning %K Internet %K mobile %K participatory surveillance %K participatory epidemiology %D 2017 %7 05.07.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Underreporting of foodborne illness makes foodborne disease burden estimation, timely outbreak detection, and evaluation of policies toward improving food safety challenging. Objective: The objective of this study was to present and evaluate Iwaspoisoned.com, an openly accessible Internet-based crowdsourcing platform that was launched in 2009 for the surveillance of foodborne illness. The goal of this system is to collect data that can be used to augment traditional approaches to foodborne disease surveillance. Methods: Individuals affected by a foodborne illness can use this system to report their symptoms and the suspected location (eg, restaurant, hotel, hospital) of infection. We present descriptive statistics of users and businesses and highlight three instances where reports of foodborne illness were submitted before the outbreaks were officially confirmed by the local departments of health. Results: More than 49,000 reports of suspected foodborne illness have been submitted on Iwaspoisoned.com since its inception by individuals from 89 countries and every state in the United States. Approximately 95.51% (42,139/44,119) of complaints implicated restaurants as the source of illness. Furthermore, an estimated 67.55% (3118/4616) of users who responded to a demographic survey were between the ages of 18 and 34, and 60.14% (2776/4616) of the respondents were female. The platform is also currently used by health departments in 90% (45/50) of states in the US to supplement existing programs on foodborne illness reporting. Conclusions: Crowdsourced disease surveillance through systems such as Iwaspoisoned.com uses the influence and familiarity of social media to create an infrastructure for easy reporting and surveillance of suspected foodborne illness events. If combined with traditional surveillance approaches, these systems have the potential to lessen the problem of foodborne illness underreporting and aid in early detection and monitoring of foodborne disease outbreaks. %M 28679492 %R 10.2196/publichealth.7076 %U http://publichealth.jmir.org/2017/3/e42/ %U https://doi.org/10.2196/publichealth.7076 %U http://www.ncbi.nlm.nih.gov/pubmed/28679492 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e41 %T HIV Strategic Information in Non-European Union Countries in the World Health Organization European Region: Capacity Development Needs %A Bozicevic,Ivana %A Handanagic,Senad %A Cakalo,Jurja Ivana %A Rinder Stengaard,Annemarie %A Rutherford,George %+ WHO Collaborating Centre for HIV Strategic Information, University of Zagreb School of Medicine, Rockefeller str. 4, Zagreb, 10000, Croatia, 385 992984138, Ivana.Bozicevic@lshtm.ac.uk %K HIV %K surveillance %K evaluation %K Europe %K aptitude %D 2017 %7 23.06.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Capacity building of the national HIV strategic information system is a core component of the response to the HIV epidemic as it enables understanding of the evolving nature of the epidemic, which is critical for program planning and identification of the gaps and deficiencies in HIV programs. Objective: The study aims to describe the results of the assessment of the needs for further development of capacities in HIV strategic information systems in the non-European Union (EU) countries in the World Health Organization European Region (EUR). Methods: Self-administered questionnaires were distributed to national AIDS programs. The first questionnaire was sent to all countries (N=18) to find out, among other issues, the priority level for strengthening a range of HIV surveillance areas and their key gaps and weaknesses. The second questionnaire was sent to 15 countries to more specifically determine capacities for the analysis of the HIV care cascade. Results: Responses to the first questionnaire were received from 10 countries, whereas 13 countries responded to the second questionnaire. Areas that were most frequently marked as being of high to moderate priority for strengthening were national electronic patient monitoring systems, evaluation of HIV interventions and impact analysis, implementation science, and data analysis. Key weaknesseses were lack of electronic reporting of HIV cases, problems with timeliness and completeness of reporting in HIV cases, under-estimates of the reported number of HIV-related deaths, and limited CD4 count testing at the time of HIV diagnosis. Migrant populations, internally displaced persons, and refugees were most commonly mentioned as groups not covered by surveillance, followed by clients of sex workers and men who have sex with men. The majority of countries reported that they were able to provide the number of people diagnosed with HIV who know their HIV status, which is important for the analysis of cross-sectional and longitudinal HIV care cascades. Ability to report on some of the key impact indicators of HIV programs—viral load suppression and mortality—should be considerably strengthened. Conclusions: The assessment found a substantial need to invest in surveillance capacities, which is a cornerstone in the development of an evidence-informed response to HIV epidemics. %M 28645888 %R 10.2196/publichealth.7357 %U https://publichealth.jmir.org/2017/2/e41/ %U https://doi.org/10.2196/publichealth.7357 %U http://www.ncbi.nlm.nih.gov/pubmed/28645888 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 6 %P e211 %T Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design %A Spreco,Armin %A Eriksson,Olle %A Dahlström,Örjan %A Cowling,Benjamin John %A Timpka,Toomas %+ Faculty of Health Sciences, Department of Medical and Health Sciences, Linköping University, IMH:s kansli, Sandbäcksgatan 7, Linköping, 581 83, Sweden, 46 737543032, armin.spreco@liu.se %K human influenza %K algorithms %K epidemiological surveillance %K public health surveillance %K evaluation research %K epidemiological methods %D 2017 %7 15.06.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic “big data” from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. Objective: The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. Methods: An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. Results: The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Conclusions: Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified. %M 28619700 %R 10.2196/jmir.7101 %U http://www.jmir.org/2017/6/e211/ %U https://doi.org/10.2196/jmir.7101 %U http://www.ncbi.nlm.nih.gov/pubmed/28619700 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e30 %T Experiences From Developing and Upgrading a Web-Based Surveillance System for Malaria Elimination in Cambodia %A Fu,Clementine %A Lopes,Sérgio %A Mellor,Steve %A Aryal,Siddhi %A Sovannaroth,Siv %A Roca-Feltrer,Arantxa %+ Malaria Consortium Cambodia, House # 91, St. 95, Boeung Trabek, Chamkar Morn, Phnom Penh, 12305, Cambodia, 855 017753032, s.lopes@malariaconsortium.org %K malaria elimination %K surveillance system %K Cambodia %D 2017 %7 14.06.2017 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Strengthening the surveillance component is key toward achieving country-wide malaria elimination in Cambodia. A Web-based upgraded malaria information system (MIS) was deemed to essentially act as the central component for surveillance strengthening. New functionality (eg, data visualization) and operational (eg, data quality) attributes of the system received particular attention. However, building from the lessons learned in previous systems’ developments, other aspects unique to Cambodia were considered to be equally important; for instance, feasibility issues, particularly at the field level (eg, user acceptability at various health levels), and sustainability needs (eg, long-term system flexibility). The Cambodian process of identifying the essential changes and critical attributes for this new information system can provide a model for other countries at various stages of the disease control and elimination continuum. Sharing these experiences not only facilitates the establishment of “best practices” but also accelerates global and regional malaria elimination efforts. In this article, Cambodia’s experience in developing and upgrading its MIS to remain responsive to country-specific needs demonstrates the necessity for considering functionality, operationalization, feasibility, and sustainability of an information system in the context of malaria elimination. %M 28615155 %R 10.2196/publichealth.6942 %U http://publichealth.jmir.org/2017/2/e30/ %U https://doi.org/10.2196/publichealth.6942 %U http://www.ncbi.nlm.nih.gov/pubmed/28615155 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e25 %T The RSVP Project: Factors Related to Disengagement From Human Immunodeficiency Virus Care Among Persons in San Francisco %A Scheer,Susan %A Chen,Miao-Jung %A Parisi,Maree Kay %A Yoshida-Cervantes,Maya %A Antunez,Erin %A Delgado,Viva %A Moss,Nicholas J %A Buchacz,Kate %+ HIV Epidemiology Section, San Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, CA, 94114, United States, 1 415 437 6259, susan.scheer@sfdph.org %K HIV care %K HIV care continuum %K engagement in HIV care %K HIV surveillance %K viral suppression %K linkage and navigation to care %D 2017 %7 04.05.2017 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: In the United States, an estimated two-thirds of persons with human immunodeficiency virus (HIV) infection do not achieve viral suppression, including those who have never engaged in HIV care and others who do not stay engaged in care. Persons with an unsuppressed HIV viral load might experience poor clinical outcomes and transmit HIV. Objective: The goal of the Re-engaging Surveillance-identified Viremic Persons (RSVP) project in San Francisco, CA, was to use routine HIV surveillance databases to identify, contact, interview, and reengage in HIV care persons who appeared to be out of care because their last HIV viral load was unsuppressed. We aimed to interview participants about their HIV care and barriers to reengagement. Methods: Using routinely collected HIV surveillance data, we identified persons with HIV who were out of care (no HIV viral load and CD4 laboratory reports during the previous 9-15 months) and with their last plasma HIV RNA viral load >200 copies/mL. We interviewed the located persons, at baseline and 3 months later, about whether and why they disengaged from HIV care and the barriers they faced to care reengagement. We offered them assistance with reengaging in HIV care from the San Francisco Department of Public Health linkage and navigation program (LINCS). Results: Of 282 persons selected, we interviewed 75 (26.6%). Of these, 67 (89%) reported current health insurance coverage, 59 (79%) had ever been prescribed and 45 (60%) were currently taking HIV medications, 59 (79%) had seen an HIV provider in the past year, and 34 (45%) had missed an HIV appointment in the past year. Reasons for not seeing a provider included feeling healthy, using alcohol or drugs, not having enough money or health insurance, and not wanting to take HIV medicines. Services needed to get to an HIV medical care appointment included transportation assistance, stable living situation or housing, sound mental health, and organizational help and reminders about appointments. A total of 52 (69%) accepted a referral to LINCS. Additionally, 64 (85%) of the persons interviewed completed a follow-up interview 3 months later and, of these, 62 (97%) had health insurance coverage and 47 (73%) reported having had an HIV-related care appointment since the baseline interview. Conclusions: Rather than being truly out of care, most participants reported intermittent HIV care, including recent HIV provider visits and health insurance coverage. Participants also frequently reported barriers to care and unmet needs. Health department assistance with HIV care reengagement was generally acceptable. Understanding why people previously in HIV care disengage from care and what might help them reengage is essential for optimizing HIV clinical and public health outcomes. %M 28473307 %R 10.2196/publichealth.7325 %U http://publichealth.jmir.org/2017/2/e25/ %U https://doi.org/10.2196/publichealth.7325 %U http://www.ncbi.nlm.nih.gov/pubmed/28473307 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 1 %P e2 %T Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance %A Smith,Michael G %A Royer,Julie %A Mann,Joshua R %A McDermott,Suzanne %+ Department of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Room 417, Columbia, SC,, United States, 1 803 777 7225, smcdermo@mailbox.sc.edu %K muscular dystrophy %K algorithm %K administrative records %D 2017 %7 12.01.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Administrative records from insurance and hospital discharge data sources are important public health tools to conduct passive surveillance of disease in populations. Identifying rare but catastrophic conditions is a challenge since approaches for maximizing valid case detection are not firmly established. Objective: The purpose of our study was to explore a number of algorithms in which International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and other administrative variables could be used to identify cases of muscular dystrophy (MD). Methods: We used active surveillance to identify possible cases of MD in medical practices in neurology, genetics, and orthopedics in 5 urban South Carolina counties and to identify the cases that had diagnostic support (ie, true cases). We then developed an algorithm to identify cases based on a combination of ICD-9-CM codes and administrative variables from a public (Medicaid) and private insurer claims-based system and a statewide hospital discharge dataset (passive surveillance). Cases of all types of MD and those with Duchenne or Becker MD (DBMD) that were common to both surveillance systems were examined to identify the most specific administrative variables for ascertainment of true cases. Results: Passive statewide surveillance identified 3235 possible cases with MD in the state, and active surveillance identified 2057 possible cases in 5 actively surveilled counties that included 2 large metropolitan areas where many people seek medical care. There were 537 common cases found in both the active and passive systems, and 260 (48.4%) were confirmed by active surveillance to be true cases. Of the 260 confirmed cases, 70 (26.9%) were recorded as DBMD. Conclusions: Accuracy of finding a true case in a passive surveillance system was improved substantially when specific diagnosis codes, number of times a code was used, age of the patient, and specialty provider variables were used. %M 28082256 %R 10.2196/publichealth.6720 %U http://publichealth.jmir.org/2017/1/e2/ %U https://doi.org/10.2196/publichealth.6720 %U http://www.ncbi.nlm.nih.gov/pubmed/28082256 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 2 %P e161 %T Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis %A Sharpe,J Danielle %A Hopkins,Richard S %A Cook,Robert L %A Striley,Catherine W %+ Rollins School of Public Health, Department of Epidemiology, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, United States, 1 912 399 2811, danielle.sharpe@emory.edu %K Internet %K social media %K Bayes theorem %K public health surveillance %K influenza, human %D 2016 %7 20.10.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective: The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods: Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC’s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package “bcp” version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. %M 27765731 %R 10.2196/publichealth.5901 %U http://publichealth.jmir.org/2016/2/e161/ %U https://doi.org/10.2196/publichealth.5901 %U http://www.ncbi.nlm.nih.gov/pubmed/27765731 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 4 %P e197 %T Existing Models of Maternal Death Surveillance Systems: Protocol for a Scoping Review %A Abouchadi,Saloua %A Shahabuddin,ASM %A Zhang,Wei Hong %A Firoz,Tabassum %A Englert,Yvon %A Nejjari,Chakib %A De Brouwere,Vincent %+ Ecole Nationale de Santé Publique, Madinat Al Irfane, Rue Lamfadel Cherkaoui, Rabat,, Morocco, 212 537683162, s.abouchadi@yahoo.fr %K maternal mortality %K surveillance systems %K completeness %K usefulness %K scoping review %K protocol %D 2016 %7 11.10.2016 %9 Protocol %J JMIR Res Protoc %G English %X Background: Maternal mortality measurement remains a critical challenge, particularly in low and middle income countries (LMICs) where little or no data are available and maternal mortality and morbidity are often the highest in the world. Despite the progress made in data collection, underreporting and translating the results into action are two major challenges that maternal death surveillance systems (MDSSs) face in LMICs. Objective: This paper presents a protocol for a scoping review aimed at synthesizing the existing models of MDSSs and factors that influence their completeness and usefulness. Methods: The methodology for scoping reviews from the Joanna Briggs Institute was used as a guide for developing this protocol. A comprehensive literature search will be conducted across relevant electronic databases. We will include all articles that describe MDSSs or assess their completeness or usefulness. At least two reviewers will independently screen all articles, and discrepancies will be resolved through discussion. The same process will be used to extract data from studies fulfilling the eligibility criteria. Data analysis will involve quantitative and qualitative methods. Results: Currently, the abstracts screening is under way and the first results are expected to be publicly available by mid-2017. The synthesis of the reviewed materials will be presented in tabular form completed by a narrative description. The results will be classified in main conceptual categories that will be obtained during the results extraction. Conclusions: We anticipate that the results will provide a broad overview of MDSSs and describe factors related to their completeness and usefulness. The results will allow us to identify research gaps concerning the barriers and facilitating factors facing MDSSs. Results will be disseminated through publication in a peer-reviewed journal and conferences as well as domestic and international agencies in charge of implementing MDSS. %M 27729305 %R 10.2196/resprot.5758 %U http://www.researchprotocols.org/2016/4/e197/ %U https://doi.org/10.2196/resprot.5758 %U http://www.ncbi.nlm.nih.gov/pubmed/27729305 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 2 %P e151 %T Testing the Feasibility of a Passive and Active Case Ascertainment System for Multiple Rare Conditions Simultaneously: The Experience in Three US States %A Reichard,Amanda %A McDermott,Suzanne %A Ruttenber,Margaret %A Mann,Joshua %A Smith,Michael G %A Royer,Julie %A Valdez,Rodolfo %+ Institute on Disability, University of New Hampshire, 10 West Edge Dr, Suite 101, Durham, NH, 03824, United States, 1 603 862 5266, amanda.reichard@unh.edu %K spina bifida %K muscular dystrophy %K fragile X syndrome %K surveillance %D 2016 %7 29.08.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Owing to their low prevalence, single rare conditions are difficult to monitor through current state passive and active case ascertainment systems. However, such monitoring is important because, as a group, rare conditions have great impact on the health of affected individuals and the well-being of their caregivers. A viable approach could be to conduct passive and active case ascertainment of several rare conditions simultaneously. This is a report about the feasibility of such an approach. Objective: To test the feasibility of a case ascertainment system with passive and active components aimed at monitoring 3 rare conditions simultaneously in 3 states of the United States (Colorado, Kansas, and South Carolina). The 3 conditions are spina bifida, muscular dystrophy, and fragile X syndrome. Methods: Teams from each state evaluated the possibility of using current or modified versions of their local passive and active case ascertainment systems and datasets to monitor the 3 conditions. Together, these teams established the case definitions and selected the variables and the abstraction tools for the active case ascertainment approach. After testing the ability of their local passive and active case ascertainment system to capture all 3 conditions, the next steps were to report the number of cases detected actively and passively for each condition, to list the local barriers against the combined passive and active case ascertainment system, and to describe the experiences in trying to overcome these barriers. Results: During the test period, the team from South Carolina was able to collect data on all 3 conditions simultaneously for all ages. The Colorado team was also able to collect data on all 3 conditions but, because of age restrictions in its passive and active case ascertainment system, it was able to report few cases of fragile X syndrome. The team from Kansas was able to collect data only on spina bifida. For all states, the implementation of an active component of the ascertainment system was problematic. The passive component appears viable with minor modifications. Conclusions: Despite evident barriers, the joint passive and active case ascertainment of rare disorders using modified existing surveillance systems and datasets seems feasible, especially for systems that rely on passive case ascertainment. %M 27574026 %R 10.2196/publichealth.5516 %U http://publichealth.jmir.org/2016/2/e151/ %U https://doi.org/10.2196/publichealth.5516 %U http://www.ncbi.nlm.nih.gov/pubmed/27574026 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 7 %P e149 %T A Social Media mHealth Solution to Address the Needs of Dengue Prevention and Management in Sri Lanka %A Lwin,May O %A Vijaykumar,Santosh %A Rathnayake,Vajira Sampath %A Lim,Gentatsu %A Panchapakesan,Chitra %A Foo,Schubert %A Wijayamuni,Ruwan %A Wimalaratne,Prasad %A Fernando,Owen Noel Newton %+ Nanyang Technological University, School of Computer Science and Engineering, 50 Nanyang Avenue, North Spine, N4-02c-75, Singapore, 639798, Singapore, 65 67905783, santoshv@ntu.edu.sg %K dengue %K public health inspector %K mhealth %K social media %K surveillance %K needs assessment %K prevention and management %D 2016 %7 01.07.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Sri Lanka has witnessed a series of dengue epidemics over the past five years, with the western province, home to the political capital of Colombo, bearing more than half of the dengue burden. Existing dengue monitoring prevention programs are exhausted as public health inspectors (PHIs) cope with increasing workloads and paper-based modes of surveillance and education, characterizing a reactive system unable to cope with the enormity of the problem. On the other hand, the unprecedented proliferation and affordability of mobile phones since 2009 and a supportive political climate have thus far remained unexploited for the use of mobile-based interventions for dengue management. Objective: To conduct a needs assessment of PHIs in Colombo with respect to their dengue-related tasks and develop a new mobile-based system to address these needs while strengthening existing systems. Methods: One-on-one in-depth interviews were conducted with 29 PHIs to a) gain a nuanced, in-depth understanding of the current state of surveillance practices, b) understand the logistical, technological and social challenges they confront, and c) identify opportunities for mobile-based interventions. Quantitative analysis included simple descriptive statistics while qualitative analysis comprised textual analysis of 209 pages of transcripts (or nearly 600 minutes of conversations) using grounded theory approaches. Results: Current paper-based data collection practices for dengue surveillance involved a circuitous, time consuming process that could take between 7-10 days to officially report and record a single case. PHIs confronted challenges in terms of unreliable, standalone GIS devices, delays in registering mosquito breeding sites and lack of engagement from communities while delivering dengue education. These findings, in concert with a high motivation to use mobile-based systems, informed the development of Mo-Buzz, a mobile-based system that integrates three components – digitized surveillance, dynamic disease mapping and digitized dengue education – on a common platform. The system was developed through an iterative, evolutionary, collaborative process, consistent with the Spiral model of software development and is currently being used by all 55 PHIs in the CMC system. Conclusions: Given the entrenched nature of existing paper-based systems in PHIs’ work habits, we expect a gradual adoption curve for Mo-Buzz in the future. Equally, we expect variable adoption of the system with respect to its specific components, and specific PHI sub-groups (younger versus older). The Mo-Buzz intervention is a response to multiple calls by the global mHealth community for collaborations in the area of mobile interventions for global health. Our experience revealed that the benefits of this paradigm lies in alleviating country-specific public health challenges through a commonly shared understanding of cultural mores, and sharing of knowledge and technologies. We call upon future researchers to further dissect the applicability of the Spiral Model of software development to mHealth interventions and contribute to the mHealth evidence debate from theoretical and applied perspectives. %M 27369296 %R 10.2196/jmir.4657 %U http://www.jmir.org/2016/7/e149/ %U https://doi.org/10.2196/jmir.4657 %U http://www.ncbi.nlm.nih.gov/pubmed/27369296 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 2 %P e122 %T A Registry for Evaluation of Efficiency and Safety of Surgical Treatment of Cartilage Defects: The German Cartilage Registry (KnorpelRegister DGOU) %A Maurer,Julia %A Grotejohann,Birgit %A Jenkner,Carolin %A Schneider,Carla %A Flury,Thomas %A Tassoni,Adrian %A Angele,Peter %A Fritz,Jürgen %A Albrecht,Dirk %A Niemeyer,Philipp %+ Clinical Trials Unit, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Elsaesser Str 2, Freiburg im Breisgau, 79110, Germany, 49 761 270 ext 77830, julia.maurer@uniklinik-freiburg.de %K ankle joint %K cartilage defect %K chondral defect %K hip joint %K knee joint %K patient registry %D 2016 %7 29.06.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: The need for documentation in cartilage defects is as obvious as in other medical specialties. Cartilage defects can cause significant pain, and lead to reduced quality of life and loss of function of the affected joint. The risk of developing osteoarthritis is high. Therefore, the socioeconomic burden of cartilage defects should not be underestimated. Objective: The objective of our study was to implement and maintain a registry of all patients undergoing surgical treatment of cartilage defects. Methods: We designed this multicenter registry for adults whose cartilage defects of a knee, ankle, or hip joint are treated surgically. The registry consists of two parts: one for the physician and one for the patient. Data for both parts will be gathered at baseline and at 6-, 12-, 24-, 36-, 60-, and 120-month follow-ups. Results: To date, a wide range of German, Swiss, and Austrian trial sites are taking part in the German Cartilage Registry, soon to be followed by further sites. More than 2124 (as of January 31, 2016) cases are already documented and the first publications have been released. Conclusions: The German Cartilage Registry addresses fundamental issues regarding the current medical care situation of patients with cartilage defects of knee, ankle, and hip joints. In addition, the registry will help to identify various procedure-specific complications, along with putative advantages and disadvantages of different chondrocyte products. It provides an expanding large-scale, unselected, standardized database for cost and care research for further retrospective studies. Trial Registration: German Clinical Trials Register: DRKS00005617; https://drks-neu.uniklinik-freiburg.de/ drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005617 (Archived by WebCite at http://www.webcitation.org/6hbFqSws0) %M 27357998 %R 10.2196/resprot.5895 %U http://www.researchprotocols.org/2016/2/e122/ %U https://doi.org/10.2196/resprot.5895 %U http://www.ncbi.nlm.nih.gov/pubmed/27357998 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e65 %T Perceptions of the Feasibility and Practicalities of Text Messaging-Based Infectious Disease Surveillance: A Questionnaire Survey %A Dang,Linh Thuy %A Vu,Nguyen Cong %A Vu,Thiem Dinh %A James,Spencer L %A Katona,Peter %A Katona,Lindsay %A Rosen,Joseph M %A Nguyen,Cuong Kieu %+ Institute of Population, Health and Development, 18 Lane 132, Hoa Bang Street, Hanoi, 122667, Vietnam, 84 437822388 ext 822, cuong.kieu.nguyen@phad.org %K SMS %K SMS-based %K infectious diseases %K text messaging %K surveillance %K Vietnam %D 2016 %7 25.05.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: In Vietnam, infectious disease surveillance data are collected via a paper-based system through four government tiers leading to a large delay. Meanwhile, mobile phones are abundant and very popular in the country, and known to be a useful tool in health care worldwide. Therefore, there is a great potential for the development of a timely disease surveillance system through the use of mobile phone short message service (SMS) text messages. Objective: This study aims to explore insights about the feasibility and practicalities of the utilization of SMS text messaging-based interventions in disease-reporting systems by identifying potential challenges and barriers in the text messaging process and looking at lessons learned. Methods: An SMS text messaging-based disease tracking system was set up in Vietnam with patient reports texted by clinic staff. Two 6-month trials utilizing this disease tracking system were designed and implemented in two northern provinces of Vietnam to report two infectious diseases: diarrhea and influenza-like illness. A structured self-reported questionnaire was developed to measure the feasibility and practicalities of the system from the participants. On the completion of the second trial in 2013, participating health staff from 40 commune health centers in the two pilot provinces were asked to complete the survey (N=80). Results: Most participants were female (61%, 49/80) and nearly half (44%, 35/80) were heads of a commune health center. Approximately two-thirds (63%, 50/80) of participants retained the basic structure of the SMS text message report and there was a strong influence (OR 28.2, 95% CI 5.3-151.2) of those people on the time they spent texting the information. The majority (88%, 70/80) felt the information conveyed in the SMS text message report was not difficult to understand. Most (86%, 69/80) believed that they could report all 28 infectious diseases asked for by the Ministry of Health by using SMS text messaging. Conclusions: From a health center staff perspective, a disease-reporting system utilizing text messaging technology is easy to use and has great potential to be implemented and expanded nationwide. The survey showed positive perceptions and feedback from the participants and contributed to a promising practical solution to improve the surveillance system of infectious disease in Vietnam. %M 27226418 %R 10.2196/mhealth.4509 %U http://mhealth.jmir.org/2016/2/e65/ %U https://doi.org/10.2196/mhealth.4509 %U http://www.ncbi.nlm.nih.gov/pubmed/27226418 %0 Journal Article %@ 2369-2960 %I Gunther Eysenbach %V 2 %N 1 %P e25 %T Shining a Light on America’s HIV Epidemic among Men who Have Sex with Men %A Valdiserri,Ronald O %+ Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, United States, rvaldis1@jhu.edu %K HIV surveillance %K HIV diagnosis %K HIV prevalence %K HIV incidence %K men who have sex with men %K gay and bisexual men %K demography %D 2016 %7 17.05.2016 %9 Guest Editorial %J JMIR Public Health Surveill %G English %X %M 27244772 %R 10.2196/publichealth.5864 %U http://publichealth.jmir.org/2016/1/e25/ %U https://doi.org/10.2196/publichealth.5864 %U http://www.ncbi.nlm.nih.gov/pubmed/27244772 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e20 %T Effectiveness of Implementation of Electronic Malaria Information System as the National Malaria Surveillance System in Thailand %A Ma,Shaojin %A Lawpoolsri,Saranath %A Soonthornworasiri,Ngamphol %A Khamsiriwatchara,Amnat %A Jandee,Kasemsak %A Taweeseneepitch,Komchaluch %A Pawarana,Rungrawee %A Jaiklaew,Sukanya %A Kijsanayotin,Boonchai %A Kaewkungwal,Jaranit %+ Department of Tropical Hygiene (Biomedical and Health Informatics), Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Road, Ratchathewi, Bangkok, 10400, Thailand, 66 23549181, jaranit.kae@mahidol.ac.th %K surveillance system %K epidemiology %K mixed-methods %K evaluation %K malaria %K eMIS %K data quality %K public health informatics %K Thailand %D 2016 %7 06.05.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In moving toward malaria elimination, one strategy is to implement an active surveillance system for effective case management. Thailand has developed and implemented the electronic Malaria Information System (eMIS) capturing individualized electronic records of suspected or confirmed malaria cases. Objective: The main purpose of this study was to determine how well the eMIS improves the quality of Thailand’s malaria surveillance system. In particular, the focus of the study was to evaluate the effectiveness of the eMIS in terms of the system users’ perception and the system outcomes (ie, quality of data) regarding the management of malaria patients. Methods: A mixed-methods technique was used with the framework based on system effectiveness attributes: data quality, timeliness, simplicity, acceptability, flexibility, stability, and usefulness. Three methods were utilized: data records review, survey of system users, and in-depth interviews with key stakeholders. From the two highest endemic provinces, paper forms matching electronic records of 4455 noninfected and 784 malaria-infected cases were reviewed. Web-based anonymous questionnaires were distributed to all 129 eMIS data entry staff throughout Thailand, and semistructured interviews were conducted with 12 management-level officers. Results: The eMIS is well accepted by system users at both management and operational levels. The data quality has enabled malaria personnel to perform more effective prevention and control activities. There is evidence of practices resulting in inconsistencies and logical errors in data reporting. Critical data elements were mostly completed, except for a few related to certain dates and area classifications. Timeliness in reporting a case to the system was acceptable with a delay of 3-4 days. The evaluation of quantitative and qualitative data confirmed that the eMIS has high levels of simplicity, acceptability, stability, and flexibility. Conclusions: Overall, the system implemented has achieved its objective. The results of the study suggested that the eMIS helps improve the quality of Thailand’s malaria surveillance system. As the national malaria surveillance system, the eMIS’s functionalities have provided the malaria staff working at the point of care with close-to-real-time case management data quality, covering case detection, case investigation, drug compliance, and follow-up visits. Such features has led to an improvement in the quality of the malaria control program; the government officials now have quicker access to both individual and aggregated data to promptly react to possible outbreak. The eMIS thus plays one of the key roles in moving toward the national goal of malaria elimination by the next decade. %M 27227156 %R 10.2196/publichealth.5347 %U http://publichealth.jmir.org/2016/1/e20/ %U https://doi.org/10.2196/publichealth.5347 %U http://www.ncbi.nlm.nih.gov/pubmed/27227156 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e12 %T Use of Electronic Health Records and Geographic Information Systems in Public Health Surveillance of Type 2 Diabetes: A Feasibility Study %A Laranjo,Liliana %A Rodrigues,David %A Pereira,Ana Marta %A Ribeiro,Rogério T %A Boavida,José Manuel %+ Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera road, Sydney, 2109, Australia, 61 413461852, liliana.laranjo@gmail.com %K electronic health records %K diabetes mellitus %K geographic information systems %K primary health care %K health records %K personal %D 2016 %7 17.03.2016 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Data routinely collected in electronic health records (EHRs) offer a unique opportunity to monitor chronic health conditions in real-time. Geographic information systems (GIS) may be an important complement in the analysis of those data. Objective: The aim of this study was to explore the feasibility of using primary care EHRs and GIS for population care management and public health surveillance of chronic conditions, in Portugal. Specifically, type 2 diabetes was chosen as a case study, and we aimed to map its prevalence and the presence of comorbidities, as well as to identify possible populations at risk for cardiovascular complications. Methods: Cross-sectional study using individual-level data from 514 primary care centers, collected from three different types of EHRs. Data were obtained on adult patients with type 2 diabetes (identified by the International Classification of Primary Care [ICPC-2] code, T90, in the problems list). GISs were used for mapping the prevalence of diabetes and comorbidities (hypertension, dyslipidemia, and obesity) by parish, in the region of Lisbon and Tagus Valley. Descriptive statistics and multivariate logistic regression were used for data analysis. Results: We identified 205,068 individuals with the diagnosis of type 2 diabetes, corresponding to a prevalence of 5.6% (205,068/3,659,868) in the study population. The mean age of these patients was 67.5 years, and hypertension was present in 71% (144,938/205,068) of all individuals. There was considerable variation in diagnosed comorbidities across parishes. Diabetes patients with concomitant hypertension or dyslipidemia showed higher odds of having been diagnosed with cardiovascular complications, when adjusting for age and gender (hypertension odds ratio [OR] 2.16, confidence interval [CI] 2.10-2.22; dyslipidemia OR 1.57, CI 1.54-1.60). Conclusions: Individual-level data from EHRs may play an important role in chronic disease surveillance, namely through the use of GIS. Promoting the quality and comprehensiveness of data, namely through patient involvement in their medical records, is crucial to enhance the feasibility and usefulness of this approach. %M 27227147 %R 10.2196/publichealth.4319 %U http://publichealth.jmir.org/2016/1/e12/ %U https://doi.org/10.2196/publichealth.4319 %U http://www.ncbi.nlm.nih.gov/pubmed/27227147 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e3 %T Improving HIV Surveillance Data for Public Health Action in Washington, DC: A Novel Multiorganizational Data-Sharing Method %A Ocampo,Joanne Michelle F %A Smart,JC %A Allston,Adam %A Bhattacharjee,Reshma %A Boggavarapu,Sahithi %A Carter,Sharon %A Castel,Amanda D %A Collmann,Jeff %A Flynn,Colin %A Hamp,Auntré %A Jordan,Diana %A Kassaye,Seble %A Kharfen,Michael %A Lum,Garret %A Pemmaraju,Raghu %A Rhodes,Anne %A Stover,Jeff %A Young,Mary A %+ The Office of the Senior Vice President for Research, Georgetown University, 2115 Wisconsin Avenue NW, suite 603, Washington, DC, 20007, United States, 1 2026874092, jfo36@georgetown.edu %K HIV %K surveillance %K data sharing %K public health %K technology %D 2016 %7 15.01.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The National HIV/AIDS Strategy calls for active surveillance programs for human immunodeficiency virus (HIV) to more accurately measure access to and retention in care across the HIV care continuum for persons living with HIV within their jurisdictions and to identify persons who may need public health services. However, traditional public health surveillance methods face substantial technological and privacy-related barriers to data sharing. Objective: This study developed a novel data-sharing approach to improve the timeliness and quality of HIV surveillance data in three jurisdictions where persons may often travel across the borders of the District of Columbia, Maryland, and Virginia. Methods: A deterministic algorithm of approximately 1000 lines was developed, including a person-matching system with Enhanced HIV/AIDS Reporting System (eHARS) variables. Person matching was defined in categories (from strongest to weakest): exact, very high, high, medium high, medium, medium low, low, and very low. The algorithm was verified using conventional component testing methods, manual code inspection, and comprehensive output file examination. Results were validated by jurisdictions using internal review processes. Results: Of 161,343 uploaded eHARS records from District of Columbia (N=49,326), Maryland (N=66,200), and Virginia (N=45,817), a total of 21,472 persons were matched across jurisdictions over various strengths in a matching process totaling 21 minutes and 58 seconds in the privacy device, leaving 139,871 uniquely identified with only one jurisdiction. No records matched as medium low or low. Over 80% of the matches were identified as either exact or very high matches. Three separate validation methods were conducted for this study, and they all found ≥90% accuracy between records matched by this novel method and traditional matching methods. Conclusions: This study illustrated a novel data-sharing approach that may facilitate timelier and better quality HIV surveillance data for public health action by reducing the effort needed for traditional person-matching reviews without compromising matching accuracy. Future analyses will examine the generalizability of these findings to other applications. %M 27227157 %R 10.2196/publichealth.5317 %U http://publichealth.jmir.org/2016/1/e3/ %U https://doi.org/10.2196/publichealth.5317 %U http://www.ncbi.nlm.nih.gov/pubmed/27227157 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e20 %T Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City %A Cantrell,Jennifer %A Ganz,Ollie %A Ilakkuvan,Vinu %A Tacelosky,Michael %A Kreslake,Jennifer %A Moon-Howard,Joyce %A Aidala,Angela %A Vallone,Donna %A Anesetti-Rothermel,Andrew %A Kirchner,Thomas R %+ Truth Initiative, Evaluation Science and Research, 900 G Street, NW, Fourth Floor, Washington, DC, 20001, United States, 1 202 436 2118, jcantrell@truthinitiative.org %K mobile technology %K public health surveillance %K tobacco %K point-of-sale %K implementation %K tobacco industry advertising %K marketing %D 2015 %7 26.11.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In tobacco control and other fields, point-of-sale surveillance of the retail environment is critical for understanding industry marketing of products and informing public health practice. Innovations in mobile technology can improve existing, paper-based surveillance methods, yet few studies describe in detail how to operationalize the use of technology in public health surveillance. Objective: The aims of this paper are to share implementation strategies and lessons learned from 2 tobacco, point-of-sale surveillance projects to inform and prepare public health researchers and practitioners to implement new mobile technologies in retail point-of-sale surveillance systems. Methods: From 2011 to 2013, 2 point-of-sale surveillance pilot projects were conducted in Washington, DC, and New York, New York, to capture information about the tobacco retail environment and test the feasibility of a multimodal mobile data collection system, which included capabilities for audio or video recording data, electronic photographs, electronic location data, and a centralized back-end server and dashboard. We established a preimplementation field testing process for both projects, which involved a series of rapid and iterative tests to inform decisions and establish protocols around key components of the project. Results: Important components of field testing included choosing a mobile phone that met project criteria, establishing an efficient workflow and accessible user interfaces for each component of the system, training and providing technical support to fieldworkers, and developing processes to integrate data from multiple sources into back-end systems that can be utilized in real-time. Conclusions: A well-planned implementation process is critical for successful use and performance of multimodal mobile surveillance systems. Guidelines for implementation include (1) the need to establish and allow time for an iterative testing framework for resolving technical and logistical challenges; (2) developing a streamlined workflow and user-friendly interfaces for data collection; (3) allowing for ongoing communication, feedback, and technology-related skill-building among all staff; and (4) supporting infrastructure for back-end data systems. Although mobile technologies are evolving rapidly, lessons learned from these case studies are essential for ensuring that the many benefits of new mobile systems for rapid point-of-sale surveillance are fully realized. %M 27227138 %R 10.2196/publichealth.4191 %U http://publichealth.jmir.org/2015/2/e20/ %U https://doi.org/10.2196/publichealth.4191 %U http://www.ncbi.nlm.nih.gov/pubmed/27227138 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e12 %T Texting-Based Reporting of Adverse Drug Reactions to Ensure Patient Safety: A Feasibility Study %A Vergeire-Dalmacion,Godofreda %A Castillo-Carandang,Nina T %A Juban,Noel R %A Amarillo,Maria Lourdes %A Tagle,Maria Pamela %A Baja,Emmanuel S %+ College of Medicine, Department of Pharmacology and Toxicology, University of the Philippines-Manila, Department of Clinical Epidemiology Room 103, Paz Mendoza Building, Pedro Gil Street, Ermita, Manila, 1000, Philippines, 63 2 525 4098, jody.dalmacion@gmail.com %K adverse drug reactions %K pharmacovigilance %K postmarketing %K spontaneous reporting %K texting %D 2015 %7 19.11.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Paper-based adverse drug reaction (ADR) reporting has been in practice for more than 6 decades. Health professionals remain the primary source of reports, while the value of patients’ reporting is yet unclear. With the increasing popularity of using electronic gadgets in health, it is expected that the electronic transmission of reports will become the norm within a few years. Objective: The aims of this study are to investigate whether short messaging service or texting can provide an alternative or supplemental method for ADR reporting given the increasing role of mobile phones in health care monitoring; to determine the usefulness of texting in addition to paper-based reporting of ADRs by resident physicians; and to describe the barriers to ADR reporting and estimate the cost for setting up and maintaining a texting-computer reporting system. Methods: This was a pre-post cross-sectional study that measured the number of ADRs texted by 51 resident physicians for 12 months from the Department of Obstetrics and Gynecology and the Department of Adult Medicine of a tertiary government hospital in Manila, Philippines, with 1350-bed capacity. Reports were captured by a texting-computer reporting system. Prior to its implementation, key informant interview and focus group discussion were conducted. Baseline information and practice on the existing paper-based reporting system were culled from the records of the hospital’s Pharmacy and Therapeutics Committee. A postintervention survey questionnaire was administered at the end of 12 months. Results: Only 3 ADRs were texted by 51 resident physicians in 12 months (reporting rate 3/51 or 6%). By contrast, 240 ADRs from the paper-based reporting system from 848 resident physicians of the study hospital were collected and tabulated (reporting rate 240/848 or 28.3%). Texting ADRs was not efficient because of power interruption, competition with the existing paper-based reporting system, and unforeseen expiration of prepaid text loads/credits. The 3 ADRs texted were a report of vivid dreams and nightmares, a report of disturbing dreams and memory lapses, both of which were due to montelukast use, and a report of hepatitis from an isoniazid/rifampicin fixed-dose combination. Nineteen of 51 resident physicians (37%) registered in the reporting system responded to the postintervention survey. The most common reasons for not reporting ADRs were no adverse reaction identified 11/19 (58%) and restrictive reporting syntax 4/19 (21%). All doctors preferred a free form of reporting. The direct cost of the texting-based reporting system was calculated to be US $5581.40 and the indirect cost was US $9989.40. The total cost for texting-based ADR reporting system for 12 months was US $15,570.79. Conclusions: Reporting of ADRs via texting could be lower compared with an existing ADR paper-based system. Problems of Internet connectivity, reporting syntax, and expiration and reliability of text loads/credits should be addressed while implementing a text-based ADR reporting system in a developing country. %M 27227130 %R 10.2196/publichealth.4605 %U http://publichealth.jmir.org/2015/2/e12/ %U https://doi.org/10.2196/publichealth.4605 %U http://www.ncbi.nlm.nih.gov/pubmed/27227130 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e15 %T The US National Tuberculosis Surveillance System: A Descriptive Assessment of the Completeness and Consistency of Data Reported from 2008 to 2012 %A Yelk Woodruff,Rachel S %A Pratt,Robert H %A Armstrong,Lori R %+ Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Division of Tuberculosis Elimination, 1600 Clifton Road NE, Mailstop E10, Atlanta, GA, 30333, United States, 1 404 639 6018, zex5@cdc.gov %K public health surveillance %K disease notification %K information systems %K data cleaning %K quality assurance %D 2015 %7 15.10.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In 2009, the Tuberculosis (TB) Information Management System transitioned into the National TB Surveillance System to allow use of 4 different types of electronic reporting schemes: state-built, commercial, and 2 schemes developed by the Centers for Disease Control and Prevention. Simultaneously, the reporting form was revised to include additional data fields. Objective: Describe data completeness for the years 2008-2012 and determine the impact of surveillance changes. Methods: Data were categorized into subgroups and assessed for completeness (eg, the percentage of patients dead at diagnosis who had a date of death reported) and consistency (eg, the percentage of patients alive at diagnosis who erroneously had a date of death reported). Reporting jurisdictions were grouped to examine differences by reporting scheme. Results: Each year less than 1% of reported cases had missing information for country of origin, race, or ethnicity. Patients reported as dead at diagnosis had death date (a new data field) missing for 3.6% in 2009 and 4.4% in 2012. From 2010 to 2012, 313 cases (1%) reported as alive at diagnosis had a death date and all of these were reported through state-built or commercial systems. The completeness of reporting for guardian country of birth for pediatric patients (a new data field) ranged from 84% in 2009 to 88.2% in 2011. Conclusions: Despite major changes, completeness has remained high for most data elements in TB surveillance. However, some data fields introduced in 2009 remain incomplete; continued training is needed to improve national TB surveillance data. %M 27227133 %R 10.2196/publichealth.4991 %U http://publichealth.jmir.org/2015/2/e15/ %U https://doi.org/10.2196/publichealth.4991 %U http://www.ncbi.nlm.nih.gov/pubmed/27227133 %0 Journal Article %@ 2291-9694 %I Gunther Eysenbach %V 3 %N 3 %P e31 %T A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation %A Tseng,Yi-Ju %A Wu,Jung-Hsuan %A Lin,Hui-Chi %A Chen,Ming-Yuan %A Ping,Xiao-Ou %A Sun,Chun-Chuan %A Shang,Rung-Ji %A Sheng,Wang-Huei %A Chen,Yee-Chun %A Lai,Feipei %A Chang,Shan-Chwen %+ Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, No. 7 Chung-Shan South Road, Taipei, 100, Taiwan, 886 223123456 ext 65054, yeechunchen@gmail.com %K health care-associated infection %K infection control %K information systems %K surveillance %K Web-based services %D 2015 %7 21.09.2015 %9 Original Paper %J JMIR Med Inform %G English %X Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. %M 26392229 %R 10.2196/medinform.4171 %U http://medinform.jmir.org/2015/3/e31/ %U https://doi.org/10.2196/medinform.4171 %U http://www.ncbi.nlm.nih.gov/pubmed/26392229 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e8 %T The Tobacco Pack Surveillance System: A Protocol for Assessing Health Warning Compliance, Design Features, and Appeals of Tobacco Packs Sold in Low- and Middle-Income Countries %A Smith,Katherine %A Washington,Carmen %A Brown,Jennifer %A Vadnais,Alison %A Kroart,Laura %A Ferguson,Jacqueline %A Cohen,Joanna %+ Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Rm 726, 624 N. Broadway, Baltimore, MD, 21215, United States, 1 4105020025, katecsmith@jhu.edu %K tobacco products %K cigarettes %K public health surveillance %K health communication %K national health policy %K marketing %K developing countries %D 2015 %7 12.08.2015 %9 Protocol %J JMIR Public Health Surveill %G English %X Background: Tobacco remains the world’s leading preventable cause of death, with the majority of tobacco-caused deaths occurring in low- and middle-income countries. The first global health treaty, the Framework Convention on Tobacco Control (FCTC), outlines a set of policy initiatives that have been demonstrated as effective in reducing tobacco use. Article 11 of the FCTC focuses on using the tobacco package to communicate tobacco-caused harms; it also seeks to restrict the delivery of misleading information about the product on the pack. Objective: The objective of this study was to establish a surveillance system for tobacco packs in the 14 low- and middle-income countries with the greatest number of smokers. The Tobacco Pack Surveillance System (TPackSS) monitors whether required health warnings on tobacco packages are being implemented as intended, and identifies pack designs and appeals that might violate or detract from the communication of harm-related information and undermine the impact of a country’s tobacco packaging laws. The protocol outlined is intended to be applicable or adaptable for surveillance efforts in other countries. Methods: Tobacco packs were collected in 14 countries during 2013. The intention was, to the extent possible, to construct a census of “unique” pack presentations available for purchase in each country. The TPackSS team partnered with in-country field staff to implement a standardized protocol for acquiring packs from 36 diverse neighborhoods across three cities in each country. At the time of purchase, data on price and place of acquisition of each pack was recorded. The field staff, according to a standardized protocol, then photographed packs before they were shipped to the United States for coding and archiving. Results: Each pack was coded for compliance with the country-specific health warning label laws, as well as for key design features of the pack and appeals of the branding elements. The coding protocols were developed based upon prior research, expert opinion, and communication theories. Each pack was coded by two independent coders, with consistency of personnel across the project. We routinely measured intercoder reliability, and only retained variables for which a good level of reliability was achieved. Variables where reliability was too low were not included in final analyses, and any inconsistencies in coding were resolved on a daily basis. Conclusions: Across the 14 countries, the TPackSS team collected 3307 tobacco packs. We have established a publicly accessible, Internet archive of these packs that is intended for use by the tobacco control policy advocacy and research community. %M 27227142 %R 10.2196/publichealth.4616 %U http://publichealth.jmir.org/2015/2/e8/ %U https://doi.org/10.2196/publichealth.4616 %U http://www.ncbi.nlm.nih.gov/pubmed/27227142 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 2 %P e75 %T A National Surveillance Survey on Noncommunicable Disease Risk Factors: Suriname Health Study Protocol %A Krishnadath,Ingrid SK %A Smits,Christel CF %A Jaddoe,Vincent WV %A Hofman,Albert %A Toelsie,Jerry R %+ Department of Public Health, Faculty of Medical Sciences, Anton de Kom University of Suriname, Kernkampweg 3-5, Paramaribo, , Suriname, 597 441007 ext 280, Ingrid.Krishnadath@uvs.edu %K ethnicity %K multistage cluster sample %K noncommunicable disease risk factors %K STEPwise approach to surveillance %K Suriname %D 2015 %7 17.06.2015 %9 Protocol %J JMIR Res Protoc %G English %X Background: Noncommunicable diseases (NCDs) are the leading cause of death in low- and middle-income countries. Therefore, the surveillance of risk factors has become an issue of major importance for planning and implementation of preventive measures. Unfortunately, in these countries data on NCDs and their risk factors are limited. This also prevails in Suriname, a middle-income country of the Caribbean, with a multiethnic/multicultural population living in diverse residential areas. For these reasons, “The Suriname Health Study” was designed. Objective: The main objective of this study is to estimate the prevalence of NCD risk factors, including metabolic syndrome, hypertension, and diabetes in Suriname. Differences between specific age groups, sexes, ethnic groups, and geographical areas will be emphasized. In addition, risk groups will be identified and targeted actions will be designed and evaluated. Methods: In this study, several methodologies were combined. A stratified multistage cluster sample was used to select the participants of 6 ethnic groups (Hindustani, Creole, Javanese, Maroon, Chinese, Amerindians, and mixed) divided into 5 age groups (between 15 and 65 years) who live in urban/rural areas or the hinterland. A standardized World Health Organization STEPwise approach to surveillance questionnaire was adapted and used to obtain information about demographic characteristics, lifestyle, and risk factors. Physical examinations were performed to measure blood pressure, height, weight, and waist circumference. Biochemical analysis of collected blood samples evaluated the levels of glucose, high-density-lipoprotein cholesterol, total cholesterol, and triglycerides. Statistical analysis will be used to identify the burden of modifiable and unmodifiable risk factors in the aforementioned subgroups. Subsequently, tailor-made interventions will be prepared and their effects will be evaluated. Results: The data as collected allow for national inference and valid analysis of the age, sex, and ethnicity subgroups in the Surinamese population. A publication of the basic survey results is anticipated in mid-2015. Secondary results on the effect of targeted lifestyle interventions are anticipated in late 2017. Conclusions: Using the data collected in this study, the national prevalence of NCD risk factors will be approximated and described in a diverse population. This study is an entry point for formulating the structure of NCD prevention and surveillance. %M 26085372 %R 10.2196/resprot.4205 %U http://www.researchprotocols.org/2015/2/e75/ %U https://doi.org/10.2196/resprot.4205 %U http://www.ncbi.nlm.nih.gov/pubmed/26085372 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 1 %P e5 %T Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study %A Broniatowski,David Andre %A Dredze,Mark %A Paul,Michael J %A Dugas,Andrea %+ Department of Engineering Management and Systems Engineering, The George Washington University, Science and Engineering Hall, 800 22nd Street NW, #2700, Washington, DC, 20052, United States, 1 2029943751, broniatowski@gwu.edu %K Web mining %K social computing %K time series analysis %D 2015 %7 29.05.2015 %9 Short Paper %J JMIR Public Health Surveill %G English %X Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions: In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report. %M 27014744 %R 10.2196/publichealth.4472 %U http://publichealth.jmir.org/2015/1/e5/ %U https://doi.org/10.2196/publichealth.4472 %U http://www.ncbi.nlm.nih.gov/pubmed/27014744 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 1 %P e3 %T The Annual American Men's Internet Survey of Behaviors of Men Who Have Sex With Men in the United States: Protocol and Key Indicators Report 2013 %A Sanchez,Travis Howard %A Sineath,R Craig %A Kahle,Erin M %A Tregear,Stephen James %A Sullivan,Patrick Sean %+ Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, United States, 1 404 727 8403, Travis.Sanchez@emory.edu %K MSM %K gay %K homosexual %K bisexual %K HIV %K STD %K Internet %K survey %K surveillance %D 2015 %7 17.04.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Men who have sex with men (MSM) are disproportionately affected by human immunodeficiency virus (HIV) and there is evidence that this population is participating in increasingly risky sexual behavior. These changes are occurring in the context of new modes of online social interaction—many MSM now report first meeting their sex partners on the Internet. Better monitoring of key behavioral indicators among MSM requires the use of surveillance strategies that capitalize on these new modes of interaction. Therefore, we developed an annual cross-sectional behavioral survey of MSM in the United States, the American Men's Internet Survey (AMIS). Objective: The purpose of this paper was to provide a description of AMIS methods. In addition we report on the first cycle of data collection (December 2013 through May 2014; AMIS-2013) on the same key indicators used for national HIV behavioral surveillance. Methods: AMIS-2013 recruited MSM from a variety of websites using banner advertisements or email blasts. Adult men currently residing in the United States were eligible to participate if they had ever had sex with a man. We examined demographic and recruitment characteristics using multivariable regression modeling (P<.05) stratified by the participants' self-reported HIV status. Results: In the AMIS-2013 round, 79,635 persons landed on the study page and 14,899 were eligible, resulting in 10,377 completed surveys from MSM representing every US state. Participants were mainly white, 40 years or older, living in the US South, living in urban areas, and recruited from a general social networking website. Self-reported HIV prevalence was 10.73% (n=1113). Compared to HIV-negative/unknown status participants, HIV-positive participants were more likely to have had anal sex without a condom with any male partner in the past 12 months (72.24% versus 61.24%, respectively; P<.001) and more likely to have had anal sex without a condom with their last male sex partner who was discordant/unknown HIV status (42.95% versus 13.62%, respectively; P<.001). Illicit substance use in the past 12 months was more likely to be reported by HIV-positive participants than HIV-negative/unknown status participants (39.17% versus 26.85%, respectively; P<.001). The vast majority of HIV-negative/unknown status participants (84.05%) had been previously HIV tested, but less than half (44.20%) had been tested in the past 12 months. Participants 18-24 years of age were more likely than those 40 years or older to have had anal sex without a condom with a discordant/unknown HIV status partner, were more likely to report substance use, and were less likely to have been HIV tested. Compared to general social networking, those from a geospatial social networking website were more likely to have reported all risk behaviors but were more likely to have been HIV tested. Conclusions: The first round of AMIS generated useful behavioral measures from more than 10,000 MSM Internet users. Preliminary findings identified some subgroups of MSM Internet users that are at potentially higher risk of HIV acquisition/transmission. AMIS will provide an ongoing data source for examining trends in sexual risk behavior of MSM. This will help to plan and monitor the impact of programs to improve this population's health. %M 27227126 %R 10.2196/publichealth.4314 %U http://publichealth.jmir.org/2015/1/e3/ %U https://doi.org/10.2196/publichealth.4314 %U http://www.ncbi.nlm.nih.gov/pubmed/27227126