TY - JOUR AU - Lin, Shuangquan AU - Duan, Lingxing AU - Xu, Xiangda AU - Cao, Haichao AU - Lu, Xiongbing AU - Wen, Xi AU - Wei, Shanzun PY - 2025/3/14 TI - Analyzing Online Search Trends for Kidney, Prostate, and Bladder Cancers in China: Infodemiology Study Using Baidu Search Data (2011-2023) JO - JMIR Cancer SP - e57414 VL - 11 KW - bladder cancer KW - kidney cancer KW - prostate cancer KW - Baidu Index KW - infodemiology KW - public interest KW - patients? concern N2 - Background: Cancers of the bladder, kidney, and prostate are the 3 major genitourinary cancers that significantly contribute to the global burden of disease (GBD) and continue to show increasing rates of morbidity and mortality worldwide. In mainland China, understanding the cancer burden on patients and their families is crucial; however, public awareness and concerns about these cancers, particularly from the patient?s perspective, remain predominantly focused on financial costs. A more comprehensive exploration of their needs and concerns has yet to be fully addressed. Objective: This study aims to analyze trends in online searches and user information?seeking behaviors related to bladder, kidney, and prostate cancers?encompassing descriptive terms (eg, ?bladder cancer,? ?kidney cancer,? ?prostate cancer?) as well as related synonyms and variations?on both national and regional scales. This study leverages data from mainland China?s leading search engine to explore the implications of these search patterns for addressing user needs and improving health management. Methods: The study analyzed Baidu Index search trends for bladder, kidney, and prostate cancers (from January 2011 to August 2023) at national and provincial levels. Search volume data were analyzed using the joinpoint regression model to calculate annual percentage changes (APCs) and average APCs (AAPCs), identifying shifts in public interest. User demand was assessed by categorizing the top 10 related terms weekly into 13 predefined topics, including diagnosis, treatment, and traditional Chinese medicine. Data visualization and statistical analyses were performed using Prism 9. Results revealed keyword trends, demographic distributions, and public information needs, offering insights into health communication and management strategies based on online information-seeking behavior. Results: Three cancer topics were analyzed using 39 search keywords, yielding a total Baidu Search Index (BSI) of 43,643,453. From 2011 to 2015, the overall APC was 15.2% (P<.05), followed by ?2.8% from 2015 to 2021, and 8.9% from 2021 to 2023, with an AAPC of 4.9%. Bladder, kidney, and prostate cancers exhibited AAPCs of 2.8%, 3.9%, and 6.8%, respectively (P<.05). The age distribution of individuals searching for these cancer topics varied across the topics. Geographically, searches for cancer were predominantly conducted by people from East China, who accounted for approximately 30% of each cancer search query. Regarding user demand, the total BSI for relevant user demand terms from August 2022 to August 2023 was 676,526,998 out of 2,570,697,380 (15.74%), representing only a limited total cancer-related search volume. Conclusions: Online searches and inquiries related to genitourinary cancers are on the rise. The depth of users? information demands appears to be influenced by regional economic levels. Cancer treatment decision-making may often involve a family-centered approach. Insights from internet search data can help medical professionals better understand public interests and concerns, enabling them to provide more targeted and reliable health care services. UR - https://cancer.jmir.org/2025/1/e57414 UR - http://dx.doi.org/10.2196/57414 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57414 ER - TY - JOUR AU - Hu, Songbo AU - Oppong, Abigail AU - Mogo, Ebele AU - Collins, Charlotte AU - Occhini, Giulia AU - Barford, Anna AU - Korhonen, Anna PY - 2025/3/5 TI - Natural Language Processing Technologies for Public Health in Africa: Scoping Review JO - J Med Internet Res SP - e68720 VL - 27 KW - public health KW - global health KW - health promotion KW - essential public health functions KW - Africa KW - natural language processing KW - artificial intelligence KW - machine learning KW - technology KW - mobile phone N2 - Background: Natural language processing (NLP) has the potential to promote public health. However, applying these technologies in African health systems faces challenges, including limited digital and computational resources to support the continent?s diverse languages and needs. Objective: This scoping review maps the evidence on NLP technologies for public health in Africa, addressing the following research questions: (1) What public health needs are being addressed by NLP technologies in Africa, and what unmet needs remain? (2) What factors influence the availability of public health NLP technologies across African countries and languages? (3) What stages of deployment have these technologies reached, and to what extent have they been integrated into health systems? (4) What measurable impact has these technologies had on public health outcomes, where such data are available? (5) What recommendations have been proposed to enhance the quality, cost, and accessibility of health-related NLP technologies in Africa? Methods: This scoping review includes academic studies published between January 1, 2013, and October 3, 2024. A systematic search was conducted across databases, including MEDLINE via PubMed, ACL Anthology, Scopus, IEEE Xplore, and ACM Digital Library, supplemented by gray literature searches. Data were extracted and the NLP technology functions were mapped to the World Health Organization?s list of essential public health functions and the United Nations? sustainable development goals (SDGs). The extracted data were analyzed to identify trends, gaps, and areas for future research. This scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines, and its protocol is publicly available. Results: Of 2186 citations screened, 54 studies were included. While existing NLP technologies support a subset of essential public health functions and SDGs, language coverage remains uneven, with limited support for widely spoken African languages, such as Kiswahili, Yoruba, Igbo, and Zulu, and no support for most of Africa?s >2000 languages. Most technologies are in prototyping phases, with only one fully deployed chatbot addressing vaccine hesitancy. Evidence of measurable impact is limited, with 15% (8/54) studies attempting health-related evaluations and 4% (2/54) demonstrating positive public health outcomes, including improved participants? mood and increased vaccine intentions. Recommendations include expanding language coverage, targeting local health needs, enhancing trust, integrating solutions into health systems, and adopting participatory design approaches. The gray literature reveals industry- and nongovernmental organizations?led projects focused on deployable NLP applications. However, these projects tend to support only a few major languages and specific use cases, indicating a narrower scope than academic research. Conclusions: Despite growth in NLP research for public health, major gaps remain in deployment, linguistic inclusivity, and health outcome evaluation. Future research should prioritize cross-sectoral and needs-based approaches that engage local communities, align with African health systems, and incorporate rigorous evaluations to enhance public health outcomes. International Registered Report Identifier (IRRID): RR2-doi:10.1101/2024.07.02.24309815 UR - https://www.jmir.org/2025/1/e68720 UR - http://dx.doi.org/10.2196/68720 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053738 ID - info:doi/10.2196/68720 ER - TY - JOUR AU - Pullano, Giulia AU - Alvarez-Zuzek, Gisele Lucila AU - Colizza, Vittoria AU - Bansal, Shweta PY - 2025/2/18 TI - Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study JO - JMIR Public Health Surveill SP - e64914 VL - 11 KW - geographical disease dynamics KW - spatial connectivity KW - mobility data KW - metapopulation modeling KW - COVID-19 KW - human mobility KW - infectious diseases KW - social distancing KW - epidemic KW - mobile apps KW - SafeGraph KW - SARS-CoV-2 KW - coronavirus KW - pandemic KW - spatio-temporal KW - US KW - public health KW - mobile health KW - mHealth KW - digital health KW - health informatics N2 - Background: Human mobility is expected to be a critical factor in the geographic diffusion of infectious diseases, and this assumption led to the implementation of social distancing policies during the early fight against the COVID-19 emergency in the United States. Yet, because of substantial data gaps in the past, what still eludes our understanding are the following questions: (1) How does mobility contribute to the spread of infection within the United States at local, regional, and national scales? (2) How do seasonality and shifts in behavior affect mobility over time? (3) At what geographic level is mobility homogeneous across the United States? Objective: This study aimed to address the questions that are critical for developing accurate transmission models, predicting the spatial propagation of disease across scales, and understanding the optimal geographical and temporal scale for the implementation of control policies. Methods: We analyzed high-resolution mobility data from mobile app usage from SafeGraph Inc, mapping daily connectivity between the US counties to grasp spatial clustering and temporal stability. Integrating this into a spatially explicit transmission model, we replicated SARS-CoV-2?s first wave invasion, assessing mobility?s spatiotemporal impact on disease predictions. Results: Analysis from 2019 to 2021 showed that mobility patterns remained stable, except for a decline in April 2020 due to lockdowns, which reduced daily movements from 45 million to approximately 25 million nationwide. Despite this reduction, intercounty connectivity remained seasonally stable, largely unaffected during the early COVID-19 phase, with a median Spearman coefficient of 0.62 (SD 0.01) between daily connectivity and gravity networks. We identified 104 geographic clusters of US counties with strong internal mobility connectivity and weaker links to counties outside these clusters. These clusters were stable over time, largely overlapping state boundaries (normalized mutual information=0.82) and demonstrating high temporal stability (normalized mutual information=0.95). Our findings suggest that intercounty connectivity is relatively static and homogeneous at the substate level. Furthermore, while county-level, daily mobility data best captures disease invasion, static mobility data aggregated to the cluster level also effectively models spatial diffusion. Conclusions: Our work demonstrates that intercounty mobility was negligibly affected outside the lockdown period in April 2020, explaining the broad spatial distribution of COVID-19 outbreaks in the United States during the early phase of the pandemic. Such geographically dispersed outbreaks place a significant strain on national public health resources and necessitate complex metapopulation modeling approaches for predicting disease dynamics and control design. We thus inform the design of such metapopulation models to balance high disease predictability with low data requirements. UR - https://publichealth.jmir.org/2025/1/e64914 UR - http://dx.doi.org/10.2196/64914 ID - info:doi/10.2196/64914 ER - TY - JOUR AU - Achangwa, Chiara AU - Han, Changhee AU - Lim, Jun-Sik AU - Cho, Seonghui AU - Choi, Sangbum AU - Ryu, Sukhyun PY - 2025/2/12 TI - Net Reproduction Number as a Real-Time Metric of Population Reproducibility JO - JMIR Public Health Surveill SP - e63603 VL - 11 KW - fertility rate KW - reproducibility KW - reproduction rate KW - population control KW - Korea KW - sex ratio KW - imbalance KW - mortality KW - woman KW - female KW - childbearing age KW - reproductive age KW - giving birth KW - assessment KW - time series KW - Korean KW - impact analysis KW - birth control KW - reproduction N2 - Abstract: The total fertility rate (TFR) is a biased estimate of the population reproductive potential when there is a sex-ratio imbalance at birth, and it does not account for the mortality of women of childbearing age. This study aimed to estimate the reproduction rate (Rt), which adjusts for the sex-ratio imbalance and the mortality of women of childbearing age, and to assess the differences in the timing of when the population reached the replacement level of the TFR and Rt. We first estimated the Rt using the probability of survival in women and the number of female births. Then, using a time-series analysis, we compared the time series of the TFR and Rt in the Korean population between 1975 and 2022. We found the Rt showed a below replacement level of the population a year earlier than the TFR. However, the estimate of the time-series analysis of Rt was not significantly different from the estimates of the TFR. Our finding suggests that the Rt can provide timely information on the adjusted population reproductive potential and is easier for the public to interpret compared to TFR. UR - https://publichealth.jmir.org/2025/1/e63603 UR - http://dx.doi.org/10.2196/63603 ID - info:doi/10.2196/63603 ER - TY - JOUR AU - Thomas, Julia AU - Lucht, Antonia AU - Segler, Jacob AU - Wundrack, Richard AU - Miché, Marcel AU - Lieb, Roselind AU - Kuchinke, Lars AU - Meinlschmidt, Gunther PY - 2025/1/29 TI - An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study JO - JMIR Public Health Surveill SP - e63809 VL - 11 KW - deep learning KW - explainable artificial intelligence (XAI) KW - large language model (LLM) KW - machine learning KW - neural network KW - prevention KW - risk monitoring KW - suicide KW - transformer model KW - suicidality KW - suicidal ideation KW - self-murder KW - self-harm KW - youth KW - adolescent KW - adolescents KW - public health KW - language model KW - language models KW - chat protocols KW - crisis helpline KW - help-seeking behaviors KW - German KW - Shapley KW - decision-making KW - mental health KW - health informatics KW - mobile phone N2 - Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text. Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model. Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language. Conclusions: Neural networks using large language model?based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk. UR - https://publichealth.jmir.org/2025/1/e63809 UR - http://dx.doi.org/10.2196/63809 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63809 ER - TY - JOUR AU - Marshall, J. Daniel AU - Gower, L. Amy AU - Katz, L. Mira AU - Bauermeister, A. José AU - Shoben, B. Abigail AU - Reiter, L. Paul PY - 2025/1/3 TI - Recruitment of Young Gay, Bisexual, and Other Men Who Have Sex With Men for a Web-Based Human Papillomavirus Vaccination Intervention: Differences in Participant Characteristics and Study Engagement by Recruitment Source in a Randomized Controlled Trial JO - J Med Internet Res SP - e64668 VL - 27 KW - study recruitment KW - gay and bisexual men KW - human papillomavirus KW - vaccination promotion KW - digital intervention KW - social media KW - dating apps KW - recruitment KW - young adults KW - adolescents KW - gay KW - bisexual KW - men who have sex with men N2 - Background: Young gay, bisexual, and other men who have sex with men have been referred to as a ?hard-to-reach? or ?hidden? community in terms of recruiting for research studies. With widespread internet use among this group and young adults in general, web-based avenues represent an important approach for reaching and recruiting members of this community. However, little is known about how participants recruited from various web-based sources may differ from one another. Objective: This study aimed to determine how young gay, bisexual, and other men who have sex with men recruited from various web-based sources differ from one another in terms of participant characteristics and study engagement. Methods: Data were collected as part of a randomized controlled trial of Outsmart HPV, a web-based human papillomavirus (HPV) vaccination intervention for young gay, bisexual, and other men who have sex with men. From 2019 to 2021, we recruited young gay, bisexual, and other men who have sex with men in the United States who were aged 18-25 years and not vaccinated against HPV (n=1227) through various web-based avenues. We classified each participant as being recruited from either (1) social media (eg, Facebook, Instagram, Snapchat), (2) a dating app (eg, Grindr, Scruff), or (3) some other digital recruitment source (eg, existing research panel, university-based organization). Analyses compared participants from these 3 groups on demographic and health-related characteristics and metrics involving study engagement. Results: Most demographic and health-related characteristics differed by web-based recruitment source, including race or ethnicity (P<.001), relationship status (P<.001), education level (P<.001), employment status (P<.001), sexual self-identity (P<.001), health insurance status (P<.001), disclosure of sexual orientation (P=.048), and connectedness to the LGBTQ (lesbian, gay, bisexual, transgender, queer) community (P<.001) The type of device used by participants during study enrollment also differed across groups, with smartphone use higher among participants recruited via dating apps (n=660, 96.6%) compared to those recruited via social media (n=318, 78.9%) or other digital sources (n=85, 60.3%; P<.001). Participants recruited via social media were more likely than those recruited via dating apps to complete follow-up surveys at 3 different timepoints (odds ratios 1.52-2.09, P=.001-.008). These participants also spent a longer amount of time viewing intervention content about HPV vaccination (3.14 minutes vs 2.67 minutes; P=.02). Conclusions: We were able to recruit a large national sample of young gay, bisexual, and other men who have sex with men for a web-based HPV vaccination intervention via multiple methodologies. Participants differed on a range of demographic and health-related characteristics, as well as metrics related to study engagement, based on whether they were recruited from social media, a dating app, or some other digital recruitment source. Findings highlight key issues and considerations that can help researchers better plan and customize future web-based recruitment efforts of young gay, bisexual, and other men who have sex with men. Trial Registration: ClinicalTrials.gov NCT04032106; https://clinicaltrials.gov/study/NCT04032106 International Registered Report Identifier (IRRID): RR2-10.2196/16294 UR - https://www.jmir.org/2025/1/e64668 UR - http://dx.doi.org/10.2196/64668 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64668 ER - TY - JOUR AU - Luo, Waylon AU - Jin, Ruoming AU - Kenne, Deric AU - Phan, NhatHai AU - Tang, Tang PY - 2024/12/30 TI - An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach JO - JMIR Form Res SP - e49567 VL - 8 KW - Twitter (X) KW - popular music KW - big data analysis KW - music KW - lyrics KW - big data KW - substance abuse KW - tweet KW - social media KW - drug KW - alcohol N2 - Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: ?tweets that reference established drug lyrics.? Objective: We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists? gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X. Methods: X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics. Results: We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98% of drug-related lyrics from these genres and 84.21% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time. Conclusions: Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use. UR - https://formative.jmir.org/2024/1/e49567 UR - http://dx.doi.org/10.2196/49567 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49567 ER - TY - JOUR AU - Yu, Bin AU - Kravchenko, Julia AU - Yashkin, Arseniy AU - Akushevich, Igor PY - 2024/11/20 TI - Decomposition of Heart Failure Prevalence and Mortality Among Older Adults in the United States: Medicare-Based Partitioning Analysis JO - JMIR Public Health Surveill SP - e51989 VL - 10 KW - heart failure KW - prevalence KW - mortality KW - partitioning KW - time trends KW - epidemiologic determinants N2 - Background: Heart failure (HF) is a challenging clinical and public health problem characterized by high prevalence and mortality among US older adults, along with a recent decline in HF prevalence and increase in mortality. The changes of prevalence can be decomposed into pre-existing disease prevalence, disease incidence, and respective survival, while the changes of mortality can be decomposed into mortality in the general population independent from HF, pre-existing HF prevalence, incidence, and respective survival. These epidemiological components may contribute differently to the changes in prevalence and mortality. Objective: We aimed to investigate and compare the relative contributions of epidemiologic determinants in HF prevalence and mortality trends. Methods: This study was a secondary data analysis of 5% of Medicare claims data for 1992?2017 in the United States. Medicare is a federal health insurance program for older adults aged 65+ years as well as people with specific disabilities and end-stage renal disease. Age-adjusted prevalence and incidence-based mortality (IBM; all-cause mortality that occurred in patients with HF) were partitioned into their respective epidemiologic determinants using the partitioning analysis approach. Results: The age-adjusted HF prevalence (1/100 person-years) increased from 11 in 1994 to 14.6 in 2005, followed by a decline to 12.6 in 2017, and the age-adjusted HF IBM (1/100,000) increased from 2220.8 in 1994 to 2563.7 in 2000, then declined to 2075.9 in 2016, followed by an increase to 2094.7 in 2017. The HF incidence (1/1000 person-years) declined from 29.4 in 1992 to 19.9 in 2017. The 1-, 3-, and 5-year survival trend showed declines in recent years. Partitioning of HF prevalence showed three phases: (1) decelerated increasing prevalence (1994?2006), (2) accelerated declining prevalence (2007?2014), and (3) decelerated declining prevalence (2015?2017). During the whole period, the decreasing HF incidence contributed to the declines in prevalence, overpowering prevalence increases contributed from survival. Likewise, partitioning of HF IBM showed three phases: (1) decelerated increasing mortality (1994?2001), (2) accelerated declining mortality (2002?2012), and (3) decelerated declining mortality (2013?2017). The decreasing HF incidence in 1994?2017 and increasing survival in 2002?2006 contributed to the declines in mortality, while the decreasing survival in 2007?2017 contributed to the mortality increase. Conclusions: Decade-long declines in HF prevalence and mortality mainly reflected decreasing incidence, while the most recent increase of mortality was predominantly due to the declining survival. If current trends persist, HF prevalence and mortality are forecasted to grow substantially in the next decade. Prevention strategies should continue the prevention of HF risk factors as well as improvement of treatment and management of HF after diagnosis. UR - https://publichealth.jmir.org/2024/1/e51989 UR - http://dx.doi.org/10.2196/51989 ID - info:doi/10.2196/51989 ER - TY - JOUR AU - Melo, Lopes Carolina AU - Mageste, Rangel Larissa AU - Guaraldo, Lusiele AU - Paula, Polessa Daniela AU - Wakimoto, Duarte Mayumi PY - 2024/11/18 TI - Use of Digital Tools in Arbovirus Surveillance: Scoping Review JO - J Med Internet Res SP - e57476 VL - 26 KW - arbovirus infections KW - dengue KW - zika virus KW - chikungunya fever KW - public health surveillance KW - digital tool KW - technology N2 - Background: The development of technology and information systems has led to important changes in public health surveillance. Objective: This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance. Methods: The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society). Results: Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance. Conclusions: In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools? data, considering ethical aspects. UR - https://www.jmir.org/2024/1/e57476 UR - http://dx.doi.org/10.2196/57476 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57476 ER - TY - JOUR AU - Rosenthal, Sarah AU - Adler-Milstein, Julia AU - Patel, Vaishali PY - 2024/11/15 TI - Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study JO - JMIR Public Health Surveill SP - e64969 VL - 10 KW - public health informatics KW - health information exchange KW - health information technology KW - data exchange KW - health information KW - national survey KW - surveillance KW - United States KW - PHA KW - HIO KW - public health agency KW - health information exchange organization N2 - Background: The COVID-19 pandemic revealed major gaps in public health agencies? (PHAs?) data and reporting infrastructure, which limited the ability of public health officials to conduct disease surveillance, particularly among racial or ethnic minorities disproportionally affected by the pandemic. Leveraging existing health information exchange organizations (HIOs) is one possible mechanism to close these technical gaps, as HIOs facilitate health information sharing across organizational boundaries. Objective: The aim of the study is to survey all HIOs that are currently operational in the United States to assess HIO connectivity with PHAs and HIOs? capabilities to support public health data exchange. Methods: Drawing on multiple sources, we identified all potential local, regional, and state HIOs that were operational in the United States as of March 1, 2022. We defined operational as HIOs that facilitated exchange between at least 2 independent entities. We fielded a survey among our census list of 135 HIOs in January-July 2023. The survey confirmed HIO status as well as captured organizational demographics and current and potential support for PHAs. We report descriptive statistics on HIO demographics and connectivity with PHAs. We also include results on services and data available to support PHAs, funding sources to support public health reporting, and barriers to public health reporting. Of the 135 potential HIOs that received the survey, 90 met our definition of an HIO, and 77 completed the survey, yielding an 86% response rate. Results: We found that 66 (86%) of HIOs in 45 states were electronically connected to at least 1 PHA, yielding 187 HIO-PHA connections across all HIOs. Among HIOs connected to PHAs, the most common type of public health reporting supported by HIOs was immunization registry (n=39, 64%), electronic laboratory result (n=37, 63%), and syndromic surveillance (n=34, 61%). In total, 58% (n=38) of HIOs connected to PHAs provided data to address COVID-19 information gaps, and an additional 30% (n=20) could do so. The most common types of data provided to PHAs were hospitalization information (n=54, 93%), other demographic data (n=53, 91%), health information (eg, chronic health conditions; n=51, 88%), and hospital laboratory results (n=51, 88%). A total of 64% (n=42) of HIOs provided at least 1 type of data analytic service to PHAs to support COVID-19 pandemic response efforts. Top HIO reported barriers to support PHA activities included limited PHA funding (n=21, 32%) and PHAs? competing priorities (n=15, 23%). Conclusions: Our results show that many HIOs are already connected to PHAs and that they are assuming an emerging role to facilitate public health reporting. HIOs are well-positioned to provide value-added support for public health data exchange and address PHAs? information gaps, as ongoing federal efforts to modernize public health data infrastructure and interoperability continue. UR - https://publichealth.jmir.org/2024/1/e64969 UR - http://dx.doi.org/10.2196/64969 ID - info:doi/10.2196/64969 ER - TY - JOUR AU - Cheng, Xuelin AU - Wu, Xiaoling AU - Ye, Wenjing AU - Chen, Yichen AU - Fu, Peihua AU - Jia, Wenchang AU - Zhang, Wei AU - Xu, Xiaoyun AU - Gong, Di AU - Mou, Changhua AU - Gu, Wen AU - Luo, Zheng AU - Jiang, Sunfang AU - Li, Xiaopan PY - 2024/11/14 TI - All-Cause and Cause-Specific Burden of Asthma in a Transitioning City in China: Population Study JO - JMIR Public Health Surveill SP - e44845 VL - 10 KW - asthma KW - mortality KW - years of life lost KW - trend analysis KW - decomposition method KW - Pudong N2 - Background: Understanding the impact of asthma on public health is crucial for evidence-based prevention and treatment strategies. Objective: This study aimed to identify the causes of asthma-related mortality in Pudong, Shanghai, China, offering insights for managing similar regions or countries in transition. Methods: Mortality statistics were obtained from the Vital Statistics System of Pudong for 2005?2021. Temporal patterns for the burden of asthma were examined. The crude mortality rate (CMR), age-standardized mortality rate by Segi?s world standard population (ASMRW), and years of life lost (YLL) for both all-cause and asthma-specific deaths were computed. Mortality rates associating with aging and other variables were categorized using the decomposition technique. The autoregressive integrated moving average model was used to forecast the asthma-related death mortality rate by 2035. Results: A total of 1568 asthma-related deaths occurred during the follow-up period, with the CMR and ASMRW being 3.25/105 and 1.22/105 person-years, respectively. The primary underlying causes of death were chronic lower respiratory diseases, coronary heart diseases, and cerebrovascular disease. The YLL due to total asthma-related deaths added up to 14,837.76 years, with a YLL rate of 30.73/105 person-years. Male individuals had more YLL (8941.81 vs 5895.95 y) and a higher YLL rate (37.12/105 vs 24.38/105 person-years) than female individuals. From 2005 to 2021, the ASMRW declined by 3.48%, and both the CMR and YLL rate decreased in the 0?29, 70?79, and ?80 years age groups (all P<.01). However, asthma-related deaths increased from 329 people between 2005 and 2008 to 472 people between 2017 and 2021. The proportion of the population aged 80 years and older gradually increased by 1.43% (95% CI 0.20%-2.68%; P=.03), and the mortality rates of asthma deaths attributable to population aging rose by 21.97% (95% CI, 11.58%-33.32%; P<.001) annually. Conclusions: Asthma remains a significant public health challenge in transitioning countries, requiring increased attention and resource allocation. UR - https://publichealth.jmir.org/2024/1/e44845 UR - http://dx.doi.org/10.2196/44845 ID - info:doi/10.2196/44845 ER - TY - JOUR AU - Davis, Kevin AU - Curry, Laurel AU - Bradfield, Brian AU - Stupplebeen, A. David AU - Williams, J. Rebecca AU - Soria, Sandra AU - Lautsch, Julie PY - 2024/11/5 TI - The Validity of Impressions as a Media Dose Metric in a Tobacco Public Education Campaign Evaluation: Observational Study JO - J Med Internet Res SP - e55311 VL - 26 KW - communication KW - public education KW - tobacco KW - media KW - public health N2 - Background: Evaluation research increasingly needs alternatives to target or gross rating points to comprehensively measure total exposure to modern multichannel public education campaigns that use multiple channels, including TV, radio, digital video, and paid social media, among others. Ratings data typically only capture delivery of broadcast media (TV and radio) and excludes other channels. Studies are needed to validate objective cross-channel metrics such as impressions against self-reported exposure to campaign messages. Objective: This study aimed to examine whether higher a volume of total media campaign impressions is predictive of individual-level self-reported campaign exposure in California. Methods: We analyzed over 3 years of advertisement impressions from the California Tobacco Prevention Program?s statewide tobacco education campaigns from August 2019 through December 2022. Impressions data varied across designated market areas (DMAs) and across time. These data were merged to individual respondents from 45 waves of panel survey data of Californians aged 18-55 years (N=151,649). Impressions were merged to respondents based on respondents? DMAs and time of survey completion. We used logistic regression to estimate the odds of respondents? campaign recall as a function of cumulative and past 3-month impressions delivered to each respondent?s DMA. Results: Cumulative impressions were positively and significantly associated with recall of each of the Flavors Hook Kids (odds ratio [OR] 1.15, P<.001), Dark Balloons and Apartment (OR 1.20, P<.001), We Are Not Profit (OR 1.36, P<.001), Tell Your Story (E-cigarette, or Vaping, product use Associated Lung Injury; OR 1.06, P<.05), and Thrown Away and Little Big Lies (OR 1.05, P<.01) campaigns. Impressions delivered in the past 3 months were associated with recall of the Flavors Hook Kids (OR 1.13, P<.001), Dark Balloons and Apartment (OR 1.08, P<.001), We Are Not Profit (OR 1.14, P<.001), and Thrown Away and Little Big Lies (OR 1.04, P<.001) campaigns. Past 3-month impressions were not significantly associated with Tell Your Story campaign recall. Overall, magnitudes of these associations were greater for cumulative impressions. We visualize recall based on postestimation predicted values from our multivariate logistic regression models. Conclusions: Variation in cumulative impressions for California Tobacco Prevention Program?s long-term multichannel tobacco education campaign is predictive of increased self-reported campaign recall, suggesting that impressions may be a valid proxy for potential campaign exposure. The use of impressions for purposes of evaluating public education campaigns may help address current methodological limitations arising from the fragmented nature of modern multichannel media campaigns. UR - https://www.jmir.org/2024/1/e55311 UR - http://dx.doi.org/10.2196/55311 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55311 ER - TY - JOUR AU - Brooks, J. Donald AU - Kim, Inae Carolyn AU - Mboussou, Fortune Franck AU - Danovaro-Holliday, Carolina M. PY - 2024/10/25 TI - Developing National Information Systems to Monitor COVID-19 Vaccination: A Global Observational Study JO - JMIR Public Health Surveill SP - e62657 VL - 10 KW - COVID-19 KW - COVID-19 vaccine KW - immunization information system KW - vaccination monitoring KW - vaccine KW - monitoring and evaluation N2 - Background: Strong information systems are essential for safe and effective immunization programs. The COVID-19 vaccine rollout presented all immunization information systems (IIS) with challenging demands?requiring in-depth vaccine implementation data at all health system levels in real time. The system development approaches taken by countries were heterogeneous, with some countries opting to adapt existing systems and others implementing new ones. Objective: Using data reported by Member States to the World Health Organization (WHO), we aim to develop a global understanding of (1) the types of IIS used to monitor COVID-19 vaccination implemented in 2021 and (2) the approaches taken by countries to develop these systems. Methods: We conducted a descriptive analysis of data reported through a supplemental questionnaire of the WHO/United Nations Children's Emergency Fund (UNICEF) Joint Reporting Form on Immunization, collecting data for 2021 on (1) the use of and developmental approaches taken for 7 IIS functions (appointments, aggregate reporting, individual-level reporting, reminders, home-based records, safety surveillance, and stock management), and (2) modifications needed for digital health frameworks to permit COVID-19 vaccination monitoring. Results: In total, 188 of 194 WHO Member States responded to the supplemental questionnaire, with 155 reporting on the IIS-related questions. Among those reporting, for each of the 7 IIS functions explored, greater than 85% of responding countries reported that the system was in place for COVID-19 vaccines. Among responding countries, ?aggregate reporting system? was the system most frequently reported as being in place (n=116, 98.3%), while ?reminder system? was the least (n=77, 89%). Among the countries reporting using a system, whether an existing system was adapted for COVID-19 vaccines or a new one was developed varied by system. Additionally, two-thirds (n=127, 67.6%) of countries reported establishing at least one new system, ranging from 72% (n=42) in high-income countries (HICs) to 62% (n=16) in low-income countries. Concurrently, 55.3% (n=104) of countries reported adapting at least one system already in place for COVID-19 vaccines, with 62% (n=36) of HICs reporting this compared to about 53% for other income groups. Of those reporting developing new systems, for each of the systems explored, more than 85% of countries reported that they intended to keep new systems specific to COVID-19 vaccines. Further, 147 of the 188 (78.2%) Member States responding to the supplemental questionnaire responded to the digital health frameworks question. Lastly, 31% (n=46) of responding countries reported needing to adapt them for COVID-19 vaccination systems. HICs had a higher percentage. Conclusions: Nearly all countries have adapted existing or developed new IIS to monitor COVID-19 vaccination. The approaches varied, notably by income group. Reflection is needed on how to sustain the investments made in IIS during the pandemic. Continued support for IIS is critical, given their essential role in program monitoring and performance. UR - https://publichealth.jmir.org/2024/1/e62657 UR - http://dx.doi.org/10.2196/62657 ID - info:doi/10.2196/62657 ER - TY - JOUR AU - Yang, Hsiao-Yu AU - Wu, Chang-Fu AU - Tsai, Kun-Hsien PY - 2024/10/16 TI - Projections of Climate Change Impact on Acute Heat Illnesses in Taiwan: Case-Crossover Study JO - JMIR Public Health Surveill SP - e57948 VL - 10 KW - climate change KW - global warming KW - heat-related illness KW - carbon reduction KW - heat KW - heat illness KW - extreme heat KW - Taiwan KW - real time KW - epidemic KW - surveillance KW - public health KW - emergency department KW - early warning system KW - nonlinear model KW - temperature KW - socioeconomic KW - environmental health KW - heat stress KW - environmental KW - epidemiology N2 - Background: With global warming, the number of days with extreme heat is expected to increase and may cause more acute heat illnesses. While decreasing emissions may mitigate the climate impacts, its effectiveness in reducing acute heat illnesses remains uncertain. Taiwan has established a real-time epidemic surveillance and early warning system to monitor acute heat illnesses since January 1, 2011. Predicting the number of acute heat illnesses requires forecasting temperature changes that are influenced by adaptation policies. Objective: The aim of this study was to estimate the changes in the number of acute heat illnesses under different adaptation policies. Methods: We obtained the numbers of acute heat illnesses in Taiwan from January 2011 to July 2023 using emergency department visit data from the real-time epidemic surveillance and early warning system. We used segmented linear regression to identify the join point as a nonoptimal temperature threshold. We projected the temperature distribution and excess acute heat illnesses through the end of the century when Taiwan adopts the ?Sustainability (shared socioeconomic pathways 1?2.6 [SSP1-2.6]),? ?Middle of the road (SSP2-4.5),? ?Regional rivalry (SSP3-7.0),? and ?Fossil-fueled development (SSP5-8.5)? scenarios. Distributed lag nonlinear models were used to analyze the attributable number (AN) and attributable fraction (AF) of acute heat illnesses caused by nonoptimal temperature. Results: We enrolled a total of 28,661 patients with a mean age of 44.5 (SD 15.3) years up to July 2023, of whom 21,619 (75.4%) were male patients. The nonoptimal temperature was 27 °C. The relative risk of acute heat illnesses with a 1-degree increase in mean temperature was 1.71 (95% CI 1.63-1.79). In the SSP5-8.5 worst-case scenario, the mean temperature was projected to rise by +5.8 °C (SD 0.26), with the AN and AF of acute heat illnesses above nonoptimal temperature being 19,021 (95% CI 2249?35,792) and 89.9% (95% CI 89.3%?90.5%) by 2090?2099. However, if Taiwan adopts the Sustainability SSP1-2.6 scenario, the AN and AF of acute heat illnesses due to nonoptimal temperature will be reduced to 12,468 (95% CI 3233?21,704) and 62.1% (95% CI 61.2?63.1). Conclusions: Adopting sustainable development policies can help mitigate the risk of acute heat illnesses caused by global warming. UR - https://publichealth.jmir.org/2024/1/e57948 UR - http://dx.doi.org/10.2196/57948 ID - info:doi/10.2196/57948 ER - TY - JOUR AU - Zhang, Kehong AU - Shen, Ganglei AU - Yuan, Yue AU - Shi, Chao PY - 2024/10/2 TI - Association Between Climatic Factors and Varicella Incidence in Wuxi, East China, 2010-2019: Surveillance Study JO - JMIR Public Health Surveill SP - e62863 VL - 10 KW - varicella KW - meteorological factors KW - Generalized Additive Model KW - Segmented Linear Regression Model KW - China KW - meteorology KW - regression KW - statistics KW - surveillance N2 - Background: Varicella is a common infectious disease and a growing public health concern in China, with increasing outbreaks in Wuxi. Analyzing the correlation between climate factors and varicella incidence in Wuxi is crucial for guiding public health prevention efforts. Objective: This study examines the impact of meteorological variables on varicella incidence in Wuxi, eastern China, from 2010 to 2019, offering insights for public health interventions. Methods: We collected daily meteorological data and varicella case records from January 1, 2010, to December 31, 2019, in Wuxi, China. Generalized cross-validation identified optimal lag days by selecting those with the lowest score. The relationship between meteorological factors and varicella incidence was analyzed using Poisson generalized additive models and segmented linear regression. Subgroup analyses were conducted by gender and age. Results: The study encompassed 64,086 varicella cases. Varicella incidence in Wuxi city displayed a bimodal annual pattern, with peak occurrences from November to January of the following year and lower peaks from May to June. Several meteorological factors influencing varicella risk were identified. A decrease of 1°C when temperatures were ?20°C corresponded to a 1.99% increase in varicella risk (95% CI 1.57-2.42, P<.001). Additionally, a decrease of 1°C below 22.38°C in ground temperature was associated with a 1.36% increase in varicella risk (95% CI 0.96-1.75, P<.001). Each 1 mm increase in precipitation above 4.88 mm was associated with a 1.62% increase in varicella incidence (95% CI 0.93-2.30, P<.001). A 1% rise in relative humidity above 57.18% increased varicella risk by 2.05% (95% CI 1.26-2.84, P<.001). An increase in air pressure of 1 hPa below 1011.277 hPa was associated with a 1.75% rise in varicella risk (95% CI 0.75-2.77, P<.001). As wind speed and evaporation increased, varicella risk decreased linearly with a 16-day lag. Varicella risk was higher with sunshine durations exceeding 1.825 hours, with a 14-day lag, increasing by 1.30% for each additional hour of sunshine (95% CI 0.62-2.00, P=.006). Subgroup analyses revealed that teenagers and children under 17 years of age faced higher varicella risks associated with temperature, average ground temperature, precipitation, relative humidity, and air pressure. Adults aged 18-64 years experienced increased risk with longer sunshine durations. Additionally, males showed higher varicella risks related to ground temperature and air pressure compared with females. However, no significant gender differences were observed regarding varicella risks associated with temperature (male: P<.001; female P<.001), precipitation (male: P=.001; female: P=.06), and sunshine duration (male: P=.53; female: P=.04). Conclusions: Our preliminary findings highlight the interplay between varicella outbreaks in Wuxi city and meteorological factors. These insights provide valuable support for developing policies aimed at reducing varicella risks through informed public health measures. UR - https://publichealth.jmir.org/2024/1/e62863 UR - http://dx.doi.org/10.2196/62863 UR - http://www.ncbi.nlm.nih.gov/pubmed/39228304 ID - info:doi/10.2196/62863 ER - TY - JOUR AU - Fesshaye, Berhaun AU - Pandya, Shivani AU - Kan, Lena AU - Kalbarczyk, Anna AU - Alland, Kelsey AU - Rahman, Mustafizur S. M. AU - Bulbul, Islam Md Mofijul AU - Mustaphi, Piyali AU - Siddique, Bakr Muhammad Abu AU - Tanim, Alam Md Imtiaz AU - Chowdhury, Mridul AU - Rumman, Tajkia AU - Labrique, B. Alain PY - 2024/9/30 TI - Quality, Usability, and Trust Challenges to Effective Data Use in the Deployment and Use of the Bangladesh Nutrition Information System Dashboard: Qualitative Study JO - J Med Internet Res SP - e48294 VL - 26 KW - digital health KW - nutrition KW - data for decision-making KW - health information systems KW - information system KW - information systems KW - LMIC KW - low- and middle-income countries KW - nutritional KW - dashboard KW - experience KW - experiences KW - interview KW - interviews KW - service KW - services KW - delivery KW - health care management N2 - Background: Evidence-based decision-making is essential to improve public health benefits and resources, especially in low- and middle-income countries (LMICs), but the mechanisms of its implementation remain less straightforward. The availability of high-quality, reliable, and sufficient data in LMICs can be challenging due to issues such as a lack of human resource capacity and weak digital infrastructure, among others. Health information systems (HISs) have been critical for aggregating and integrating health-related data from different sources to support evidence-based decision-making. Nutrition information systems (NISs), which are nutrition-focused HISs, collect and report on nutrition-related indicators to improve issues related to malnutrition and food security?and can assist in improving populations? nutritional statuses and the integration of nutrition programming into routine health services. Data visualization tools (DVTs) such as dashboards have been recommended to support evidence-based decision-making, leveraging data from HISs or NISs. The use of such DVTs to support decision-making has largely been unexplored within LMIC contexts. In Bangladesh, the Mukto dashboard was developed to display and visualize nutrition-related performance indicators at the national and subnational levels. However, despite this effort, the current use of nutrition data to guide priorities and decisions remains relatively nascent and underused. Objective: The goal of this study is to better understand how Bangladesh?s NIS, including the Mukto dashboard, has been used and areas for improvement to facilitate its use for evidence-based decision-making toward ameliorating nutrition-related service delivery and the health status of communities in Bangladesh. Methods: Primary data collection was conducted through qualitative semistructured interviews with key policy-level stakeholders (n=24). Key informants were identified through purposive sampling and were asked questions about the experiences and challenges with the NIS and related nutrition dashboards. Results: Main themes such as trust, data usability, personal power, and data use for decision-making emerged from the data. Trust in both data collection and quality was lacking among many stakeholders. Poor data usability stemmed from unstandardized indicators, irregular data collection, and differences between rural and urban data. Insufficient personal power and staff training coupled with infrastructural challenges can negatively affect data at the input stage. While stakeholders understood and expressed the importance of evidence-based decision-making, ultimately, they noted that the data were not being used to their maximum potential. Conclusions: Leveraging DVTs can improve the use of data for evidence-based decision-making, but decision makers must trust that the data are believable, credible, timely, and responsive. The results support the significance of a tailored data ecosystem, which has not reached its full potential in Bangladesh. Recommendations to reach this potential include ensuring a clear intended user base and accountable stakeholders are present. Systems should also have the capacity to ensure data credibility and support ongoing personal power requirements. UR - https://www.jmir.org/2024/1/e48294 UR - http://dx.doi.org/10.2196/48294 UR - http://www.ncbi.nlm.nih.gov/pubmed/39348172 ID - info:doi/10.2196/48294 ER - TY - JOUR AU - Kim, Yeji AU - Kim, Soeun AU - Lee, Somin AU - Park, Jaeyu AU - Koyanagi, Ai AU - Smith, Lee AU - Kim, Seo Min AU - Fond, Guillaume AU - Boyer, Laurent AU - López Sánchez, Felipe Guillermo AU - Dragioti, Elena AU - Kim, Jin Hyeon AU - Lee, Hayeon AU - Son, Yejun AU - Kim, Minji AU - Kim, Sunyoung AU - Yon, Keon Dong PY - 2024/9/18 TI - National Trends in the Prevalence of Unmet Health Care and Dental Care Needs During the COVID-19 Pandemic: Longitudinal Study in South Korea, 2009-2022 JO - JMIR Public Health Surveill SP - e51481 VL - 10 KW - COVID-19 KW - pandemic KW - epidemiology KW - South Korea KW - unmet health care KW - unmet dental care. N2 - Background: Although previous studies have investigated trends in unmet health care and dental care needs, most have focused on specific groups, such as patients with chronic conditions and older adults, and have been limited by smaller data sets. Objective: This study aims to investigate the trends and relative risk factors for unmet health care and dental care needs, as well as the impact of the COVID-19 pandemic on these needs. Methods: We assessed unmet health care and dental care needs from 2009 to 2022 using data from the Korea Community Health Survey (KCHS). Our analysis included responses from 2,750,212 individuals. Unmet health care or dental care needs were defined as instances of not receiving medical or dental services deemed necessary by experts or desired by patients. Results: From 2009 to 2022, the study included 2,700,705 individuals (1,229,671 men, 45.53%; 673,780, 24.95%, aged 19-39 years). Unmet health care needs decreased before the COVID-19 pandemic; however, during the pandemic, there was a noticeable increase (?diff 0.10, 95% CI 0.09-0.11). Unmet dental care needs declined before the pandemic and continued to decrease during the pandemic (?diff 0.23, 95% CI 0.22-0.24). Overall, the prevalence of unmet dental care needs was significantly higher than that for unmet health care needs. While the prevalence of unmet health care needs generally decreased over time, the ? difference during the pandemic increased compared with prepandemic values. Conclusions: Our study is the first to analyze national unmet health care and dental care needs in South Korea using nationally representative, long-term, and large-scale data from the KCHS. We found that while unmet health care needs decreased during COVID-19, the decline was slower compared with previous periods. This suggests a need for more targeted interventions to prevent unmet health care and dental care needs. UR - https://publichealth.jmir.org/2024/1/e51481 UR - http://dx.doi.org/10.2196/51481 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51481 ER - TY - JOUR AU - Zhao, Yusui AU - Xu, Yue AU - Yao, Dingming AU - Wu, Qingqing AU - Chen, Heni AU - Hu, Xiujing AU - Huang, Yu AU - Zhang, Xuehai PY - 2024/8/30 TI - Changes in Infectious Disease?Specific Health Literacy in the Post?COVID-19 Pandemic Period: Two-Round Cross-Sectional Survey Study JO - JMIR Public Health Surveill SP - e52666 VL - 10 KW - survey KW - infectious disease?specific health literacy KW - COVID-19 KW - health education KW - factors KW - postpandemic N2 - Background: Infectious disease?specific health literacy (IDSHL) is a crucial factor in the development of infectious diseases. It plays a significant role not only in mitigating the resurgence of infectious diseases but also in effectively averting the emergence of novel infections such as COVID-19. During the 3 years of the COVID-19 pandemic, China primarily adopted nonpharmaceutical interventions, advocating for people to avoid crowded places and wear masks to prevent the spread of COVID-19. Consequently, there has been a dearth of research concerning IDSHL and its corresponding focal points for health education. Objective: This study aimed to (1) evaluate the changes in IDSHL scores between 2019 (before the COVID-19 pandemic) and 2022 (the postepidemic period of COVID-19) and (2) explore the risk factors affecting IDSHL using a multivariate logistic regression analysis. Methods: This study used 2-round cross-sectional surveys, conducted in 2019 and 2022, respectively, in 30 counties in Zhejiang Province, China. Multiple-stage stratified random sampling was used to select households, and a Kish grid was used to identify participants. An identical standardized questionnaire consisting of 12 closed-ended questions was used to measure IDSHL scores before and after the COVID-19 pandemic (2019 and 2022). Standard descriptive statistics, chi-square tests, t tests, and multivariate logistic regression analyses were used to analyze the data. Results: The 2-round cross-sectional surveys conducted in 2019 and 2022 yielded, out of 19,366 and 19,221 total questionnaires, 19,257 (99.44% response rate) and 18,857 (98.11% response rate) valid questionnaires, respectively. The correct response rate for the respiratory infectious diseases question ?When coughing or sneezing, which of the following is correct?? increased from 29.10% in 2019 to 37.92% in 2022 (?²1=332.625; P<.001). The correct response rate for the nonrespiratory infectious diseases question ?In which of the following ways can hepatitis B be transmitted to others?? decreased from 64.28% to 59.67% (?²1=86.059; P<.001). In terms of IDSHL scores, a comparison between 2022 and 2019 revealed notable statistical differences in the overall scores (t1=10.829; P<.001) and across the 3 dimensions of knowledge (t1=8.840; P<.001), behavior (t1=16.170; P<.001), and skills (t1=9.115; P<.001). With regard to the questions, all but 4 exhibited statistical differences (P<.001). Multivariate logistic regression analyses indicated that the 2022 year group had a higher likelihood of possessing acquired IDSHL than the 2019 group (odds ratio 1.323, 95% CI 1.264?1.385; P<.001). Conclusions: When conducting health education, it is imperative to enhance efforts in nonrespiratory infectious disease health education, as well as respiratory infectious diseases such as COVID-19. Health education interventions should prioritize ethnic minority populations with a poor self-health status and low education. UR - https://publichealth.jmir.org/2024/1/e52666 UR - http://dx.doi.org/10.2196/52666 ID - info:doi/10.2196/52666 ER - TY - JOUR AU - Gomez, Manchikanti Anu AU - Reed, Diane Reiley AU - Bennett, H. Ariana AU - Kavanaugh, Megan PY - 2024/8/20 TI - Integrating Sexual and Reproductive Health Equity Into Public Health Goals and Metrics: Comparative Analysis of Healthy People 2030?s Approach and a Person-Centered Approach to Contraceptive Access Using Population-Based Data JO - JMIR Public Health Surveill SP - e58009 VL - 10 KW - contraception KW - public health objectives KW - public health metrics KW - person-centeredness KW - sexual and reproductive health equity N2 - Background: The Healthy People initiative is a national effort to lay out public health goals in the United States every decade. In its latest iteration, Healthy People 2030, key goals related to contraception focus on increasing the use of effective birth control (contraceptive methods classified as most or moderately effective for pregnancy prevention) among women at risk of unintended pregnancy. This narrow focus is misaligned with sexual and reproductive health equity, which recognizes that individuals? self-defined contraceptive needs are critical for monitoring contraceptive access and designing policy and programmatic strategies to increase access. Objective: We aimed to compare 2 population-level metrics of contraceptive access: a conventional metric, use of contraceptive methods considered most or moderately effective for pregnancy prevention among those considered at risk of unintended pregnancy (approximating the Healthy People 2030 approach), and a person-centered metric, use of preferred contraceptive method among current and prospective contraceptive users. Methods: We used nationally representative data collected in 2022 to construct the 2 metrics of contraceptive access; the overall sample included individuals assigned female at birth not using female sterilization or otherwise infecund and who were not pregnant or trying to become pregnant (unweighted N=2760; population estimate: 43.9 million). We conducted a comparative analysis to examine the convergence and divergence of the metrics by examining whether individuals met the inclusion criteria for the denominators of both metrics, neither metric, only the conventional metric, or only the person-centered metric. Results: Comparing the 2 approaches to measuring contraceptive access, we found that 79% of respondents were either included in or excluded from both metrics (reflecting that the metrics converged when individuals were treated the same by both). The remaining 21% represented divergence in the metrics, with an estimated 5.7 million individuals who did not want to use contraception included only in the conventional metric denominator and an estimated 3.5 million individuals who were using or wanted to use contraception but had never had penile-vaginal sex included only in the person-centered metric denominator. Among those included only in the conventional metric, 100% were content nonusers?individuals who were not using contraception, nor did they want to. Among those included only in the person-centered metric, 68% were currently using contraception. Despite their current or desired contraceptive use, these individuals were excluded from the conventional metric because they had never had penile-vaginal sex. Conclusions: Our analysis highlights that a frequently used metric of contraceptive access misses the needs of millions of people by simultaneously including content nonusers and excluding those who are using or want to use contraception who have never had sex. Documenting and quantifying the gap between current approaches to assessing contraceptive access and more person-centered ones helps clearly identify where programmatic and policy efforts should focus going forward. UR - https://publichealth.jmir.org/2024/1/e58009 UR - http://dx.doi.org/10.2196/58009 UR - http://www.ncbi.nlm.nih.gov/pubmed/39163117 ID - info:doi/10.2196/58009 ER - TY - JOUR AU - Gan, Ting AU - Liu, Yunning AU - Bambrick, Hilary AU - Zhou, Maigeng AU - Hu, Wenbiao PY - 2024/8/8 TI - Liver Cancer Mortality Disparities at a Fine Scale Among Subpopulations in China: Nationwide Analysis of Spatial and Temporal Trends JO - JMIR Public Health Surveill SP - e54967 VL - 10 KW - liver cancer KW - mortality KW - year of life lost KW - spatial distribution KW - temporal trend N2 - Background: China has the highest number of liver cancers worldwide, and liver cancer is at the forefront of all cancers in China. However, current research on liver cancer in China primarily relies on extrapolated data or relatively lagging data, with limited focus on subregions and specific population groups. Objective: The purpose of this study is to identify geographic disparities in liver cancer by exploring the spatial and temporal trends of liver cancer mortality and the years of life lost (YLL) caused by it within distinct geographical regions, climate zones, and population groups in China. Methods: Data from the National Death Surveillance System between 2013 and 2020 were used to calculate the age-standardized mortality rate of liver cancer (LASMR) and YLL from liver cancer in China. The spatial distribution and temporal trends of liver cancer were analyzed in subgroups by sex, age, region, and climate classification. Estimated annual percentage change was used to describe liver cancer trends in various regions, and partial correlation was applied to explore associations between LASMR and latitude. Results: In China, the average LASMR decreased from 28.79 in 2013 to 26.38 per 100,000 in 2020 among men and 11.09 to 9.83 per 100,000 among women. This decline in mortality was consistent across all age groups. Geographically, Guangxi had the highest LASMR for men in China, with a rate of 50.15 per 100,000, while for women, it was Heilongjiang, with a rate of 16.64 per 100,000. Within these regions, the LASMR among men in most parts of Guangxi ranged from 32.32 to 74.98 per 100,000, whereas the LASMR among women in the majority of Heilongjiang ranged from 13.72 to 21.86 per 100,000. The trend of LASMR varied among regions. For both men and women, Guizhou showed an increasing trend in LASMR from 2013 to 2020, with estimated annual percentage changes ranging from 10.05% to 29.07% and from 10.09% to 21.71%, respectively. Both men and women observed an increase in LASMR with increasing latitude below the 40th parallel. However, overall, LASMR in men was positively correlated with latitude (R=0.225; P<.001), while in women, it showed a negative correlation (R=0.083; P=.04). High LASMR areas among men aligned with subtropical zones, like Cwa and Cfa. The age group 65 years and older, the southern region, and the Cwa climate zone had the highest YLL rates at 4850.50, 495.50, and 440.17 per 100,000, respectively. However, the overall trends in these groups showed a decline over the period. Conclusions: Despite the declining overall trend of liver cancer in China, there are still marked disparities between regions and populations. Future prevention and control should focus on high-risk regions and populations to further reduce the burden of liver cancer in China. UR - https://publichealth.jmir.org/2024/1/e54967 UR - http://dx.doi.org/10.2196/54967 ID - info:doi/10.2196/54967 ER - TY - JOUR AU - Bellmann, Louis AU - Wiederhold, Johannes Alexander AU - Trübe, Leona AU - Twerenbold, Raphael AU - Ückert, Frank AU - Gottfried, Karl PY - 2024/7/24 TI - Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data JO - JMIR Med Inform SP - e49865 VL - 12 KW - data exploration KW - cohort studies KW - data visualization KW - big data KW - statistical models KW - medical knowledge KW - data analysis KW - cardiovascular diseases KW - usability N2 - Background: Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria. Objective: This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set. Methods: We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks. Results: We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported. Conclusions: The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference. UR - https://medinform.jmir.org/2024/1/e49865 UR - http://dx.doi.org/10.2196/49865 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49865 ER - TY - JOUR AU - Holdefer, A. Ashley AU - Pizarro, Jeno AU - Saunders-Hastings, Patrick AU - Beers, Jeffrey AU - Sang, Arianna AU - Hettinger, Zachary Aaron AU - Blumenthal, Joseph AU - Martinez, Erik AU - Jones, Daniel Lance AU - Deady, Matthew AU - Ezzeldin, Hussein AU - Anderson, A. Steven PY - 2024/7/15 TI - Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study JO - JMIR Public Health Surveill SP - e49811 VL - 10 KW - adverse event KW - vaccine safety KW - computable phenotype KW - postmarket surveillance system KW - real-world data KW - validation study KW - Food and Drug Administration KW - electronic health records KW - COVID-19 vaccine N2 - Background: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. Objective: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration?s postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. Methods: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. Results: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. Conclusions: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection. UR - https://publichealth.jmir.org/2024/1/e49811 UR - http://dx.doi.org/10.2196/49811 UR - http://www.ncbi.nlm.nih.gov/pubmed/39008361 ID - info:doi/10.2196/49811 ER - TY - JOUR AU - Hopcroft, EM Lisa AU - Curtis, J. Helen AU - Croker, Richard AU - Pretis, Felix AU - Inglesby, Peter AU - Evans, David AU - Bacon, Sebastian AU - Goldacre, Ben AU - Walker, J. Alex AU - MacKenna, Brian PY - 2024/6/5 TI - Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study JO - JMIR Public Health Surveill SP - e51323 VL - 10 KW - electronic health records KW - primary care KW - general practice KW - opioid analgesics KW - data science KW - implementation science KW - data-driven KW - identification KW - intervention KW - implementations KW - proof of concept KW - opioid KW - unbiased KW - prescribing data KW - analysis tool N2 - Background: We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made. Objective: We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. Methods: We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures?total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate. Results: We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. Conclusions: By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation. UR - https://publichealth.jmir.org/2024/1/e51323 UR - http://dx.doi.org/10.2196/51323 UR - http://www.ncbi.nlm.nih.gov/pubmed/38838327 ID - info:doi/10.2196/51323 ER - TY - JOUR AU - Fujii, Yusaku PY - 2024/5/14 TI - An Engineering Alternative to Lockdown During COVID-19 and Other Airborne Infectious Disease Pandemics: Feasibility Study JO - JMIR Biomed Eng SP - e54666 VL - 9 KW - COVID-19 KW - airborne infectious diseases KW - lockdown KW - powered air purifying respirator (PAPR) KW - infectious dose KW - airborne transmission KW - emergency evacuation KW - herd immunity KW - pandemic KW - aerosol KW - air KW - quality KW - infection control KW - infectious KW - respiratory KW - purifier KW - purifiers KW - purifying KW - respirator KW - respirators KW - device KW - devices KW - airborne N2 - Background: Now and in the future, airborne diseases such as COVID-19 could become uncontrollable and lead the world into lockdowns. Finding alternatives to lockdowns, which limit individual freedoms and cause enormous economic losses, is critical. Objective: The purpose of this study was to assess the feasibility of achieving a society or a nation that does not require lockdown during a pandemic due to airborne infectious diseases through the mass production and distribution of high-performance, low-cost, and comfortable powered air purifying respirators (PAPRs). Methods: The feasibility of a social system using PAPR as an alternative to lockdown was examined from the following perspectives: first, what PAPRs can do as an alternative to lockdown; second, how to operate a social system utilizing PAPR; third, directions of improvement of PAPR as an alternative to lockdown; and finally, balancing between efficiency of infection control and personal freedom through the use of Internet of Things (IoT). Results: PAPR was shown to be a possible alternative to lockdown through the reduction of airborne and droplet transmissions and through a temporary reduction of infection probability per contact. A social system in which individual constraints imposed by lockdown are replaced by PAPRs was proposed, and an example of its operation is presented in this paper. For example, the government determines the type and intensity of the lockdown and activates it. At that time, the government will also indicate how PAPR can be substituted for the different activity and movement restrictions imposed during a lockdown, for example, a curfew order may be replaced with the permission to go outside if wearing a PAPR. The following 7 points were raised as directions for improvement of PAPR as an alternative method to lockdown: flow optimization, precise differential pressure control, design improvement, maintenance method, variation development such as booth type, information terminal function, and performance evaluation method. In order to achieve the effectiveness and efficiency in controlling the spread of infection and the individual freedom at a high level in a social system that uses PAPRs as an alternative to lockdown, it was considered effective to develop a PAPR wearing rate network management system utilizing IoT. Conclusions: This study shows that using PAPR with infection control ability and with less economic and social damage as an alternative to nationwide lockdown is possible during a pandemic due to airborne infectious diseases. Further, the efficiency of the government?s infection control and each citizen?s freedom can be balanced by using the PAPR wearing rate network management system utilizing an IoT system. UR - https://biomedeng.jmir.org/2024/1/e54666 UR - http://dx.doi.org/10.2196/54666 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875692 ID - info:doi/10.2196/54666 ER - TY - JOUR AU - Resendez, Skyler AU - Brown, H. Steven AU - Ruiz Ayala, Sebastian Hugo AU - Rangan, Prahalad AU - Nebeker, Jonathan AU - Montella, Diane AU - Elkin, L. Peter PY - 2024/4/30 TI - Defining the Subtypes of Long COVID and Risk Factors for Prolonged Disease: Population-Based Case-Crossover Study JO - JMIR Public Health Surveill SP - e49841 VL - 10 KW - long COVID KW - PASC KW - postacute sequelae of COVID-19 KW - public health KW - policy initiatives KW - pandemic KW - diagnosis KW - COVID-19 treatment KW - long COVID cause KW - health care support KW - public safety KW - COVID-19 KW - Veterans Affairs KW - United States KW - COVID-19 testing KW - clinician KW - mobile phone N2 - Background: There have been over 772 million confirmed cases of COVID-19 worldwide. A significant portion of these infections will lead to long COVID (post?COVID-19 condition) and its attendant morbidities and costs. Numerous life-altering complications have already been associated with the development of long COVID, including chronic fatigue, brain fog, and dangerous heart rhythms. Objective: We aim to derive an actionable long COVID case definition consisting of significantly increased signs, symptoms, and diagnoses to support pandemic-related clinical, public health, research, and policy initiatives. Methods: This research employs a case-crossover population-based study using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) data generated at Veterans Affairs medical centers nationwide between January 1, 2020, and August 18, 2022. In total, 367,148 individuals with ICD-10-CM data both before and after a positive COVID-19 test were selected for analysis. We compared ICD-10-CM codes assigned 1 to 7 months following each patient?s positive test with those assigned up to 6 months prior. Further, 350,315 patients had novel codes assigned during this window of time. We defined signs, symptoms, and diagnoses as being associated with long COVID if they had a novel case frequency of ?1:1000, and they significantly increased in our entire cohort after a positive test. We present odds ratios with CIs for long COVID signs, symptoms, and diagnoses, organized by ICD-10-CM functional groups and medical specialty. We used our definition to assess long COVID risk based on a patient?s demographics, Elixhauser score, vaccination status, and COVID-19 disease severity. Results: We developed a long COVID definition consisting of 323 ICD-10-CM diagnosis codes grouped into 143 ICD-10-CM functional groups that were significantly increased in our 367,148 patient post?COVID-19 population. We defined 17 medical-specialty long COVID subtypes such as cardiology long COVID. Patients who were COVID-19?positive developed signs, symptoms, or diagnoses included in our long COVID definition at a proportion of at least 59.7% (268,320/449,450, based on a denominator of all patients who were COVID-19?positive). The long COVID cohort was 8 years older with more comorbidities (2-year Elixhauser score 7.97 in the patients with long COVID vs 4.21 in the patients with non?long COVID). Patients who had a more severe bout of COVID-19, as judged by their minimum oxygen saturation level, were also more likely to develop long COVID. Conclusions: An actionable, data-driven definition of long COVID can help clinicians screen for and diagnose long COVID, allowing identified patients to be admitted into appropriate monitoring and treatment programs. This long COVID definition can also support public health, research, and policy initiatives. Patients with COVID-19 who are older or have low oxygen saturation levels during their bout of COVID-19, or those who have multiple comorbidities should be preferentially watched for the development of long COVID. UR - https://publichealth.jmir.org/2024/1/e49841 UR - http://dx.doi.org/10.2196/49841 UR - http://www.ncbi.nlm.nih.gov/pubmed/38687984 ID - info:doi/10.2196/49841 ER - TY - JOUR AU - Wang, Di AU - Li, Peifan AU - Huang, Xiaoling AU - Liu, Yixuan AU - Mao, Shihang AU - Yin, Haoning AU - Wang, Na AU - Luo, Yan AU - Sun, Shan PY - 2024/4/24 TI - Exploring the Prevalence of Tinnitus and Ear-Related Symptoms in China After the COVID-19 Pandemic: Online Cross-Sectional Survey JO - JMIR Form Res SP - e54326 VL - 8 KW - COVID-19 pandemic KW - tinnitus KW - ear-related symptoms KW - online survey KW - prevalence KW - ear-related KW - China KW - cross-sectional KW - complex KW - heterogeneous KW - symptom KW - symptoms KW - Chinese KW - population KW - investigate KW - health care KW - exploratory KW - teen KW - teens KW - teenager KW - teenagers KW - older adult KW - older adults KW - elder KW - elderly KW - older person KW - older people KW - COVID-19 KW - regression analysis N2 - Background: Tinnitus is a complex and heterogeneous disease that has been identified as a common manifestation of COVID-19. To gain a comprehensive understanding of tinnitus symptoms in individuals following COVID-19 infection, we conducted an online survey called the China Ear Nose and Throat Symptom Survey in the COVID-19 Pandemic (CENTSS) among the Chinese population. Objective: Our objective was to investigate tinnitus and ear-related symptoms after COVID-19 infection in the Chinese population, with the aim of providing a solid empirical foundation for improved health care. The findings from CENTSS can contribute to the development of enhanced management strategies for tinnitus in the context of long COVID. By gaining a better understanding of the factors contributing to tinnitus in individuals with COVID-19, health care providers can tailor interventions to address the specific needs of affected patients. Furthermore, this study serves as a basis for research on the long-term consequences of COVID-19 infection and its associated tinnitus symptoms. Methods: A quantitative, online, cross-sectional survey study design was used to explore the impact of the COVID-19 pandemic on experiences with tinnitus in China. Data were collected through an online questionnaire designed to identify the presence of tinnitus and its impacts. Descriptive statistics were used to analyze individuals' demographic characteristics, COVID-19 infection?related ear symptoms, and the cognitive and emotional implications of tinnitus. Univariable and multivariable logistic regression analyses were used to model the cross-sectional baseline associations between demographic characteristics, noise exposure, educational level, health and lifestyle factors, and the occurrence of tinnitus. Results: Between December 19, 2022, and February 1, 2023, we obtained responses from 1262 Chinese participants representing 24 regions, with an average age of 37 years. Among them, 540 patients (42.8%) reported experiencing ear-related symptoms after COVID-19 infection. Only 114 (9%) of these patients sought medical attention specifically for their ear symptoms, while 426 (33.8%) did not seek hospital care. Tinnitus emerged as the most prevalent and impactful symptom among all ear-related symptoms experienced after COVID-19 infection. Of the respondents, female participants (688/888, 77.78%), younger individuals (<30 years), individuals with lower education levels, participants residing in western China, and those with a history of otolaryngology diseases were more likely to develop tinnitus following COVID-19 infection. Conclusions: In summary, tinnitus was identified as the most common ear-related symptom during COVID-19 infection. Individuals experiencing tinnitus after COVID-19 infection were found to have poorer cognitive and emotional well-being. Different ear-related symptoms in patients post?COVID-19 infection may suggest viral invasion of various parts of the ear. It is therefore crucial to monitor and manage hearing-related changes resulting from COVID-19 as clinical services resume. UR - https://formative.jmir.org/2024/1/e54326 UR - http://dx.doi.org/10.2196/54326 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54326 ER - TY - JOUR AU - García, E. Yury AU - Schmidt, J. Alec AU - Solis, Leslie AU - Daza-Torres, L. María AU - Montesinos-López, Cricelio J. AU - Pollock, H. Brad AU - Nuño, Miriam PY - 2024/4/17 TI - Assessing SARS-CoV-2 Testing Adherence in a University Town: Recurrent Event Modeling Analysis JO - JMIR Public Health Surveill SP - e48784 VL - 10 KW - Healthy Davis Together KW - COVID-19 KW - COVID-19 surveillance program KW - community surveillance KW - HDT: HYT KW - Healthy Yolo Together KW - SARS-CoV-2 KW - severe acute respiratory syndrome coronavirus 2 KW - coronavirus KW - demographic KW - demographics KW - testing KW - adherence KW - compliance KW - USA KW - United States KW - response program KW - response programs KW - engagement KW - participation KW - infectious KW - trend KW - trends KW - community based KW - surveillance KW - public health KW - infection control KW - PCR KW - polymerase chain reaction KW - RT-qPCR KW - reverse transcription quantitative polymerase chain reaction KW - viral KW - virus KW - viruses N2 - Background: Healthy Davis Together was a program launched in September 2020 in the city of Davis, California, to mitigate the spread of COVID-19 and facilitate the return to normalcy. The program involved multiple interventions, including free saliva-based asymptomatic testing, targeted communication campaigns, education efforts, and distribution of personal protective equipment, community partnerships, and investments in the local economy. Objective: This study identified demographic characteristics of individuals that underwent testing and assessed adherence to testing over time in a community pandemic-response program launched in a college town in California, United States. Methods: This study outlines overall testing engagement, identifies demographic characteristics of participants, and evaluates testing participation changes over 4 periods of the COVID-19 pandemic, distinguished by the dominant variants Delta and Omicron. Additionally, a recurrent model is employed to explore testing patterns based on the participants? frequency, timing, and demographic characteristics. Results: A total of 770,165 tests were performed between November 18, 2020, and June 30, 2022, among 89,924 (41.1% of total population) residents of Yolo County, with significant participation from racially or ethnically diverse participants and across age groups. Most positive cases (6351 of total) and highest daily participation (895 per 100,000 population) were during the Omicron period. There were some gender and age-related differences in the pattern of recurrent COVID-19 testing. Men were slightly less likely (hazard ratio [HR] 0.969, 95% CI 0.943-0.996) to be retested and more likely (HR 1.104, 95% CI 1.075-1.134) to stop testing altogether than women. People aged between 20 and 34 years were less likely to be retested (HR 0.861, 95% CI 0.828-0.895) and more likely to stop testing altogether (HR 2.617, 95% CI 2.538-2.699). However, older age groups were less likely to stop testing, especially those aged between 65-74 years and 75-84 years, than those aged between 0 and 19 years. The likelihood of stopping testing was lower (HR 0.93, 95% CI 0.889-0.976) for the Asian group and higher for the Hispanic or Latino (HR 1.185, 95% CI 1.148-1.223) and Black or African American (HR 1.198, 95% CI 1.054-1.350) groups than the White group. Conclusions: The unique features of a pandemic response program that supported community-wide access to free asymptomatic testing provide a unique opportunity to evaluate adherence to testing recommendations and testing trends over time. Identification of individual and group-level factors associated with testing behaviors can provide insights for identifying potential areas of improvement in future testing initiatives. UR - https://publichealth.jmir.org/2024/1/e48784 UR - http://dx.doi.org/10.2196/48784 UR - http://www.ncbi.nlm.nih.gov/pubmed/38631033 ID - info:doi/10.2196/48784 ER - TY - JOUR AU - Carter, Sarah AU - Lin, C. Jane AU - Chow, Ting AU - Martinez, P. Mayra AU - Qiu, Chunyuan AU - Feldman, Klara R. AU - McConnell, Rob AU - Xiang, H. Anny PY - 2024/4/17 TI - Preeclampsia Onset, Days to Delivery, and Autism Spectrum Disorders in Offspring: Clinical Birth Cohort Study JO - JMIR Public Health Surveill SP - e47396 VL - 10 KW - autism spectrum disorders KW - autism KW - clinical management KW - diagnosis KW - expectant management KW - fetal exposure KW - fetal KW - management KW - preeclampsia KW - pregnancy KW - pregnant women KW - risk N2 - Background: Maternal preeclampsia is associated with a risk of autism spectrum disorders (ASD) in offspring. However, it is unknown whether the increased ASD risk associated with preeclampsia is due to preeclampsia onset or clinical management of preeclampsia after onset, as clinical expectant management of preeclampsia allows pregnant women with this complication to remain pregnant for potentially weeks depending on the onset and severity. Identifying the risk associated with preeclampsia onset and exposure provides evidence to support the care of high-risk pregnancies and reduce adverse effects on offspring. Objective: This study aimed to fill the knowledge gap by assessing the ASD risk in children associated with the gestational age of preeclampsia onset and the number of days from preeclampsia onset to delivery. Methods: This retrospective population-based clinical cohort study included 364,588 mother-child pairs of singleton births between 2001 and 2014 in a large integrated health care system in Southern California. Maternal social demographic and pregnancy health data, as well as ASD diagnosis in children by the age of 5 years, were extracted from electronic medical records. Cox regression models were used to assess hazard ratios (HRs) of ASD risk in children associated with gestational age of the first occurrence of preeclampsia and the number of days from first occurrence to delivery. Results: Preeclampsia occurred in 16,205 (4.4%) out of 364,588 pregnancies; among the 16,205 pregnancies, 2727 (16.8%) first occurred at <34 weeks gestation, 4466 (27.6%) first occurred between 34 and 37 weeks, and 9012 (55.6%) first occurred at ?37 weeks. Median days from preeclampsia onset to delivery were 4 (IQR 2,16) days, 1 (IQR 1,3) day, and 1 (IQR 0,1) day for those first occurring at <34, 34-37, and ?37 weeks, respectively. Early preeclampsia onset was associated with greater ASD risk (P=.003); HRs were 1.62 (95% CI 1.33-1.98), 1.43 (95% CI 1.20-1.69), and 1.23 (95% CI 1.08-1.41), respectively, for onset at <34, 34-37, and ?37 weeks, relative to the unexposed group. Within the preeclampsia group, the number of days from preeclampsia onset to delivery was not associated with ASD risk in children; the HR was 0.995 (95% CI 0.986-1.004) after adjusting for gestational age of preeclampsia onset. Conclusions: Preeclampsia during pregnancy was associated with ASD risk in children, and the risk was greater with earlier onset. However, the number of days from first preeclampsia onset to delivery was not associated with ASD risk in children. Our study suggests that ASD risk in children associated with preeclampsia is not increased by expectant management of preeclampsia in standard clinical practice. Our results emphasize the need to identify effective approaches to preventing the onset of preeclampsia, especially during early pregnancy. Further research is needed to confirm if this finding applies across different populations and clinical settings. UR - https://publichealth.jmir.org/2024/1/e47396 UR - http://dx.doi.org/10.2196/47396 UR - http://www.ncbi.nlm.nih.gov/pubmed/38630528 ID - info:doi/10.2196/47396 ER - TY - JOUR AU - Lee, Mi-Sun AU - Lee, Hooyeon PY - 2024/4/10 TI - Chronic Disease Patterns and Their Relationship With Health-Related Quality of Life in South Korean Older Adults With the 2021 Korean National Health and Nutrition Examination Survey: Latent Class Analysis JO - JMIR Public Health Surveill SP - e49433 VL - 10 KW - chronic disease KW - latent class analysis KW - multimorbidity KW - older adults KW - quality of life N2 - Background: Improved life expectancy has increased the prevalence of older adults living with multimorbidities, which likely deteriorates their health-related quality of life (HRQoL). Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific multimorbidity profiles. Objective: The study objectives were to (1) examine the prevalence of multimorbidity among Korean older adults (ie, those aged 65 years and older), (2) investigate chronic disease patterns using latent class analysis, and (3) assess which chronic disease patterns are more strongly associated with HRQoL. Methods: A sample of 1806 individuals aged 65 years and older from the 2021 Korean National Health and Nutrition Examination Survey was analyzed. Latent class analysis was conducted to identify the clustering pattern of chronic diseases. HRQoL was assessed by an 8-item health-related quality of life scale (HINT-8). Multiple linear regression was used to analyze the association with the total score of the HINT-8. Logistic regression analysis was performed to evaluate the odds ratio of having problems according to the HINT-8 items. Results: The prevalence of multimorbidity in the sample was 54.8%. Three chronic disease patterns were identified: relatively healthy, cardiometabolic condition, arthritis, allergy, or asthma. The total scores of the HINT-8 were the highest in participants characterized as arthritis, allergy, or asthma group, indicating the lowest quality of life. Conclusions: Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities. Identifying chronic disease patterns and their impact on overall health and well-being is critical for guiding integrated care. UR - https://publichealth.jmir.org/2024/1/e49433 UR - http://dx.doi.org/10.2196/49433 UR - http://www.ncbi.nlm.nih.gov/pubmed/38598275 ID - info:doi/10.2196/49433 ER - TY - JOUR AU - Rieckmann, Andreas AU - Nielsen, Sebastian AU - Dworzynski, Piotr AU - Amini, Heresh AU - Mogensen, Wengel Søren AU - Silva, Bartolomeu Isaquel AU - Chang, Y. Angela AU - Arah, A. Onyebuchi AU - Samek, Wojciech AU - Rod, Hulvej Naja AU - Ekstrøm, Thorn Claus AU - Benn, Stabell Christine AU - Aaby, Peter AU - Fisker, Bærent Ane PY - 2024/4/9 TI - Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study JO - JMIR Public Health Surveill SP - e48060 VL - 10 KW - child mortality KW - causal discovery KW - Guinea-Bissau KW - inductive-deductive KW - machine learning KW - targeted preventive and risk-mitigating interventions N2 - Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. Methods: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. Results: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. Conclusions: The study?s results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups. UR - https://publichealth.jmir.org/2024/1/e48060 UR - http://dx.doi.org/10.2196/48060 UR - http://www.ncbi.nlm.nih.gov/pubmed/38592761 ID - info:doi/10.2196/48060 ER - TY - JOUR AU - Xuan, Kun AU - Zhang, Ning AU - Li, Tao AU - Pang, Xingya AU - Li, Qingru AU - Zhao, Tianming AU - Wang, Binbing AU - Zha, Zhenqiu AU - Tang, Jihai PY - 2024/4/5 TI - Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study JO - JMIR Public Health Surveill SP - e50673 VL - 10 KW - varicella KW - incidence KW - epidemiology KW - spatial autocorrelation KW - contagious disease KW - chicken pox KW - varicella zoster virus KW - China N2 - Background: Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. Objective: The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. Methods: Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. Results: A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. Conclusions: This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella. UR - https://publichealth.jmir.org/2024/1/e50673 UR - http://dx.doi.org/10.2196/50673 UR - http://www.ncbi.nlm.nih.gov/pubmed/38579276 ID - info:doi/10.2196/50673 ER - TY - JOUR AU - Loeb, Talia AU - Willis, Kalai AU - Velishavo, Frans AU - Lee, Daniel AU - Rao, Amrita AU - Baral, Stefan AU - Rucinski, Katherine PY - 2024/4/4 TI - Leveraging Routinely Collected Program Data to Inform Extrapolated Size Estimates for Key Populations in Namibia: Small Area Estimation Study JO - JMIR Public Health Surveill SP - e48963 VL - 10 KW - female sex workers KW - HIV KW - key populations KW - men who have sex with men KW - Namibia KW - population size estimation KW - small area estimation N2 - Background: Estimating the size of key populations, including female sex workers (FSW) and men who have sex with men (MSM), can inform planning and resource allocation for HIV programs at local and national levels. In geographic areas where direct population size estimates (PSEs) for key populations have not been collected, small area estimation (SAE) can help fill in gaps using supplemental data sources known as auxiliary data. However, routinely collected program data have not historically been used as auxiliary data to generate subnational estimates for key populations, including in Namibia. Objective: To systematically generate regional size estimates for FSW and MSM in Namibia, we used a consensus-informed estimation approach with local stakeholders that included the integration of routinely collected HIV program data provided by key populations? HIV service providers. Methods: We used quarterly program data reported by key population implementing partners, including counts of the number of individuals accessing HIV services over time, to weight existing PSEs collected through bio-behavioral surveys using a Bayesian triangulation approach. SAEs were generated through simple imputation, stratified imputation, and multivariable Poisson regression models. We selected final estimates using an iterative qualitative ranking process with local key population implementing partners. Results: Extrapolated national estimates for FSW ranged from 4777 to 13,148 across Namibia, comprising 1.5% to 3.6% of female individuals aged between 15 and 49 years. For MSM, estimates ranged from 4611 to 10,171, comprising 0.7% to 1.5% of male individuals aged between 15 and 49 years. After the inclusion of program data as priors, the estimated proportion of FSW derived from simple imputation increased from 1.9% to 2.8%, and the proportion of MSM decreased from 1.5% to 0.75%. When stratified imputation was implemented using HIV prevalence to inform strata, the inclusion of program data increased the proportion of FSW from 2.6% to 4.0% in regions with high prevalence and decreased the proportion from 1.4% to 1.2% in regions with low prevalence. When population density was used to inform strata, the inclusion of program data also increased the proportion of FSW in high-density regions (from 1.1% to 3.4%) and decreased the proportion of MSM in all regions. Conclusions: Using SAE approaches, we combined epidemiologic and program data to generate subnational size estimates for key populations in Namibia. Overall, estimates were highly sensitive to the inclusion of program data. Program data represent a supplemental source of information that can be used to align PSEs with real-world HIV programs, particularly in regions where population-based data collection methods are challenging to implement. Future work is needed to determine how best to include and validate program data in target settings and in key population size estimation studies, ultimately bridging research with practice to support a more comprehensive HIV response. UR - https://publichealth.jmir.org/2024/1/e48963 UR - http://dx.doi.org/10.2196/48963 UR - http://www.ncbi.nlm.nih.gov/pubmed/38573760 ID - info:doi/10.2196/48963 ER - TY - JOUR AU - Ko, Yousang AU - Park, Seuk Jae AU - Min, Jinsoo AU - Kim, Woo Hyung AU - Koo, Hyeon-Kyoung AU - Oh, Youn Jee AU - Jeong, Yun-Jeong AU - Lee, Eunhye AU - Yang, Bumhee AU - Kim, Sang Ju AU - Lee, Sung-Soon AU - Kwon, Yunhyung AU - Yang, Jiyeon AU - Han, yeon Ji AU - Jang, Jin You AU - Kim, Jinseob PY - 2024/4/1 TI - Timely Pulmonary Tuberculosis Diagnosis Based on the Epidemiological Disease Spectrum: Population-Based Prospective Cohort Study in the Republic of Korea JO - JMIR Public Health Surveill SP - e47422 VL - 10 KW - pulmonary tuberculosis KW - disease spectrum KW - timely diagnosis KW - patient delay KW - health care delay KW - risk factor KW - epidemiological disease KW - tuberculosis KW - treatment KW - TB KW - PTB disease spectrum KW - mortality KW - early diagnosis N2 - Background: Timely pulmonary tuberculosis (PTB) diagnosis is a global health priority for interrupting transmission and optimizing treatment outcomes. The traditional dichotomous time-divided approach for addressing time delays in diagnosis has limited clinical application because the time delay significantly varies depending on each community in question. Objective: We aimed to reevaluate the diagnosis time delay based on the PTB disease spectrum using a novel scoring system that was applied at the national level in the Republic of Korea. Methods: The Pulmonary Tuberculosis Spectrum Score (PTBSS) was developed based on previously published proposals related to the disease spectrum, and its validity was assessed by examining both all-cause and PTB-related mortality. In our analysis, we integrated the PTBSS into the Korea Tuberculosis Cohort Registry. We evaluated various time delays, including patient, health care, and overall delays, and their system-associated variables in line with each PTBSS. Furthermore, we reclassified the scores into distinct categories of mild (PTBSS=0-1), moderate (PBTBSS=2-3), and severe (PBTBSS=4-6) using a multivariate regression approach. Results: Among the 14,031 Korean patients with active PTB whose data were analyzed from 2018 to 2020, 37% (n=5191), 38% (n=5328), and 25% (n=3512) were classified as having a mild, moderate, and severe disease status, respectively, according to the PTBSS. This classification can therefore reflect the disease spectrum of PTB by considering the correlation of the score with mortality. The time delay patterns differed according to the PTBSS. In health care delays according to the PTBSS, greater PTB disease progression was associated with a shorter diagnosis period, since the condition is microbiologically easy to diagnose. However, with respect to patient delays, the change in elapsed time showed a U-shaped pattern as PTB progressed. This means that a remarkable patient delay in the real-world setting might occur at both apical ends of the spectrum (ie, in both mild and severe cases of PTB). Independent risk factors for a severe PTB pattern were age (adjusted odds ratio 1.014) and male sex (adjusted odds ratio 1.422), whereas no significant risk factor was found for mild PTB. Conclusions: Timely PTB diagnosis should be accomplished. This can be improved with use of the PTBSS, a simple and intuitive scoring system, which can be more helpful in clinical and public health applications compared to the traditional dichotomous time-only approach. UR - https://publichealth.jmir.org/2024/1/e47422 UR - http://dx.doi.org/10.2196/47422 UR - http://www.ncbi.nlm.nih.gov/pubmed/38557939 ID - info:doi/10.2196/47422 ER - TY - JOUR AU - Rigby, C. Ryan AU - Ferdinand, O. Alva AU - Kum, Hye-Chung AU - Schmit, Cason PY - 2024/3/28 TI - Data Sharing in a Decentralized Public Health System: Lessons From COVID-19 Syndromic Surveillance JO - JMIR Public Health Surveill SP - e52587 VL - 10 KW - syndromic surveillance KW - federalism KW - COVID-19 KW - public health KW - SARS-CoV-2 KW - COVID-19 pandemic KW - United States KW - decentralized KW - data sharing KW - digital health KW - ethical guidelines KW - risk score KW - technology KW - innovation KW - information system KW - collaborative framework KW - infodemiology KW - digital technology KW - health information KW - health data KW - health policy KW - surveillance UR - https://publichealth.jmir.org/2024/1/e52587 UR - http://dx.doi.org/10.2196/52587 UR - http://www.ncbi.nlm.nih.gov/pubmed/38546731 ID - info:doi/10.2196/52587 ER - TY - JOUR AU - Bindhu, Shwetha AU - Nattam, Anunita AU - Xu, Catherine AU - Vithala, Tripura AU - Grant, Tiffany AU - Dariotis, K. Jacinda AU - Liu, Hexuan AU - Wu, Y. Danny T. PY - 2024/3/20 TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e50898 VL - 16 KW - health literacy KW - social determinants of health KW - SDoH KW - social determinants KW - systematic review KW - patient education KW - health education KW - health information KW - information needs KW - information comprehension KW - patient counseling KW - barriers to care KW - language proficiency N2 - Background: Health literacy (HL) is the ability to make informed decisions using health information. As health data and information availability increase due to online clinic notes and patient portals, it is important to understand how HL relates to social determinants of health (SDoH) and the place of informatics in mitigating disparities. Objective: This systematic literature review aims to examine the role of HL in interactions with SDoH and to identify feasible HL-based interventions that address low patient understanding of health information to improve clinic note-sharing efficacy. Methods: The review examined 2 databases, Scopus and PubMed, for English-language articles relating to HL and SDoH. We conducted a quantitative analysis of study characteristics and qualitative synthesis to determine the roles of HL and interventions. Results: The results (n=43) were analyzed quantitatively and qualitatively for study characteristics, the role of HL, and interventions. Most articles (n=23) noted that HL was a result of SDoH, but other articles noted that it could also be a mediator for SdoH (n=6) or a modifiable SdoH (n=14) itself. Conclusions: The multivariable nature of HL indicates that it could form the basis for many interventions to combat low patient understandability, including 4 interventions using informatics-based solutions. HL is a crucial, multidimensional skill in supporting patient understanding of health materials. Designing interventions aimed at improving HL or addressing poor HL in patients can help increase comprehension of health information, including the information contained in clinic notes shared with patients. UR - https://ojphi.jmir.org/2024/1/e50898 UR - http://dx.doi.org/10.2196/50898 UR - http://www.ncbi.nlm.nih.gov/pubmed/38506914 ID - info:doi/10.2196/50898 ER - TY - JOUR AU - Wang, Limin AU - Liu, Shimeng AU - Jiang, Shan AU - Li, Chaofan AU - Lu, Liyong AU - Fang, Yunhai AU - Li, Shunping PY - 2024/3/13 TI - Authors? Reply: ?The Need for a Bleed Type?Specific Annual Bleeding Rate in Hemophilia Studies? JO - JMIR Public Health Surveill SP - e54756 VL - 10 KW - benefit-risk assessment KW - discrete choice experiment KW - hemophilia A KW - patient preference KW - prophylactic treatment UR - https://publichealth.jmir.org/2024/1/e54756 UR - http://dx.doi.org/10.2196/54756 UR - http://www.ncbi.nlm.nih.gov/pubmed/38478903 ID - info:doi/10.2196/54756 ER - TY - JOUR AU - Huang, Kun PY - 2024/3/13 TI - The Need for a Bleed Type?Specific Annual Bleeding Rate in Hemophilia Studies JO - JMIR Public Health Surveill SP - e51372 VL - 10 KW - benefit-risk assessment KW - discrete choice experiment KW - hemophilia A KW - patient preference KW - prophylactic treatment UR - https://publichealth.jmir.org/2024/1/e51372 UR - http://dx.doi.org/10.2196/51372 UR - http://www.ncbi.nlm.nih.gov/pubmed/38478908 ID - info:doi/10.2196/51372 ER - TY - JOUR AU - Allen, S. Katie AU - Valvi, Nimish AU - Gibson, Joseph P. AU - McFarlane, Timothy AU - Dixon, E. Brian PY - 2024/3/13 TI - Electronic Health Records for Population Health Management: Comparison of Electronic Health Record?Derived Hypertension Prevalence Measures Against Established Survey Data JO - Online J Public Health Inform SP - e48300 VL - 16 KW - public health informatics KW - surveillance KW - chronic conditions KW - electronic health record KW - health management KW - hypertension KW - public health KW - prevalence KW - population-based survey N2 - Background: Hypertension is the most prevalent risk factor for mortality globally. Uncontrolled hypertension is associated with excess morbidity and mortality, and nearly one-half of individuals with hypertension do not have the condition under control. Data from electronic health record (EHR) systems may be useful for community hypertension surveillance, filling a gap in local public health departments? community health assessments and supporting the public health data modernization initiatives currently underway. To identify patients with hypertension, computable phenotypes are required. These phenotypes leverage available data elements?such as vitals measurements and medications?to identify patients diagnosed with hypertension. However, there are multiple methodologies for creating a phenotype, and the identification of which method most accurately reflects real-world prevalence rates is needed to support data modernization initiatives. Objective: This study sought to assess the comparability of 6 different EHR-based hypertension prevalence estimates with estimates from a national survey. Each of the prevalence estimates was created using a different computable phenotype. The overarching goal is to identify which phenotypes most closely align with nationally accepted estimations. Methods: Using the 6 different EHR-based computable phenotypes, we calculated hypertension prevalence estimates for Marion County, Indiana, for the period from 2014 to 2015. We extracted hypertension rates from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period. We used the two 1-sided t test (TOST) to test equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race. Results: Using both 80% and 90% CIs, the TOST analysis resulted in 2 computable phenotypes demonstrating rough equivalence to BRFSS estimates. Variation in performance was noted across phenotypes as well as demographics. TOST with 80% CIs demonstrated that the phenotypes had less variance compared to BRFSS estimates within subpopulations, particularly those related to racial categories. Overall, less variance occurred on phenotypes that included vitals measurements. Conclusions: This study demonstrates that certain EHR-derived prevalence estimates may serve as rough substitutes for population-based survey estimates. These outcomes demonstrate the importance of critically assessing which data elements to include in EHR-based computer phenotypes. Using comprehensive data sources, containing complete clinical data as well as data representative of the population, are crucial to producing robust estimates of chronic disease. As public health departments look toward data modernization activities, the EHR may serve to assist in more timely, locally representative estimates for chronic disease prevalence. UR - https://ojphi.jmir.org/2024/1/e48300 UR - http://dx.doi.org/10.2196/48300 UR - http://www.ncbi.nlm.nih.gov/pubmed/38478904 ID - info:doi/10.2196/48300 ER - TY - JOUR AU - Baines, Rebecca AU - Stevens, Sebastian AU - Austin, Daniela AU - Anil, Krithika AU - Bradwell, Hannah AU - Cooper, Leonie AU - Maramba, Daniel Inocencio AU - Chatterjee, Arunangsu AU - Leigh, Simon PY - 2024/3/5 TI - Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review JO - J Med Internet Res SP - e50421 VL - 26 KW - data sharing KW - personal health data KW - patient KW - public attitudes KW - systematic review KW - secondary use KW - third party KW - willingness to share KW - data privacy and security N2 - Background: International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses. Objective: This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses. Methods: A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach. Results: Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level. Conclusions: There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized. UR - https://www.jmir.org/2024/1/e50421 UR - http://dx.doi.org/10.2196/50421 UR - http://www.ncbi.nlm.nih.gov/pubmed/38441944 ID - info:doi/10.2196/50421 ER - TY - JOUR AU - Yin, Shuhua AU - Chen, Shi AU - Ge, Yaorong PY - 2024/1/23 TI - Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study JO - JMIR Infodemiology SP - e49756 VL - 4 KW - infoveillance KW - social media KW - COVID-19 KW - US Centers for Disease Control and Prevention KW - CDC KW - topic modeling KW - multivariate time series analysis N2 - Background: Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC?s social media communications and the actual epidemic metrics to improve public health agencies? communication strategies during health emergencies. Objective: This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC?s digital health communication strategies for future health emergencies. Methods: Two types of data were collected: (1) a total of 17,524 COVID-19?related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19?related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. Results: Four major topics from the CDC?s COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC?s topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. Conclusions: Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively. UR - https://infodemiology.jmir.org/2024/1/e49756 UR - http://dx.doi.org/10.2196/49756 UR - http://www.ncbi.nlm.nih.gov/pubmed/38261367 ID - info:doi/10.2196/49756 ER - TY - JOUR AU - Galvez-Hernandez, Pablo AU - Gonzalez-Viana, Angelina AU - Gonzalez-de Paz, Luis AU - Shankardass, Ketan AU - Muntaner, Carles PY - 2024/1/8 TI - Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis JO - JMIR Public Health Surveill SP - e50379 VL - 10 KW - web scraping KW - text mining KW - spatial overlay analysis KW - program evaluation KW - social environment KW - contextual variables KW - health assets KW - social connection KW - multilevel analysis KW - health services research N2 - Background: Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data. Objective: We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas. Methods: This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults? social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas. Results: We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources. Conclusions: The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables. UR - https://publichealth.jmir.org/2024/1/e50379 UR - http://dx.doi.org/10.2196/50379 UR - http://www.ncbi.nlm.nih.gov/pubmed/38190245 ID - info:doi/10.2196/50379 ER - TY - JOUR AU - De La Cerda, Isela AU - Bauer, X. Cici AU - Zhang, Kehe AU - Lee, Miryoung AU - Jones, Michelle AU - Rodriguez, Arturo AU - McCormick, B. Joseph AU - Fisher-Hoch, P. Susan PY - 2023/12/20 TI - Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health JO - JMIR Public Health Surveill SP - e47981 VL - 9 KW - community interventions KW - emergency preparedness KW - health disparities KW - intervention evaluation KW - precision public health KW - public health informatics KW - public health intervention KW - public health KW - spatial epidemiology KW - surveillance N2 - Background: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named ?Boots on the Ground? (BOG) in the City of Brownsville (COB). Objective: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. Methods: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. Results: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. Conclusions: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community. UR - https://publichealth.jmir.org/2023/1/e47981 UR - http://dx.doi.org/10.2196/47981 UR - http://www.ncbi.nlm.nih.gov/pubmed/38117549 ID - info:doi/10.2196/47981 ER - TY - JOUR AU - Kast, Kristina AU - Otten, Sara-Marie AU - Konopik, Jens AU - Maier, B. Claudia PY - 2023/12/14 TI - Web-Based Public Reporting as a Decision-Making Tool for Consumers of Long-Term Care in the United States and the United Kingdom: Systematic Analysis of Report Cards JO - JMIR Form Res SP - e44382 VL - 7 KW - long-term care KW - medical decision-making KW - nursing homes KW - public reporting KW - quality improvement KW - report cards N2 - Background: Report cards can help consumers make an informed decision when searching for a long-term care facility. Objective: This study aims to examine the current state of web-based public reporting on long-term care facilities in the United States and the United Kingdom. Methods: We conducted an internet search for report cards, which allowed for a nationwide search for long-term care facilities and provided freely accessible quality information. On the included report cards, we drew a sample of 1320 facility profiles by searching for long-term care facilities in 4 US and 2 UK cities. Based on those profiles, we analyzed the information provided by the included report cards descriptively. Results: We found 40 report cards (26 in the United States and 14 in the United Kingdom). In total, 11 of them did not state the source of information. Additionally, 7 report cards had an advanced search field, 24 provided simplification tools, and only 3 had a comparison function. Structural quality information was always provided, followed by consumer feedback on 27 websites, process quality on 15 websites, prices on 12 websites, and outcome quality on 8 websites. Inspection results were always displayed as composite measures. Conclusions: Apparently, the identified report cards have deficits. To make them more helpful for users and to bring public reporting a bit closer to its goal of improving the quality of health care services, both countries are advised to concentrate on optimizing the existing report cards. Those should become more transparent and improve the reporting of prices and consumer feedback. Advanced search, simplification tools, and comparison functions should be integrated more widely. UR - https://formative.jmir.org/2023/1/e44382 UR - http://dx.doi.org/10.2196/44382 UR - http://www.ncbi.nlm.nih.gov/pubmed/38096004 ID - info:doi/10.2196/44382 ER - TY - JOUR AU - Sigalo, Nekabari AU - Frias-Martinez, Vanessa PY - 2023/11/30 TI - Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study JO - JMIR Infodemiology SP - e43700 VL - 3 KW - social media KW - Twitter KW - COVID-19 KW - vaccine KW - surveys KW - SARS-CoV-2 KW - vaccinations KW - hesitancy N2 - Background: Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect. Objective: This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data. Methods: COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter?s streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS. Results: The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey. Conclusions: These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies. UR - https://infodemiology.jmir.org/2023/1/e43700 UR - http://dx.doi.org/10.2196/43700 UR - http://www.ncbi.nlm.nih.gov/pubmed/37903294 ID - info:doi/10.2196/43700 ER - TY - JOUR AU - Solberg, M. Laurence AU - Duckworth, J. Laurie AU - Dunn, M. Elizabeth AU - Dickinson, Theresa AU - Magoc, Tanja AU - Snigurska, A. Urszula AU - Ser, E. Sarah AU - Celso, Brian AU - Bailey, Meghan AU - Bowen, Courtney AU - Radhakrishnan, Nila AU - Patel, R. Chirag AU - Lucero, Robert AU - Bjarnadottir, I. Ragnhildur PY - 2023/11/30 TI - Use of a Data Repository to Identify Delirium as a Presenting Symptom of COVID-19 Infection in Hospitalized Adults: Cross-Sectional Cohort Pilot Study JO - JMIR Aging SP - e43185 VL - 6 KW - COVID-19 KW - delirium KW - neurocognitive disorder KW - data repository KW - adults KW - pilot study KW - symptom KW - electronic health record KW - viral infection KW - clinical KW - patient KW - research KW - diagnosis KW - disorder KW - memory KW - covid KW - memory loss KW - old KW - old age N2 - Background: Delirium, an acute confusional state highlighted by inattention, has been reported to occur in 10% to 50% of patients with COVID-19. People hospitalized with COVID-19 have been noted to present with or develop delirium and neurocognitive disorders. Caring for patients with delirium is associated with more burden for nurses, clinicians, and caregivers. Using information in electronic health record data to recognize delirium and possibly COVID-19 could lead to earlier treatment of the underlying viral infection and improve outcomes in clinical and health care systems cost per patient. Clinical data repositories can further support rapid discovery through cohort identification tools, such as the Informatics for Integrating Biology and the Bedside tool. Objective: The specific aim of this research was to investigate delirium in hospitalized older adults as a possible presenting symptom in COVID-19 using a data repository to identify neurocognitive disorders with a novel group of International Classification of Diseases, Tenth Revision (ICD-10) codes. Methods: We analyzed data from 2 catchment areas with different demographics. The first catchment area (7 counties in the North-Central Florida) is predominantly rural while the second (1 county in North Florida) is predominantly urban. The Integrating Biology and the Bedside data repository was queried for patients with COVID-19 admitted to inpatient units via the emergency department (ED) within the health center from April 1, 2020, and April 1, 2022. Patients with COVID-19 were identified by having a positive COVID-19 laboratory test or a diagnosis code of U07.1. We identified neurocognitive disorders as delirium or encephalopathy, using ICD-10 codes. Results: Less than one-third (1437/4828, 29.8%) of patients with COVID-19 were diagnosed with a co-occurring neurocognitive disorder. A neurocognitive disorder was present on admission for 15.8% (762/4828) of all patients with COVID-19 admitted through the ED. Among patients with both COVID-19 and a neurocognitive disorder, 56.9% (817/1437) were aged ?65 years, a significantly higher proportion than those with no neurocognitive disorder (P<.001). The proportion of patients aged <65 years was significantly higher among patients diagnosed with encephalopathy only than patients diagnosed with delirium only and both delirium and encephalopathy (P<.001). Most (1272/4828, 26.3%) patients with COVID-19 admitted through the ED during our study period were admitted during the Delta variant peak. Conclusions: The data collected demonstrated that an increased number of older patients with neurocognitive disorder present on admission were infected with COVID-19. Knowing that delirium increases the staffing, nursing care needs, hospital resources used, and the length of stay as previously noted, identifying delirium early may benefit hospital administration when planning for newly anticipated COVID-19 surges. A robust and accessible data repository, such as the one used in this study, can provide invaluable support to clinicians and clinical administrators in such resource reallocation and clinical decision-making. UR - https://aging.jmir.org/2023/1/e43185 UR - http://dx.doi.org/10.2196/43185 UR - http://www.ncbi.nlm.nih.gov/pubmed/37910448 ID - info:doi/10.2196/43185 ER - TY - JOUR AU - Herrera-Espejel, Sofia Paula AU - Rach, Stefan PY - 2023/11/20 TI - The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review JO - JMIR Public Health Surveill SP - e50814 VL - 9 KW - machine translation KW - public health KW - epidemiology KW - population-based KW - recruitment KW - outreach KW - multilingual KW - culturally and linguistically diverse communities N2 - Background: Culturally and linguistically diverse groups are often underrepresented in population-based research and surveillance efforts, leading to biased study results and limited generalizability. These groups, often termed ?hard-to-reach,? commonly encounter language barriers in the public health (PH) outreach material and information campaigns, reducing their involvement with the information. As a result, these groups are challenged by 2 effects: the medical and health knowledge is less tailored to their needs, and at the same time, it is less accessible for to them. Modern machine translation (MT) tools might offer a cost-effective solution to PH material language accessibility problems. Objective: This scoping review aims to systematically investigate current use cases of MT specific to the fields of PH and epidemiology, with a particular interest in its use for population-based recruitment methods. Methods: PubMed, PubMed Central, Scopus, ACM Digital Library, and IEEE Xplore were searched to identify articles reporting on the use of MT in PH and epidemiological research for this PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews)?compliant scoping review. Information on communication scenarios, study designs and the principal findings of each article were mapped according to a settings approach, the World Health Organization monitoring and evaluation framework and the service readiness level framework, respectively. Results: Of the 7186 articles identified, 46 (0.64%) were included in this review, with the earliest study dating from 2009. Most of the studies (17/46, 37%) discussed the application of MT to existing PH materials, limited to one-way communication between PH officials and addressed audiences. No specific article investigated the use of MT for recruiting linguistically diverse participants to population-based studies. Regarding study designs, nearly three-quarters (34/46, 74%) of the articles provided technical assessments of MT from 1 language (mainly English) to a few others (eg, Spanish, Chinese, or French). Only a few (12/46, 26%) explored end-user attitudes (mainly of PH employees), whereas none examined the legal or ethical implications of using MT. The experiments primarily involved PH experts with language proficiencies. Overall, more than half (38/70, 54% statements) of the summarizing results presented mixed and inconclusive views on the technical readiness of MT for PH information. Conclusions: Using MT in epidemiology and PH can enhance outreach to linguistically diverse populations. The translation quality of current commercial MT solutions (eg, Google Translate and DeepL Translator) is sufficient if postediting is a mandatory step in the translation workflow. Postediting of legally or ethically sensitive material requires staff with adequate content knowledge in addition to sufficient language skills. Unsupervised MT is generally not recommended. Research on whether machine-translated texts are received differently by addressees is lacking, as well as research on MT in communication scenarios that warrant a response from the addressees. UR - https://publichealth.jmir.org/2023/1/e50814 UR - http://dx.doi.org/10.2196/50814 UR - http://www.ncbi.nlm.nih.gov/pubmed/37983078 ID - info:doi/10.2196/50814 ER - TY - JOUR AU - Murakami, Kentaro AU - Shinozaki, Nana AU - Kimoto, Nana AU - Onodera, Hiroko AU - Oono, Fumi AU - McCaffrey, A. Tracy AU - Livingstone, E. M. Barbara AU - Okuhara, Tsuyoshi AU - Matsumoto, Mai AU - Katagiri, Ryoko AU - Ota, Erika AU - Chiba, Tsuyoshi AU - Nishida, Yuki AU - Sasaki, Satoshi PY - 2023/11/16 TI - Web-Based Content on Diet and Nutrition Written in Japanese: Infodemiology Study Based on Google Trends and Google Search JO - JMIR Form Res SP - e47101 VL - 7 KW - diet KW - nutrition KW - information KW - internet KW - web KW - Japanese language N2 - Background: The increased availability of content of uncertain integrity obtained through the internet is a major concern. To date, however, there has been no comprehensive scrutiny of the fitness-for-purpose of web-based content on diet and nutrition. Objective: This cross-sectional study aims to describe diet- and nutrition-related web-based content written in Japanese, identified via a systematic extraction strategy using Google Trends and Google Search. Methods: We first identified keywords relevant for extracting web-based content (eg, blogs) on diet and nutrition written in Japanese using Google Trends. This process included identification of 638 seed terms, identification of approximately 1500 pairs of related queries (top) and search terms, the top 10% of which were extracted to identify 160 relevant pairs of related queries (top) and search terms, and identification of 107 keywords for search. We then extracted relevant web-based content using Google Search. Results: The content (N=1703) examined here was extracted following a search based on 107 keywords. The most common themes included food and beverages (390/1703, 22.9%), weight management (366/1703, 21.49%), health benefits (261/1703, 15.33%), and healthy eating (235/1703, 13.8%). The main disseminators were information technology companies and mass media (474/1703, 27.83%), food manufacturers (246/1703, 14.45%), other (236/1703, 13.86%), and medical institutions (214/1703, 12.57%). Less than half of the content (790/1703, 46.39%) clearly indicated the involvement of editors or writers. More than half of the content (983/1703, 57.72%) was accompanied by one or more types of advertisement. The proportion of content with any type of citation reference was 40.05% (682/1703). The themes and disseminators of content were significantly associated with the involvement of editors or writers, accompaniment with advertisement, and citation of reference. In particular, content focusing on weight management was more likely to clearly indicate the involvement of editors or writers (212/366, 57.9%) and to be accompanied by advertisement (273/366, 74.6%), but less likely to have references cited (128/366, 35%). Content from medical institutions was less likely to have citation references (62/214, 29%). Conclusions: This study highlights concerns regarding the authorship, conflicts of interest (advertising), and the scientific credibility of web-based diet- and nutrition-related information written in Japanese. Nutrition professionals and experts should take these findings seriously because exposure to nutritional information that lacks context or seems contradictory can lead to confusion and backlash among consumers. However, more research is needed to draw firm conclusions about the accuracy and quality of web-based diet- and nutrition-related content and whether similar results can be obtained in other major mass media or social media outlets and even other languages. UR - https://formative.jmir.org/2023/1/e47101 UR - http://dx.doi.org/10.2196/47101 UR - http://www.ncbi.nlm.nih.gov/pubmed/37971794 ID - info:doi/10.2196/47101 ER - TY - JOUR AU - Hou, Xinran AU - Xu, Wei AU - Zhang, Chengliang AU - Song, Zongbin AU - Zhu, Maoen AU - Guo, Qulian AU - Wang, Jian PY - 2023/11/13 TI - L-Shaped Association of Serum Chloride Level With All-Cause and Cause-Specific Mortality in American Adults: Population-Based Prospective Cohort Study JO - JMIR Public Health Surveill SP - e49291 VL - 9 KW - serum chloride KW - all-cause mortality KW - cause-specific mortality KW - National Health and Nutrition Examination Survey KW - National Death Index N2 - Background: Chloride is the most abundant anion in the human extracellular fluid and plays a crucial role in maintaining homeostasis. Previous studies have demonstrated that hypochloremia can act as an independent risk factor for adverse outcomes in various clinical settings. However, the association of variances of serum chloride with long-term mortality risk in general populations has been rarely investigated. Objective: This study aims to assess the association of serum chloride with all-cause and cause-specific mortality in the general American adult population. Methods: Data were collected from 10 survey cycles (1999-2018) of the National Health and Nutrition Examination Survey. All-cause mortality, cardiovascular disease (CVD) mortality, cancer mortality, and respiratory disease mortality data were obtained by linkage to the National Death Index through December 31, 2019. After adjusting for demographic factors and relevant lifestyle, laboratory items, and comorbid factors, weighted Cox proportional risk models were constructed to estimate hazard ratios and 95% CIs for all-cause and cause-specific mortality. Results: A total of 51,060 adult participants were included, and during a median follow-up of 111 months, 7582 deaths were documented, 2388 of CVD, 1639 of cancer, and 567 of respiratory disease. The weighted Kaplan-Meier survival analyses showed consistent highest mortality risk in individuals with the lowest quartiles of serum chloride. The multivariate-adjusted hazard ratios from lowest to highest quartiles of serum chloride (?101.2, 101.3-103.2, 103.2-105.0, and ?105.1 mmol/L) were 1.00 (95% CI reference), 0.77 (95% CI 0.67-0.89), 0.72 (95% CI 0.63-0.82), and 0.77 (95% CI 0.65-0.90), respectively, for all-cause mortality (P for linear trend<.001); 1.00 (95% CI reference), 0.63 (95% CI 0.51-0.79), 0.56 (95% CI 0.43-0.73), and 0.67 (95% CI 0.50-0.89) for CVD mortality (P for linear trend=.004); 1.00 (95% CI reference), 0.67 (95% CI 0.54-0.84), 0.65 (95% CI 0.50-0.85), and 0.65 (95% CI 0.48-0.87) for cancer mortality (P for linear trend=.004); and 1.00 (95% CI reference), 0.68 (95% CI 0.41-1.13), 0.59 (95% CI 0.40-0.88), and 0.51 (95% CI 0.31-0.84) for respiratory disease mortality (P for linear trend=.004). The restricted cubic spline analyses revealed the nonlinear and L-shaped associations of serum chloride with all-cause and cause-specific mortality (all P for nonlinearity<.05), in which lower serum chloride was prominently associated with higher mortality risk. The associations of serum chloride with mortality risk were robust, and no significant additional interaction effect was detected for all-cause mortality and CVD mortality (P for interaction>.05). Conclusions: In American adults, decreased serum chloride concentrations were independently associated with increased all-cause mortality, CVD mortality, cancer mortality, and respiratory disease mortality. Our findings suggested that serum chloride may serve as a promising cost-effective health indicator in the general adult population. Further studies are warranted to explore the potential pathophysiological mechanisms underlying the association between serum chloride and mortality. UR - https://publichealth.jmir.org/2023/1/e49291 UR - http://dx.doi.org/10.2196/49291 UR - http://www.ncbi.nlm.nih.gov/pubmed/37955964 ID - info:doi/10.2196/49291 ER - TY - JOUR AU - Yang, Wenyi AU - Wang, Baohua AU - Ma, Shaobo AU - Wang, Jingxin AU - Ai, Limei AU - Li, Zhengyu AU - Wan, Xia PY - 2023/11/6 TI - Optimal Look-Back Period to Identify True Incident Cases of Diabetes in Medical Insurance Data in the Chinese Population: Retrospective Analysis Study JO - JMIR Public Health Surveill SP - e46708 VL - 9 KW - diabetes KW - incident cases KW - administrative data KW - look-back period KW - retrograde survival function N2 - Background: Accurate estimation of incidence and prevalence is vital for preventing and controlling diabetes. Administrative data (including insurance data) could be a good source to estimate the incidence of diabetes. However, how to determine the look-back period (LP) to remove cases with preceding records remains a problem for administrative data. A short LP will cause overestimation of incidence, whereas a long LP will limit the usefulness of a database. Therefore, it is necessary to determine the optimal LP length for identifying incident cases in administrative data. Objective: This study aims to offer different methods to identify the optimal LP for diabetes by using medical insurance data from the Chinese population with reference to other diseases in the administrative data. Methods: Data from the insurance database of the city of Weifang, China from between January 2016 and December 2020 were used. To identify the incident cases in 2020, we removed prevalent patients with preceding records of diabetes between 2016 and 2019 (ie, a 4-year LP). Using this 4-year LP as a reference, consistency examination indexes (CEIs), including positive predictive values, the ? coefficient, and overestimation rate, were calculated to determine the level of agreement between different LPs and an LP of 4 years (the longest LP). Moreover, we constructed a retrograde survival function, in which survival (ie, incident cases) means not having a preceding record at the given time and the survival time is the difference between the date of the last record in 2020 and the most recent previous record in the LP. Based on the survival outcome and survival time, we established the survival function and survival hazard function. When the survival probability, S(t), remains stable, and survival hazard converges to zero, we obtain the optimal LP. Combined with the results of these two methods, we determined the optimal LP for Chinese diabetes patients. Results: The ? agreement was excellent (0.950), with a high positive predictive value (92.2%) and a low overestimation rate (8.4%) after a 2-year LP. As for the retrograde survival function, S(t) dropped rapidly during the first 1-year LP (from 1.00 to 0.11). At a 417-day LP, the hazard function reached approximately zero (ht=0.000459), S(t) remained at 0.10, and at 480 days, the frequency of S(t) did not increase. Combining the two methods, we found that the optimal LP is 2 years for Chinese diabetes patients. Conclusions: The retrograde survival method and CEIs both showed effectiveness. A 2-year LP should be considered when identifying incident cases of diabetes using insurance data in the Chinese population. UR - https://publichealth.jmir.org/2023/1/e46708 UR - http://dx.doi.org/10.2196/46708 UR - http://www.ncbi.nlm.nih.gov/pubmed/37930785 ID - info:doi/10.2196/46708 ER - TY - JOUR AU - Fisher, Andrew AU - Young, Maclaren Matthew AU - Payer, Doris AU - Pacheco, Karen AU - Dubeau, Chad AU - Mago, Vijay PY - 2023/9/19 TI - Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework JO - J Med Internet Res SP - e43630 VL - 25 KW - early warning system KW - social media KW - law enforcement KW - public health KW - new psychoactive substances KW - development KW - drug KW - dosage KW - Canada KW - Twitter KW - poisoning KW - monitoring KW - community KW - public safety KW - machine learning KW - Fleiss KW - tweet KW - tweet annotations KW - pharmacology KW - addiction N2 - Background: A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada. Objective: The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities. Methods: To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2. Results: When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of ~84.5%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of ~94.1%) with the subject matter experts. Conclusions: This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain. UR - https://www.jmir.org/2023/1/e43630 UR - http://dx.doi.org/10.2196/43630 UR - http://www.ncbi.nlm.nih.gov/pubmed/37725410 ID - info:doi/10.2196/43630 ER - TY - JOUR AU - Malhotra, Kashish AU - Kempegowda, Punith PY - 2023/9/11 TI - Appraising Unmet Needs and Misinformation Spread About Polycystic Ovary Syndrome in 85,872 YouTube Comments Over 12 Years: Big Data Infodemiology Study JO - J Med Internet Res SP - e49220 VL - 25 KW - polycystic ovary syndrome KW - PCOS KW - public KW - YouTube KW - global health KW - online trends KW - global equity KW - infodemiology KW - big data KW - comments KW - sentiment KW - network analysis KW - contextualization KW - word association KW - misinformation KW - endocrinopathy KW - women KW - gender KW - users KW - treatment KW - fatigue KW - pain KW - motherhood N2 - Background: Polycystic ovary syndrome (PCOS) is the most common endocrinopathy in women, resulting in substantial burden related to metabolic, reproductive, and psychological complications. While attempts have been made to understand the themes and sentiments of the public regarding PCOS at the local and regional levels, no study has explored worldwide views, mainly due to financial and logistical limitations. YouTube is one of the largest sources of health-related information, where many visitors share their views as questions or comments. These can be used as a surrogate to understand the public?s perceptions. Objective: We analyzed the comments of all videos related to PCOS published on YouTube from May 2011 to April 2023 and identified trends over time in the comments, their context, associated themes, gender-based differences, and underlying sentiments. Methods: After extracting all the comments using the YouTube application programming interface, we contextually studied the keywords and analyzed gender differences using the Benjamini-Hochberg procedure. We applied a multidimensional approach to analyzing the content via association mining using Mozdeh. We performed network analysis to study associated themes using the Fruchterman-Reingold algorithm and then manually screened the comments for content analysis. The sentiments associated with YouTube comments were analyzed using SentiStrength. Results: A total of 85,872 comments from 940 PCOS videos on YouTube were extracted. We identified a specific gender for 13,106 comments. Of these, 1506 were matched to male users (11.5%), and 11,601 comments to female users (88.5%). Keywords including diagnosing PCOS, symptoms of PCOS, pills for PCOS (medication), and pregnancy were significantly associated with female users. Keywords such as herbal treatment, natural treatment, curing PCOS, and online searches were significantly associated with male users. The key themes associated with female users were symptoms of PCOS, positive personal experiences (themes such as helpful and love), negative personal experiences (fatigue and pain), motherhood (infertility and trying to conceive), self-diagnosis, and use of professional terminology detailing their journey. The key themes associated with male users were misinformation regarding the ?cure? for PCOS, using natural and herbal remedies to cure PCOS, fake testimonies from spammers selling their courses and consultations, finding treatment for PCOS, and sharing perspectives of female family members. The overall average positive sentiment was 1.6651 (95% CI 1.6593-1.6709), and the average negative sentiment was 1.4742 (95% CI 1.4683-1.4802) with a net positive difference of 0.1909. Conclusions: There may be a disparity in views on PCOS between women and men, with the latter associated with non?evidence-based approaches and misinformation. The improving sentiment noticed with YouTube comments may reflect better health care services. Prioritizing and promoting evidence-based care and disseminating pragmatic online coverage is warranted to improve public sentiment and limit misinformation spread. UR - https://www.jmir.org/2023/1/e49220 UR - http://dx.doi.org/10.2196/49220 UR - http://www.ncbi.nlm.nih.gov/pubmed/37695666 ID - info:doi/10.2196/49220 ER - TY - JOUR AU - Deng, Yuhan AU - Ma, Yuan AU - Fu, Jingzhu AU - Wang, Xiaona AU - Yu, Canqing AU - Lv, Jun AU - Man, Sailimai AU - Wang, Bo AU - Li, Liming PY - 2023/9/7 TI - Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study JO - JMIR Public Health Surveill SP - e47095 VL - 9 KW - machine learning KW - carotid plaque KW - health check-up KW - prediction KW - fatty liver KW - risk assessment KW - risk stratification KW - cardiovascular KW - logistic regression N2 - Background: Carotid plaque can progress into stroke, myocardial infarction, etc, which are major global causes of death. Evidence shows a significant increase in carotid plaque incidence among patients with fatty liver disease. However, unlike the high detection rate of fatty liver disease, screening for carotid plaque in the asymptomatic population is not yet prevalent due to cost-effectiveness reasons, resulting in a large number of patients with undetected carotid plaques, especially among those with fatty liver disease. Objective: This study aimed to combine the advantages of machine learning (ML) and logistic regression to develop a straightforward prediction model among the population with fatty liver disease to identify individuals at risk of carotid plaque. Methods: Our study included 5,420,640 participants with fatty liver from Meinian Health Care Center. We used random forest, elastic net (EN), and extreme gradient boosting ML algorithms to select important features from potential predictors. Features acknowledged by all 3 models were enrolled in logistic regression analysis to develop a carotid plaque prediction model. Model performance was evaluated based on the area under the receiver operating characteristic curve, calibration curve, Brier score, and decision curve analysis both in a randomly split internal validation data set, and an external validation data set comprising 32,682 participants from MJ Health Check-up Center. Risk cutoff points for carotid plaque were determined based on the Youden index, predicted probability distribution, and prevalence rate of the internal validation data set to classify participants into high-, intermediate-, and low-risk groups. This risk classification was further validated in the external validation data set. Results: Among the participants, 26.23% (1,421,970/5,420,640) were diagnosed with carotid plaque in the development data set, and 21.64% (7074/32,682) were diagnosed in the external validation data set. A total of 6 features, including age, systolic blood pressure, low-density lipoprotein cholesterol (LDL-C), total cholesterol, fasting blood glucose, and hepatic steatosis index (HSI) were collectively selected by all 3 ML models out of 27 predictors. After eliminating the issue of collinearity between features, the logistic regression model established with the 5 independent predictors reached an area under the curve of 0.831 in the internal validation data set and 0.801 in the external validation data set, and showed good calibration capability graphically. Its predictive performance was comprehensively competitive compared with the single use of either logistic regression or ML algorithms. Optimal predicted probability cutoff points of 25% and 65% were determined for classifying individuals into low-, intermediate-, and high-risk categories for carotid plaque. Conclusions: The combination of ML and logistic regression yielded a practical carotid plaque prediction model, and was of great public health implications in the early identification and risk assessment of carotid plaque among individuals with fatty liver. UR - https://publichealth.jmir.org/2023/1/e47095 UR - http://dx.doi.org/10.2196/47095 UR - http://www.ncbi.nlm.nih.gov/pubmed/37676713 ID - info:doi/10.2196/47095 ER - TY - JOUR AU - Marashi, Amir AU - Warren, David AU - Call, Gary AU - Dras, Mark PY - 2023/9/1 TI - Trends in Opioid Medication Adherence During the COVID-19 Pandemic: Retrospective Cohort Study JO - JMIR Public Health Surveill SP - e42495 VL - 9 KW - COVID-19 KW - opioid crisis KW - opioids KW - medication for opioid use disorder KW - MOUD KW - pandemic KW - public health KW - opioid KW - medication KW - treatment KW - care KW - patient KW - opioid use disorder KW - beta regression analysis KW - breakpoint analysis N2 - Background: The recent pandemic had the potential to worsen the opioid crisis through multiple effects on patients? lives, such as the disruption of care. In particular, good levels of adherence with respect to medication for opioid use disorder (MOUD), recognized as being important for positive outcomes, may be disrupted. Objective: This study aimed to investigate whether patients on MOUD experienced a drop in medication adherence during the recent COVID-19 pandemic. Methods: This retrospective cohort study used Medicaid claims data from 6 US states from 2018 until the start of 2021. We compared medication adherence for people on MOUD before and after the beginning of the COVID-19 pandemic in March 2020. Our main measure was the proportion of days covered (PDC), a score that measures patients? adherence to their MOUD. We carried out a breakpoint analysis on PDC, followed by a patient-level beta regression analysis with PDC as the dependent variable while controlling for a set of covariates. Results: A total of 79,991 PDC scores were calculated for 37,604 patients (age: mean 37.6, SD 9.8 years; sex: n=17,825, 47.4% female) between 2018 and 2021. The coefficient for the effect of COVID-19 on PDC score was ?0.076 and was statistically significant (odds ratio 0.925, 95% CI 0.90-0.94). Conclusions: The COVID-19 pandemic was negatively associated with patients? adherence to their medication, which had declined since the beginning of the pandemic. UR - https://publichealth.jmir.org/2023/1/e42495 UR - http://dx.doi.org/10.2196/42495 UR - http://www.ncbi.nlm.nih.gov/pubmed/37656492 ID - info:doi/10.2196/42495 ER - TY - JOUR AU - Kang, Danbee AU - Kim, Hyunsoo AU - Cho, Juhee AU - Kim, Zero AU - Chung, Myungjin AU - Lee, Eon Jeong AU - Nam, Jin Seok AU - Kim, Won Seok AU - Yu, Jonghan AU - Chae, Joo Byung AU - Ryu, Min Jai AU - Lee, Kyung Se PY - 2023/8/24 TI - Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning?Based Analysis JO - JMIR Public Health Surveill SP - e45212 VL - 9 KW - breast cancer survivor KW - quality of life KW - machine learning KW - trajectory KW - predict KW - develop KW - breast cancer KW - survivor KW - cancer KW - oncology KW - algorithm KW - model KW - QoL N2 - Background: Breast cancer is the most common cancer and the most common cause of cancer death in women. Although survival rates have improved, unmet psychosocial needs remain challenging because the quality of life (QoL) and QoL-related factors change over time. In addition, traditional statistical models have limitations in identifying factors associated with QoL over time, particularly concerning the physical, psychological, economic, spiritual, and social dimensions. Objective: This study aimed to identify patient-centered factors associated with QoL among patients with breast cancer using a machine learning (ML) algorithm to analyze data collected along different survivorship trajectories. Methods: The study used 2 data sets. The first data set was the cross-sectional survey data from the Breast Cancer Information Grand Round for Survivorship (BIG-S) study, which recruited consecutive breast cancer survivors who visited the outpatient breast cancer clinic at the Samsung Medical Center in Seoul, Korea, between 2018 and 2019. The second data set was the longitudinal cohort data from the Beauty Education for Distressed Breast Cancer (BEST) cohort study, which was conducted at 2 university-based cancer hospitals in Seoul, Korea, between 2011 and 2016. QoL was measured using European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 questionnaire. Feature importance was interpreted using Shapley Additive Explanations (SHAP). The final model was selected based on the highest mean area under the receiver operating characteristic curve (AUC). The analyses were performed using the Python 3.7 programming environment (Python Software Foundation). Results: The study included 6265 breast cancer survivors in the training data set and 432 patients in the validation set. The mean age was 50.6 (SD 8.66) years and 46.8% (n=2004) had stage 1 cancer. In the training data set, 48.3% (n=3026) of survivors had poor QoL. The study developed ML models for QoL prediction based on 6 algorithms. Performance was good for all survival trajectories: overall (AUC 0.823), baseline (AUC 0.835), within 1 year (AUC 0.860), between 2 and 3 years (AUC 0.808), between 3 and 4 years (AUC 0.820), and between 4 and 5 years (AUC 0.826). Emotional and physical functions were the most important features before surgery and within 1 year after surgery, respectively. Fatigue was the most important feature between 1 and 4 years. Despite the survival period, hopefulness was the most influential feature on QoL. External validation of the models showed good performance with AUCs between 0.770 and 0.862. Conclusions: The study identified important factors associated with QoL among breast cancer survivors across different survival trajectories. Understanding the changing trends of these factors could help to intervene more precisely and timely, and potentially prevent or alleviate QoL-related issues for patients. The good performance of our ML models in both training and external validation sets suggests the potential use of this approach in identifying patient-centered factors and improving survivorship care. UR - https://publichealth.jmir.org/2023/1/e45212 UR - http://dx.doi.org/10.2196/45212 UR - http://www.ncbi.nlm.nih.gov/pubmed/37309655 ID - info:doi/10.2196/45212 ER - TY - JOUR AU - Wang, Limin AU - Liu, Shimeng AU - Jiang, Shan AU - Li, Chaofan AU - Lu, Liyong AU - Fang, Yunhai AU - Li, Shunping PY - 2023/7/26 TI - Quantifying Benefit-Risk Trade-Offs Toward Prophylactic Treatment Among Adult Patients With Hemophilia A in China: Discrete Choice Experiment Study JO - JMIR Public Health Surveill SP - e45747 VL - 9 KW - benefit-risk assessment KW - discrete choice experiment KW - hemophilia A KW - patient preference KW - prophylactic treatment N2 - Background: Hemophilia A is a chronic condition that requires meticulous treatment and management. Patient preferences for prophylactic treatment can substantially influence adherence, outcomes, and quality of life, yet these preferences remain underexplored, particularly in China. Objective: This study aimed to investigate the preferences for prophylactic treatment among Chinese adult patients with hemophilia A without inhibitors, considering clinical effectiveness, side effects, dosing mode, and dosing frequency. Methods: A discrete choice experiment was used to elicit patient preferences for prophylactic treatment of hemophilia. The study was conducted across 7 provinces in China with socioeconomic and geographical diversity. Subgroup analysis was performed according to education level, geographic location, and treatment type, alongside the exploration of benefit-risk trade-offs. Results: A total of 113 patients completed the discrete choice experiment questionnaire, and we included 102 responses for analysis based on predetermined exclusion criteria. The study found that patients prioritized reducing annual bleeding times and avoiding the risk of developing inhibitors over treatment process attributes. Subgroup analysis revealed that lower-educated patients and those from rural areas attached more importance to the dosing mode, likely due to barriers to self-administration. Patients demonstrated a clear understanding of benefit-risk trade-offs, exhibiting a willingness to accept an increased risk of developing inhibitors for improved clinical outcomes. Conclusions: This study provides valuable insights into the preferences of patients with hemophilia A for prophylactic treatment in China. Understanding these preferences can enhance shared decision-making between patients and clinicians, fostering personalized prophylactic treatment plans that may optimize adherence and improve clinical outcomes. UR - https://publichealth.jmir.org/2023/1/e45747 UR - http://dx.doi.org/10.2196/45747 UR - http://www.ncbi.nlm.nih.gov/pubmed/37494098 ID - info:doi/10.2196/45747 ER - TY - JOUR AU - Qin, Lang AU - Zheng, Ming AU - Schwebel, C. David AU - Li, Li AU - Cheng, Peixia AU - Rao, Zhenzhen AU - Peng, Ruisha AU - Ning, Peishan AU - Hu, Guoqing PY - 2023/6/30 TI - Content Quality of Web-Based Short-Form Videos for Fire and Burn Prevention in China: Content Analysis JO - J Med Internet Res SP - e47343 VL - 25 KW - fire KW - burn KW - prevention KW - first aid KW - short video KW - content quality KW - public impact KW - China N2 - Background: Web-based short-form videos are increasingly popular for disseminating fire and burn prevention information, but their content quality is unknown. Objective: We aimed to systematically assess the characteristics, content quality, and public impact of web-based short-form videos offering primary and secondary (first aid) prevention recommendations for fires and burns in China between 2018 and 2021. Methods: We retrieved short-form videos offering both primary and secondary (first aid) information to prevent fire and burn injuries published on the 3 most popular web-based short-form video platforms in China: TikTok, Kwai, and Bilibili. To assess video content quality, we calculated the proportion of short-form videos that included information on each of the 15 recommendations for burn prevention education from the World Health Organization (WHO; P1) and that correctly disseminated each recommendation (P2). High P1 and P2 indicated better content quality. To assess their public impact, we calculated the median (IQR) of 3 indicators: the number of comments, likes, and saves as a favorite by viewers. Chi-square test, trend chi-square test, and Kruskal-Wallis H test examined differences in indicators across the 3 platforms, years, content, and time duration of videos and between videos disseminating correct versus incorrect information. Results: Overall, 1459 eligible short-form videos were included. The number of short-form videos increased by 16 times between 2018 and 2021. Of them, 93.97% (n=1371) were about secondary prevention (first aid) and 86.02% (n=1255) lasted <2 minutes. The proportion of short-form videos including each of the 15 WHO recommendations ranged from 0% to 77.86% (n=1136). Recommendations 8, 13, and 11 had the highest proportions (n=1136, 77.86%; n=827, 56.68%; and n=801, 54.9%, respectively), whereas recommendations 3 and 5 were never mentioned. Among the short-form videos that included the WHO recommendations, recommendations 1, 2, 4, 6, 9, and 12 were always disseminated correctly, but the other 9 recommendations were correctly disseminated in 59.11% (120/203) to 98.68% (1121/1136) of videos. The proportion of short-form videos including and correctly disseminating the WHO recommendations varied across platforms and years. The public impact of short videos varied greatly across videos, with a median (IQR) of 5 (0-34) comments, 62 (7-841) likes, and 4 (0-27) saves as a favorite. Short-form videos disseminating correct recommendations had larger public impact than those disseminating either partially correct or incorrect knowledge (median 5 vs 4 comments, 68 vs 51 likes, and 5 vs 3 saves as a favorite, respectively; all P<.05). Conclusions: Despite the rapid increase in the number of web-based short-form videos about fire and burn prevention available in China, their content quality and public impact were generally low. Systematic efforts are recommended to improve the content quality and public impact of short-form videos on injury prevention topics such as fire and burn prevention. UR - https://www.jmir.org/2023/1/e47343 UR - http://dx.doi.org/10.2196/47343 UR - http://www.ncbi.nlm.nih.gov/pubmed/37389906 ID - info:doi/10.2196/47343 ER - TY - JOUR AU - Carrasco-Ribelles, A. Lucía AU - Cabrera-Bean, Margarita AU - Danés-Castells, Marc AU - Zabaleta-del-Olmo, Edurne AU - Roso-Llorach, Albert AU - Violán, Concepción PY - 2023/6/27 TI - Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People JO - JMIR Public Health Surveill SP - e45848 VL - 9 KW - multimorbidity KW - frailty KW - clustering KW - electronic health record KW - primary care KW - trajectory N2 - Background: Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. Objective: This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. Methods: Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d?Informació pel Desenvolupament de la Investigació a l?Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. Results: The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. Conclusions: Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories. UR - https://publichealth.jmir.org/2023/1/e45848 UR - http://dx.doi.org/10.2196/45848 UR - http://www.ncbi.nlm.nih.gov/pubmed/37368462 ID - info:doi/10.2196/45848 ER - TY - JOUR AU - Archambault, M. Patrick AU - Rosychuk, J. Rhonda AU - Audet, Martyne AU - Bola, Rajan AU - Vatanpour, Shabnam AU - Brooks, C. Steven AU - Daoust, Raoul AU - Clark, Gregory AU - Grant, Lars AU - Vaillancourt, Samuel AU - Welsford, Michelle AU - Morrison, J. Laurie AU - Hohl, M. Corinne AU - PY - 2023/6/16 TI - Accuracy of Self-Reported COVID-19 Vaccination Status Compared With a Public Health Vaccination Registry in Québec: Observational Diagnostic Study JO - JMIR Public Health Surveill SP - e44465 VL - 9 KW - electronic vaccination registry KW - self-reported vaccination status KW - COVID-19 KW - accuracy KW - diagnostic study KW - interrater agreement N2 - Background: The accuracy of self-reported vaccination status is important to guide real-world vaccine effectiveness studies and policy making in jurisdictions where access to electronic vaccine registries is restricted. Objective: This study aimed to determine the accuracy of self-reported vaccination status and reliability of the self-reported number of doses, brand, and time of vaccine administration. Methods: This diagnostic accuracy study was completed by the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive patients presenting to 4 emergency departments (EDs) in Québec between March 24, 2020, and December 25, 2021. We included adult patients who were able to consent, could speak English or French, and had a proven COVID-19 infection. We compared the self-reported vaccination status of the patients with their vaccination status in the electronic Québec Vaccination Registry. Our primary outcome was the accuracy of the self-reported vaccination status (index test) ascertained during telephone follow-up compared with the Québec Vaccination Registry (reference standard). The accuracy was calculated by dividing all correctly self-reported vaccinated and unvaccinated participants by the sum of all correctly and incorrectly self-reported vaccinated and unvaccinated participants. We also reported interrater agreement with the reference standard as measured by unweighted Cohen ? for self-reported vaccination status at telephone follow-up and at the time of their index ED visit, number of vaccine doses, and brand. Results: During the study period, we included 1361 participants. At the time of the follow-up interview, 932 participants reported at least 1 dose of a COVID-19 vaccine. The accuracy of the self-reported vaccination status was 96% (95% CI 95%-97%). Cohen ? for self-reported vaccination status at phone follow-up was 0.91 (95% CI 0.89-0.93) and 0.85 (95% CI 0.77-0.92) at the time of their index ED visit. Cohen ? was 0.89 (95% CI 0.87-0.91) for the number of doses, 0.80 (95% CI 0.75-0.84) for the brand of the first dose, 0.76 (95% CI 0.70-0.83) for the brand of the second dose, and 0.59 (95% CI 0.34-0.83) for the brand of the third dose. Conclusions: We reported a high accuracy of self-reported vaccination status for adult patients without cognitive disorders who can express themselves in English or French. Researchers can use self-reported COVID-19 vaccination data on the number of doses received, vaccine brand name, and timing of vaccination to guide future research with patients who are capable of self-reporting their vaccination data. However, access to official electronic vaccine registries is still needed to determine the vaccination status in certain susceptible populations where self-reported vaccination data remain missing or impossible to obtain. Trial Registration: Clinicaltrials.gov NCT04702945; https://clinicaltrials.gov/ct2/show/NCT04702945 UR - https://publichealth.jmir.org/2023/1/e44465 UR - http://dx.doi.org/10.2196/44465 UR - http://www.ncbi.nlm.nih.gov/pubmed/37327046 ID - info:doi/10.2196/44465 ER - TY - JOUR AU - Maddah, Noha AU - Verma, Arpana AU - Almashmoum, Maryam AU - Ainsworth, John PY - 2023/5/19 TI - Effectiveness of Public Health Digital Surveillance Systems for Infectious Disease Prevention and Control at Mass Gatherings: Systematic Review JO - J Med Internet Res SP - e44649 VL - 25 KW - public health KW - digital surveillance system KW - infectious disease prevention and control KW - mass gathering event KW - systematic review N2 - Background: Mass gatherings (MGs; eg, religious, sporting, musical, sociocultural, and other occasions that draw large crowds) pose public health challenges and concerns related to global health. A leading global concern regarding MGs is the possible importation and exportation of infectious diseases as they spread from the attendees to the general population, resulting in epidemic outbreaks. Governments and health authorities use technological interventions to support public health surveillance and prevent and control infectious diseases. Objective: This study aims to review the evidence on the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MG events. Methods: A systematic literature search was conducted in January 2022 using the Ovid MEDLINE, Embase, CINAHL, and Scopus databases to examine relevant articles published in English up to January 2022. Interventional studies describing or evaluating the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs were included in the analysis. Owing to the lack of appraisal tools for interventional studies describing and evaluating public health digital surveillance systems at MGs, a critical appraisal tool was developed and used to assess the quality of the included studies. Results: In total, 8 articles were included in the review, and 3 types of MGs were identified: religious (the Hajj and Prayagraj Kumbh), sporting (the Olympic and Paralympic Games, the Federation International Football Association World Cup, and the Micronesian Games), and cultural (the Festival of Pacific Arts) events. In total, 88% (7/8) of the studies described surveillance systems implemented at MG events, and 12% (1/8) of the studies described and evaluated an enhanced surveillance system that was implemented for an event. In total, 4 studies reported the implementation of a surveillance system: 2 (50%) described the enhancement of the system that was implemented for an event, 1 (25%) reported a pilot implementation of a surveillance system, and 1 (25%) reported an evaluation of an enhanced system. The types of systems investigated were 2 syndromic, 1 participatory, 1 syndromic and event-based, 1 indicator- and event-based, and 1 event-based surveillance system. In total, 62% (5/8) of the studies reported timeliness as an outcome generated after implementing or enhancing the system without measuring its effectiveness. Only 12% (1/8) of the studies followed the Centers for Disease Control and Prevention guidelines for evaluating public health surveillance systems and the outcomes of enhanced systems based on the systems? attributes to measure their effectiveness. Conclusions: On the basis of the review of the literature and the analysis of the included studies, there is limited evidence of the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs because of the absence of evaluation studies. UR - https://www.jmir.org/2023/1/e44649 UR - http://dx.doi.org/10.2196/44649 UR - http://www.ncbi.nlm.nih.gov/pubmed/37204833 ID - info:doi/10.2196/44649 ER - TY - JOUR AU - Lee, Heui Yoon AU - Jang, Yu-Jin AU - Lee, Soo-Kyoung PY - 2023/4/13 TI - Obstacles to Health Big Data Utilization Based on the Perceptions and Demands of Health Care Workers in South Korea: Web-Based Survey Study JO - JMIR Form Res SP - e45913 VL - 7 KW - demand KW - health big data KW - health care worker KW - obstacles KW - perception KW - utilization N2 - Background: This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes. Objective: The aim of this study was to identify the perceptions of and demands for health big data analysis and use among workers in health care?related occupations and to clarify the obstacles to the use of health big data. Methods: From May 8 to May 18, 2022, we conducted a web-based survey among 390 health care?related workers in South Korea. We used Fisher exact test and analysis of variance to estimate the differences among occupations. We expressed the analysis results by item in frequency and percentage and expressed the difficulties in analyzing health big data by mean and standard deviation. Results: The respondents who revealed the need to use health big data in health care work?related fields accounted for 86.4% (337/390); 65.6% (256/390) of the respondents had never used health big data. The lack of awareness about the source of the desired data was the most cited reason for nonuse by 39.6% (153/386) of the respondents. The most cited obstacle to using health big data by the respondents was the difficulty in data integration and expression unit matching, followed by missing value processing and noise removal. Thus, the respondents experienced the greatest difficulty in the data preprocessing stage during the health big data analysis process, regardless of occupation. Approximately 91.8% (358/390) of the participants responded that they were willing to use the system if a system supporting big data analysis was developed. As suggestions for the specific necessary support system, the reporting and provision of appropriate data and expert advice on questions arising during the overall process of big data analysis were mentioned. Conclusions: Our findings indicate respondents? high awareness of and demand for health big data. Our findings also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of such data. Hence, we recommend the development of a customized support system that meets the specific requirements of big data analysis by users such as individuals, nongovernmental agencies, and academia. Our study is significant because it identified important but overlooked failure factors. Thus, it is necessary to prepare practical measures to increase the utilization of health big data in the future. UR - https://formative.jmir.org/2023/1/e45913 UR - http://dx.doi.org/10.2196/45913 UR - http://www.ncbi.nlm.nih.gov/pubmed/37052992 ID - info:doi/10.2196/45913 ER - TY - JOUR AU - Vo, Ace AU - Tao, Youyou AU - Li, Yan AU - Albarrak, Abdulaziz PY - 2023/3/29 TI - The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis JO - JMIR Public Health Surveill SP - e44070 VL - 9 KW - social determinants of health KW - public policy KW - health outcomes KW - policy recommendation KW - cities N2 - Background: With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions. Objective: This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity. Methods: This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level. Results: The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ?2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis. Conclusions: The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health. UR - https://publichealth.jmir.org/2023/1/e44070 UR - http://dx.doi.org/10.2196/44070 UR - http://www.ncbi.nlm.nih.gov/pubmed/36989028 ID - info:doi/10.2196/44070 ER - TY - JOUR AU - Cevasco, E. Kevin AU - Roess, A. Amira PY - 2023/3/22 TI - Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study JO - JMIR Public Health Surveill SP - e38633 VL - 9 KW - COVID-19 KW - contact tracing KW - postmarketing KW - mobile apps KW - public health KW - digital KW - interventions KW - tool KW - adoption KW - effectiveness KW - prevention KW - application KW - transmission N2 - Background: Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers. Objective: The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework. Methods: We analyzed data from a nationally representative survey of US adults about their COVID-19?related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates. Results: An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12). Conclusions: Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters? friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety. UR - https://publichealth.jmir.org/2023/1/e38633 UR - http://dx.doi.org/10.2196/38633 UR - http://www.ncbi.nlm.nih.gov/pubmed/36947135 ID - info:doi/10.2196/38633 ER - TY - JOUR AU - Mishra, Ninad AU - Grant, Reynaldo AU - Patel, Toth Megan AU - Guntupalli, Siva AU - Hamilton, Andrew AU - Carr, Jeremy AU - McKnight, Elizabeth AU - Wise, Wendy AU - deRoode, David AU - Jellison, Jim AU - Collins, Viator Natalie AU - Pérez, Alejandro AU - Karki, Saugat PY - 2023/3/14 TI - Automating Case Reporting of Chlamydia and Gonorrhea to Public Health Authorities in Illinois Clinics: Implementation and Evaluation of Findings JO - JMIR Public Health Surveill SP - e38868 VL - 9 KW - public health surveillance KW - sexually transmitted diseases KW - gonorrhoea KW - chlamydia KW - electronic case reporting KW - eCR KW - health information interoperability KW - electronic health records KW - EHR KW - case reporting KW - automated KW - reporting KW - recording KW - patient records KW - cases KW - health care system KW - semantic KW - interoperability KW - implementation N2 - Background: Chlamydia and gonorrhea cases continue to rise in Illinois, increasing by 16.4% and 70.9% in 2019, respectively, compared with 2015. Providers are required to report both chlamydia and gonorrhea, as mandated by public health laws. Manual reporting remains a huge burden; 90%-93% of cases were reported to Illinois Department of Public Health (IDPH) via electronic laboratory reporting (ELR), and the remaining were reported through web-based data entry platforms, faxes, and phone calls. However, cases reported via ELRs only contain information available to a laboratory facility and do not contain additional data needed for public health. Such data are typically found in an electronic health record (EHR). Electronic case reports (eCRs) were developed and automated the generation of case reports from EHRs to be reported to public health agencies. Objective: Prior studies consolidated trigger criteria for eCRs, and compared with manual reporting, found it to be more complete. The goal of this project is to pilot standards-based eCR for chlamydia and gonorrhea. We evaluated the throughput, completeness, and timeliness of eCR compared to ELR, as well as the implementation experience at a large health center?controlled network in Illinois. Methods: For this study, we selected 8 clinics located on the north, west, and south sides of Chicago to implement the eCRs; these cases were reported to IDPH. The study period was 52 days. The centralized EHR used by these clinics leveraged 2 of the 3 case detection scenarios, which were previously defined as the trigger, to generate an eCR. These messages were successfully transmitted via Health Level 7 electronic initial case report standard. Upon receipt by IDPH, these eCRs were parsed and housed in a staging database. Results: During the study period, 183 eCRs representing 135 unique patients were received by IDPH. eCR reported 95% (n=113 cases) of all the chlamydia cases and 97% (n=70 cases) of all the gonorrhea cases reported from the participating clinical sites. eCR found an additional 14 (19%) cases of gonorrhea that were not reported via ELR. However, ELR reported an additional 6 cases of chlamydia and 2 cases of gonorrhea, which were not reported via eCR. ELR reported 100% of chlamydia cases but only 81% of gonorrhea cases. While key elements such as patient and provider names were complete in both eCR and ELR, eCR was found to report additional clinical data, including history of present illness, reason for visit, symptoms, diagnosis, and medications. Conclusions: eCR successfully identified and created automated reports for chlamydia and gonorrhea cases in the implementing clinics in Illinois. eCR demonstrated a more complete case report and represents a promising future of reducing provider burden for reporting cases while achieving greater semantic interoperability between health care systems and public health. UR - https://publichealth.jmir.org/2023/1/e38868 UR - http://dx.doi.org/10.2196/38868 UR - http://www.ncbi.nlm.nih.gov/pubmed/36917153 ID - info:doi/10.2196/38868 ER - TY - JOUR AU - Lin, Senlin AU - Ma, Yingyan AU - Xu, Yi AU - Lu, Lina AU - He, Jiangnan AU - Zhu, Jianfeng AU - Peng, Yajun AU - Yu, Tao AU - Congdon, Nathan AU - Zou, Haidong PY - 2023/2/23 TI - Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data JO - JMIR Public Health Surveill SP - e41624 VL - 9 KW - artificial intelligence KW - cost KW - diabetic retinopathy KW - utility KW - low- and middle-income countries KW - screening N2 - Background: Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)?based and manual grading?based telemedicine screening is inadequate for policy making. Objective: The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China. Methods: We conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita. Results: The total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy. Conclusions: Our study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI. UR - https://publichealth.jmir.org/2023/1/e41624 UR - http://dx.doi.org/10.2196/41624 UR - http://www.ncbi.nlm.nih.gov/pubmed/36821353 ID - info:doi/10.2196/41624 ER - TY - JOUR AU - Kishore, Kamal AU - Jaswal, Vidushi AU - Pandey, Kumar Anuj AU - Verma, Madhur AU - Koushal, Vipin PY - 2023/2/10 TI - Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study JO - JMIR Public Health Surveill SP - e38371 VL - 9 KW - COVID-19 KW - government response KW - nonpharmaceutical interventions KW - lockdown KW - Comprehensive Health Index KW - Stringency Index KW - time-series modeling KW - ARIMA KW - SARIMA KW - Oxford COVID-19 Government Response Tracker KW - public health KW - surveillance KW - Oxford tracker KW - ecological study KW - health data KW - health policy KW - Bayesian information criteria N2 - Background: Many nations swiftly designed and executed government policies to contain the rapid rise in COVID-19 cases. Government actions can be broadly segmented as movement and mass gathering restrictions (such as travel restrictions and lockdown), public awareness (such as face covering and hand washing), emergency health care investment, and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government, University of Oxford, tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using the Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the COVID-19 pandemic. Objective: This study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of COVID-19. We expect a significant inverse relationship between policy indices (CHI and SI) and COVID-19 severity indices (morbidity and mortality). Methods: In this ecological study, we analyzed data from 2 publicly available data sources released between March 2020 and October 2021: the Oxford Covid-19 Government Response Tracker and the World Health Organization. We used autoregressive integrated moving average (ARIMA) and seasonal ARIMA to model the data. The performance of different models was assessed using a combination of evaluation criteria: adjusted R2, root mean square error, and Bayesian information criteria. Results: implementation of policies by the government to contain the COVID-19 crises resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell, they were consistently higher at values of >80% points. During the initial investigation, we found that cases per million (CPM) and deaths per million (DPM) followed the same trend. However, the final CPM and DPM models were seasonal ARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. This study does not support the hypothesis that COVID-19 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI). Conclusions: Our study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. Twenty-first?century problems thus demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage, and data communication. Thus, digital epidemiology can and should be successfully integrated into existing surveillance systems for better disease monitoring, management, and evaluation. UR - https://publichealth.jmir.org/2023/1/e38371 UR - http://dx.doi.org/10.2196/38371 UR - http://www.ncbi.nlm.nih.gov/pubmed/36395334 ID - info:doi/10.2196/38371 ER - TY - JOUR AU - Malden, E. Deborah AU - Tartof, Y. Sara AU - Ackerson, K. Bradley AU - Hong, Vennis AU - Skarbinski, Jacek AU - Yau, Vincent AU - Qian, Lei AU - Fischer, Heidi AU - Shaw, F. Sally AU - Caparosa, Susan AU - Xie, Fagen PY - 2022/12/30 TI - Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System JO - JMIR Public Health Surveill SP - e41529 VL - 8 IS - 12 KW - natural language processing KW - NLP KW - COVID-19 KW - symptoms KW - disease characterization KW - artificial intelligence KW - application KW - data KW - cough KW - fever KW - headache KW - surveillance N2 - Background: Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. Objective: The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. Methods: This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2?positive cases were used to validate the algorithm performance. Results: A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). Conclusions: These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone. UR - https://publichealth.jmir.org/2022/12/e41529 UR - http://dx.doi.org/10.2196/41529 UR - http://www.ncbi.nlm.nih.gov/pubmed/36446133 ID - info:doi/10.2196/41529 ER - TY - JOUR AU - Thompson, M. Hale AU - Sharma, Brihat AU - Smith, L. Dale AU - Bhalla, Sameer AU - Erondu, Ihuoma AU - Hazra, Aniruddha AU - Ilyas, Yousaf AU - Pachwicewicz, Paul AU - Sheth, K. Neeral AU - Chhabra, Neeraj AU - Karnik, S. Niranjan AU - Afshar, Majid PY - 2022/12/8 TI - Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study JO - JMIR Public Health Surveill SP - e38158 VL - 8 IS - 12 KW - unhealthy opioid use KW - substance misuse KW - COVID-19 KW - severity of illness KW - overdose KW - topic modeling KW - machine learning KW - opioid use KW - pandemic KW - health outcome KW - public health KW - disease severity KW - electronic health record KW - COVID-19 outcome KW - risk factor KW - patient data N2 - Background: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. Objective: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. Methods: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ?18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. Results: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier?s predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). Conclusions: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health. UR - https://publichealth.jmir.org/2022/12/e38158 UR - http://dx.doi.org/10.2196/38158 UR - http://www.ncbi.nlm.nih.gov/pubmed/36265163 ID - info:doi/10.2196/38158 ER - TY - JOUR AU - van der Ploeg, Tjeerd AU - Gobbens, J. Robbert J. PY - 2022/10/20 TI - Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation JO - JMIR Public Health Surveill SP - e38450 VL - 8 IS - 10 KW - municipality properties KW - data merging KW - modeling technique KW - variable selection KW - prediction model KW - public health KW - COVID-19 KW - surveillance KW - static data KW - Dutch public domain KW - pandemic KW - Wuhan KW - virus KW - public KW - infections KW - fever KW - cough KW - congestion KW - fatigue KW - symptoms KW - pneumonia KW - dyspnea KW - death N2 - Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 ?m (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared. UR - https://publichealth.jmir.org/2022/10/e38450 UR - http://dx.doi.org/10.2196/38450 UR - http://www.ncbi.nlm.nih.gov/pubmed/36219835 ID - info:doi/10.2196/38450 ER - TY - JOUR AU - Hota, Bala AU - Casey, Paul AU - McIntyre, F. Anne AU - Khan, Jawad AU - Rab, Shafiq AU - Chopra, Aneesh AU - Lateef, Omar AU - Layden, E. Jennifer PY - 2022/9/27 TI - A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study JO - JMIR Public Health Surveill SP - e35973 VL - 8 IS - 9 KW - public health KW - informatics KW - surveillance KW - disease surveillance KW - epidemiology KW - health data KW - electronic health record KW - data hub KW - acute care hospital KW - COVID-19 KW - pandemic KW - data governance N2 - Background: Disease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards. Objective: We sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources. Methods: Following a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems?a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds. Results: Data from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability. Conclusions: We described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance. UR - https://publichealth.jmir.org/2022/9/e35973 UR - http://dx.doi.org/10.2196/35973 UR - http://www.ncbi.nlm.nih.gov/pubmed/35544440 ID - info:doi/10.2196/35973 ER - TY - JOUR AU - Huang, Yuru AU - Burgoine, Thomas AU - Essman, Michael AU - Theis, Z. Dolly R. AU - Bishop, P. Tom R. AU - Adams, Jean PY - 2022/9/8 TI - Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study JO - JMIR Public Health Surveill SP - e39033 VL - 8 IS - 9 KW - nutritional database KW - web scraping KW - food prepared out of the home KW - out-of-home KW - data science KW - chains N2 - Background: Hand transcribing nutrient composition data from websites requires extensive human resources and is prone to error. As a result, there are limited nutrient composition data on food prepared out of the home in the United Kingdom. Such data are crucial for understanding and monitoring the out-of-home food environment, which aids policy making. Automated data collection from publicly available sources offers a potential low-resource solution to address this gap. Objective: In this paper, we describe the first UK longitudinal nutritional database of food prepared out of the home, MenuTracker. As large chains will be required to display calorie information on their UK menus from April 2022, we also aimed to identify which chains reported their nutritional information online in November 2021. In a case study to demonstrate the utility of MenuTracker, we estimated the proportions of menu items exceeding recommended energy and nutrient intake (eg, >600 kcal per meal). Methods: We have collated nutrient composition data of menu items sold by large chain restaurants quarterly since March 2021. Large chains were defined as those with 250 employees or more (those covered by the new calorie labeling policy) or belonging to the top 100 restaurants based on sales volume. We developed scripts in Python to automate the data collection process from business websites. Various techniques were used to harvest web data and extract data from nutritional tables in PDF format. Results: Automated Python programs reduced approximately 85% of manual work, totaling 500 hours saved for each wave of data collection. As of January 2022, MenuTracker has 76,405 records from 88 large out-of-home food chains at 4 different time points (ie, March, June, September, and December) in 2021. In constructing the database, we found that one-quarter (24.5%, 256/1043) of large chains, which are likely to be subject to the United Kingdom?s calorie menu labeling regulations, provided their nutritional information online in November 2021. Across these chains, 24.7% (16,391/66,295) of menu items exceeded the UK government?s recommendation of a maximum of 600 kcal for a single meal. Comparable figures were 46.4% (29,411/63,416) for saturated fat, 34.7% (21,964/63,388) for total fat, 17.6% (11,260/64,051) for carbohydrates, 17.8% (11,434/64,059) for sugar, and 35.2% (22,588/64,086) for salt. Furthermore, 0.7% to 7.1% of the menu items exceeded the maximum daily recommended intake for these nutrients. Conclusions: MenuTracker is a valuable resource that harnesses the power of data science techniques to use publicly available data online. Researchers, policy makers, and consumers can use MenuTracker to understand and assess foods available from out-of-home food outlets. The methods used in development are available online and can be used to establish similar databases elsewhere. UR - https://publichealth.jmir.org/2022/9/e39033 UR - http://dx.doi.org/10.2196/39033 UR - http://www.ncbi.nlm.nih.gov/pubmed/36074559 ID - info:doi/10.2196/39033 ER - TY - JOUR AU - Templ, Matthias AU - Kanjala, Chifundo AU - Siems, Inken PY - 2022/9/2 TI - Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization JO - JMIR Public Health Surveill SP - e34472 VL - 8 IS - 9 KW - longitudinal data and event history data KW - low- and middle-income countries KW - LMIC KW - anonymization KW - health and demographic surveillance system N2 - Background: Data anonymization and sharing have become popular topics for individuals, organizations, and countries worldwide. Open-access sharing of anonymized data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations. Objective: This study aimed to highlight the requirements and possible solutions for sharing health surveillance event history data. The challenges lie in the anonymization of multiple event dates and time-varying variables. Methods: A sequential approach that adds noise to event dates is proposed. This approach maintains the event order and preserves the average time between events. In addition, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding the key variables that change over time, such as educational level or occupation, we make 2 proposals: one based on limiting the intermediate statuses of the individual and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information. Results: An anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created. Conclusions: The proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker?s database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing. UR - https://publichealth.jmir.org/2022/9/e34472 UR - http://dx.doi.org/10.2196/34472 UR - http://www.ncbi.nlm.nih.gov/pubmed/36053573 ID - info:doi/10.2196/34472 ER - TY - JOUR AU - Meza-Torres, Bernardo AU - Delanerolle, Gayathri AU - Okusi, Cecilia AU - Mayor, Nikhil AU - Anand, Sneha AU - Macartney, Jack AU - Gatenby, Piers AU - Glampson, Ben AU - Chapman, Martin AU - Curcin, Vasa AU - Mayer, Erik AU - Joy, Mark AU - Greenhalgh, Trisha AU - Delaney, Brendan AU - de Lusignan, Simon PY - 2022/8/16 TI - Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study JO - JMIR Public Health Surveill SP - e37668 VL - 8 IS - 8 KW - medical record systems KW - computerized KW - Systematized Nomenclature of Medicine KW - post?acute COVID-19 syndrome KW - phenotype KW - COVID-19 KW - post?COVID-19 syndrome KW - long COVID KW - ethnicity KW - social class KW - general practitioners KW - data accuracy KW - data extracts KW - biomedical ontologies KW - SARS-CoV-2 KW - hospitalization N2 - Background: Most studies of long COVID (symptoms of COVID-19 infection beyond 4 weeks) have focused on people hospitalized in their initial illness. Long COVID is thought to be underrecorded in UK primary care electronic records. Objective: We sought to determine which symptoms people present to primary care after COVID-19 infection and whether presentation differs in people who were not hospitalized, as well as post?long COVID mortality rates. Methods: We used routine data from the nationally representative primary care sentinel cohort of the Oxford?Royal College of General Practitioners Research and Surveillance Centre (N=7,396,702), applying a predefined long COVID phenotype and grouped by whether the index infection occurred in hospital or in the community. We included COVID-19 infection cases from March 1, 2020, to April 1, 2021. We conducted a before-and-after analysis of long COVID symptoms prespecified by the Office of National Statistics, comparing symptoms presented between 1 and 6 months after the index infection matched with the same months 1 year previously. We conducted logistic regression analysis, quoting odds ratios (ORs) with 95% CIs. Results: In total, 5.63% (416,505/7,396,702) and 1.83% (7623/416,505) of the patients had received a coded diagnosis of COVID-19 infection and diagnosis of, or referral for, long COVID, respectively. People with diagnosis or referral of long COVID had higher odds of presenting the prespecified symptoms after versus before COVID-19 infection (OR 2.66, 95% CI 2.46-2.88, for those with index community infection and OR 2.42, 95% CI 2.03-2.89, for those hospitalized). After an index community infection, patients were more likely to present with nonspecific symptoms (OR 3.44, 95% CI 3.00-3.95; P<.001) compared with after a hospital admission (OR 2.09, 95% CI 1.56-2.80; P<.001). Mental health sequelae were more strongly associated with index hospital infections (OR 2.21, 95% CI 1.64-2.96) than with index community infections (OR 1.36, 95% CI 1.21-1.53; P<.001). People presenting to primary care after hospital infection were more likely to be men (OR 1.43, 95% CI 1.25-1.64; P<.001), more socioeconomically deprived (OR 1.42, 95% CI 1.24-1.63; P<.001), and with higher multimorbidity scores (OR 1.41, 95% CI 1.26-1.57; P<.001) than those presenting after an index community infection. All-cause mortality in people with long COVID was associated with increasing age, male sex (OR 3.32, 95% CI 1.34-9.24; P=.01), and higher multimorbidity score (OR 2.11, 95% CI 1.34-3.29; P<.001). Vaccination was associated with reduced odds of mortality (OR 0.10, 95% CI 0.03-0.35; P<.001). Conclusions: The low percentage of people recorded as having long COVID after COVID-19 infection reflects either low prevalence or underrecording. The characteristics and comorbidities of those presenting with long COVID after a community infection are different from those hospitalized. This study provides insights into the presentation of long COVID in primary care and implications for workload. UR - https://publichealth.jmir.org/2022/8/e37668 UR - http://dx.doi.org/10.2196/37668 UR - http://www.ncbi.nlm.nih.gov/pubmed/35605170 ID - info:doi/10.2196/37668 ER - TY - JOUR AU - Ostropolets, Anna AU - Ryan, B. Patrick AU - Schuemie, J. Martijn AU - Hripcsak, George PY - 2022/6/17 TI - Characterizing Anchoring Bias in Vaccine Comparator Selection Due to Health Care Utilization With COVID-19 and Influenza: Observational Cohort Study JO - JMIR Public Health Surveill SP - e33099 VL - 8 IS - 6 KW - COVID-19 KW - vaccine KW - anchoring KW - comparator selection KW - time-at-risk KW - vaccination KW - bias KW - observational KW - utilization KW - flu KW - influenza KW - index KW - cohort N2 - Background: Observational data enables large-scale vaccine safety surveillance but requires careful evaluation of the potential sources of bias. One potential source of bias is the index date selection procedure for the unvaccinated cohort or unvaccinated comparison time (?anchoring?). Objective: Here, we evaluated the different index date selection procedures for 2 vaccinations: COVID-19 and influenza. Methods: For each vaccine, we extracted patient baseline characteristics on the index date and up to 450 days prior and then compared them to the characteristics of the unvaccinated patients indexed on (1) an arbitrary date or (2) a date of a visit. Additionally, we compared vaccinated patients indexed on the date of vaccination and the same patients indexed on a prior date or visit. Results: COVID-19 vaccination and influenza vaccination differ drastically from each other in terms of the populations vaccinated and their status on the day of vaccination. When compared to indexing on a visit in the unvaccinated population, influenza vaccination had markedly higher covariate proportions, and COVID-19 vaccination had lower proportions of most covariates on the index date. In contrast, COVID-19 vaccination had similar covariate proportions when compared to an arbitrary date. These effects attenuated, but were still present, with a longer lookback period. The effect of day 0 was present even when the patients served as their own controls. Conclusions: Patient baseline characteristics are sensitive to the choice of the index date. In vaccine safety studies, unexposed index event should represent vaccination settings. Study designs previously used to assess influenza vaccination must be reassessed for COVID-19 to account for a potentially healthier population and lack of medical activity on the day of vaccination. UR - https://publichealth.jmir.org/2022/6/e33099 UR - http://dx.doi.org/10.2196/33099 UR - http://www.ncbi.nlm.nih.gov/pubmed/35482996 ID - info:doi/10.2196/33099 ER - TY - JOUR AU - Brown, Joan AU - Bhatnagar, Manas AU - Gordon, Hugh AU - Goodner, Jared AU - Cobb, Perren J. AU - Lutrick, Karen PY - 2022/6/9 TI - An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study JO - JMIR Hum Factors SP - e35032 VL - 9 IS - 2 KW - clinical research design KW - disaster management KW - informatics KW - public health emergencies KW - electronic data capture KW - design tenet KW - public health emergency KW - electronic data KW - EDCT KW - real time data N2 - Background: The Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery PREP) partnered with a third-party technology vendor to design and implement an electronic data capture tool that addressed multisite data collection challenges during public health emergencies (PHE) in the United States. The basis of the work was to design an electronic data capture tool and to prospectively gather data on usability from bedside clinicians during national health system stress queries and influenza observational studies. Objective: The aim of this paper is to describe the lessons learned in the design and implementation of a novel electronic data capture tool with the goal of significantly increasing the nation?s capability to manage real-time data collection and analysis during PHE. Methods: A multiyear and multiphase design approach was taken to create an electronic data capture tool, which was used to pilot rapid data capture during a simulated PHE. Following the pilot, the study team retrospectively assessed the feasibility of automating the data captured by the electronic data capture tool directly from the electronic health record. In addition to user feedback during semistructured interviews, the System Usability Scale (SUS) questionnaire was used as a basis to evaluate the usability and performance of the electronic data capture tool. Results: Participants included Discovery PREP physicians, their local administrators, and data collectors from tertiary-level academic medical centers at 5 different institutions. User feedback indicated that the designed system had an intuitive user interface and could be used to automate study communication tasks making for more efficient management of multisite studies. SUS questionnaire results classified the system as highly usable (SUS score 82.5/100). Automation of 17 (61%) of the 28 variables in the influenza observational study was deemed feasible during the exploration of automated versus manual data abstraction.The creation and use of the Project Meridian electronic data capture tool identified 6 key design requirements for multisite data collection, including the need for the following: (1) scalability irrespective of the type of participant; (2) a common data set across sites; (3) automated back end administrative capability (eg, reminders and a self-service status board); (4) multimedia communication pathways (eg, email and SMS text messaging); (5) interoperability and integration with local site information technology infrastructure; and (6) natural language processing to extract nondiscrete data elements. Conclusions: The use of the electronic data capture tool in multiple multisite Discovery PREP clinical studies proved the feasibility of using the novel, cloud-based platform in practice. The lessons learned from this effort can be used to inform the improvement of ongoing global multisite data collection efforts during the COVID-19 pandemic and transform current manual data abstraction approaches into reliable, real time, and automated information exchange. Future research is needed to expand the ability to perform automated multisite data extraction during a PHE and beyond. UR - https://humanfactors.jmir.org/2022/2/e35032 UR - http://dx.doi.org/10.2196/35032 UR - http://www.ncbi.nlm.nih.gov/pubmed/35679114 ID - info:doi/10.2196/35032 ER - TY - JOUR AU - Garcia, Cristian AU - Rehman, Nadia AU - Lawson, O. Daeria AU - Djiadeu, Pascal AU - Mbuagbaw, Lawrence PY - 2022/5/13 TI - Developing Reporting Guidelines for Studies of HIV Drug Resistance Prevalence: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e35969 VL - 11 IS - 5 KW - HIV KW - drug resistance KW - reporting guideline KW - prevalence KW - surveillance KW - antiretroviral therapy KW - report KW - global health KW - problem N2 - Background: HIV drug resistance is a global health problem that limits the effectiveness of antiretroviral therapy. Adequate surveillance of HIV drug resistance is challenged by heterogenous and inadequate data reporting, which compromises the accuracy, interpretation, and usability of prevalence estimates. Previous research has found that the quality of reporting in studies of HIV drug resistance prevalence is low, and thus better guidance is needed to ensure complete and uniform reporting. Objective: This paper contributes to the process of developing reporting guidelines for prevalence studies of HIV drug resistance by reporting the methodology used in creating a reporting item checklist and generating key insights on items that are important to report. Methods: We will conduct a sequential explanatory mixed methods study among authors and users of studies of HIV drug resistance. The two-phase design will include a cross-sectional electronic survey (quantitative phase) followed by a focus group discussion (qualitative phase). Survey participants will rate the essentiality of various reporting items. This data will be analyzed using content validity ratios to determine the items that will be retained for focus group discussions. Participants in these discussions will revise the items and any additionally suggested items and settle on a complete reporting item checklist. We will also conduct a thematic analysis of the group discussions to identify emergent themes regarding the agreement process. Results: As of November 2021, data collection for both phases of the study is complete. In July 2021, 51 participants had provided informed consent and completed the electronic survey. In October 2021, focus group discussions were held. Nine participants in total participated in two virtual focus group discussions. As of May 2022, data are being analyzed. Conclusions: This study supports the development of a reporting checklist for studies of HIV drug resistance by achieving agreement among experts on what items should be reported in these studies. The results of this work will be refined and elaborated on by a writing committee of HIV drug resistance experts and external reviewers to develop finalized reporting guidelines. International Registered Report Identifier (IRRID): DERR1-10.2196/35969 UR - https://www.researchprotocols.org/2022/5/e35969 UR - http://dx.doi.org/10.2196/35969 UR - http://www.ncbi.nlm.nih.gov/pubmed/35559984 ID - info:doi/10.2196/35969 ER - TY - JOUR AU - Ye, Wenjing AU - Lu, Weiwei AU - Li, Xiaopan AU - Chen, Yichen AU - Wang, Lin AU - Zeng, Guangwang AU - Xu, Cheng AU - Ji, Chen AU - Cai, Yuyang AU - Yang, Ling AU - Luo, Zheng PY - 2022/4/20 TI - Long-term Changes in the Premature Death Rate in Lung Cancer in a Developed Region of China: Population-based Study JO - JMIR Public Health Surveill SP - e33633 VL - 8 IS - 4 KW - lung cancer KW - mortality KW - years of life lost KW - trend analysis KW - decomposition method N2 - Background: Lung cancer is a leading cause of death worldwide, and its incidence shows an upward trend. A study of the long-term changes in the premature death rate in lung cancer in a developed region of China has great exploratory significance to further clarify the effectiveness of intervention measures. Objective: This study examined long-term changes in premature lung cancer death rates in order to understand the changes in mortality and to design future prevention plans in Pudong New Area (PNA), Shanghai, China. Methods: Cancer death data were collected from the Mortality Registration System of PNA. We analyzed the crude mortality rate (CMR), age-standardized mortality rate by Segi?s world standard population (ASMRW), and years of life lost (YLL) of patients with lung cancer from 1973 to 2019. Temporal trends in the CMR, ASMRW, and YLL rate were calculated by joinpoint regression expressed as an average annual percentage change (AAPC) with the corresponding 95% CI. Results: All registered permanent residents in PNA (80,543,137 person-years) from 1973 to 2019 were enrolled in this study. There were 42,229 deaths from lung cancer. The CMR and ASMRW were 52.43/105 and 27.79/105 person-years, respectively. The YLL due to premature death from lung cancer was 481779.14 years, and the YLL rate was 598.16/105 person-years. The CMR and YLL rate showed significantly increasing trends in men, women, and the total population (P<.001). The CMR of the total population increased by 2.86% (95% CI 2.66-3.07, P<.001) per year during the study period. The YLL rate increased with an AAPC of 2.21% (95% CI 1.92-2.51, P<.001) per year. The contribution rates of increased CMR values caused by demographic factors were more evident than those caused by nondemographic factors. Conclusions: Lung cancer deaths showed an increasing trend in PNA from 1973 to 2019. Demographic factors, such as the aging population, contributed more to an increased CMR. Our research can help us understand the changes in lung cancer mortality and can be used for similar cities in designing future prevention plans. UR - https://publichealth.jmir.org/2022/4/e33633 UR - http://dx.doi.org/10.2196/33633 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442209 ID - info:doi/10.2196/33633 ER - TY - JOUR AU - Röbbelen, Alice AU - Schmieding, L. Malte AU - Kopka, Marvin AU - Balzer, Felix AU - Feufel, A. Markus PY - 2022/4/15 TI - Interactive Versus Static Decision Support Tools for COVID-19: Randomized Controlled Trial JO - JMIR Public Health Surveill SP - e33733 VL - 8 IS - 4 KW - clinical decision support KW - usability KW - COVID-19 KW - consumer health KW - medical informatic KW - symptom checker KW - decision support KW - symptom KW - support KW - decision making KW - algorithm KW - flowchart KW - agent N2 - Background: During the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs? underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree?s nodes step-by-step in an interactive manner. Objective: This study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts. Methods: We developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention?s guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants? accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants? ratings of the DSTs? usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants? perception of the tools. We used ANOVAs and t tests to assess statistical significance. Results: Our survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants? perception of the DSTs. Conclusions: When the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other. Trial Registration: Deutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered) UR - https://publichealth.jmir.org/2022/4/e33733 UR - http://dx.doi.org/10.2196/33733 UR - http://www.ncbi.nlm.nih.gov/pubmed/34882571 ID - info:doi/10.2196/33733 ER - TY - JOUR AU - Tilahun, Binyam AU - Endehabtu, F. Berhanu AU - Gashu, D. Kassahun AU - Mekonnen, A. Zeleke AU - Animut, Netsanet AU - Belay, Hiwot AU - Denboba, Wubshet AU - Alemu, Hibret AU - Mohammed, Mesoud AU - Abate, Biruk PY - 2022/4/12 TI - Current and Future Needs for Human Resources for Ethiopia?s National Health Information System: Survey and Forecasting Study JO - JMIR Med Educ SP - e28965 VL - 8 IS - 2 KW - forecasting KW - human resources KW - health information system KW - workforce KW - Ethiopia KW - health informatics KW - healthcare professionals N2 - Background: Strengthening the national health information system is one of Ethiopia?s priority transformation agendas. A well-trained and competent workforce is the essential ingredient to a strong health information system. However, this workforce has neither been quantified nor characterized well, and there is no roadmap of required human resources to enhance the national health information system. Objective: We aimed to determine the current state of the health information system workforce and to forecast the human resources needed for the health information system by 2030. Methods: We conducted a survey to estimate the current number of individuals employed in the health information system unit and the turnover rate. Document review and key-informant interviews were used to collect current human resources and available health information system position data from 110 institutions, including the Ministry of Health, federal agencies, regional health bureaus, zonal health departments, district health offices, and health facilities. The Delphi technique was used to forecast human resources required for the health information system in the next ten years: 3 rounds of workshops with experts from the Ministry of Health, universities, agencies, and regional health bureaus were held. In the first expert meeting, we set criteria, which was followed by expert suggestions and feedback. Results: As of April 2020, there were 10,344 health information system professionals working in the governmental health system. Nearly 95% (20/21) of district health offices and 86.7% (26/30) of health centers reported that the current number of health information system positions was inadequate. In the period from June 2015 to June 2019, health information technicians had high turnover (48/244, 19.7%) at all levels of the health system. In the next ten years, we estimate that 50,656 health information system professionals will be needed to effectively implement the Ethiopia's national health information system. Conclusions: Current health information system?related staffing levels were found to be inadequate. To meet the estimated need of 50,656 multidisciplinary health information system professionals by 2030, the Ministry of Health and regional health bureaus, in collaboration with partners and academic institutions, need to work on retaining existing and training additional health information system professionals. UR - https://mededu.jmir.org/2022/2/e28965 UR - http://dx.doi.org/10.2196/28965 UR - http://www.ncbi.nlm.nih.gov/pubmed/35412469 ID - info:doi/10.2196/28965 ER - TY - JOUR AU - Maaß, Laura AU - Pan, Chen-Chia AU - Freye, Merle PY - 2022/3/31 TI - Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e33404 VL - 11 IS - 3 KW - digital public health KW - telemedicine KW - electronic health records KW - ePrescription KW - eReferral KW - eConsultation KW - eSurveillance KW - eVaccination registries KW - scoping review KW - protocol N2 - Background: Rapid developments and implementation of digital technologies in public health domains throughout the last decades have changed the landscape of health delivery and disease prevention globally. A growing number of countries are introducing interventions such as online consultations, electronic health records, or telemedicine to their health systems to improve their populations? health and improve access to health care. Despite multiple definitions for digital public health and the development of different digital interventions, no study has analyzed whether the utilized technologies fit the definition or the core characteristics of digital public health interventions. A scoping review is therefore needed to explore the extent of the literature on this topic. Objective: The main aim of this scoping review is to outline real-world digital public health interventions on all levels of health care, prevention, and health. The second objective will be the mapping of reported intervention characteristics. These will include nontechnical elements and the technical features of an intervention. Methods: We searched for relevant literature in the following databases: PubMed, Web of Science, CENTRAL (Cochrane Central Register of Controlled Trials), IEEE (Institute of Electrical and Electronics Engineers) Xplore, and the Association for Computing Machinery (ACM) Full-Text Collection. All original study types (observational studies, experimental trials, qualitative studies, and health-economic analyses), as well as governmental reports, books, book chapters, or peer-reviewed full-text conference papers were included when the evaluation and description of a digital health intervention was the primary intervention component. Two authors screened the articles independently in three stages (title, abstract, and full text). Two independent authors will also perform the data charting. We will report our results following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Results: An additional systematic search in IEEE Xplore and ACM, performed on December 1, 2021, identified another 491 titles. We identified a total of 13,869 papers after deduplication. As of March 2022, the abstract screening state is complete, and we are in the state of screening the 1417 selected full texts for final inclusion. We estimate completing the review in April 2022. Conclusions: To our knowledge, this will be the first scoping review to fill the theoretical definitions of digital public health with concrete interventions and their characteristics. Our scoping review will display the landscape of worldwide existing digital public health interventions that use information and communication technologies. The results of this review will be published in a peer-reviewed journal in early 2022, which can serve as a blueprint for the development of future digital public health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/33404 UR - https://www.researchprotocols.org/2022/3/e33404 UR - http://dx.doi.org/10.2196/33404 UR - http://www.ncbi.nlm.nih.gov/pubmed/35357321 ID - info:doi/10.2196/33404 ER - TY - JOUR AU - Caskey, John AU - McConnell, L. Iain AU - Oguss, Madeline AU - Dligach, Dmitriy AU - Kulikoff, Rachel AU - Grogan, Brittany AU - Gibson, Crystal AU - Wimmer, Elizabeth AU - DeSalvo, E. Traci AU - Nyakoe-Nyasani, E. Edwin AU - Churpek, M. Matthew AU - Afshar, Majid PY - 2022/3/8 TI - Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline JO - JMIR Public Health Surveill SP - e36119 VL - 8 IS - 3 KW - natural language processing KW - public health informatics KW - named entity recognition KW - contact tracing KW - COVID-19 KW - outbreaks KW - neural language model KW - disease surveillance KW - digital health KW - electronic surveillance KW - public health KW - digital surveillance tool N2 - Background: In Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted policy making. We developed an automated pipeline to ingest the free text into a pretrained neural language model to identify businesses and facilities as outbreaks. Objective: We aimed to examine the precision and recall of our natural language processing pipeline against existing outbreaks and potentially new clusters. Methods: Data on cases of COVID-19 were extracted from the Wisconsin Electronic Disease Surveillance System (WEDSS) for Dane County between July 1, 2020, and June 30, 2021. Features from the case interview forms were fed into a Bidirectional Encoder Representations from Transformers (BERT) model that was fine-tuned for named entity recognition (NER). We also developed a novel location-mapping tool to provide addresses for relevant NER. Precision and recall were measured against manually verified outbreaks and valid addresses in WEDSS. Results: There were 46,798 cases of COVID-19, with 4,183,273 total BERT tokens and 15,051 unique tokens. The recall and precision of the NER tool were 0.67 (95% CI 0.66-0.68) and 0.55 (95% CI 0.54-0.57), respectively. For the location-mapping tool, the recall and precision were 0.93 (95% CI 0.92-0.95) and 0.93 (95% CI 0.92-0.95), respectively. Across monthly intervals, the NER tool identified more potential clusters than were verified in WEDSS. Conclusions: We developed a novel pipeline of tools that identified existing outbreaks and novel clusters with associated addresses. Our pipeline ingests data from a statewide database and may be deployed to assist local health departments for targeted interventions. UR - https://publichealth.jmir.org/2022/3/e36119 UR - http://dx.doi.org/10.2196/36119 UR - http://www.ncbi.nlm.nih.gov/pubmed/35144241 ID - info:doi/10.2196/36119 ER - TY - JOUR AU - Shakeri Hossein Abad, Zahra AU - Butler, P. Gregory AU - Thompson, Wendy AU - Lee, Joon PY - 2022/1/18 TI - Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk JO - J Med Internet Res SP - e28749 VL - 24 IS - 1 KW - crowdsourcing KW - machine learning KW - digital public health surveillance KW - public health database KW - social media analysis N2 - Background: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. Objective: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Methods: We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning?based classification model, and a hybrid convolutional neural network model. Results: Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. Conclusions: Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models. UR - https://www.jmir.org/2022/1/e28749 UR - http://dx.doi.org/10.2196/28749 UR - http://www.ncbi.nlm.nih.gov/pubmed/35040794 ID - info:doi/10.2196/28749 ER - TY - JOUR AU - Shaikh, Ahmed AU - Bhatia, Abhishek AU - Yadav, Ghanshyam AU - Hora, Shashwat AU - Won, Chung AU - Shankar, Mark AU - Heerboth, Aaron AU - Vemulapalli, Prakash AU - Navalkar, Paresh AU - Oswal, Kunal AU - Heaton, Clay AU - Saunik, Sujata AU - Khanna, Tarun AU - Balsari, Satchit PY - 2022/1/10 TI - Applying Human-Centered Design Principles to Digital Syndromic Surveillance at a Mass Gathering in India: Viewpoint JO - J Med Internet Res SP - e27952 VL - 24 IS - 1 KW - mHealth KW - design KW - human centered design KW - intervention KW - syndromic surveillance KW - digital health UR - https://www.jmir.org/2022/1/e27952 UR - http://dx.doi.org/10.2196/27952 UR - http://www.ncbi.nlm.nih.gov/pubmed/35006088 ID - info:doi/10.2196/27952 ER - TY - JOUR AU - Link, Elena AU - Baumann, Eva AU - Klimmt, Christoph PY - 2021/12/10 TI - Explaining Online Information Seeking Behaviors in People With Different Health Statuses: German Representative Cross-sectional Survey JO - J Med Internet Res SP - e25963 VL - 23 IS - 12 KW - online health information seeking behavior KW - Planned Risk Information Seeking Model KW - health status KW - theory building KW - personal survey N2 - Background: Worldwide, the internet is an increasingly important channel for health information. Many theories have been applied in research on online health information seeking behaviors (HISBs), with each model integrating a different set of predictors; thus, a common understanding of the predictors of (online) HISB is still missing. Another shortcoming of the theories explaining (online) HISB is that most existing models, so far, focus on very specific health contexts such as cancer. Therefore, the assumptions of the Planned Risk Information Seeking Model (PRISM) as the latest integrative model are applied to study online HISB, because this model identifies the general cognitive and sociopsychological factors that explain health information seeking intention. We shift away from single diseases and explore cross-thematic patterns of online HISB intention and compare predictors concerning different health statuses as it can be assumed that groups of people perceiving themselves as ill or healthy will differ concerning their drivers of online HISB. Considering the specifics of online HISB and variation in individual context factors is key for the development of generalizable theories. Objective: The objective of our study was to contribute to the development of the concept of online HISB in 2 areas. First, this study aimed to explore individual-level predictors of individuals? online HISB intention by applying the postulates of PRISM. Second, we compared relevant predictors of online HISB in groups of people with different health statuses to identify cross-thematic central patterns of online HISB. Methods: Data from a representative sample of German internet users (n=822) served to explain online HISB intentions and influencing patterns in different groups of people. The applicability of the PRISM to online HISB intention was tested by structural equation modeling and multigroup comparison. Results: Our results revealed PRISM to be an effective framework for explaining online HISB intention. For online HISB, attitudes toward seeking health information online provided the most important explanatory power followed by risk perceptions and affective risk responses. The multigroup comparison revealed differences both regarding the explanatory power of the model and the relevance of predictors of online HISB. The online HISB intention could be better explained for people facing a health threat, suggesting that the predictors adopted from PRISM were more suitable to explain a problem-driven type of information-seeking behavior. Conclusions: Our findings indicate that attitudes toward seeking health information online and risk perceptions are of central importance for online HISB across different health-conditional contexts. Predictors such as self-efficacy and perceived knowledge insufficiency play a context-dependent role?they are more influential when individuals are facing health threats and the search for health information is of higher personal relevance and urgency. These findings can be understood as the first step to develop a generalized theory of online HISB. UR - https://www.jmir.org/2021/12/e25963 UR - http://dx.doi.org/10.2196/25963 UR - http://www.ncbi.nlm.nih.gov/pubmed/34890348 ID - info:doi/10.2196/25963 ER - TY - JOUR AU - Iyamu, Ihoghosa AU - Xu, T. Alice X. AU - Gómez-Ramírez, Oralia AU - Ablona, Aidan AU - Chang, Hsiu-Ju AU - Mckee, Geoff AU - Gilbert, Mark PY - 2021/11/26 TI - Defining Digital Public Health and the Role of Digitization, Digitalization, and Digital Transformation: Scoping Review JO - JMIR Public Health Surveill SP - e30399 VL - 7 IS - 11 KW - digital public health KW - digital transformation KW - digitalization KW - scoping review KW - digitization KW - definition KW - mobile phone N2 - Background: The recent proliferation and application of digital technologies in public health has spurred interest in digital public health. However, as yet, there appears to be a lack of conceptual clarity and consensus on its definition. Objective: In this scoping review, we seek to assess formal and informal definitions of digital public health in the literature and to understand how these definitions have been conceptualized in relation to digitization, digitalization, and digital transformation. Methods: We conducted a scoping literature search in Ovid MEDLINE, Embase, Google Scholar, and 14 government and intergovernmental agency websites encompassing 6 geographic regions. Among a total of 409 full articles identified, we reviewed 11 publications that either formally defined digital public health or informally described the integration of digital technologies into public health in relation to digitization, digitalization, and digital transformation, and we conducted a thematic analysis of the identified definitions. Results: Two explicit definitions of digital public health were identified, each with divergent meanings. The first definition suggested digital public health was a reimagination of public health using new ways of working, blending established public health wisdom with new digital concepts and tools. The second definition highlighted digital public health as an asset to achieve existing public health goals. In relation to public health, digitization was used to refer to the technical process of converting analog records to digital data, digitalization referred to the integration of digital technologies into public health operations, and digital transformation was used to describe a cultural shift that pervasively integrates digital technologies and reorganizes services on the basis of the health needs of the public. Conclusions: The definition of digital public health remains contested in the literature. Public health researchers and practitioners need to clarify these conceptual definitions to harness opportunities to integrate digital technologies into public health in a way that maximizes their potential to improve public health outcomes. International Registered Report Identifier (IRRID): RR2-10.2196/preprints.27686 UR - https://publichealth.jmir.org/2021/11/e30399 UR - http://dx.doi.org/10.2196/30399 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842555 ID - info:doi/10.2196/30399 ER - TY - JOUR AU - Shi, Xin AU - Lima, Silva Simone Maria da AU - Mota, Miranda Caroline Maria de AU - Lu, Ying AU - Stafford, S. Randall AU - Pereira, Viana Corintho PY - 2021/11/25 TI - Prevalence of Multimorbidity of Chronic Noncommunicable Diseases in Brazil: Population-Based Study JO - JMIR Public Health Surveill SP - e29693 VL - 7 IS - 11 KW - multimorbidity KW - prevalence KW - health care KW - public health KW - Brazil KW - logistic regression N2 - Background: Multimorbidity is the co-occurrence of two or more chronic diseases. Objective: This study, based on self-reported medical diagnosis, aims to investigate the dynamic distribution of multimorbidity across sociodemographic levels and its impacts on health-related issues over 15 years in Brazil using national data. Methods: Data were analyzed using descriptive statistics, hypothesis tests, and logistic regression. The study sample comprised 679,572 adults (18-59 years of age) and 115,699 elderly people (?60 years of age) from the two latest cross-sectional, multiple-cohort, national-based studies: the National Sample Household Survey (PNAD) of 1998, 2003, and 2008, and the Brazilian National Health Survey (PNS) of 2013. Results: Overall, the risk of multimorbidity in adults was 1.7 times higher in women (odds ratio [OR] 1.73, 95% CI 1.67-1.79) and 1.3 times higher among people without education (OR 1.34, 95% CI 1.28-1.41). Multiple chronic diseases considerably increased with age in Brazil, and people between 50 and 59 years old were about 12 times more likely to have multimorbidity than adults between 18 and 29 years of age (OR 11.89, 95% CI 11.27-12.55). Seniors with multimorbidity had more than twice the likelihood of receiving health assistance in community services or clinics (OR 2.16, 95% CI 2.02-2.31) and of being hospitalized (OR 2.37, 95% CI 2.21-2.56). The subjective well-being of adults with multimorbidity was often worse than people without multiple chronic diseases (OR=12.85, 95% CI: 12.07-13.68). These patterns were similar across all 4 cohorts analyzed and were relatively stable over 15 years. Conclusions: Our study shows little variation in the prevalence of the multimorbidity of chronic diseases in Brazil over time, but there are differences in the prevalence of multimorbidity across different social groups. It is hoped that the analysis of multimorbidity from the two latest Brazil national surveys will support policy making on epidemic prevention and management. UR - https://publichealth.jmir.org/2021/11/e29693 UR - http://dx.doi.org/10.2196/29693 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842558 ID - info:doi/10.2196/29693 ER - TY - JOUR AU - Bhimaraju, Hari AU - Nag, Nitish AU - Pandey, Vaibhav AU - Jain, Ramesh PY - 2021/11/23 TI - Understanding ?Atmosome?, the Personal Atmospheric Exposome: Comprehensive Approach JO - JMIR Biomed Eng SP - e28920 VL - 6 IS - 4 KW - exposome KW - exposomics KW - personal health KW - indoor air quality KW - health state estimation KW - health informatics KW - public health policy KW - epidemiology KW - embedded systems KW - internet of things N2 - Background: Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the ?atmosome.? The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health. Objective: The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health. Methods: An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access. Results: The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality. Conclusions: Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases. UR - https://biomedeng.jmir.org/2021/4/e28920 UR - http://dx.doi.org/10.2196/28920 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/28920 ER - TY - JOUR AU - Rajamani, Geetanjali AU - Rodriguez Espinosa, Patricia AU - Rosas, G. Lisa PY - 2021/11/19 TI - Intersection of Health Informatics Tools and Community Engagement in Health-Related Research to Reduce Health Inequities: Scoping Review JO - J Particip Med SP - e30062 VL - 13 IS - 3 KW - community engagement KW - stakeholder involvement KW - underserved communities KW - health informatics KW - health information technology KW - health inequities KW - health-related research N2 - Background: The exponential growth of health information technology has the potential to facilitate community engagement in research. However, little is known about the use of health information technology in community-engaged research, such as which types of health information technology are used, which populations are engaged, and what are the research outcomes. Objective: The objectives of this scoping review were to examine studies that used health information technology for community engagement and to assess (1) the types of populations, (2) community engagement strategies, (3) types of health information technology tools, and (4) outcomes of interest. Methods: We searched PubMed and PCORI Literature Explorer using terms related to health information technology, health informatics, community engagement, and stakeholder involvement. This search process yielded 967 papers for screening. After inclusion and exclusion criteria were applied, a total of 37 papers were analyzed for key themes and for approaches relevant to health information technology and community engagement research. Results: This analysis revealed that the communities engaged were generally underrepresented populations in health-related research, including racial or ethnic minority communities such as Black/African American, American Indian/Alaska Native, Latino ethnicity, and communities from low socioeconomic backgrounds. The studies focused on various age groups, ranging from preschoolers to older adults. The studies were also geographically spread across the United States and the world. Community engagement strategies included collaborative development of health information technology tools and partnerships to promote use (encompassing collaborative development, use of community advisory boards, and focus groups for eliciting information needs) and use of health information technology to engage communities in research (eg, through citizen science). The types of technology varied across studies, with mobile or tablet-based apps being the most common platform. Outcomes measured included eliciting user needs and requirements, assessing health information technology tools and prototypes with participants, measuring knowledge, and advocating for community change. Conclusions: This study illustrates the current landscape at the intersection of health information technology tools and community-engaged research approaches. It highlights studies in which various community-engaged research approaches were used to design culturally centered health information technology tools, to promote health information technology uptake, or for engagement in health research and advocacy. Our findings can serve as a platform for generating future research upon which to expand the scope of health information technology tools and their use for meaningful stakeholder engagement. Studies that incorporate community context and needs have a greater chance of cocreating culturally centered health information technology tools and better knowledge to promote action and improve health outcomes. UR - https://jopm.jmir.org/2021/3/e30062 UR - http://dx.doi.org/10.2196/30062 UR - http://www.ncbi.nlm.nih.gov/pubmed/34797214 ID - info:doi/10.2196/30062 ER - TY - JOUR AU - Elkin, L. Peter AU - Mullin, Sarah AU - Mardekian, Jack AU - Crowner, Christopher AU - Sakilay, Sylvester AU - Sinha, Shyamashree AU - Brady, Gary AU - Wright, Marcia AU - Nolen, Kimberly AU - Trainer, JoAnn AU - Koppel, Ross AU - Schlegel, Daniel AU - Kaushik, Sashank AU - Zhao, Jane AU - Song, Buer AU - Anand, Edwin PY - 2021/11/9 TI - Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record?s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study JO - J Med Internet Res SP - e28946 VL - 23 IS - 11 KW - afib KW - atrial fibrillation KW - artificial intelligence KW - NVAF KW - natural language processing KW - stroke risk KW - bleed risk KW - CHA2DS2-VASc KW - HAS-BLED KW - bio-surveillance N2 - Background: Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. Objective: The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record?s (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars. Methods: We abstracted 96,681 participants from the University of Buffalo faculty practice?s EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ?75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA2DS2-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA2DS2?VASc ?2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year?s costs after stroke. Results: The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion. Conclusions: Artificial intelligence?informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences. UR - https://www.jmir.org/2021/11/e28946 UR - http://dx.doi.org/10.2196/28946 UR - http://www.ncbi.nlm.nih.gov/pubmed/34751659 ID - info:doi/10.2196/28946 ER - TY - JOUR AU - Vromans, D. Ruben AU - van Eenbergen, C. Mies AU - Geleijnse, Gijs AU - Pauws, Steffen AU - van de Poll-Franse, V. Lonneke AU - Krahmer, J. Emiel PY - 2021/10/25 TI - Exploring Cancer Survivor Needs and Preferences for Communicating Personalized Cancer Statistics From Registry Data: Qualitative Multimethod Study JO - JMIR Cancer SP - e25659 VL - 7 IS - 4 KW - breast cancer KW - cancer statistics KW - personalization KW - prostate cancer KW - risk communication KW - cancer registry KW - cancer KW - patient needs and preferences N2 - Background: Disclosure of cancer statistics (eg, survival or incidence rates) based on a representative group of patients can help increase cancer survivors? understanding of their own diagnostic and prognostic situation, and care planning. More recently, there has been an increasing interest in the use of cancer registry data for disclosing and communicating personalized cancer statistics (tailored toward personal and clinical characteristics) to cancer survivors and relatives. Objective: The aim of this study was to explore breast cancer (BCa) and prostate cancer (PCa) survivor needs and preferences for disclosing (what) and presenting (how) personalized statistics from a large Dutch population-based data set, the Netherlands Cancer Registry (NCR). Methods: To elicit survivor needs and preferences for communicating personalized NCR statistics, we created different (non)interactive tools visualizing hypothetical scenarios and adopted a qualitative multimethod study design. We first conducted 2 focus groups (study 1; n=13) for collecting group data on BCa and PCa survivor needs and preferences, using noninteractive sketches of what a tool for communicating personalized statistics might look like. Based on these insights, we designed a revised interactive tool, which was used to further explore the needs and preferences of another group of cancer survivors during individual think-aloud observations and semistructured interviews (study 2; n=11). All sessions were audio-recorded, transcribed verbatim, analyzed using thematic (focus groups) and content analysis (think-aloud observations), and reported in compliance with qualitative research reporting criteria. Results: In both studies, cancer survivors expressed the need to receive personalized statistics from a representative source, with especially a need for survival and conditional survival rates (ie, survival rate for those who have already survived for a certain period). Personalized statistics adjusted toward personal and clinical factors were deemed more relevant and useful to know than generic or average-based statistics. Participants also needed support for correctly interpreting the personalized statistics and putting them into perspective, for instance by adding contextual or comparative information. Furthermore, while thinking aloud, participants experienced a mix of positive (sense of hope) and negative emotions (feelings of distress) while viewing the personalized survival data. Overall, participants preferred simplicity and conciseness, and the ability to tailor the type of visualization and amount of (detailed) statistical information. Conclusions: The majority of our sample of cancer survivors wanted to receive personalized statistics from the NCR. Given the variation in patient needs and preferences for presenting personalized statistics, designers of similar information tools may consider potential tailoring strategies on multiple levels, as well as effective ways for providing supporting information to make sure that the personalized statistics are properly understood. This is encouraging for cancer registries to address this unmet need, but also for those who are developing or implementing personalized data-driven information tools for patients and relatives. UR - https://cancer.jmir.org/2021/4/e25659 UR - http://dx.doi.org/10.2196/25659 UR - http://www.ncbi.nlm.nih.gov/pubmed/34694237 ID - info:doi/10.2196/25659 ER - TY - JOUR AU - Wong, Chi-Yin Kenneth AU - Xiang, Yong AU - Yin, Liangying AU - So, Hon-Cheong PY - 2021/9/30 TI - Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach JO - JMIR Public Health Surveill SP - e29544 VL - 7 IS - 9 KW - prediction KW - COVID-19 KW - risk factors KW - machine learning KW - pandemic KW - biobank KW - public health KW - prediction models KW - medical informatics N2 - Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance. Objective: Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved. Methods: We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes. Results: A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC?ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the ?lite? models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM. Conclusions: We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings. UR - https://publichealth.jmir.org/2021/9/e29544 UR - http://dx.doi.org/10.2196/29544 UR - http://www.ncbi.nlm.nih.gov/pubmed/34591027 ID - info:doi/10.2196/29544 ER - TY - JOUR AU - Argyris, Anna Young AU - Monu, Kafui AU - Tan, Pang-Ning AU - Aarts, Colton AU - Jiang, Fan AU - Wiseley, Anne Kaleigh PY - 2021/6/24 TI - Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study JO - JMIR Public Health Surveill SP - e23105 VL - 7 IS - 6 KW - antivaccination movement KW - Twitter messaging KW - public health informatics KW - supervised machine learning algorithm KW - unsupervised machine learning algorithm KW - qualitative content analysis KW - data visualization KW - infodemiology KW - infodemic KW - health misinformation KW - infoveillance KW - social listening N2 - Background: Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading the public to accept immunization. Many prior studies have associated the diversity of topics discussed by antivaccine advocates with the public?s higher engagement with such content. Nonetheless, a comprehensive comparison of discursive topics in pro- and antivaccine content in the engagement-persuasion spectrum remains unexplored. Objective: We aimed to compare discursive topics chosen by pro- and antivaccine advocates in their attempts to influence the public to accept or reject immunization in the engagement-persuasion spectrum. Our overall objective was pursued through three specific aims as follows: (1) we classified vaccine-related tweets into provaccine, antivaccine, and neutral categories; (2) we extracted and visualized discursive topics from these tweets to explain disparities in engagement between pro- and antivaccine content; and (3) we identified how those topics frame vaccines using Entman?s four framing dimensions. Methods: We adopted a multimethod approach to analyze discursive topics in the vaccine debate on public social media sites. Our approach combined (1) large-scale balanced data collection from a public social media site (ie, 39,962 tweets from Twitter); (2) the development of a supervised classification algorithm for categorizing tweets into provaccine, antivaccine, and neutral groups; (3) the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides; and (4) a multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics. In so doing, we alleviated methodological challenges that have hindered previous analyses of pro- and antivaccine discursive topics. Results: Our results indicated that antivaccine topics have greater intertopic distinctiveness (ie, the degree to which discursive topics are distinct from one another) than their provaccine counterparts (t122=2.30, P=.02). In addition, while antivaccine advocates use all four message frames known to make narratives persuasive and influential, provaccine advocates have neglected having a clear problem statement. Conclusions: Based on our results, we attribute higher engagement among antivaccine advocates to the distinctiveness of the topics they discuss, and we ascribe the influence of the vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates. UR - https://publichealth.jmir.org/2021/6/e23105/ UR - http://dx.doi.org/10.2196/23105 UR - http://www.ncbi.nlm.nih.gov/pubmed/34185004 ID - info:doi/10.2196/23105 ER - TY - JOUR AU - Chen, Hong AU - Yu, Ping AU - Hailey, David AU - Cui, Tingru PY - 2021/5/10 TI - Validation of 4D Components for Measuring Quality of the Public Health Data Collection Process: Elicitation Study JO - J Med Internet Res SP - e17240 VL - 23 IS - 5 KW - data quality KW - data collection KW - HIV/AIDS KW - public health informatics KW - health information systems KW - component validation KW - expert elicitation KW - public health KW - health informatics N2 - Background: Identification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice. Objective: This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework. Methods: Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework. Results: A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices. Conclusions: This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction. UR - https://www.jmir.org/2021/5/e17240 UR - http://dx.doi.org/10.2196/17240 UR - http://www.ncbi.nlm.nih.gov/pubmed/33970112 ID - info:doi/10.2196/17240 ER - TY - JOUR AU - Chew, Robert AU - Kery, Caroline AU - Baum, Laura AU - Bukowski, Thomas AU - Kim, Annice AU - Navarro, Mario PY - 2021/3/16 TI - Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation JO - JMIR Public Health Surveill SP - e25807 VL - 7 IS - 3 KW - Reddit KW - social media KW - age KW - machine learning KW - classification N2 - Background: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users? demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. Objective: We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. Methods: This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users? age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. Results: The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. Conclusions: We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users? posting behavior, linguistic patterns, and account features that distinguish adolescents from adults. UR - https://publichealth.jmir.org/2021/3/e25807 UR - http://dx.doi.org/10.2196/25807 UR - http://www.ncbi.nlm.nih.gov/pubmed/33724195 ID - info:doi/10.2196/25807 ER - TY - JOUR AU - Sambaturu, Prathyush AU - Bhattacharya, Parantapa AU - Chen, Jiangzhuo AU - Lewis, Bryan AU - Marathe, Madhav AU - Venkatramanan, Srinivasan AU - Vullikanti, Anil PY - 2020/9/4 TI - An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study JO - JMIR Public Health Surveill SP - e12842 VL - 6 IS - 3 KW - epidemic data analysis KW - summarization KW - spatio-temporal patterns KW - transactional data mining N2 - 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. UR - http://publichealth.jmir.org/2020/3/e12842/ UR - http://dx.doi.org/10.2196/12842 UR - http://www.ncbi.nlm.nih.gov/pubmed/32701458 ID - info:doi/10.2196/12842 ER - TY - JOUR AU - Rosso, Nicholas AU - Giabbanelli, Philippe PY - 2018/05/30 TI - Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey JO - JMIR Public Health Surveill SP - e56 VL - 4 IS - 2 KW - diet, food, and nutrition KW - public health informatics KW - supervised machine learning N2 - Background: National surveys in public health nutrition commonly record the weight of every food consumed by an individual. However, if the goal is to identify whether individuals are in compliance with the 5 main national nutritional guidelines (sodium, saturated fats, sugars, fruit and vegetables, and fats), much less information may be needed. A previous study showed that tracking only 2.89% of all foods (113/3911) was sufficient to accurately identify compliance. Further reducing the data needs could lower participation burden, thus decreasing the costs for monitoring national compliance with key guidelines. Objective: This study aimed to assess whether national public health nutrition surveys can be further simplified by only recording whether a food was consumed, rather than having to weigh it. Methods: Our dataset came from a generalized sample of inhabitants in the United Kingdom, more specifically from the National Diet and Nutrition Survey 2008-2012. After simplifying food consumptions to a binary value (1 if an individual consumed a food and 0 otherwise), we built and optimized decision trees to find whether the foods could accurately predict compliance with the major 5 nutritional guidelines. Results: When using decision trees of a similar size to previous studies (ie, involving as many foods), we were able to correctly infer compliance for the 5 guidelines with an average accuracy of 80.1%. This is an average increase of 2.5 percentage points over a previous study, showing that further simplifying the surveys can actually yield more robust estimates. When we allowed the new decision trees to use slightly more foods than in previous studies, we were able to optimize the performance with an average increase of 3.1 percentage points. Conclusions: Although one may expect a further simplification of surveys to decrease accuracy, our study found that public health dietary surveys can be simplified (from accurately weighing items to simply checking whether they were consumed) while improving accuracy. One possibility is that the simplification reduced noise and made it easier for patterns to emerge. Using simplified surveys will allow to monitor public health nutrition in a more cost-effective manner and possibly decrease the number of errors as participation burden is reduced. UR - http://publichealth.jmir.org/2018/2/e56/ UR - http://dx.doi.org/10.2196/publichealth.9536 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/publichealth.9536 ER - TY - JOUR AU - Ben Ramadan, Ahmed Awatef AU - Jackson-Thompson, Jeannette AU - Schmaltz, Lee Chester PY - 2018/05/03 TI - Improving Visualization of Female Breast Cancer Survival Estimates: Analysis Using Interactive Mapping Reports JO - JMIR Public Health Surveill SP - e42 VL - 4 IS - 2 KW - survival KW - female breast cancer KW - Missouri KW - cancer registry N2 - Background: The Missouri Cancer Registry collects population-based cancer incidence data on Missouri residents diagnosed with reportable malignant neoplasms. The Missouri Cancer Registry wanted to produce data that would be of interest to lawmakers as well as public health officials at the legislative district level on breast cancer, the most common non-skin cancer among females. Objective: The aim was to measure and interactively visualize survival data of female breast cancer cases in the Missouri Cancer Registry. Methods: Female breast cancer data were linked to Missouri death records and the Social Security Death Index. Unlinked female breast cancer cases were crossmatched to the National Death Index. Female breast cancer cases in subcounty senate districts were geocoded using TIGER/Line shapefiles to identify their district. A database was created and analyzed in SEER*Stat. Senatorial district maps were created using US Census Bureau?s cartographic boundary files. The results were loaded with the cartographic data into InstantAtlas software to produce interactive mapping reports. Results: Female breast cancer survival profiles of 5-year cause-specific survival percentages and 95% confidence intervals, displayed in tables and interactive maps, were created for all 34 senatorial districts. The maps visualized survival data by age, race, stage, and grade at diagnosis for the period from 2004 through 2010. Conclusions: Linking cancer registry data to the National Death Index database improved accuracy of female breast cancer survival data in Missouri and this could positively impact cancer research and policy. The created survival mapping report could be very informative and usable by public health professionals, policy makers, at-risk women, and the public. UR - http://publichealth.jmir.org/2018/2/e42/ UR - http://dx.doi.org/10.2196/publichealth.8163 UR - http://www.ncbi.nlm.nih.gov/pubmed/29724710 ID - info:doi/10.2196/publichealth.8163 ER - TY - JOUR AU - Zhang, Zhizun AU - Gonzalez, C. Mila AU - Morse, S. Stephen AU - Venkatasubramanian, Venkat PY - 2017/10/11 TI - Knowledge Management Framework for Emerging Infectious Diseases Preparedness and Response: Design and Development of Public Health Document Ontology JO - JMIR Res Protoc SP - e196 VL - 6 IS - 10 KW - EIDs KW - public health KW - systems engineering KW - knowledge representation KW - teleological function KW - knowledge management KW - ontology KW - semantic reasoning N2 - Background: There are increasing concerns about our preparedness and timely coordinated response across the globe to cope with emerging infectious diseases (EIDs). This poses practical challenges that require exploiting novel knowledge management approaches effectively. Objective: This work aims to develop an ontology-driven knowledge management framework that addresses the existing challenges in sharing and reusing public health knowledge. Methods: We propose a systems engineering-inspired ontology-driven knowledge management approach. It decomposes public health knowledge into concepts and relations and organizes the elements of knowledge based on the teleological functions. Both knowledge and semantic rules are stored in an ontology and retrieved to answer queries regarding EID preparedness and response. Results: A hybrid concept extraction was implemented in this work. The quality of the ontology was evaluated using the formal evaluation method Ontology Quality Evaluation Framework. Conclusions: Our approach is a potentially effective methodology for managing public health knowledge. Accuracy and comprehensiveness of the ontology can be improved as more knowledge is stored. In the future, a survey will be conducted to collect queries from public health practitioners. The reasoning capacity of the ontology will be evaluated using the queries and hypothetical outbreaks. We suggest the importance of developing a knowledge sharing standard like the Gene Ontology for the public health domain. UR - http://www.researchprotocols.org/2017/10/e196/ UR - http://dx.doi.org/10.2196/resprot.7904 UR - http://www.ncbi.nlm.nih.gov/pubmed/29021130 ID - info:doi/10.2196/resprot.7904 ER - TY - JOUR AU - Hoyt, Eugene Robert AU - Snider, Dallas AU - Thompson, Carla AU - Mantravadi, Sarita PY - 2016/10/11 TI - IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics JO - JMIR Public Health Surveill SP - e157 VL - 2 IS - 2 KW - data analysis KW - data mining KW - machine learning KW - statistical data analysis KW - natural language processing N2 - Background: We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective: To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods: The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results: Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions: IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. UR - http://publichealth.jmir.org/2016/2/e157/ UR - http://dx.doi.org/10.2196/publichealth.5810 UR - http://www.ncbi.nlm.nih.gov/pubmed/27729304 ID - info:doi/10.2196/publichealth.5810 ER - TY - JOUR AU - Bell, Cameron AU - Guerinet, Julien AU - Atkinson, M. Katherine AU - Wilson, Kumanan PY - 2016/06/23 TI - Feasibility and Limitations of Vaccine Two-Dimensional Barcoding Using Mobile Devices JO - J Med Internet Res SP - e143 VL - 18 IS - 6 KW - vaccines KW - feasibility studies KW - immunization KW - automatic data processing KW - cell phones KW - vaccinations/standards N2 - Background: Two-dimensional (2D) barcoding has the potential to enhance documentation of vaccine encounters at the point of care. However, this is currently limited to environments equipped with dedicated barcode scanners and compatible record systems. Mobile devices may present a cost-effective alternative to leverage 2D vaccine vial barcodes and improve vaccine product-specific information residing in digital health records. Objective: Mobile devices have the potential to capture product-specific information from 2D vaccine vial barcodes. We sought to examine the feasibility, performance, and potential limitations of scanning 2D barcodes on vaccine vials using 4 different mobile phones. Methods: A unique barcode scanning app was developed for Android and iOS operating systems. The impact of 4 variables on the scan success rate, data accuracy, and time to scan were examined: barcode size, curvature, fading, and ambient lighting conditions. Two experimenters performed 4 trials 10 times each, amounting to a total of 2160 barcode scan attempts. Results: Of the 1832 successful scans performed in this evaluation, zero produced incorrect data. Five-millimeter barcodes were the slowest to scan, although only by 0.5 seconds on average. Barcodes with up to 50% fading had a 100% success rate, but success rate deteriorated beyond 60% fading. Curved barcodes took longer to scan compared with flat, but success rate deterioration was only observed at a vial diameter of 10 mm. Light conditions did not affect success rate or scan time between 500 lux and 20 lux. Conditions below 20 lux impeded the device?s ability to scan successfully. Variability in scan time was observed across devices in all trials performed. Conclusions: 2D vaccine barcoding is possible using mobile devices and is successful under the majority of conditions examined. Manufacturers utilizing 2D barcodes should take into consideration the impact of factors that limit scan success rates. Future studies should evaluate the effect of mobile barcoding on workflow and vaccine administrator acceptance. UR - http://www.jmir.org/2016/6/e143/ UR - http://dx.doi.org/10.2196/jmir.5591 UR - http://www.ncbi.nlm.nih.gov/pubmed/27339043 ID - info:doi/10.2196/jmir.5591 ER - TY - JOUR AU - Cutrona, L. Sarah AU - Sreedhara, Meera AU - Goff, L. Sarah AU - Fisher, D. Lloyd AU - Preusse, Peggy AU - Jackson, Madeline AU - Sundaresan, Devi AU - Garber, D. Lawrence AU - Mazor, M. Kathleen PY - 2016/05/06 TI - Improving Rates of Influenza Vaccination Through Electronic Health Record Portal Messages, Interactive Voice Recognition Calls and Patient-Enabled Electronic Health Record Updates: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e56 VL - 5 IS - 2 KW - electronic health records KW - influenza vaccines KW - clinical decision support KW - Internet KW - Telephone KW - Electronic Mail KW - Health Records, Personal KW - Medical Informatics Applications N2 - Background: Clinical decision support (CDS), including computerized reminders for providers and patients, can improve health outcomes. CDS promoting influenza vaccination, delivered directly to patients via an electronic health record (EHR) patient portal and interactive voice recognition (IVR) calls, offers an innovative approach to improving patient care. Objective: To test the effectiveness of an EHR patient portal and IVR outreach to improve rates of influenza vaccination in a large multispecialty group practice in central Massachusetts. Methods: We describe a nonblinded, randomized controlled trial of EHR patient portal messages and IVR calls designed to promote influenza vaccination. In our preparatory phase, we conducted qualitative interviews with patients, providers, and staff to inform development of EHR portal messages with embedded questionnaires and IVR call scripts. We also provided practice-wide education on influenza vaccines to all physicians and staff members, including information on existing vaccine-specific EHR CDS. Outreach will target adult patients who remain unvaccinated for more than 2 months after the start of the influenza season. Using computer-generated randomization and a factorial design, we will assign 20,000 patients who are active users of electronic patient portals to one of the 4 study arms: (1) receipt of a portal message promoting influenza vaccines and offering online appointment scheduling; (2) receipt of an IVR call with similar content but without appointment facilitation; (3) both (1) and (2); or (4) neither (1) nor (2) (usual care). We will randomize patients without electronic portals (10,000 patients) to (1) receipt of IVR call or (2) usual care. Both portal messages and IVR calls promote influenza vaccine completion. Our primary outcome is percentage of eligible patients with influenza vaccines administered at our group practice during the 2014-15 influenza season. Both outreach methods also solicit patient self-report on influenza vaccinations completed outside the clinic or on barriers to influenza vaccination. Self-reported data from both outreach modes will be uploaded into the EHR to increase accuracy of existing provider-directed EHR CDS (vaccine alerts). Results: With our proposed sample size and using a factorial design, power calculations using baseline vaccination rate estimates indicated that 4286 participants per arm would give 80% power to detect a 3% improvement in influenza vaccination rates between groups (?=.05; 2-sided). Intention-to-treat unadjusted chi-square analyses will be performed to assess the impact of portal messages, either alone or in combination with the IVR call, on influenza vaccination rates. The project was funded in January 2014. Patient enrollment for the project described here completed in December 2014. Data analysis is currently under way and first results are expected to be submitted for publication in 2016. Conclusions: If successful, this study?s intervention may be adapted by other large health care organizations to increase vaccination rates among their eligible patients. ClinicalTrial: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 (Archived by WebCite at http://www.webcitation.org/6fbLviHLH). UR - http://www.researchprotocols.org/2016/2/e56/ UR - http://dx.doi.org/10.2196/resprot.5478 UR - http://www.ncbi.nlm.nih.gov/pubmed/27153752 ID - info:doi/10.2196/resprot.5478 ER -