Online journal of public health informatics最新文献

{"title":"Vaping: Public Health, Social Media, and Toxicity.","authors":"Yehao Sun, Prital Prabhu, Dongmei Li, Scott McIntosh, Irfan Rahman","doi":"10.2196/53245","DOIUrl":"https://doi.org/10.2196/53245","url":null,"abstract":"This viewpoint aims to provide a comprehensive understanding of vaping from various perspectives that contribute to the invention, development, spread, and consequences of e-cigarette products and vaping. Our analysis showed that the specific characteristics of e-cigarette products as well as marketing strategies, especially social media marketing, fostered the spread of vaping and the subsequent effects on human health and toxicity. We analyzed the components of e-cigarette devices and e-liquids, including the latest variants whose impacts were often overlooked. The different forms of nicotine, including salts and freebase nicotine, tobacco-derived nicotine, tobacco-free nicotine, and cooling agents (WS3 and WS23), have brought more choices for vapers along with more ways for e-cigarette manufacturers to advertise false understandings and present a greater threat to vapers' health. Our work emphasized the products of brands that have gained significant influence recently, which are contributing to severe public health issues. On the other hand, we also discussed in detail the toxicity of e-liquid components and proposed a toxicity mechanism. We also noticed that nicotine and other chemicals in e-liquids promote each other's negative effects through the oxidative stress and inflammatory nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway, a mechanism leading to pulmonary symptoms and addiction. The impact of government regulations on the products themselves, including flavor bans or regulations, has been limited. Therefore, we proposed further interventions or harm reduction strategies from a public health perspective.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"31 7","pages":"e53245"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Machine Learning Model for Predicting Mortality Risk in Patients With Complex Chronic Conditions: Retrospective Analysis.","authors":"Guillem Hernández Guillamet, Ariadna Ning Morancho Pallaruelo, Laura Miró Mezquita, Ramón Miralles, Miquel Àngel Mas, María José Ulldemolins Papaseit, Oriol Estrada Cuxart, Francesc López Seguí","doi":"10.2196/58453","DOIUrl":"10.2196/58453","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/52782.].</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e58453"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10995781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review.","authors":"Shwetha Bindhu, Anunita Nattam, Catherine Xu, Tripura Vithala, Tiffany Grant, Jacinda K Dariotis, Hexuan Liu, Danny T Y Wu","doi":"10.2196/50898","DOIUrl":"10.2196/50898","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e50898"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10993137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140178139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic Health Records for Population Health Management: Comparison of Electronic Health Record-Derived Hypertension Prevalence Measures Against Established Survey Data.","authors":"Katie S Allen, Nimish Valvi, P Joseph Gibson, Timothy McFarlane, Brian E Dixon","doi":"10.2196/48300","DOIUrl":"10.2196/48300","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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 ","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e48300"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140121544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Semantic Approach to Describe Social and Economic Characteristics That Impact Health Outcomes (Social Determinants of Health): Ontology Development Study.","authors":"Daniela Dally, Muhammad Amith, Rebecca L Mauldin, Latisha Thomas, Yifang Dang, Cui Tao","doi":"10.2196/52845","DOIUrl":"10.2196/52845","url":null,"abstract":"<p><strong>Background: </strong>Social determinants of health (SDoH) have been described by the World Health Organization as the conditions in which individuals are born, live, work, and age. These conditions can be grouped into 3 interrelated levels known as macrolevel (societal), mesolevel (community), and microlevel (individual) determinants. The scope of SDoH expands beyond the biomedical level, and there remains a need to connect other areas such as economics, public policy, and social factors.</p><p><strong>Objective: </strong>Providing a computable artifact that can link health data to concepts involving the different levels of determinants may improve our understanding of the impact SDoH have on human populations. Modeling SDoH may help to reduce existing gaps in the literature through explicit links between the determinants and biological factors. This in turn can allow researchers and clinicians to make better sense of data and discover new knowledge through the use of semantic links.</p><p><strong>Methods: </strong>An experimental ontology was developed to represent knowledge of the social and economic characteristics of SDoH. Information from 27 literature sources was analyzed to gather concepts and encoded using Web Ontology Language, version 2 (OWL2) and Protégé. Four evaluators independently reviewed the ontology axioms using natural language translation. The analyses from the evaluations and selected terminologies from the Basic Formal Ontology were used to create a revised ontology with a broad spectrum of knowledge concepts ranging from the macrolevel to the microlevel determinants.</p><p><strong>Results: </strong>The literature search identified several topics of discussion for each determinant level. Publications for the macrolevel determinants centered around health policy, income inequality, welfare, and the environment. Articles relating to the mesolevel determinants discussed work, work conditions, psychosocial factors, socioeconomic position, outcomes, food, poverty, housing, and crime. Finally, sources found for the microlevel determinants examined gender, ethnicity, race, and behavior. Concepts were gathered from the literature and used to produce an ontology consisting of 383 classes, 109 object properties, and 748 logical axioms. A reasoning test revealed no inconsistent axioms.</p><p><strong>Conclusions: </strong>This ontology models heterogeneous social and economic concepts to represent aspects of SDoH. The scope of SDoH is expansive, and although the ontology is broad, it is still in its early stages. To our current understanding, this ontology represents the first attempt to concentrate on knowledge concepts that are currently not covered by existing ontologies. Future direction will include further expanding the ontology to link with other biomedical ontologies, including alignment for granular semantics.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e52845"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Information Seeking Behavior on Social Networking Sites and Self-Treatment: Pilot Survey Study.","authors":"Reginald A Silver, Chandrika Johnson","doi":"10.2196/51984","DOIUrl":"10.2196/51984","url":null,"abstract":"<p><strong>Background: </strong>Social networking site use and social network-based health information seeking behavior have proliferated to the point that the lines between seeking health information from credible social network-based sources and the decision to seek medical care or attempt to treat oneself have become blurred.</p><p><strong>Objective: </strong>We contribute to emerging research on health information seeking behavior by investigating demographic factors, social media use for health information seeking purposes, and the relationship between health information seeking and occurrences of self-treatment.</p><p><strong>Methods: </strong>Data were collected from an online survey in which participants were asked to describe sociodemographic factors about themselves, social media use patterns, perceptions about their motivations for health information seeking on social media platforms, and whether or not they attempted self-treatment after their social media-related health information seeking. We conducted a binomial logistic regression with self-treatment as a dichotomous categorical dependent variable.</p><p><strong>Results: </strong>Results indicate that significant predictors of self-treatment based on information obtained from social networking sites include race, exercise frequency, and degree of trust in the health-related information received.</p><p><strong>Conclusions: </strong>With an understanding of how sociodemographic factors might influence the decision to self-treat based on information obtained from social networking sites, health care providers can assist patients by educating them on credible social network-based sources of health information and discussing the importance of seeking medical advice from a health care provider.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"15 ","pages":"e51984"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10765284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intersection of Perceived COVID-19 Risk, Preparedness, and Preventive Health Behaviors: Latent Class Segmentation Analysis","authors":"Osaro Mgbere, Sorochi Iloanusi, Ismaeel Yunusa, Nchebe-Jah R Iloanusi, Shrey Gohil, Ekere James Essien","doi":"10.2196/50967","DOIUrl":"https://doi.org/10.2196/50967","url":null,"abstract":"Background COVID-19 risk perception is a factor that influences the pandemic spread. Understanding the potential behavioral responses to COVID-19, including preparedness and adoption of preventive measures, can inform interventions to curtail its spread. Objective We assessed self-perceived and latent class analysis (LCA)–based risks of COVID-19 and their associations with preparedness, misconception, information gap, and preventive practices among residents of a densely populated city in Nigeria. Methods We used data from a cross-sectional survey conducted among residents (N=140) of Onitsha, Nigeria, in March 2020, before the government-mandated lockdown. Using an iterative expectation-maximization algorithm, we applied LCA to systematically segment participants into the most likely distinct risk clusters. Furthermore, we used bivariate and multivariable logistic regression models to determine the associations among knowledge, attitude, preventive practice, perceived preparedness, misconception, COVID-19 information gap, and self-perceived and LCA-based COVID-19 risks. Results Most participants (85/140, 60.7%) had good knowledge and did not perceive themselves as at risk of contracting COVID-19. Three-quarters of the participants (102/137, 74.6%; P<.001) experienced COVID-19–related information gaps, while 62.9% (88/140; P=.04) of the participants had some misconceptions about the disease. Conversely, most participants (93/140, 66.4%; P<.001) indicated that they were prepared for the COVID-19 pandemic. The majority of the participants (94/138, 68.1%; P<.001) self-perceived that they were not at risk of contracting COVID-19 compared to 31.9% (44/138) who professed to be at risk of contracting COVID-19. Using the LCA, we identified 3 distinct risk clusters (P<.001), namely, prudent or low-risk takers, skeptics or high-risk takers, and carefree or very high-risk takers with prevalence rates (probabilities of cluster membership that represent the prevalence rate [γc]) of 47.5% (95% CI 40%-55%), 16.2% (95% CI 11.4%-20.9%), and 36.4% (95% CI 28.8%-43.9%), respectively. We recorded a significantly negative agreement between self-perceived risk and LCA-based segmentation of COVID-19 risk (κ=–0.218, SD 0.067; P=.01). Knowledge, attitude, and perceived need for COVID-19 information were significant predictors of COVID-19 preventive practices among the Onitsha city residents. Conclusions The clustering patterns highlight the impact of modifiable risk behaviors on COVID-19 preventive practices, which can provide strong empirical support for health prevention policies. Consequently, clusters with individuals at high risk of contracting COVID-19 would benefit from multicomponent interventions delivered in diverse settings to improve the population-based response to the pandemic.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135268314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toxicology Test Results for Public Health Surveillance of the Opioid Epidemic: Retrospective Analysis","authors":"Titus Schleyer, Bill Robinson, Samir Parmar, Diane Janowiak, P Joseph Gibson, Val Spangler","doi":"10.2196/50936","DOIUrl":"https://doi.org/10.2196/50936","url":null,"abstract":"Background Addressing the opioid epidemic requires timely insights into population-level factors, such as trends in prevalence of legal and illegal substances, overdoses, and deaths. Objective This study aimed to examine whether toxicology test results of living individuals from a variety of sources could be useful in surveilling the opioid epidemic. Methods A retrospective analysis standardized, merged, and linked toxicology results from 24 laboratories in Marion County, Indiana, United States, from September 1, 2018, to August 31, 2019. The data set consisted of 33,787 Marion County residents and their 746,681 results. We related the data to general Marion County demographics and compared alerts generated by toxicology results to opioid overdose–related emergency department visits. Nineteen domain experts helped prototype analytical visualizations. Main outcome measures included test positivity in the county and by ZIP code; selected demographics of individuals with toxicology results; and correlation of toxicology results with opioid overdose–related emergency department visits. Results Four percent of Marion County residents had at least 1 toxicology result. Test positivity rates ranged from 3% to 19% across ZIP codes. Males were underrepresented in the data set. Age distribution resembled that of Marion County. Alerts for opioid toxicology results were not correlated with opioid overdose–related emergency department visits. Conclusions Analyzing toxicology results at scale was impeded by varying data formats, completeness, and representativeness; changes in data feeds; and patient matching difficulties. In this study, toxicology results did not predict spikes in opioid overdoses. Larger, more rigorous and well-controlled studies are needed to assess the utility of toxicology tests in predicting opioid overdose spikes.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Model for Predicting Mortality Risk in Complex Chronic Patients: Retrospective Analysis from the ProPCC Program in Catalonia (Preprint)","authors":"Guillem Hernández Guillamet, Ariadna Ning Morancho Pallaruelo, Laura Miró Mezquita, Ramón Miralles Basseda, Miquel Angel Mas Bergas, María José Ulldemolins Papaseit, Oriol Estrada Cuxart, Francesc López Seguí","doi":"10.2196/52782","DOIUrl":"https://doi.org/10.2196/52782","url":null,"abstract":"","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"2015 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139339585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Health Impact of mHealth Interventions in India: Systematic Review and Meta-Analysis.","authors":"Vibha Joshi, Nitin Kumar Joshi, Pankaj Bhardwaj, Kuldeep Singh, Deepika Ojha, Yogesh Kumar Jain","doi":"10.2196/50927","DOIUrl":"10.2196/50927","url":null,"abstract":"<p><strong>Background: </strong>Considerable use of mobile health (mHealth) interventions has been seen, and these interventions have beneficial effects on health and health service delivery processes, especially in resource-limited settings. Various functionalities of mobile phones offer a range of opportunities for mHealth interventions.</p><p><strong>Objective: </strong>This review aims to assess the health impact of mHealth interventions in India.</p><p><strong>Methods: </strong>This systematic review and meta-analysis was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies conducted in India, and published between April 1, 2011, and March 31, 2021, were considered. A literature search was conducted using a combination of MeSH (Medical Subject Headings) terms in different databases to identify peer-reviewed publications. Thirteen out of 1350 articles were included for the final review. Risk of bias was assessed using the Risk of Bias 2 tool for RCTs and Risk Of Bias In Non-randomised Studies - of Interventions tool (for nonrandomized trials), and a meta-analysis was performed using RevMan for 3 comparable studies on maternal, neonatal, and child health.</p><p><strong>Results: </strong>The meta-analysis showed improved usage of maternal and child health services including iron-folic acid supplementation (odds ratio [OR] 14.30, 95% CI 6.65-30.75), administration of both doses of the tetanus toxoid (OR 2.47, 95% CI 0.22-27.37), and attending 4 or more antenatal check-ups (OR 1.82, 95% CI 0.65-5.09). Meta-analysis for studies concerning economic evaluation and chronic diseases could not be performed due to heterogeneity. However, a positive economic impact was observed from a societal perspective (ReMiND [reducing maternal and newborn deaths] and ImTeCHO [Innovative Mobile Technology for Community Health Operation] interventions), and chronic disease interventions showed a positive impact on clinical outcomes, patient and provider satisfaction, app usage, and improvement in health behaviors.</p><p><strong>Conclusions: </strong>This review provides a comprehensive overview of mHealth technology in all health sectors in India, analyzing both health and health care usage indicators for interventions focused on maternal and child health and chronic diseases.</p><p><strong>Trial registration: </strong>PROSPERO 2021 CRD42021235315; https://tinyurl.com/yh4tp2j7.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e50927"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49058699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}