Anthony Paulo Sunjaya, Myron Anthony Godinho, Jitendra Jonnagaddala, Craig Kuziemsky, Karen Tu, Rafiqul Islam, Tasuku Okui, Naoki Nakashima, Javier Silva-Valencia, Leonardo Rojas-Mezarina, Alvin Marcelo, Sabrina Wong Kay Wye, Chien-Yeh Hsu, Uy Hoang, Jack Westfall, Simon de Lusignan, Siaw-Teng Liaw
{"title":"关于健康的社会决定因素的初级保健电子病历数据:精确/个性化医疗的质量和适用性。","authors":"Anthony Paulo Sunjaya, Myron Anthony Godinho, Jitendra Jonnagaddala, Craig Kuziemsky, Karen Tu, Rafiqul Islam, Tasuku Okui, Naoki Nakashima, Javier Silva-Valencia, Leonardo Rojas-Mezarina, Alvin Marcelo, Sabrina Wong Kay Wye, Chien-Yeh Hsu, Uy Hoang, Jack Westfall, Simon de Lusignan, Siaw-Teng Liaw","doi":"10.1055/s-0044-1800716","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Precision and personalised medicine requires comprehensive genetic, epigenetic, lifestyle, social, community and environmental knowledge of the patient. This approach highlights the importance of the social determinants of health (SDoH), described by the World Health Organization (WHO) as 'the non-medical factors that influence health outcomes, the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life such as economic policies and systems, development agendas, social norms, social policies and political systems'.</p><p><strong>Methods: </strong>This study examined if countries collect SDoH indicators and, if they do, the quality of the data and whether they are fit for clinical and population health purposes. The sources of data were EHR networks and, where not available, national data collections.</p><p><strong>Results: </strong>While demographic details (age, gender) and rurality were well documented in most countries, we found that data availability and quality for education, occupation, income, socio-economic status, and residential care varied considerably between countries. Data for smoking, obesity, alcohol use, mental health, and substance use were generally poorly recorded.</p><p><strong>Conclusion: </strong>Recommendations include a universal set of indicators and taxonomy for SDoH; common data model and metadata standards for national and global harmonisation and monitoring; benchmarks for data quality and fitness-for-purpose; capacity building at national and subnational levels in data collection, data analysis, communication and dissemination of results; ethical and transparent data stewardship; and governance, leadership and diplomacy across multiple sectors to co-create an enabling policy and regulatory environment.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"32-44"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020554/pdf/","citationCount":"0","resultStr":"{\"title\":\"Primary Care EHR data on Social Determinants of Health: Quality and Fitness for Purpose in Precision/Personalised Medicine.\",\"authors\":\"Anthony Paulo Sunjaya, Myron Anthony Godinho, Jitendra Jonnagaddala, Craig Kuziemsky, Karen Tu, Rafiqul Islam, Tasuku Okui, Naoki Nakashima, Javier Silva-Valencia, Leonardo Rojas-Mezarina, Alvin Marcelo, Sabrina Wong Kay Wye, Chien-Yeh Hsu, Uy Hoang, Jack Westfall, Simon de Lusignan, Siaw-Teng Liaw\",\"doi\":\"10.1055/s-0044-1800716\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Precision and personalised medicine requires comprehensive genetic, epigenetic, lifestyle, social, community and environmental knowledge of the patient. This approach highlights the importance of the social determinants of health (SDoH), described by the World Health Organization (WHO) as 'the non-medical factors that influence health outcomes, the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life such as economic policies and systems, development agendas, social norms, social policies and political systems'.</p><p><strong>Methods: </strong>This study examined if countries collect SDoH indicators and, if they do, the quality of the data and whether they are fit for clinical and population health purposes. The sources of data were EHR networks and, where not available, national data collections.</p><p><strong>Results: </strong>While demographic details (age, gender) and rurality were well documented in most countries, we found that data availability and quality for education, occupation, income, socio-economic status, and residential care varied considerably between countries. Data for smoking, obesity, alcohol use, mental health, and substance use were generally poorly recorded.</p><p><strong>Conclusion: </strong>Recommendations include a universal set of indicators and taxonomy for SDoH; common data model and metadata standards for national and global harmonisation and monitoring; benchmarks for data quality and fitness-for-purpose; capacity building at national and subnational levels in data collection, data analysis, communication and dissemination of results; ethical and transparent data stewardship; and governance, leadership and diplomacy across multiple sectors to co-create an enabling policy and regulatory environment.</p>\",\"PeriodicalId\":40027,\"journal\":{\"name\":\"Yearbook of medical informatics\",\"volume\":\"33 1\",\"pages\":\"32-44\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020554/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Yearbook of medical informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1055/s-0044-1800716\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yearbook of medical informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1055/s-0044-1800716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Primary Care EHR data on Social Determinants of Health: Quality and Fitness for Purpose in Precision/Personalised Medicine.
Introduction: Precision and personalised medicine requires comprehensive genetic, epigenetic, lifestyle, social, community and environmental knowledge of the patient. This approach highlights the importance of the social determinants of health (SDoH), described by the World Health Organization (WHO) as 'the non-medical factors that influence health outcomes, the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life such as economic policies and systems, development agendas, social norms, social policies and political systems'.
Methods: This study examined if countries collect SDoH indicators and, if they do, the quality of the data and whether they are fit for clinical and population health purposes. The sources of data were EHR networks and, where not available, national data collections.
Results: While demographic details (age, gender) and rurality were well documented in most countries, we found that data availability and quality for education, occupation, income, socio-economic status, and residential care varied considerably between countries. Data for smoking, obesity, alcohol use, mental health, and substance use were generally poorly recorded.
Conclusion: Recommendations include a universal set of indicators and taxonomy for SDoH; common data model and metadata standards for national and global harmonisation and monitoring; benchmarks for data quality and fitness-for-purpose; capacity building at national and subnational levels in data collection, data analysis, communication and dissemination of results; ethical and transparent data stewardship; and governance, leadership and diplomacy across multiple sectors to co-create an enabling policy and regulatory environment.
期刊介绍:
Published by the International Medical Informatics Association, this annual publication includes the best papers in medical informatics from around the world.