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Open Access
CC BY 4.0 · Yearb Med Inform 2024; 33(01): 032-044
DOI: 10.1055/s-0044-1800716
Special Section: Digital Health for Precision in Prevention
Working Group Contributions

Primary Care EHR data on Social Determinants of Health: Quality and Fitness for Purpose in Precision/Personalised Medicine

Authors

  • Anthony Paulo Sunjaya

    1   The George Institute for Global Health, UNSW Sydney, Australia

  • Myron Anthony Godinho

    2   Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Australia

  • Jitendra Jonnagaddala

    3   School of Population Health, UNSW Sydney, Australia

  • Craig Kuziemsky

    4   MacEwan University, Edmonton, Alberta, Canada

  • Karen Tu

    5   University of Toronto, Ontario, Canada

  • Rafiqul Islam

    6   Kyushu University, Japan

  • Tasuku Okui

    6   Kyushu University, Japan

  • Naoki Nakashima

    6   Kyushu University, Japan

  • Javier Silva-Valencia

    7   Telehealth Unit Department, National University of San Marcos, Lima, Peru

  • Leonardo Rojas-Mezarina

    7   Telehealth Unit Department, National University of San Marcos, Lima, Peru

  • Alvin Marcelo

    8   University of the Philippines, Manila, Philippines

  • Sabrina Wong Kay Wye

    9   Clinical Research Unit, Nanyang Technological University, Singapore

  • Chien-Yeh Hsu

    10   Taipei Medical University, Taiwan

  • Uy Hoang

    11   Department of Primary Care Health Sciences, University of Oxford, UK

  • Jack Westfall

    12   University of Colorado, USA

  • Simon de Lusignan

    11   Department of Primary Care Health Sciences, University of Oxford, UK

  • Siaw-Teng Liaw

    3   School of Population Health, UNSW Sydney, Australia
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Summary

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.

Keywords

Primary Care - Electronic Health Records - Social Determinants of Health - Precision Medicine


Publikationsverlauf

Artikel online veröffentlicht:
08. April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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