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

TL;DR

This paper examines how well primary care electronic health records capture social factors affecting health and finds significant gaps in data quality.

Contribution

The study proposes a framework for improving SDoH data collection and standardization for precision medicine.

Findings

Demographic data like age and gender are well documented, but socioeconomic factors vary widely in quality.

Data on smoking, obesity, and mental health are poorly recorded across most countries.

Recommendations include standardized indicators and data models for global health monitoring.

Abstract

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.

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