ArticleJournal of the American Medical Informatics Association : JAMIA2025
Incorporating area-level social drivers of health in predictive algorithms using electronic health record data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Perspective: Machine Learning for Health Should Consider Social Drivers of Health.Proceedings of machine learning research · 2026Article
- Precision risk assessment for pediatric hospitalization using address-level data in Cincinnati, Ohio.Journal of clinical and translational science · 2026Article
- Article
- A microsimulation-based framework for mitigating societal bias in primary care data.medRxiv : the preprint server for health sciences · 2025Article
- Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms.medRxiv : the preprint server for health sciences · 2025Article
- Beyond the individual.Journal of the American Medical Informatics Association : JAMIA · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesThe inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do. We argue that practitioners should consider the use of social indices and factors-a class of area-level measurements-given their accessibility, transparency, and quality.
resultsWe illustrate the process of using such indices in predictive algorithms, which includes the selection of appropriate indices for the outcome, measurement time, and geographic level, in a demonstrative example with the Kidney Failure Risk Equation. DISCUSSION: Identifying settings where incorporating SDOH may be beneficial and incorporating them rigorously can help validate algorithms and assess generalizability.
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