aceAssessing the Utility of Social Determinants of Health Data in Suicide Prediction Models
Objective This study seeks to explore the utility of social determinants of health (SDoH) variables in suicide prediction models. We aim to assess the impact of individual- and geographic-level SDoH factors on improving the performance of suicide prediction models and the identification of individuals at high risk for suicide. Methods A retrospective sample of 1214 deaths by suicide and 815,544 living patients was identified in the Maryland Suicide Data Warehouse (MSDW) and linked to census tract data through ge…
Adding individual-level social determinants of health and clinical features to machine learning models improved suicide risk prediction over demographic-only baselines in a Maryland sample of 1214 suicide deaths and 815,544 living patients.
Evidence
- Peer-reviewedArchives of Suicide Research2026-08-25
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Truvace Impact Record TRV-2026-0891, v1: “Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models.” Truvace, 2026-08-26. /record/TRV-2026-0891 (accessed at citation time). sha256 1b006aa05e50699c…
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