Practical considerations for social determinant-based disease prediction in the All of Us research program.

Background: Growing recognition that social determinants of health (SDoH) strongly influence health outcomes has expanded their inclusion in biomedical research, underscoring the need to evaluate how best to incorporate these data into disease prediction models. Methods: The Results: Here we show that requiring sufficient individual-level SDoH survey data results in significant selection bias and sample reduction in AoU. We also show that SES alone captures a substantial proportion of the predictive signal from individual-level SDoH data while preserving sample size and mitigating selection bias. Moreover, SES measures provide greater predictive utility than self-reported race and ethnicity, without excluding underrepresented groups. We find disease-specific patterns of association with SDoH and that area-level SDoH metrics contribute to disease prediction independently of individual-level measures. Conclusions: Altogether, we emphasize key analytical considerations and disease-specific trade-offs for the integration of SDoH data into disease prediction models in AoU and similar cohorts.