  Social determinants of health (SDOH) -- the conditions in which people live,
grow, and age -- play a crucial role in a person's health and well-being. There
is a large, compelling body of evidence in population health studies showing
that a wide range of SDOH is strongly correlated with health outcomes. Yet, a
majority of the risk prediction models based on electronic health records (EHR)
do not incorporate a comprehensive set of SDOH features as they are often noisy
or simply unavailable. Our work links a publicly available EHR database,
MIMIC-IV, to well-documented SDOH features. We investigate the impact of such
features on common EHR prediction tasks across different patient populations.
We find that community-level SDOH features do not improve model performance for
a general patient population, but can improve data-limited model fairness for
specific subpopulations. We also demonstrate that SDOH features are vital for
conducting thorough audits of algorithmic biases beyond protective attributes.
We hope the new integrated EHR-SDOH database will enable studies on the
relationship between community health and individual outcomes and provide new
benchmarks to study algorithmic biases beyond race, gender, and age.
