Abstract Maternal and infant mortality remain disproportionately high in the United States compared to other high-income nations, particularly among Medicaid recipients, who account for nearly half of all U.S. births. While the association between social determinants of health (SDoH) and adverse maternal outcomes is well-established, implementing this knowledge in real-time risk stratification has remained challenging. We developed and validated machine learning models that integrate healthcare and SDoH data from 190,698 Medicaid-enrolled women across 26 states and Washington, DC, to support earlier, actionable risk identification in clinical practice. A model using only demographic and clinical data achieved 86.3% accuracy, 93.1% AUC, and 71.3% sensitivity in predicting adverse pregnancy outcomes. Incorporating SDoH—particularly healthcare access variables such as provider availability, distance to care, and infrastructure—improved sensitivity to 81.3% (a 10.0 percentage point gain) while maintaining high specificity (94.3%) and eliminating algorithmic sensitivity disparities between Black and White patients. The model identified risk a median of 55 days before traditional clinical indicators emerged, providing a substantial window for proactive, community-based intervention. Simulation of targeted SDoH improvements, particularly in maternal healthcare workforce availability and infrastructure, predicted a 31.8% reduction in adverse pregnancy outcomes, with the greatest absolute benefit for Black women. These findings suggest that systematically integrating clinical and social data has the potential to identify high-risk pregnancies months before complications emerge, which could enable earlier intervention within existing Medicaid care management frameworks and help address persistent racial inequities in maternal health outcomes.
Patel et al. (Wed,) studied this question.