Preprint
Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts
A county-level machine learning study combining CDC WONDER mortality data, the Social Vulnerability Index, health behavior data, and healthcare workforce files across 975 U.S. counties. An XGBoost model with SHAP interpretation identified disability rate, hypertension, smoking, and lack of vehicle access as the strongest predictors of fentanyl overdose mortality, correctly flagging 71% of high-risk counties (R² = 0.457). The analysis surfaced 143 "silent risk counties" with elevated structural vulnerability but not-yet-elevated death counts, and found treatment-desert counties had 52.6% higher mortality, pointing to where prevention investment matters most.