Socioeconomic and Behavioral Drivers of Geographic Disparities in U.S. Cardiovascular Mortality: A Machine Learning Analysis

Background: Substantial geographic disparities in cardiovascular disease (CVD) mortality persist across the United States. The extent to which "place" reflects underlying socioeconomic and behavioral risk factors remains insufficiently explained. This study applies machine learning to quantify the determinants of these disparities. Methods: A cross-sectional analysis linked county-level 2019-2020 age-adjusted CVD mortality rates from the CDC with health determinant metrics from the 2023 County Health Rankings dataset. The analytic sample included [N counties] with complete data. A Random Forest regressor modeled mortality outcomes, incorporating socioeconomic, healthcare access, and behavioral predictors. Model interpretation used SHAP to assess feature-level contributions. Results: The model explained [R 2 value] of variance in CVD mortality. Socioeconomic factors, particularly median household income and poverty rates, were the most influential predictors, followed by county-level smoking prevalence. Geographic identifiers alone had limited explanatory value after accounting for socioeconomic and behavioral metrics. Conclusions: Geographic disparities in CVD mortality are explained by underlying socioeconomic disadvantage and community health behaviors. Effective reduction of disparities requires public health interventions addressing poverty, education, and behavioral risk factors beyond clinical care.