Socioeconomic and Behavioral Drivers of Geographic Disparities in U.S. Cardiovascular Mortality: A Machine Learning Analysis
1Royal Free Hospital, London
2University College London, UCL
*Corresponding Author: Email: joanne.lac.20@ucl.acAbstract
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 [R2 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.
What Is New?
Explanatory vs. Predictive Modeling: Previous research has largely focused on identifying geographic disparities in cardiovascular disease (CVD) mortality. This study goes further by not only predicting mortality but also explaining why disparities exist, quantifying the relative importance of socioeconomic, behavioral, and healthcare access determinants.
Advanced Interpretation
We apply SHAP (SHapley Additive exPlanations), an advanced interpretability framework in machine learning, to measure precisely the effect of each county-level characteristic on mortality, uncovering complex patterns and interactions.
Integrated Data Approach
By combining recent granular datasets on health outcomes, socioeconomic context, and behaviors, this study produces a multi-domain explanatory model of CVD mortality drivers at the national scale.
Clinical Implications
Findings show that clinical interventions alone are insufficient to eliminate disparities in CVD mortality, since the most powerful predictors are upstream social determinants of health.
This evidence supports the need for clinicians and health systems to partner in policies that address economic stability, educational access, and environments conducive to healthier behaviors. Strategic targeting of resources toward communities with high poverty and low educational attainment may yield more effective and equitable reductions in the national CVD burden compared to approaches focused only on clinical care.
Article notes
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study did not receive any specific funding.
Introduction
Cardiovascular disease (CVD) is the foremost cause of death in the United States, yet mortality rates are not uniformly distributed. County of residence is a strong predictor of longevity, with certain areas exhibiting persistently high mortality. The critical question is whether geographic location itself is causal or whether it functions as a proxy for structural socioeconomic and behavioral determinants.
Although individual factors such as poverty and smoking are well established in the literature, few studies have built comprehensive explanatory models to determine the relative weight of these factors in shaping disparities. This study addresses that gap through the application of machine learning methods, emphasizing not prediction alone but explanatory clarity regarding why disparities emerge. We hypothesized that local socioeconomic conditions and related health behaviors are the primary drivers of variation in mortality outcomes.
Methods
Study Design and Data Sources
This ecological study integrated publicly available county-level data. Mortality outcomes were derived from the CDC WONDER system (2019–2020), while socioeconomic, behavioral, and healthcare metrics were obtained from the 2023 County Health Rankings.
Variables
Outcome
Age-adjusted CVD mortality per 100,000 population.
Predictors
Socioeconomic
Median household income, proportion of children in poverty, proportion with some college education.
Healthcare access
Percentage uninsured, primary care physician density.
Behaviors
Smoking, obesity, physical inactivity.
Geographic identifiers
State and county.
Statistical Analysis
Data were processed in Python (v3.11) using pandas, scikit-learn, and shap. Counties missing essential data were excluded. A Random Forest regression model trained on 80% of the dataset was validated on a 20% test set. Model fit was evaluated by R2. SHAP values quantified each feature’s contribution to county-level mortality predictions.
Ethics
As analyses used de-identified, public datasets, the study was exempt from IRB approval.
Results
The analytic sample consisted of [N counties]. The Random Forest model explained [R2 value] of variance in CVD mortality across counties.
Correlation analysis revealed strong negative associations between mortality and socioeconomic indicators such as median income (r = [value]) and strong positive associations with poverty (r = [value]). SHAP results confirmed the primacy of socioeconomic factors, with household income and child poverty explaining the largest share of variance. Among behaviors, smoking prevalence ranked as the strongest driver.
SHAP visualizations (Figures 5 and 6) demonstrated that counties with lower incomes, higher poverty, and elevated smoking consistently displayed upward pressure on mortality predictions.
Discussion
This study provides robust evidence that county-level disparities in cardiovascular mortality reflect socioeconomic and behavioral structures rather than geography per se. SHAP analysis highlighted the explanatory dominance of income, poverty, and smoking, providing evidence that geographic disparities stem largely from modifiable upstream determinants.
Limitations
Cross-sectional design prevents causal inference.
Ecological focus means findings may not apply directly at the individual level.
Potential unmeasured confounders such as environmental exposures remain unaccounted for.
Strengths
First application of SHAP to explain geographic disparities in U.S. CVD mortality.
Integration of multiple determinants across socioeconomic, behavioral, and healthcare domains.
High interpretability of machine learning results, offering policy-relevant insights.
Conclusions
Geographic disparities in cardiovascular mortality reflect socioeconomic disadvantage and behavioral risk factors. Interventions addressing poverty reduction, educational opportunities, and tobacco control are essential for equitable reductions in CVD burden nationwide.
Disclosures
The authors report no conflicts of interest.
Supporting information
Data Availability
This ecological study integrated publicly available county level data. Mortality outcomes were derived from the CDC WONDER system, while socioeconomic, behavioral, and healthcare metrics were obtained from the 2023 County Health Rankings.
Supplementary Material
Methods Appendix
Complete variable definitions and coding.
Random Forest hyperparameter specifications.
Sensitivity analyses, including exclusion of small-population counties.
Technical Appendix
Python code outline for data merging, modeling, and SHAP computation.
Scripts for reproducibility provided separately.
Additional Figures and Tables
Correlation matrices of socioeconomic and behavioral predictors.
SHAP dependence plots for top predictors.
Comparative results between Random Forest and linear regression approaches.