Predicting Asthma Prevalence Across U.S. Communities by Integrating Social Vulnerability and Environmental Risk with Machine Learning.

BACKGROUND: Asthma is one of the most common chronic conditions in the United States and disproportionately affects racial and ethnic minority populations and low-income communities. While both social and environmental factors are known contributors to asthma prevalence, the extent to which they interact to shape geographic disparities remains insufficiently understood. OBJECTIVE: This study aimed to explore how these factors vary across regions with differing asthma prevalence to identify patterns that may inform targeted public health responses. METHODS: We used the CDC's 2022 Environmental Justice Index (EJI), which includes standardized social and environmental measures at the census tract level. K-means clustering identified geographic clusters with varying asthma prevalence based on asthma rates and related health outcomes. A random forest model was trained to predict asthma rates using social and environmental variables, and feature importance was analyzed. Socioeconomic variables consistently ranked as stronger predictors than environmental exposures. CONCLUSIONS: Geographic areas with high asthma prevalence are shaped by overlapping social and environmental vulnerabilities. Socioeconomic disparities emerged as particularly influential. These findings support integrated, data-driven public health strategies to address asthma inequities.