Association Between Social and Environmental Exposure and Chronic Disease Burden in the United States: An Explainable AI Analysis of the Center for Disease Control Environmental Justice Index

Jonah Treitler, Tadas Vasaitis (), Linda Zanin (), Alexander Libin () and Yijun Shao ()
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Jonah Treitler: Thomas Jefferson High School for Science and Technology
Tadas Vasaitis: University of Maryland Eastern Shore, School of Pharmacy and Health Professions
Linda Zanin: George Washington University, Biomedical Informatics Center
Alexander Libin: Georgetown University, AIM AHEAD Consortium, Georgetown-Howard Universities Center for Clinical and Translational Science, Medstar Research Health Institute
Yijun Shao: George Washington University, Biomedical Informatics Center

A chapter in Health Technologies and Demographic Challenges, 2025, pp 93-101 from Springer

Abstract: Abstract This study investigates the association between social and environmental exposures and chronic disease burden in the US, using the Environmental Justice Index (EJI) and explainable AI (XAI) methods. The EJI dataset contains social vulnerability, environmental burden, and health vulnerability indicators for over 71,000 census tracts. Using both traditional statistical approaches (Spearman correlation and linear regression) and machine learning (ML) models (Random Forests and Neural Networks), this study assessed the association between social/environmental factors and health indicators. Our results show that ML outperformed linear regression in model fitting, achieving 82–91% in R2. This indicates that, to a very large extent, variance in disease prevalence can be explained by social/environmental factors, and their relationships are non-linear. The XAI-generated impact scores further identified individual factors positively and negatively associated with specific diseases, which can inform publica health policies and interventions.

Keywords: Environmental justice; Explainable AI; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-94901-2_8

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DOI: 10.1007/978-3-031-94901-2_8

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