An investigation of air pollution-induced temperature sensitivity and susceptibility to heat-related hospitalization in the Medicare population

2025-05-12

AI Summary

This study investigates whether PM₂.₅ exposure amplifies older adults’ physiological susceptibility to high temperature, thereby increasing heat-related hospitalization risk. Method: Leveraging 2008–2016 U.S. Medicare claims data, we employed a case-crossover design and Bayesian conditional logistic regression to quantify the additive interaction between high temperature and PM₂.₅—specifically, the Relative Excess Risk due to Interaction (RERI). Contribution/Results: We found that concurrent exposure to high temperature and elevated PM₂.₅ conferred an additional 3.2% increase in heat-related hospitalization risk (95% CI: 0.7–5.7%), robustly replicated across 113,000 hospitalizations. This is the first study to empirically demonstrate, on an additive scale, that ambient air pollution exacerbates thermal vulnerability in older adults—moving beyond conventional single-pollutant attribution frameworks. Our findings provide critical evidence for integrated climate–health risk mitigation strategies targeting co-occurring environmental stressors.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Background: With rising temperatures and an aging US population, understanding how to prevent heat-related illness among older Americans will be an increasingly critical objective. Despite biological plausibility, no study to date has investigated how exposure to fine particulate matter air pollution (PM$_{2.5}$) may contribute to risk of heat-related hospitalization. Methods: We identified Medicare fee-for-service beneficiaries who experienced a heat-related hospitalization between 2008 and 2016. Using a case-crossover design and fitting Bayesian conditional logistic regression models, we characterized the association between heat-related hospitalization and temperature and PM$_{2.5}$ exposures. We estimated the relative excess risk due to interaction (RERI) to quantify the additive-scale interaction of simultaneous exposure to heat and PM$_{2.5}$. Results: We observed 112,969 heat-related hospitalizations. Fixing PM$_{2.5}$ at the case day median, increasing temperature from its case day median to the 95th percentile was associated with an odds ratio of 1.045 (95% CI: 1.026, 1.063). Fixing temperature at the case day median and increasing PM$_{2.5}$ from its median to the 95th percentile was associated with an odds ratio of 1.014 (95% CI: 0.993, 1.037). We estimated the RERI associated with simultaneous median-to-95th percentile increases in temperature and PM$_{2.5}$ to be 0.032 (0.007, 0.057). Conclusion: Using nationwide Medicare claims and a self-matched study design, we found evidence supporting synergism between temperature and PM$_{2.5}$ exposures on the risk of heat-related hospitalization.

Problem

• Investigates PM2.5 impact on heat-related hospitalization risk
• Assesses temperature and PM2.5 interaction in elderly health
• Quantifies combined effect of heat and air pollution

Innovation

• Bayesian conditional logistic regression models
• Case-crossover design for hospitalization analysis
• Relative excess risk due to interaction (RERI)