Predicting Urban Heat-Related Illness Across U.S. Climate Regions and Demographics

Area of Science:

• Environmental Science
• Public Health
• Computational Modeling

Background:

• Limited understanding of climate, demographic, and behavioral interactions driving hospitalizations during extreme heat events (EHEs).
• Need for predictive models to assess future heat-related illness (HRI) burdens and costs.

Purpose of the Study:

• To model the impact of changing EHEs on HRI morbidity across diverse demographic groups in US Metropolitan Statistical Areas (MSAs).
• To project the economic costs of HRIs in MSAs under different emissions scenarios through 2040.

Main Methods:

• Development of a transdisciplinary discrete event system dynamics (SD) modeling approach.
• Simulation of HRI morbidity and costs for 53 large MSAs in the contiguous US (CONUS) through 2040.
• Integration of climate projections, demographic data, and socioeconomic disparities.

Main Results:

• Projected increases in HRI morbidity and associated costs, varying by region and emissions scenario.
• Identification of demographic and socioeconomic disparities as key factors influencing HRI risk.
• Model outputs highlight regional differences driven by EHE profiles and underlying risk distributions.

Conclusions:

• System dynamics modeling provides a framework for understanding and projecting EHE impacts on public health and costs.
• Findings underscore the need for targeted interventions to address community vulnerabilities to extreme heat.
• The approach facilitates improved calibration and coordination of regional heat-response strategies.