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

We still know relatively little about how climate, demographics, built environment, and behaviors interact to drive hospitalizations during extreme heat events (EHEs).This study employed a discrete event system dynamics modeling approach to address two research questions about the relationship between EHEs and heat-related illnesses (HRIs): (1) How will changes in EHE frequencies, intensities, and durations in major Metropolitan Statistical Areas (MSAs) across the contiguous United States (CONUS) Climate Regions drive HRI morbidity across demographic, health-status, and household groups over the coming decades? and (2) What are the anticipated HRI costs for these MSAs over the coming decades and how will the distribution of those costs evolve, across a range of plausible low- and high-emissions scenarios? We drew on extant literature to develop a transdisciplinary system dynamics simulation model which we ran on a sample of the 53 largest MSAs in CONUS, by population. We produced stratified HRI and cost projections across regions out to the year 2040. We found striking regional differences in HRIs and HRIs per EHE. We also found race disparities in HRIs and costs, and these disparities varied by US climate region. Differences across regions and scenarios furthermore appear to be the result of changing EHE profiles combined with underlying changes in the geographic distribution of demographic and socioeconomic disparities in risk. By combining continuous and discrete event modeling, this approach makes possible the construction of models which can be empirically tested at discrete points, both structural and parametric, along its causal chains. Such models may help align specific interventions that address vulnerabilities to extreme heat while improving the calibration, coordination, and timing of regional responses.