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). To address this paucity in understanding, we draw on extant literature to develop a transdisciplinary discrete event system dynamics (SD) modeling approach to address two research questions about the relationship between EHEs and heat-related illnesses (HRIs): (a) 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 (b) What are the anticipated HRI costs for these MSAs over the coming decades and how will the distribution of those costs evolve, across plausible low- and high-emissions scenarios? By employing a system dynamics simulation model on a sample of the 53 largest population MSAs in CONUS, we produced stratified HRI and cost projections across regions out to the year 2040. The results suggest that differences across regions and scenarios depend on 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 their causal chains. Such models may help align specific interventions that address community vulnerabilities to extreme heat while improving the calibration, coordination, and timing of regional responses.