High-Dimensional Heterogeneity in Heat-Related Health Impacts Across Germany Districts.

Background: Multiple factors can drive heat susceptibility but are typically analyzed individually. This study investigates high-dimensional heterogeneity across multiple domains in the effect of hot days on emergency hospitalizations across German districts. Methods: We obtained emergency hospitalization rates per 1,000 in 3-day windows for the summers of 2005-2023 from the German Diagnosis-Related Groups-Statistic, linked with spatio-temporal temperature information. A hot day was defined as at least one day above the district-level 98th percentile heat index. Multiple characteristics of the demographic, health/social, and environmental domains were selected as effect modifiers. We implemented a generalized random forest, where outcome and propensity scores were modeled with fixed effects estimators to control for seasonality and district-level confounding. We divided the conditional average treatment effects into quartiles to map their spatial distribution and identify key effect modifiers via the variable importance and observed indicator profiles across quartiles. Results: While the average effect of heat on emergency hospitalizations was close to null (-0.3 per 1,000), effects in the lowest and highest effect quartiles ranged from -2.3 to +1.4 per 1,000. Demographic indicators explained the largest share of heterogeneity (34.5%), followed by environmental (14.53%) and health/social factors (10.28%). The indicator profile for the most susceptible districts was characterized by lower air pollution levels, a larger percentage of vulnerable populations, and higher car density. Conclusion: We highlight that heat susceptibility cannot be summarized by one number per region or be explained by a single effect modifier, which needs to be considered in heat-health warning systems and adaptation planning.