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 potential 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 (CATE) into quartiles to map their spatial distribution and identify key effect modifiers via the variable importance and observed indicator profiles across quartiles. Results: We found substantial variation across districts regarding heat-related emergency hospitalizations. The top three indicators for predicting this effect heterogeneity were the percentage of people seeking protection, population below age 6, and air pollution levels. The observed indicator profile for the most susceptible districts was characterized by a larger percentage of vulnerable populations, higher car density, and fewer hospital beds. Conclusion: There is substantial variation in heat susceptibility across and within districts in Germany. We propose an approach that can help understanding such complex interplay of indicators from different domains driving heterogeneity.