Determinants of heat risk in an aging population : a machine learning approach

This paper identifies individual and regional risk factors for hospitalizations caused by heat within the German population over 65 years of age. Using administrative insurance claims data and a machine-learning-based regression model, we causally estimate heterogeneous heat effects and explore the geographic, morbidity, and socioeconomic correlates of heat vulnerability. Our results indicate that health effects distribute highly unevenly across the population. The most vulnerable are more likely to suffer from chronic diseases such as dementia and Alzheimer's disease and live in rural areas with more old-age poverty and less nursing care. We project that unabated climate change might bring heat to areas with particularly vulnerable populations, which could lead to a five-fold increase in heat-related hospitalization by 2100.