High-resolution modeling and projection of heat-related mortality in Germany under climate change

• Published in: Communications medicine Vol. 4; no. 1; pp. 206 - 8
• Main Authors: Wang, Junyu, Nikolaou, Nikolaos, an der Heiden, Matthias, Irrgang, Christopher
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.10.2024; Springer Nature B.V; Nature Portfolio
• Subjects: 692/700/478; 692/700/478/174; Adaptation; Age groups; Agglomeration; Climate change; Datasets; Emission standards; Gender; Heat; Medicine; Medicine & Public Health; Mortality; Population; Public health; Temperature; Europe; Germany
• ISSN: 2730-664X; 2730-664X
• DOI: 10.1038/s43856-024-00643-3

Background Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Method Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate change scenarios. Results We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014–2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7–14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Conclusions Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning. Plain Language Summary Heat is becoming a major cause of preventable deaths during the summer. We developed a computer model to estimate heat-related deaths at specific times and in different districts. Using this model for Germany, we estimate that over the past decade (2014–2023), around 48,000 deaths were heat-related, with most occurring during heatwaves. For example, a heatwave from July 7–14, 2023, contributed to 1100 out of 3900 heat-related deaths that year. Our model also suggests that, without adaptation, heat-related deaths in Germany could increase remarkably due to climate change. The insights from our model can help identify areas at high risk and support long-term public health planning to reduce the impact of heatwaves. Wang et al. developed a multi-scale machine learning model with high spatial and temporal resolution to estimate heat-related mortality in Germany. The model indicates that 48,000 deaths between 2014 and 2023 were heat related, and, without adaptation, climate change could increase heat-related mortality by 2.5 to 9 times by 2100.