Ensemble Bayesian modelling with dynamic population to estimate excess deaths due to extreme temperatures.

Extreme heat has been linked to increased mortality. Monitoring the mortality burden attributable to extreme heat is crucial to inform policies, such as heat warnings, and prevent heat-related deaths. In this study, we evaluate excess mortality during summer 2022 in Switzerland, identify vulnerable populations and estimate temperature thresholds relevant for heat adaptation policies. We use nationwide mortality and population data during 2011-2022 by age, sex, day and canton. We develop a Bayesian ensemble modelling approach with dynamic population to predict expected mortality in summer 2022 and calculate excess by comparing expected with observed mortality. We account for covariates associated with mortality such as national holidays, and spatiotemporal random effects to improve predictions. After accounting for the effect of the COVID-19 pandemic, we observed a total of 487 (95% Credible Interval: 10-935) excess deaths during summer 2022 in people older than 80 years. We illustrate that for periods of extreme heat longer than four days, the minimum excess mortality temperature threshold in the oldest age group is the 70th percentile of the temperature. Our approach highlights the importance of lower temperature thresholds during prolonged periods of extreme heat and advocates for integrating this insight in heat adaptation policies.