Abstract / Summary
Objectives: Heatwaves drive surges in acute care demand, yet heat-health action plans (HHAPs) lack tools to scale emergency care responses. This study validated a forecasting tool for heatwave-related emergency department (ED) demand across four clusters comprising the emergency care network of Ticino Canton, Switzerland. Methods: This retrospective, ecological, time-series study modeled daily mean temperature and ED presentations using a distributed lag non-linear model framework with autoregressive and calendar adjustment components. The model was trained on summers (June-August) 2016-2024 and tested on summer 2025, defining heatwaves as daily mean temperature >25 {degrees}C for [≥]3 consecutive days. Results: A non-linear temperature-demand association was shown across the clusters, with ED visits increasing at higher temperatures. Model fit varied across clusters (R2 0.389-0.672), and the reference model consistently outperformed naive baselines. During two heatwaves in 2025, prediction errors remained within acceptable ranges. Baseline demand, heatwave occurrence, and model performance varied substantially within the canton. Conclusions: These findings have implications for the design of HHAPs, suggesting that emergency care preparedness should rely on locally calibrated demand forecast rather than standardized activation protocols.