Mapping of local heat-related mortality risk using a hybrid statistical and machine learning framework

Background Climate change is increasing the frequency and severity of extreme heat, raising risks to population health. These trends highlight the need for early-warning systems that can anticipate heat-related impacts and support timely public health action. In Portugal, the national heat warning system effectively triggers national alerts but does not account for differences in risk between municipalities. Methods We conducted a nationwide ecological retrospective study using a hybrid statistical–machine learning framework to estimate heat-related mortality risk across all municipalities in Portugal. Associations between extreme heat and mortality were estimated using a Generalised Linear Mixed Model with random intercepts per municipality and random slopes for extreme heat, while robustness and predictive performance were evaluated using GPBoost, a machine learning method combining gradient-boosted decision trees with Gaussian process–based random effects. Findings Extreme heat was associated with a 14.7% increase in mortality on extreme heat days (incidence rate ratio 1.147, 95% CI 1.133–1.161; p < 0.001). Nearly half of the total variance in mortality (47%) was attributable to between-municipality differences, stressing the need for spatially resolved modelling. These results were validated using machine learning, which estimated an 11.29% (9.98%–12.87%) relative increase in mortality associated with extreme heat. Interpretation By integrating formal statistical inference with machine-learning validation, this study demonstrates that reliable, policy-relevant downscaling of heat-related mortality risk is both feasible and essential at national scale. This framework is replicable to other countries, supporting the development of high-resolution heat–health warning systems and more targeted public health interventions to reduce the escalating health burden of extreme heat.