{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T09:40:57Z","timestamp":1772271657276,"version":"3.50.1"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Background: Climate change is increasing the frequency and severity of heatwaves, 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\u2013machine learning framework to downscale heat-related mortality risk across all municipalities in Portugal. Associations between extreme heat and mortality were estimated using a Generalized Linear Mixed Model with random intercepts per municipality and random slopes for extreme heat, robustness and predictive performance were evaluated using GPBoost, a machine learning method combining gradient-boosted decision trees with Gaussian process\u2013based 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\u20131.161; p&lt;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 by machine-learning, which estimated an 11.82% 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\u2013health warning systems and more targeted public health interventions to reduce the escalating health burden of extreme heat.<\/jats:p>","DOI":"10.2139\/ssrn.6296607","type":"posted-content","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:12:11Z","timestamp":1772255531000},"source":"Crossref","is-referenced-by-count":0,"title":["Downscaling Heat-Related Mortality Risk Using a Hybrid Statistical and Machine Learning Framework"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9160-0157","authenticated-orcid":true,"given":"Maria Miguel","family":"Oliveira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7374-2270","authenticated-orcid":true,"given":"Ana Margarida","family":"Alho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1564-2180","authenticated-orcid":true,"given":"Ana Patr\u00edcia","family":"Oliveira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8316-5035","authenticated-orcid":true,"given":"Paulo  Jorge","family":"Nogueira","sequence":"additional","affiliation":[]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:12:11Z","timestamp":1772255531000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=6296607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.6296607","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}