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

2026-07Artigo científico Acesso aberto
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dc.contributor.authorOliveira, Maria Miguel
dc.contributor.authorAlho, Ana Margarida
dc.contributor.authorOliveira, Ana Patrícia
dc.contributor.authorNogueira, Paulo
dc.contributor.institutionCentro de Investigação em Saúde Pública (CISP/PHRC)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - Pólo ENSP
dc.contributor.institutionEscola Nacional de Saúde Pública (ENSP)
dc.contributor.institutionGlobal Health and Tropical Medicine (GHTM)
dc.contributor.institutionInstituto de Higiene e Medicina Tropical (IHMT)
dc.contributor.pblElsevier
dc.date.accessioned2026-06-22T03:33:01Z
dc.date.available2026-06-22T03:33:01Z
dc.date.issued2026-07
dc.descriptionFunding information: This work was funded by the Portuguese Foundation for Science and Technology (FCT) under the AI4HeatHealth project (Reference: 2024.07710.IACDC). The authors thank Statistics Portugal (INE) for providing access to mortality data. Publisher Copyright: © 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
dc.description.abstractBackground: 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent3474399
dc.identifier.doi10.1016/j.envadv.2026.100713
dc.identifier.issn2666-7657
dc.identifier.otherPURE: 163213234
dc.identifier.otherPURE UUID: 973065f6-c281-41c4-8d75-a79d702a19c4
dc.identifier.otherScopus: 105037472489
dc.identifier.urihttp://hdl.handle.net/10362/203963
dc.identifier.urlhttps://www.scopus.com/pages/publications/105037472489
dc.language.isoeng
dc.peerreviewedyes
dc.subjectExcess mortality
dc.subjectExtreme heat
dc.subjectGPBoost
dc.subjectMachine learning
dc.subjectMixed-effects models
dc.subjectPublic health surveillance
dc.subjectGlobal and Planetary Change
dc.subjectEnvironmental Chemistry
dc.subjectEnvironmental Science (miscellaneous)
dc.subjectSDG 3 - Good Health and Well-being
dc.subjectSDG 13 - Climate Action
dc.titleMapping of local heat-related mortality risk using a hybrid statistical and machine learning frameworken
dc.typejournal article
degois.publication.titleEnvironmental Advances
degois.publication.volume24
dspace.entity.typePublication
rcaap.rightsopenAccess

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