Exploring the relationship between medications and heat-related community deaths during the 2021 heat dome: a hybrid approach using machine learning.

Boudreault, Jérémie ORCID: https://orcid.org/0000-0002-3086-2635; McLean, Kathleen E. et Henderson, Sarah B. (2025). Exploring the relationship between medications and heat-related community deaths during the 2021 heat dome: a hybrid approach using machine learning. eBioMedicine , vol. 117 . p. 105788. DOI: 10.1016/j.ebiom.2025.105788.

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Résumé

Background
Extreme heat events (EHEs) are a growing threat to health worldwide. To date, only a limited number of studies have evaluated medications as risk or protective factors for mortality during EHEs.
Methods
We explored the relationship between dispensed pharmaceuticals and heat-related community deaths using linked administrative health data and both logistic regression (LR) and machine learning (ML) models. We conducted a case-control study during the 2021 EHE in British Columbia, Canada, including 504 community deaths from heat exposure as cases and 2520 similar controls who survived the EHE. We used medications dispensed 30, 60 and 90 days prior to death (or 30, 60 and 90 days before the end of the EHE for controls) as predictors, grouped by Anatomical Therapeutic Chemical (ATC) classification at level 2 for LR (28 classes) and level 4 for ML (270 subclasses). Models were adjusted for multiple covariates, including common chronic diseases.
Findings
Results from LR showed increased odds of mortality associated with dispensations of antiepileptics, anti-Parkinson drugs, psycholeptics, diuretics, drugs for diabetes, beta blocking agents, analgesics, urologicals and drugs for treatment of bone diseases. We observed a protective association with dispensations of calcium channel blockers and ophthalmologicals. Results varied by sex, age, and other covariates. The ML model highlighted the most computationally important subclasses of medications within each of the ATC level 2 classes.
Interpretation
This study leveraged both LR and ML to generate insights about medications and mortality during EHEs. The results add to the existing evidence on pharmaceutical risks during EHEs and provide new avenues for further research. They can be used to help develop more targeted messages to inform individuals whose medications put them at greater risk during EHEs.
Funding
BC Centre for Disease Control and Ministère de l'Enseignement supérieur du Québec.

Type de document:	Article
Mots-clés libres:	extreme heat; climate change; mortality; pharmaceuticals; machine learning; light gradient boosting
Centre:	Centre Eau Terre Environnement
Date de dépôt:	26 août 2025 14:52
Dernière modification:	26 août 2025 14:52
URI:	https://espace.inrs.ca/id/eprint/16536

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