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Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning

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Abstract

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.

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Data Availability

The authors do not have permission to share health data. Weather and air pollution data are freely available from Environment and Climate Change Canada (ECCC).

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Acknowledgements

The authors would like to thank Denis Hamel and Louis Rochette for the help with the mortality data extraction. They would also like to thank Yohann Chiu, Magalie Canuel, Ray Bustinza, Felix Lamothe, Natalie Gravel and Annabel Ruf for their comments on early versions of this work. The authors also thank the editor and two anonymous reviewers who helped improve the quality of this paper.

Funding

The main author has received funding from the Natural Sciences and Engineering Research Council of Canada (Vanier Scholarship, #CGV-180 821), the Canadian Institute of Health Research (Health System Impact Fellowship, #IF1-184093), Ouranos (Real-Décoste Excellence Scholarship, #RDX-317725) and the National Institute of Public Health of Quebec (no grant number).

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Authors and Affiliations

  1. Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada

    Jérémie Boudreault, Céline Campagna & Fateh Chebana

  2. Direction de la santé environnementale, au travail et de la toxicologie, Institut national de santé publique du Québec (INSPQ), 945 Avenue Wolfe, Quebec, QC, G1V 5B3, Canada

    Jérémie Boudreault & Céline Campagna

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  1. Jérémie Boudreault
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Contributions

Jérémie Boudreault: conceptualization, methodology, data curation, formal analysis, visualization, software, writing — original draft, review and editing, funding acquisition. Céline Campagna: conceptualization, writing — review and editing, supervision, project administration, funding acquisition. Fateh Chebana: conceptualization, writing — review and editing, supervision, project administration, funding acquisition.

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Correspondence to Jérémie Boudreault.

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This project received ethics approval from the Human Research Ethics Committee of the National Institute of Scientific Research (CER-22–693).

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Not applicable. Consent to participate was not required due to the nature of the data used (i.e., anonymized administrative health database).

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Not applicable. Consent to publish was not required due to the nature of the data used (i.e., anonymized administrative health database).

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The authors declare no competing interests.

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Responsible Editor: Lotfi Aleya

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Boudreault, J., Campagna, C. & Chebana, F. Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning. Environ Sci Pollut Res 31, 14059–14070 (2024). https://doi.org/10.1007/s11356-024-31969-z

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