Boudreault, Jérémie 
ORCID: https://orcid.org/0000-0002-3086-2635; Campagna, Céline et Chebana, Fateh ORCID: https://orcid.org/0000-0002-3329-8179
(2024).
Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning.
Environmental Science and Pollution Research
, vol. 31
, nº 9.
pp. 14059-14070.
DOI: 10.1007/s11356-024-31969-z.
Résumé
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.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | mortality; temperature; temperature variations; weather; air pollution; machine learning |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 31 juill. 2024 20:04 |
| Dernière modification: | 31 juill. 2024 20:04 |
| URI: | https://espace.inrs.ca/id/eprint/15486 |
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