Do we need flexible machine-learning algorithms to assess the effect of long-term exposure to fine particulate matter on mortality?: An example from a Canadian national cohort
All three methods of reducing long-term exposure to PM<sub>2.5</sub> yielded tangible public health benefits in Canada where PM<sub>2.5</sub> levels are among the lowest worldwide. However, the advantage of employing machine-learning algorithms with a doubly robust estimator remains minimal, especially considering the variance-bias tradeoff.
Authors
- Jay S. Kaufman (ORCID: https://orcid.org/0000-0003-1606-401X)
- Juwel Rana (ORCID: https://orcid.org/0000-0001-5597-664X)
- Tarik Benmarhnia (ORCID: https://orcid.org/0000-0002-4018-3089)
- Chen Chen (ORCID: https://orcid.org/0000-0002-0632-946X)
- Hong Chen (ORCID: https://orcid.org/0000-0003-0353-3622)
Institutions
- Health Canada (CA)
- Scripps Institution of Oceanography (US)
- Inserm (FR)
- École des Hautes Études en Santé Publique (FR)
- University of Toronto (CA)
- Thales (Australia) (AU)
- Analytical Services (US)
- University of California San Diego (US)
- Institut de Recherche en Santé, Environnement et Travail (FR)
- Public Health Ontario (CA)
- Asian University of Bangladesh (BD)
- Institute for Clinical Evaluative Sciences (CA)
- McGill University (CA)
- Université de Rennes (FR)
Publication Details
- Journal
- Environmental Epidemiology
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1097/ee9.0000000000000375
- Citations
- 2
- Primary Topic
- Air Quality and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 2.40
Funders
- Government of Canada
- Health Canada