Evidence map›Paper›PMID 40046729›Full record

ArticleEnvironmental epidemiology (Philadelphia, Pa.)2025

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.

Chen Chen, Jay S Kaufman, Juwel Rana, Tarik Benmarhnia, Hong Chen

Abstract read
In one paragraph

Article in Environmental epidemiology (Philadelphia, Pa.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. When does algorithmic complexity add causal value in environmental epidemiology?Environmental epidemiology (Philadelphia, Pa.) · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chen ChenScripps Institution of Oceanography, University of California San Diego, La Jolla, California.ORCID https://orcid.org/0000-0002-0632-946X
Jay S KaufmanDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
Juwel RanaDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
Tarik BenmarhniaScripps Institution of Oceanography, University of California San Diego, La Jolla, California.
Hong ChenEnvironmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.

Funding

Susceptibility and adverse health outcomes related to climate-sensitive events among older Medicare beneficiaries with Alzheimer and DementiaRF1AG080948 · NIA · YALE UNIVERSITY · PI BELL, MICHELLE L, BENMARHNIA, TARIK · 2023 to 2023
$2.6M
NIA NIH HHS RF1 AG080948
6 · The paper itself

Abstract

Background: Evidence suggests the existence of nonlinearity in the relationship between long-term fine particulate matter (PM Methods: Using a cohort of the Canadian Community Health Survey respondents (followed from 2005 until 2014), we obtained consented respondents' baseline characteristics, time-varying annual average PM Results: Among 62,365 participants, the 10-year cumulative mortality rate differences per 1000 participants were -0.23 (95% confidence intervals: -0.46, 0.00), -0.83 (-1.24, -0.43), and -0.67 (-1.27, -0.06) for parametric g-computation, targeted minimum loss-based estimator using parametric models, and targeted minimum loss-based estimator with SuperLearner and six candidate algorithms of high flexibility, respectively. Changing the hyperparameters did not meaningful change estimates or algorithm weights. Conclusions: All three methods of reducing long-term exposure to PM

Indexed as

Air pollutionCausal inferenceDoubly robust estimationEnvironmental epidemiologyLong-term exposureMachine learningMortalitySuperLearner

Identifiers

PMID40046729
PMCPMC11882297

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.