Associations between all-cause and ischemic heart disease mortality and long-term ambient ultrafine particles exposure: a comparison of statistical and machine learning exposure models
Bibliographic record
Abstract
BACKGROUND: Evidence about the health effects of ultrafine particles (UFPs) remains limited, especially due to challenges in estimating exposure in epidemiological studies. OBJECTIVE: We estimated the associations between annual mean UFPs and all-cause and ischemic heart disease (IHD) mortality and investigated whether methods used to assess exposure influence these associations. METHODS: We conducted a retrospective cohort study in Quebec City, Canada, between 2000 and 2017. Annual mean UFPs exposure at participants postal code were estimated from different statistical and machine learning methods previously developed based on a one-year mobile monitoring campaign. Association with mortality were assessed using Cox models adjusted for potential confounders. We considered single and multipollutant models and assessed potential non-linearity. We evaluated effect modification by age (<65 vs. ≥ 65 years). RESULTS: , slopes were positive and steeper at lower ranges of concentrations with a flattening at higher concentrations. Exposure-response curves varied across exposure methods, particularly at lower concentrations. In multipollutant models, estimated hazard ratio (HR) for all-cause mortality was 1.18 (50th vs.10th percentiles; 95% CI: 1.14, 1.23) using XGBoost, which was our best performing model. Corresponding HR using Forward Stepwise Linear Regression model, our least accurate model, was 1.04 (95% CI: 1.00, 1.08). Exposure-response functions for IHD mortality were very similar to those of all-cause mortality. For both all-cause and IHD mortality, associations were smaller in the elderly compared with the nonelderly. CONCLUSIONS: Long-term exposure to ambient UFPs is associated with all-cause and IHD mortality. The estimated response function varied depending on the exposure assessment method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".