Associations between long-term PM2.5 and ozone exposure and mortality in the Canadian Census Health and Environment Cohort (CANCHEC), by spatial synoptic classification zone
Bibliographic record
Abstract
Studies suggest that long-term chronic exposure to fine particulate matter air pollution can increase lung cancer mortality. We analyzed the association between long term PM2.5 and ozone exposure and mortality due to lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease, accounting for geographic location, socioeconomic status, and residential mobility. Subjects in the 1991 Canadian Census Health and Environment Cohort (CanCHEC) were followed for 20 years, and assigned to regions across Canada based on spatial synoptic classification weather types. Hazard ratios (HR) for mortality, were related to PM2.5 and ozone using Cox proportional hazards survival models, adjusting for socioeconomic characteristics and individual confounders. An increase of 10 μg/m3 in long term PM2.5 exposure resulted in an HR for lung cancer mortality of 1.26 (95% CI 1.04, 1.53); the inclusion in the model of SSC zone as a stratum increased the risk estimate to HR 1.29 (95% CI 1.06, 1.57). After adjusting for ozone, HRs increased to 1.49 (95% CI 1.23, 1.88), and HR 1.54 (95% CI 1.27, 1.87), with and without zone as a model stratum. HRs for ischemic heart disease fell from 1.25 (95% CI 1.21, 1.29) for exposure to PM2.5, to 1.13 (95% CI 1.08, 1.19) when PM2.5 was adjusted for ozone. For COPD, the 95% confidence limits included 1.0 when climate zone was included in the model. HRs for all causes of death showed spatial differences when compared to zone 3, the most populated climate zone. Exposure to PM2.5 was related to an increased risk of mortality from lung cancer, and both ozone and PM2.5 exposure were related to risk of mortality from ischemic heart disease, and the risk varied spatially by climate zone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".