Long-term Exposure to Fine Particulate Matter Air Pollution and Mortality Among Canadian Women
Bibliographic record
Abstract
BACKGROUND: Long-term exposure to fine particulate matter (PM2.5) has been associated with increased mortality, especially from cardiovascular disease. There are, however, uncertainties about the nature of the exposure-response relation at lower concentrations. In Canada, where ambient air pollution levels are substantially lower than in most other countries, there have been few attempts to study associations between long-term exposure to PM2.5 and mortality. METHODS: We present a prospective cohort analysis of 89,248 women who enrolled in the Canadian National Breast Screening Study between 1980 and 1985, and for whom residential measures of PM2.5 could be assigned. We derived individual-level estimates of long-term exposure to PM2.5 from satellite observations. We linked cohort records to national mortality data to ascertain mortality between 1980 and 2005. We used Cox proportional hazards models to characterize associations between PM2.5 and several causes of death. The hazard ratios (HRs) and 95% confidence intervals (CIs) computed from these models were adjusted for several individual and neighborhood-level characteristics. RESULTS: The cohort was composed predominantly of Canadian-born (82%) and married (80%) women. The median residential concentration of PM2.5 was 9.1 μg/m(3) (standard deviation = 3.4). In fully adjusted models, a 10 μg/m(3) increase in PM2.5 exposure was associated with elevated risks of nonaccidental (HR: 1.12; 95% CI = 1.04, 1.19), and ischemic heart disease mortality (HR: 1.34; 95% CI = 1.09, 1.66). CONCLUSIONS: The findings from this study provide additional support for the hypothesis that exposure to very low levels of ambient PM2.5 increases the risk of cardiovascular mortality.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".