Exposure to ambient air pollution and the incidence of lung cancer and breast cancer in the Ontario Population Health and Environment Cohort
Bibliographic record
Abstract
Lung and female breast cancers are highly prevalent worldwide. Although the association between exposure to ambient fine particulate matter (PM2.5) and lung cancer has been recognized, there is less evidence for associations with other common air pollutants such as nitrogen dioxide (NO2) and ozone (O3). Even less is known about potential associations between these pollutants and breast cancer. We conducted a population‐based cohort study to investigate the associations of chronic exposure to PM2.5, NO2, O3 and redox‐weighted average of NO2 and O3 (Ox) with incident lung and breast cancer, using the Ontario Population Health and Environment Cohort (ONPHEC), which includes all long‐term residents aged 35–85 years who lived in Ontario, Canada, 2001–2015. Incident lung and breast cancers were ascertained using the Ontario Cancer Registry. Annual estimates of exposures were assigned to the residential postal codes of subjects for each year during follow‐up. We used Cox proportional‐hazards models adjusting for personal‐ and neighborhood‐level covariates. Our cohorts for lung and breast cancer analyses included ~4.9 million individuals and ~2.5 million women, respectively. During follow‐up, 100,146 incident cases of lung cancer and 91,146 incident cases of breast cancer were diagnosed. The fully adjusted analyses showed positive associations of lung cancer incidence with PM2.5 (hazard ratio [HR] = 1.02 [95% CI: 1.01–1.05] per 5.3 μg/m3) and NO2 (HR = 1.05 [95% CI: 1.03–1.07] per 14 ppb). No associations with lung cancer were observed for O3 or Ox. Relationships between PM2.5 and NO2 with lung cancer exhibited a sublinear shape. We did not find compelling evidence linking air pollution to breast cancer.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".