Lung cancer burden attributable to ambient particulate matter: a nationally representative population-based case-control study
Bibliographic record
Abstract
Abstract Background Particulate matter with a diameter of 2.5 micrometers or less (PM 2.5 ) is a known lung carcinogen, but its impact in low-pollution settings is less understood. We assessed the association between long-term PM 2.5 exposure and lung cancer risk in Northern Ireland (NI), a region with relatively low air pollution levels. Methods We conducted a population-based case-control study using data from the Northern Ireland Cancer Registry and the Northern Ireland Cohort for the Longitudinal Study of Ageing. The study included 917 lung cancer cases diagnosed in 2014 and 8,088 controls without lung cancer. Eight-year average PM 2.5 exposure was estimated by linking residential postcodes to 1 km² resolution pollution maps. Fully adjusted logistic regression models were used, controlling for key confounders including smoking status and deprivation index to estimate odds ratios (ORs) and their 95% confidence intervals (95% CI), and population attributable fractions (PAFs). Results Individuals in the highest PM 2.5 tertile (>9.6 µg/m³) had a 37% increased lung cancer risk (OR: 1.37; 95% CI: 1.12–1.68) compared to the lowest tertile (<7.4 µg/m³). The association was stronger in women (OR: 1.79; 95% CI: 1.32–2.44) and not detected in men. Exposure above 10 µg/m³ accounted for 10% of cases, approximately 137 preventable lung cancers annually. Discussion Even in low-pollution regions, PM 2.5 contributes to lung cancer risk, especially in women. Strengthened air quality measures are needed to reduce preventable disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".