Impact of long-term exposure to ambient particulate matter and nitrogen dioxide on chronic obstructive pulmonary disease: results from the Women’s Health Initiative cohort
Bibliographic record
Abstract
Abstract Rationale: Although COPD prevalence and exacerbations have been linked to ambient pollutants, evidence on the impact of ambient pollutants on COPD incidence is relatively sparse. Objectives: To evaluate the associations of long-term ambient particulate matter (PM 2.5 ; PM 10 ), nitrogen dioxide (NO 2 ), and incident self-reported COPD in the Women’s Health Initiative (WHI), a large prospective cohort study of post-menopausal women across the United States. Methods: We estimated annual average residential pollutant concentrations using validated spatiotemporal models and monitored data. We estimated pollutant-COPD associations as hazard ratios (HRs) and 95% confidence intervals (CI) per inter-quartile range (IQR) increase in pollutant using time-varying Cox proportional hazards models adjusted for potential confounders including sociodemographic characteristics, lifestyle and health factors, and WHI Clinical Center at baseline. Finally, we assessed the joint impact of exposure to multiple pollutants using quantile-based G-computation for survival outcomes. Measurements and Main Results: During the median follow-up time of 11.1 years, the study participants experienced 3532 cases of COPD. HRs ranged from 1.20 (95% CI:1.15, 1.26) per IQR increase in PM 2.5 , to 1.19 (95% CI:1.13, 1.26) per IQR increase in NO 2 , to 1.10 (95% CI:1.06, 1.15) per IQR increase in PM 10 . In our multi-pollutant model, a quartile increase in PM 2.5 and NO 2 was associated with a HR of 1.16 (95% CI:1.11, 1.20). Conclusions: In this national cohort of post-menopausal women, the long-term residential concentrations of ambient particulate matter (PM 2.5 and PM 10 ), and NO 2 were associated with a higher risk of incident COPD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".