Associations of Ambient Air Pollution with Chronic Obstructive Pulmonary Disease Hospitalization and Mortality
Bibliographic record
Abstract
RATIONALE: Ambient air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD). However, there is a lack of longitudinal studies to support this assertion. OBJECTIVES: To investigate the associations of long-term exposure to elevated traffic-related air pollution and woodsmoke pollution with the risk of COPD hospitalization and mortality. METHODS: This population-based cohort study included a 5-year exposure period and a 4-year follow-up period. All residents aged 45-85 years who resided in Metropolitan Vancouver, Canada, during the exposure period and did not have known COPD at baseline were included in this study (n = 467,994). Residential exposures to traffic-related air pollutants (black carbon, particulate matter <2.5 μm in aerodynamic diameter, nitrogen dioxide, and nitric oxide) and woodsmoke were estimated using land-use regression models and integrating changes in residences during the exposure period. COPD hospitalizations and deaths during the follow-up period were identified from provincial hospitalization and death registration databases. MEASUREMENTS AND MAIN RESULTS: An interquartile range elevation in black carbon concentrations (0.97 × 10(-5)/m, equivalent to 0.78 μg/m(3) elemental carbon) was associated with a 6% (95% confidence interval, 2-10%) increase in COPD hospitalizations and a 7% (0-13%) increase in COPD mortality after adjustment for covariates. Exposure to higher levels of woodsmoke pollution (tertile 3 vs. tertile 1) was associated with a 15% (2-29%) increase in COPD hospitalizations. There were positive exposure-response trends for these observed associations. CONCLUSIONS: Ambient air pollution, including traffic-related fine particulate pollution and woodsmoke pollution, is associated with an increased risk of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".