Long-Term Exposure to Traffic-Related Air Pollution and Cardiovascular Mortality
Bibliographic record
Abstract
BACKGROUND: Findings from previous cohort studies suggest a positive association between traffic-related air pollution and cardiovascular mortality. However, few studies have assessed intraurban variation in traffic-related pollution or evaluated cardiovascular effects at lower levels of pollution that are typically seen in Canadian cities. METHODS: We conducted a cohort study of traffic-related air pollution and cardiovascular mortality among adults who lived in three cities in Ontario, Canada. Study members of the cohort were a random sample from the federal family income tax database, comprising 205,440 adults age 35-85 years, who lived in Toronto, Hamilton, or Windsor between 1982 and 1986. Follow-up ended on 31 December 2004. Mortality from cardiovascular and cerebrovascular diseases was ascertained using the Canadian Mortality Database. We estimated time-dependent concentrations of ambient nitrogen dioxide (NO2) from land-use regression models and assigned exposures to residences of subjects. Rate ratios (RRs) were estimated from Cox proportional hazard model adjusted for individual risk factors and selected contextual covariables. We adjusted indirectly for smoking and obesity. RESULTS: The spatial distributions of NO2 did not change appreciably over the follow-up period. Cumulative exposure to NO2 was associated with a 12% increase in mortality from cardiovascular disease for each increase of 5 parts per billion of NO2 (95% confidence interval [CI] = 7%-17%) and a 15% increase (8%-21%) in mortality from ischemic heart disease. Risks of cardiovascular mortality were also increased with shorter term exposures, but the RRs were somewhat smaller. No association was found for cerebrovascular mortality (RR = 0.99 [95% CI = 0.91-1.08]). CONCLUSION: Traffic-related air pollution at relatively low concentrations in Ontario was associated with increased mortality from cardiovascular 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".