LONG-TERM EXPOSURE TO TRAFFIC NOISE AND TRAFFIC-RELATED AIR POLLUTION AND CORONARY HEART DISEASE MORTALITY
Bibliographic record
Abstract
Background and Aims: In metropolitan areas, road traffic is a major contributor to ambient air pollution, and also the dominant source of environmental noise. Therefore, observed associations between air pollution and adverse cardiovascular outcomes may be confounded by coexistent traffic noise or vice versa. This population-based cohort study investigated the independent and joint effects of traffic noise and traffic-related air pollution on coronary heart disease (CHD) mortality. Methods: This 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 during the exposure period and without known CHD at baseline were included (n = 445,795). Individual exposures to traffic noise and traffic-related air pollutants including black carbon, PM2.5, NO2, and NO were estimated at subjects’ residences using a noise prediction model (Cadna A) and land use regression models, respectively. CHD deaths during the follow-up period were identified from the provincial death registration database. Results: Traffic noise level was moderately correlated with black carbon (0.44), but weakly correlated with PM2.5 (0.14), NO2 (0.33), and NO (0.39). After adjusting for baseline age, sex, preexisting comorbidity, neighborhood socioeconomic status, PM2.5, NO2, and black carbon, a 10 dBA elevation in residential noise was associated with a 9% (95% CI: 1-17%) increase in CHD mortality; subjects in the highest noise decile (>70 dBA) had a 19% (95% CI: 2-40%) increase in CHD mortality compared with those in the lowest decile (≤57 dBA). After similar adjustments including noise, an interquartile range (0.97×10–5/m) elevation in black carbon concentration was associated with a 4% (95% CI: 1-8%) increase in CHD mortality. We did not find any interaction between traffic noise and black carbon on CHD mortality. Conclusions: Traffic noise and black carbon may both be independently responsible for observed associations between exposure to road traffic and adverse cardiovascular outcomes.
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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 | 0.000 | 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".