Exposure to Road Traffic Noise and Incidence of Acute Myocardial Infarction and Congestive Heart Failure: A Population-Based Cohort Study in Toronto, Canada
Bibliographic record
Abstract
Background: Epidemiological evidence for the association between traffic-related noise and the incidence of major cardiovascular events such as acute myocardial infarction (AMI) and congestive heart failure (CHF) is inconclusive, especially in North America. Objectives: We evaluated the associations between long-term exposure to road traffic noise and the incidence of AMI and CHF. Methods: Our study population comprised ∼1 million people 30–100 years of age who lived in Toronto, Canada, from 2001 to 2015 and were free of AMI (referred to as the AMI cohort) or CHF (the CHF cohort) at baseline. Outcomes were ascertained from health administrative databases using validated algorithms. Annual average noise levels were estimated as the A-weighted equivalent sound pressure level over the 24-h period (LAeq24) and during nighttime (LAeqNight), respectively, using propagation modeling, and assigned to participants’ annual six-digit postal code addresses during follow-up. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for incident AMI and CHF in relation to LAeq24 and LAeqNight using random-effects Cox proportional hazards models adjusting for individual- and census tract–level covariates, including traffic-related air pollutants [e.g., ultrafine particles (UFPs) and nitrogen dioxide]. Results: During follow-up, there were 37,441 AMI incident cases and 95,138 CHF incident cases. Each interquartile range change in LAeq24 was associated with an increased risk of incident AMI (HR=1.07; 95% CI: 1.06, 1.08) and CHF (HR=1.07; 95% CI: 1.06, 1.09). Similarly, LAeqNight was associated with incident AMI (HR=1.07; 95% CI: 1.05, 1.08) and CHF (HR=1.06; 95% CI: 1.05, 1.07). These results were robust to various sensitivity analyses and remained elevated after controlling for long-term exposure to UFPs and nitrogen dioxide. We found near-linear relationships between noise and the incidence of AMI and CHF with no evidence of threshold values. Conclusion: In this large cohort study in Toronto, Canada, chronic exposure to road traffic noise was associated with elevated risks for AMI and CHF incidence. https://doi.org/10.1289/EHP5809
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".