Incidence of acute myocardial infarction in the workforce: Findings from the Occupational Disease Surveillance System
Bibliographic record
Abstract
BACKGROUND: Increased risks of acute myocardial infarction (AMI) may be attributable to the workplace, however, associations are not well-established. Using the Occupational Disease Surveillance System (ODSS), we sought to estimate associations between occupation and industry of employment and AMI risk among workers in Ontario, Canada. METHODS: The study population was derived by linking provincial accepted lost-time workers' compensation claims data, to inpatient hospitalization records. Workers aged 15-65 years with an accepted non-AMI compensation claim were followed for an AMI event between 2007 and 2016. Adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each industry and occupation group, compared to all other workers in the cohort. Sex-stratified analyses were also performed. RESULTS: In all, 24,514 incident cases of AMI were identified among 1,502,072 Ontario workers. Increased incidence rates of AMI were found across forestry (HR 1.37, 95% CI 1.19-1.58) and wood processing (HR 1.50, 1.27-1.77) job-titles. Elevated rates were also detected within industries and occupations both broadly related to mining and quarrying (HR 1.52, 1.17-1.97), trucking (HR 1.32, 1.27-1.38), construction (HR 1.32, 1.14-1.54), and the manufacturing and processing of metal (HR 1.41, 1.19-1.68), textile (HR 1.41, 1.07-1.88), non-metallic mineral (HR 1.30, 0.82-2.07), and rubber and plastic (HR 1.42, 1.27-1.60) products. Female food service workers also had elevated AMI rates (HR 1.36, 1.23-1.51). CONCLUSION: This study found occupational variation in AMI incidence. Future studies should examine work-related hazards possibly contributing to such excess risks, like noise, vibration, occupational physical activity, shift work, and chemical and particulate exposures.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".