Incidence of mesothelioma and asbestosis by occupation in a diverse workforce
Bibliographic record
Abstract
OBJECTIVE: We sought to characterize detailed patterns of mesothelioma and asbestosis incidence in the workforce as part of an occupational disease surveillance program in Ontario, Canada. METHODS: The Occupational Disease Surveillance System (ODSS) cohort was established using workers' compensation claims data and includes 2.18 million workers employed from 1983 to 2014. Workers were followed for mesothelioma and asbestosis diagnoses in Ontario Cancer Registry, physician, hospital, and ambulatory care records through 2016. Trends in incidence rates were estimated over the study period. Cox proportional hazard models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: A total of 854 mesothelioma and 737 asbestosis cases were diagnosed during follow-up. Compared with all other workers in the ODSS, those employed in construction trades occupations had the greatest adjusted incidence rate of both mesothelioma (223 cases; HR, 2.38; 95% CI: 2.03-2.78) and asbestosis (261 cases; HR, 3.64; 95% CI: 3.11-4.25). Rates were particularly elevated for insulators, pipefitters and plumbers, and carpenters. Workers in welding and flame cutting, boiler making, and mechanic and machinery repair occupations, as well as those in industrial chemical and primary metal manufacturing industries, had strongly elevated rates of both diseases. Rates were greater than anticipated for workers in electrical utility occupations and education and related services. CONCLUSIONS: Results substantiate the risk of mesothelioma and asbestosis in occupation and industry groups in the Ontario workforce with known or suspected asbestos exposure. Sustained efforts to prevent the occurrence of additional cases of disease in high-risk groups are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".