Establishing the Occupational Disease Surveillance System (ODSS) for Ontario: a linkage of administrative data
Bibliographic record
Abstract
IntroductionWorkplace conditions and exposures are important determinants of health. However, identifying and monitoring population-level trends in work-related disease is challenged by existing data limitations. Administrative health databases capture timely and accurate information about disease diagnoses among the Ontario population, but these data do not include work history. Objectives and ApproachThe Occupational Disease Surveillance System (ODSS), launched in 2017, captures and reports trends in work-related disease in Ontario. A cohort of 2+ million workers was identified from compensation claims (1983-2014). Records were linked through probabilistic and deterministic methods to the Registered Persons Database (1990-2015), and administrative health databases including the Ontario Cancer Registry (1964-2016), hospitalization (2006-2016), ambulatory care (2006-2016) and provincial health insurance plan billing data (1999-2016). Preliminary applications of ODSS have examined risks of 28 cancer sites and 11 non-cancer health conditions. Risks are estimated with Cox proportional hazards models for thousands of industry and occupation groups. ResultsLinkage of existing administrative databases is an efficient approach for examining risk factors for work-related disease at the population level. ODSS can identify groups of workers by industry or occupation that are at increased risk of disease due to known or suspected workplace conditions and risk factors. For example, ODSS detected elevated risk of lung cancer among known at-risk workers employed in mining and quarrying (HR 1.47, 95% CI 1.33-1.61), transport equipment operating (HR 1.39, 95% CI 1.34-1.44), and construction (HR 1.09, 95% CI 1.06-1.13). Exploratory analyses can also detect previously unknown associations between work-related risk factors and disease. For example, although dermatitis and asthma are common occupational diseases, many causative exposures remain unclear. ODSS is currently being used to further explore potential risk factors. Conclusion/ImplicationsTimely information about work-related disease is crucial to support prevention initiatives to protect workers. This novel linkage identifies existing and emerging trends in occupational disease in Ontario. By capturing work-related risk factors, ODSS serves as a model for other provinces to overcome existing gaps in disease surveillance.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".