Linkage of administrative and compensation databases for work-related asthma surveillance in Ontario: A proof of concept study
Bibliographic record
Abstract
RATIONALE: Approximately 15% of all asthma cases are work-related and eligible for workers’ compensation in Ontario. However, compensation rates of work-related asthma (WRA) are far less than predicted, making it difficult to estimate the prevalence of the disease.OBJECTIVES We aimed to estimate prevalence of compensated WRA in Ontario; profile the pattern of compensated WRA by demographic, temporal and geographic factors; and demonstrate the potential for database linkage to monitor rates of compensated WRA cases.METHODS Compensated WRA claims data were linked to asthma cases in the Institute of Clinical Evaluative Sciences (ICES) asthma database via encrypted health card numbers. WRA claims between April 1998 and March 2002 were accessed from: i) the Ontario Workplace Safety and Insurance Board (WSIB) Occupational Disease Information Surveillance System (ODISS); and ii) a University of Toronto research database (RD) created by abstracting the same WSIB ODISS claim files.MAIN RESULTS: The estimated prevalence of WRA among individuals with asthma in the asthma database was less than 1% compared to an expected prevalence of 15-20%. Sensitivity of the Asthma database for including individuals with asthma with WRA was very good but differed significantly based on claims category (p < 0.001) compared to the RD as the gold standard.CONCLUSIONS Our findings suggest WRA is severely under-reported. Approximately 11-15% of compensated WRA claims are not captured by the asthma database. Factors accounting for discordance between databases should be explored in order for administrative data linkage to be used to monitor the rates of compensated WRA cases in Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".