Costs of productivity loss due to occupational cancer in Canada: estimation using claims data from Workers’ Compensation Boards
Bibliographic record
Abstract
INTRODUCTION: Cancer is a leading cause of illness globally, yet our understanding of the financial implications of cancer caused by working conditions and environments is limited. The goal of this study is to estimate the costs of productivity losses due to occupational cancer in Canada, and to evaluate the factors associated with these costs. METHODS: Two sources of data are used: (i) Individual level administrative claims data from the Workers Compensation Board of Nova Scotia; and (ii) provincial aggregated cancer claims statistics from the Association of Workers Compensation Boards of Canada. Benefits paid to claimants are based on actuarial estimates of wage-loss, but do not include medical costs that are covered by the Canadian publicly funded healthcare system. Regional claims level data are used to estimate the total and average (per claim) cost of occupational cancer to the insurance system, and to assess which characteristics of the claim/claimant influence costs. Cost estimates from one region are weighted using regional multipliers to adjust for system differences between regions, and extrapolated to estimate national costs of occupational cancer. RESULTS/DISCUSSION: We estimate that the total cost of occupational cancer to the Workers' Compensation system in Canada between 1996 and 2013 was $1.2 billion. The average annual cost was $68 million. The cancer being identified as asbestos related were significantly positively associated with costs, whereas the age of the claimant was significantly negatively associated with costs. The industry type/region, injury type or part of body affected by cancer were not significant cost determinants. CONCLUSION: Given the severity of the cancer burden, it is important to understand the financial implications of the disease on workers. Our study shows that productivity losses associated with cancer in the workplace are not negligible, particularly for workers exposed to asbestos.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".