Usefulness of the results of a surveillance program for uncompensated work-related diseases in France
Bibliographic record
Abstract
Abstract Background French employees receive compensations for diseases officially recognized as professionnal disease. Reimbursment data are thus used to produce statistics. Such data do not integrate uncompensated work-related diseases (UUWRD) defined as likely to be of occupational origin but not recognized as well. In 2003, the National Public Health Agency implemented a surveillance program on UWRD. This program is a complement to the compensation system for occupational diseases. This communication presents results UWRD program can provide. Methods Twice a year, a network of volunteer occupational physicians (OP) reports ill health and associated work exposures of employees. Employee sociodemographics are notified. In 2018, half of French regions are integrated in the program. Prevalence rates are calculated for UWRD. Chi-squared tests are used to compare prevalence rates between groups. Multivariate logistic regressions are conducted to evaluate:1- risks to report UWRD between groups, 2- prevalence rates trends. Underreporting rates of UWRD are approximated using an indicator capturing differences between figures produced by UWRD program and the compensation system. Results Over the 2009-2014 period, women working in mass food retail were observed at higher risk to present musuculoskeletical disorders than women of other sectors (ORa = 2.0). Same results were noted for men (ORa = 1.3). In mass food retail, decreases in musculoskeletal disorder prevalence rates were reported. Estimated average annual change rates were of 7.0 % for women and 11.0% for men. In 2011, UWRD data highlighted that between half and three-quarter of work-related musculoskeletal disorders were unreported by the compensation system. Conclusions UWRD data are used to identify vulnerable groups, analyse temporal trends and evaluate underreporting of professionnal disease. Key messages URWD program is a complement to the compensation system and let to better monitor health status of communities. Such informations are of interest to guide prevention policies.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".