Estimating the social cost of respiratory cancer cases attributable to occupational exposures in France
Bibliographic record
Abstract
Purpose The objective of this article was to estimate the social cost of respiratory cancer cases attributable to occupational risk factors in France in 2010. Methods According to the attributable fraction method and based on available epidemiological data from the lit- erature, we estimated the number of respiratory cancer cases due to each identified risk factor. We used the cost- of-illness method with a prevalence-based approach. We took into account the direct and indirect costs. We estimated the cost of production losses due to morbidity (absenteeism and presenteeism) and mortality costs (years of production losses) in the market and nonmarket spheres. Results The social cost of lung, larynx, sinonasal and mesothelioma cancer caused by exposure to asbestos, chromium, diesel engine exhaust, paint, crystalline silica, wood and leather dust in France in 2010 were estimated at between 917 and 2,181 million euros. Between 795 and 2,011 million euros (87-92 %) of total costs were due to lung cancer alone. Asbestos was by far the risk factor representing the greatest cost to French society in 2010 at between 531 and 1,538 million euros (58-71 %), ahead of diesel engine exhaust, representing an estimated social cost of between 233 and 336 million euros, and crystalline silica (119-229 million euros). Indirect costs represented about 66 % of total costs. Conclusion Our assessment shows the magnitude of the economic impact of occupational respiratory cancers. It allows comparisons between countries and provides valu- able information for policy-makers responsible for defining public health priorities.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".