O2C.6 The economic burden of occupational injuries and diseases in five european union countries
Bibliographic record
Abstract
The objective of this study was to estimate the economic burden of occupational injuries and diseases in five European Union countries for the reference year 2015. We used a ‘bottom up’ approach to estimate the economic burden from a societal perspective for Finland, Germany, Italy, The Netherlands, and Poland. Three broad cost categories were considered—direct health care, indirect productivity, and intangible health-related quality of life costs. The methods started with data on newly diagnosed occupational injuries and diseases from calendar year 2015. We considered lifetime costs for cases across all cost categories. Sensitivity analysis was undertaken to assess the impact of key parameters. Indirect costs represent the largest proportion of total costs (with the exception is Poland), ranging from 66% for The Netherland to 43% for Poland. Intangible costs are the second highest, ranging from 49% for Poland to 21% for Finland and The Netherlands. Direct costs range from 16% for Finland to 8% for Poland. Average per case costing is highest for The Netherlands (€75,342), followed by Italy (€58,411), German (€44,919), Finland (€43,069) and lastly Poland (€38,918). Total costs as a percentage of GDP are highest for Poland (10.4%), followed by Italy (6.7%), The Netherlands (3.6%), Germany (3.3%) and lastly Finland (2.7%). In terms of costs per working population, the value is highest for Italy (€4,956), followed by The Netherland (€2,930), Poland (€2,793), Germany (€2,527) and lastly Finland (€2,331). The economic burden of occupational injuries and diseases in the countries considered are substantial, despite efforts to reduce adverse workplace exposures. Our case costs and total economic burden estimates provide a basis for undertaking economic evaluations of prevention efforts and can serve as a template for monitoring and evaluation at the country level. We advance the methods on several fronts.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".