Cancer Premature Mortality Costs in Europe in 2020: A Comparison of the Human Capital Approach and the Friction Cost Approach
Bibliographic record
Abstract
The inclusion of productivity costs can affect the outcome of cost-effectiveness analyses. We estimated the value of cancer premature mortality productivity costs for Europe in 2020 using the Human Capital Approach (HCA) and compared these to the Friction Cost Approach (FCA). Cancer mortality data were obtained from GLOBOCAN 2020 by sex and five-year age groups. Twenty-three cancer sites for 31 European countries were included. The HCA and the FCA were valued using average annual gross wages by sex and age group and applied to Years of Potential Productive Life Lost. 2020 friction periods were calculated and all costs were in 2020 euros. Estimated cancer premature mortality costs for Europe in 2020 were EUR 54.0 billion (HCA) and EUR 1.57 billion (FCA). The HCA/FCA cost ratio for Europe was 34.4, but considerable variation arose across countries (highest in Ireland: 64.5 v lowest in Czech Republic: 11.1). Both the HCA and the FCA ranked lung, breast and colorectal as the top three most costly cancers in Europe, but cost per death altered rankings substantially. Significant cost differences were observed following sensitivity analysis. Our study provides a unique perspective of the difference between HCA and FCA estimates of productivity costs by cancer site and country in Europe.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".