Global paid and unpaid productivity losses due to cancer-related mortality
Bibliographic record
Abstract
BACKGROUND: Cancer is among the most important causes of premature deaths globally. We estimated the value of paid and unpaid productivity losses due to premature mortality in 2022 from all cancers worldwide. METHODS: Years of productive life lost were derived from cancer mortality data for 36 cancer types among people of working age (15-64 years) in 185 countries for the year 2022. Paid productivity losses were estimated using the human capital approach, while unpaid activities were valued using the opportunity cost approach. Lost productivity was estimated using wages, workforce statistics, and time spent on unpaid activities from various sources. All analyses were performed by sex and age group for each country. RESULTS: In 2022, productivity losses from premature cancer mortality were valued at an estimated US$566 billion, equivalent to 0.6% of the global gross domestic product. Of the total value, 53.9% (US$305 billion) was attributable to paid productivity losses, and 46.1% (US$260 billion) to unpaid productivity losses. Paid productivity losses were generally higher among men, while unpaid productivity losses were greater among women, with variations seen across world regions. The total value of lost productivity was greatest for lung cancer, followed by breast and liver cancers. Per cancer death, testicular cancer, melanoma of the skin, and brain and nervous system cancer generated the highest value of productivity losses. CONCLUSION: The substantial value of productivity losses from premature cancer mortality highlights its marked societal burden. Continuous investments in global cancer control efforts, including in less common cancers, will yield substantial returns-on-investment to national economies, especially in transitioning countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".