Economic burden attributable to high BMI-caused cancers: a global level analysis between 2002 and 2021
Bibliographic record
Abstract
BACKGROUND: Obesity and overweight are prevailing concerns in modern society, but high BMI shows an established correlation with the risk of cancers that impacts not only medical issues but also economic performance. This study analyzes the economic loss due to high BMI-caused cancers (HBCCs). METHODS: This study used the comprehensive Global Burden of Disease (GBD) 2021 database and estimated the economic loss of HBCCs through the Value of Statistical Life approach (VSLA), incorporating a willingness-to-pay metric. Health burdens are expressed in age-standardized DALYs and death rates, and economic burdens are shown in dollars lost (2021 PPP) calculated from total DALYs. A joinpoint regression analysis was utilized to capture the temporal trends, cancer incidence, and economic losses attributed to high BMI across various countries and income levels. We calculated the average annual percentage change (AAPC) in total economic loss to evaluate the trend over the study period. RESULTS: There is a growing trend in both economic loss and disease burden of HBCCs on a global level. Colon and rectum cancer (CRC) show the highest economic loss ($2593.159 million, UI: 1109.04-4119.61, to $7294.52 million, UI: 3134.75-11,511.13), with pancreatic (AAPC: 10.47*, CI: 8.01-13.51) and liver cancer (AAPC: 8.08*, CI: 5.77-10.35) being the fastest growing cause. The cancer burden for all measures positively correlates with the country's income level; high-income countries are the only group to experience a decreasing trend in the health burden, but they are still increasing in economic burden. Differences in loss of certain types of cancer and gender gap are observed in different income tiers. CONCLUSIONS: These findings indicate a significant upward trend in economic loss, highlighting the urgency for strengthened policy measures. It is crucial for policymakers to implement effective risk reduction and resilience-building strategies to mitigate future economic loss and better protect vulnerable communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".