Global, regional and national burden of liver cancer 1990–2021: a systematic analysis of the global burden of disease study 2021
Bibliographic record
Abstract
BACKGROUND: Liver cancer is a growing global health issue, with significant geographical disparities in prevalence and mortality. Understanding these differences is key to developing effective prevention and treatment strategies. METHODS: We analyzed liver cancer trends from 1990 to 2021 across 204 countries using data from the Global Burden of Disease (GBD) study. We modeled mortality from vital registration data and estimated non-fatal burden using primary studies, hospital discharges, and claims data. We calculated prevalence, mortality, YLLs, YLDs, and DALYs, adjusting for age and reporting rates per 100,000 population with 95% UI. FINDINGS: In 2021, there were 739,299 (673114-821948) cases of liver cancer worldwide. The age-standardized prevalence rate increased from (7.75 [6.91-8.43] per 100,000 people) in 1990 to (8.68[7.90-9.67] per 100,000 people) in 2021, while the mortality rate slightly decreased from(4.48 [4.10-4.93] per 100,000 people) to (6.13 [5.58-6.84] per 100,000 people). High-income North America had the highest prevalence rate, and Southern Latin America had the lowest. Mongolia had the highest prevalence and mortality rates, while Morocco had the lowest. The total YLDs attributed to liver cancer nearly tripled from 1990 to 2021, and the age-standardized DALY rate decreased. In the frontier analysis, countries or regions with higher SDI have greater potential for burden improvement. In the frontier analysis of SDI and age-standardized liver cancer DALY rates in 2021, countries with higher SDI (> 0.85) and higher effective differences relative to their level of development include America, Canada, Germany, Netherlands, etc., while frontier countries with lower SDI (< 0.5) and lower effective differences include Somalia, Papua New Guinea, Yemen, Lao People's Democratic Republic, etc. Countries with larger effective differences include Togo, Gambia, Australia, Norway, etc. CONCLUSION: The global burden of liver cancer is decreasing, but the prevalence of liver cancer is increasing, with significant differences across regions worldwide. These findings can inform health policy and research to address this global challenge. INTERPRETATION: From 1990 to 2021, the incidence of liver cancer in many regions has increased significantly, which is expected to impose a huge social and economic burden on governments and health systems in the coming years. Our research findings may assist policymakers in devising strategies to combat liver cancer, including educating professionals to address the burden of this complex disease.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".