The Burden and Trends of Gynecological Cancers in Asia from 1980 to 2021, with Projections to 2050: A Systematic Analysis for the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Gynecological cancers pose a significant threat to women's health. This study aimed to investigate the disease burden of cervical, uterine, and ovarian cancers in Asia from 1980 to 2021. The Global Burden of Disease 2021 database (GBD 2021) was used to conduct a cross-sectional study. The incidence, mortality rates, and disability-adjusted life years (DALYs) were obtained as indicators to estimate the burden. The effects of age, period, and cohort on the incidence of gynecological cancers were analyzed via the age-period-cohort web tool (APC-Web). The future trends of the gynecological cancer burden in Asia from 2025 to 2050 were predicted via a Bayesian age-period-cohort model. In 2021, cervical cancer exhibited the highest age-standardized mortality burden (3.1 deaths per 100,000; 95% UI: 2.7-3.4), whereas uterine cancer had the lowest (0.7 deaths per 100,000; 95% UI: 0.6-0.9). Geographically, South Asia has experienced the highest cervical cancer burden, with Seychelles, Mongolia, Cambodia, and Nepal ranking among the most affected nations. In contrast, Central Asia had the highest ovarian cancer burden, led by Georgia, followed by the United Arab Emirates, Seychelles, and Brunei Darussalam. Similarly, the uterine cancer burden was most pronounced in Central Asia, with Georgia, Armenia, Mauritius, and the United Arab Emirates exhibiting elevated rates. Finally, increasing trends in the burden of gynecological cancers were predicted across all age groups from 2025 to 2050, with women aged 60 to 64 years being the most affected. In conclusion, gynecological cancers are significant contributors to the disease burden in Asia. Improved early screening methods are essential to mitigate this increasing burden.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| 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".