Evolving global burden and trend of cervical cancer in G20 countries: Age, sex, regional disparities from 1990-2021.
Bibliographic record
Abstract
5541 Background: Cervical Cancer (CC) is the 6 th leading cause of death and 4 th leading cause of disability amongst all cancer in G20 countries. The G20 nations, which represent about 85% of the global GDP, hold a pivotal role in shaping the world's economic and health landscapes. This economic dominance underscores the substantial impact that public health issues, like CC, can have not only on individual countries but on global stability and productivity. Methods: We estimated the incidence, prevalence, deaths, disability-adjusted life years (DALYs), and years lived with disability (YLDs) attributed to CC across the G20 countries from 1990 - 2021, disaggregated by age, sex, year, and location, using the standardized methodology of the Global Burden of Disease Study 2021. Non-fatal health outcomes were modeled using DISMOD MR 2.1, a machine learning tool, while fatal health outcomes were assessed using the Cause of Death Ensemble Model (CODEm). The results are reported as absolute counts and age-standardized rates per 100,000 population. Results: The total prevalence count of cervical cancer increased from 1.3 (95% uncertainty interval: 1.2–1.3) million in 1990 to 2.2 (2.0–2.5 ) million in 2021. Deaths rose from 140,740 (128,861–152,835) to 183,343 (166,174–200,956), while disability-adjusted life years (DALYs) increased from 4.8 (4.4–5.2 ) million to 5.9 (5.3–6.4 ) million during the same period. The highest annual percentage change (APC) in the age-standardized incidence rate (ASIR) was observed in Italy (1.59%), followed by South Africa (1.14%), China (0.43%), Argentina (0.3%), Bulgaria (0.2%), and Canada (0.1%). In contrast, the majority of countries, including the United States, experienced a decline in ASIR, with the United States showing a 1.5% reduction from 1990 to 2021. For age-standardized mortality rate (ASMR), South Africa (1.12%) and Italy (0.47%) demonstrated an increase in APC, while all other countries observed a decline. Age-wise analysis revealed that for the 20–54 age group, APC in incidence increased by 1.12%, while for the 55+ age group, it rose by 1.77%. In terms of mortality, the 20–54 age group recorded a 0.26% increase in APC, and the 55+ age group experienced a 1.25% increase. Regarding DALYs, APC for the 20–54 age group increased by 1.4%, while the 55+ age group saw a rise of 2.15% from 1990 to 2021. Conclusions: Death due to Cervical Cancer accounted for 2.35% of all cancer causalities in 2021. Study findings underscore the need for urgent public health interventions. Disparities in incidence and mortality trends reflect unequal access to healthcare, screening, and HPV vaccination. The higher APC in older age groups (55+ years) highlight the importance of targeted healthcare access, while modest increases among younger populations (20–54 years) emphasize sustaining vaccination and screening efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".