Human Papillomavirus Vaccination Coverage Estimates Among the Primary Target Cohort (9–14-Year-Old Girls) in the World (2010–2024)
Bibliographic record
Abstract
Background/Objectives: Monitoring human papillomavirus (HPV) vaccine coverage worldwide can provide valuable insight into cervical cancer prevention. The aim of this manuscript was to assess the HPV vaccination coverage among the primary target cohort (9–14-year-old girls) in the world from 2010 to 2024. Methods: A descriptive epidemiological study (with an ecological study design) was carried out. Trends in HPV vaccination coverage were examined using the joinpoint regression analysis. Results: The HPV vaccination was introduced into the national schedule of 147 countries in 2024. Globally, coverage with the first dose of the HPV vaccine in the primary target cohort (9–14-year-old girls) was estimated at 56.9% in 2024. The growth trend in HPV vaccination coverage was significant mainly in the most developed countries (e.g., such as the USA, Canada and Germany), while trends were 10 times faster in other countries such as Armenia, Indonesia and Tanzania. A decline in trends of HPV vaccination coverage was significant in some developing countries (e.g., such as Panama, Sri Lanka, and Suriname) and in one of the most developed countries—the United Kingdom. Conclusions: A better understanding of changes in HPV vaccination coverage worldwide and further efforts to improve coverage to the target of 90% may contribute to more effective disease prevention.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".