Global estimates of expected and preventable cervical cancers among girls born between 2005 and 2014: a birth cohort analysis
Bibliographic record
Abstract
BACKGROUND: WHO has launched an initiative aiming to eliminate cervical cancer as a public health problem. Elimination is a long-term target that needs long-lasting commitment. To support local authorities in implementing human papillomavirus (HPV) vaccination, we provide regional and country-specific estimates of cervical cancer burden and the projected impact of HPV vaccination among today's young girls who could develop cervical cancer if not vaccinated. METHODS: The expected number of cervical cancer cases in the absence of vaccination among girls born between 2005 and 2014 was quantified by combining age-specific incidence rates from GLOBOCAN 2018 and cohort-specific mortality rates by age from UN demographic projections. Preventable cancers were estimated on the basis of HPV prevalence reduction attributable to vaccination and the relative contribution of each HPV type to cervical cancer incidence. We assessed the number of cervical cancer cases preventable through vaccines targeting HPV types 16 and 18, with and without cross-protection, and through vaccines targeting HPV types 16, 18, 31, 33, 45, 52, and 58. FINDINGS: Globally, without vaccination, the burden of cervical cancer in these birth cohorts is expected to reach 11·6 million (95% uncertainty interval 11·4-12·0) cases by 2094. Approximately 75% of the burden will be concentrated in 25 countries mostly located in Africa and Asia, where the future number of cases is expected to increase manyfold, reaching 5·6 million (5·4-6·0) cases in Africa and 4·5 million (4·4-4·6) cases in Asia. Worldwide immunisation with an HPV vaccine targeted to HPV types 16 and 18, with cross-protection against HPV types 31, 33, and 45, could prevent about 8·7 million (8·5-9·0) cases. INTERPRETATION: Detailed estimates of the increasing burden of cervical cancer and projected impact of HPV vaccination is of immediate relevance to public health decision makers. Shifting the focus of projections towards recently born girls who could develop cervical cancer if not vaccinated is fundamental to overcome stakeholders' hesitancy towards HPV vaccination. FUNDING: Bill & Melinda Gates Foundation, Canadian Institutes of Health Research.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".