Estimation of the potential overall impact of human papillomavirus vaccination on cervical cancer cases and deaths
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) vaccination offers potential for primary prevention of HPV-related pre-cancers and cancers as demonstrated in clinical trials. Mathematical models have estimated the potential real-life impact of vaccination on the burden of cervical cancer (CC). However, these are restricted to evaluations in a limited number of countries. METHODS: Potential decline in CC cases and deaths with the AS04-adjuvanted HPV-16/18 vaccine of young girls naïve to HPV, was estimated at steady-state (vaccine coverage: 0-100%) based on clinical trial and country-specific incidence data. Data on vaccine efficacy were taken from the end of study PATRICIA trial of the AS04-adjuvanted HPV-16/18 vaccine. The numbers of cases and deaths due to HPV-16/18 were estimated and compared with those due to any HPV type to estimate the additional cases prevented. This difference estimates CC cases and deaths avoided due to protection against non-vaccine HPV types. Cost-offsets due to reductions in CC treatment were estimated for five countries (Brazil, Canada, Italy, Malaysia and South African Republic) using country-specific unit cost data. Additionally, cervical intraepithelial neoplasia grade 2 or 3 (CIN2/3)-related burden (cases and treatment costs) prevented by vaccination were estimated for two countries (Italy and Malaysia). RESULTS: HPV vaccination could prevent a substantial number of CC cases and deaths in countries worldwide, with associated cost-offsets due to reduced CC treatment. Cross-protection increased the estimated potential number of CC cases and deaths prevented by 34 and 18% in Africa and Oceania, respectively. Moreover, vaccination could result in a substantial reduction in the number of CIN2/3 lesions and associated costs. CONCLUSION: HPV vaccination could reduce the burden of CC and precancerous lesions in countries worldwide, part of disease burden reduction being related to protection against non HPV-16/18 related types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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 teacher head, 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".