Population-Level Impact of the Bivalent, Quadrivalent, and Nonavalent Human Papillomavirus Vaccines: A Model–Based Analysis
Bibliographic record
Abstract
BACKGROUND: Bivalent and quadrivalent human papillomavirus (HPV) vaccines are now licensed in several countries. Furthermore, clinical trials examining the efficacy of a nonavalent vaccine are underway. We aimed to compare the potential population-level effectiveness of the bivalent, quadrivalent, and candidate nonavalent HPV vaccines. METHODS: We developed an individual-based, transmission-dynamic model of HPV infection and disease in a population stratified by age, gender, sexual activity, and screening behavior. The model was calibrated to highly stratified sexual behavior, HPV epidemiology, and cervical screening data from Canada. RESULTS: Under base case assumptions, vaccinating 12-year-old girls (70% coverage) with the bivalent (quadrivalent) vaccine is predicted to reduce the cumulative incidence of anogenital warts (AGWs) by 0.0% (72.1%), diagnosed cervical intraepithelial neoplasia lesions 2 and 3 (CIN2 and -3) by 51.0% (46.1%), and cervical squamous cell carcinoma (SCC) by 31.9% (30.5%), over 70 years. Changing from a bivalent (quadrivalent) to a nonavalent vaccine is predicted to reduce the cumulative number of AGW episodes by an additional 66.7% (0.0%), CIN2 and -3 episodes by an additional 9.3% (12.5%), and SCC cases by an additional 4.8% (6.6%) over 70 years. Differences in predicted population-level effectiveness between the vaccines were most sensitive to duration of protection and the time horizon of analysis. The vaccines produced similar effectiveness at preventing noncervical HPV-related cancers. CONCLUSIONS: The bivalent vaccine is expected to be slightly more effective at preventing CIN2 and -3 and SCC in the longer term, whereas the quadrivalent vaccine is expected to substantially reduce AGW cases shortly after the start of vaccination programs. Switching to a nonavalent vaccine has the potential to further reduce precancerous lesions and cervical cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".