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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".