Vaccinating Girls and Boys with Different Human Papillomavirus Vaccines: Can It Optimise Population-Level Effectiveness?
Bibliographic record
Abstract
BACKGROUND: Decision-makers may consider vaccinating girls and boys with different HPV vaccines to benefit from their respective strengths; the quadrivalent (HPV4) prevents anogenital warts (AGW) whilst the bivalent (HPV2) may confer greater cross-protection. We compared, to a girls-only vaccination program with HPV4, the impact of vaccinating: 1) both genders with HPV4, and 2) boys with HPV4 and girls with HPV2. METHODS: We used an individual-based transmission-dynamic model of heterosexual HPV infection and diseases. Our base-case scenario assumed lifelong efficacy of 100% against vaccine types, and 46,29,8,18,6% and 77,43,79,8,0% efficacy against HPV-31,-33,-45,-52,-58 for HPV4 and HPV2, respectively. RESULTS: Assuming 70% vaccination coverage and lifelong cross-protection, vaccinating boys has little additional benefit on AGW prevention, irrespective of the vaccine used for girls. Furthermore, using HPV4 for boys and HPV2 for girls produces greater incremental reductions in SCC incidence than using HPV4 for both genders (12 vs 7 percentage points). At 50% vaccination coverage, vaccinating boys produces incremental reductions in AGW of 17 percentage points if both genders are vaccinated with HPV4, but increases female incidence by 16 percentage points if girls are switched to HPV2 (heterosexual male incidence is incrementally reduced by 24 percentage points in both scenarios). Higher incremental reductions in SCC incidence are predicted when vaccinating boys with HPV4 and girls with HPV2 versus vaccinating both genders with HPV4 (16 vs 12 percentage points). Results are sensitive to vaccination coverage and the relative duration of protection of the vaccines. CONCLUSION: Vaccinating girls with HPV2 and boys with HPV4 can optimize SCC prevention if HPV2 has higher/longer cross-protection, but can increase AGW incidence if vaccination coverage is low among boys.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".