Potential impact of switching from a two- to one-dose gender-neutral routine HPV vaccination program in Canada: A mathematical modeling analysis
Bibliographic record
Abstract
ABSTRACT Background Worldwide, countries are examining whether to implement one-dose HPV vaccination. To inform policy recommendations in Canada, we used mathematical modeling to project the population-level impact and efficiency of switching from two-to one-dose gender-neutral routine HPV vaccination. Methods We used HPV-ADVISE, an individual-based transmission-dynamic model of HPV infections/diseases, to model 2 provinces (Quebec, Ontario), which represent higher (≈85%) and lower (≈65%) HPV vaccination coverage in Canada. We examined non-inferior and pessimistic scenarios of one-dose efficacy (VE=98%, 90%) and average duration (VD=lifelong, 30 years, 25 years) versus two doses (VE=98%, VD=lifelong). Our main outcomes were the relative reduction in HPV-16 (among females/males) and cervical cancers, and the number of doses needed to prevent one cervical cancer (NNV). Results Our model projects that one-dose HPV vaccination would avert a similar number of cervical cancers as two doses in Canada, under various non-inferior and pessimistic scenarios. Under the most pessimistic scenario (VD=25 years), one-dose vaccination would avert ∼3 percentage-points fewer cervical cancers than two doses over 100 years. All one-dose scenarios were projected to lead to cervical cancer elimination and were projected to be a substantially more efficient use of vaccine doses compared to two doses (NNVs one-dose vs no vaccination=800-1000; incremental NNVs two-dose vs one-dose vaccination >10,000). Interpretation If the average duration of one-dose protection is longer than 25 years, individuals would be protected during their peak ages of sexual activity and one-dose vaccination would prevent a similar number of HPV-related cancers, while being a more efficient use of vaccine doses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".