Substantial Decline in Vaccine-Type Human Papillomavirus (HPV) Among Vaccinated Young Women During the First 8 Years After HPV Vaccine Introduction in a Community
Bibliographic record
Abstract
Background. Human papillomavirus (HPV) vaccine effectiveness and herd protection are not well established in community settings. Our objective was to determine trends in vaccine-type HPV in young women during the 8 years after vaccine introduction, to assess changes in HPV prevalence and characterize herd protection in a community. Methods. We recruited 3 samples of sexually experienced, 13–26-year-old adolescent girls and young women (hereafter women; N = 1180) from 2006–2014: before widespread vaccine introduction (wave 1) and 3 (wave 2) and 7 (wave 3) years after vaccine introduction. We determined the prevalence of vaccine-type HPV (HPV-6, -11, -16, and -18) among all, vaccinated, and unvaccinated women at waves 1, 2, and 3, adjusted for differences in participant characteristics, then examined whether changes in HPV prevalence were significant using inverse propensity score–weighted logistic regression. Results. Vaccination rates increased from 0% to 71.3% across the 3 waves. Adjusted vaccine-type HPV prevalence changed from 34.8% to 8.7% (75.0% decline) in all women, from 34.9% to 3.2% (90.8% decline) in vaccinated women, and from 32.5% to 22.0% (32.3% decline) in unvaccinated women. Among vaccinated participants, vaccine-type HPV prevalence decreased significantly from wave 1 to wave 2 (adjusted odds ratio, 0.21; 95% confidence interval, .13–.34) and from wave 1 to wave 3 (0.06; .03–.13). The same decreases were also significant among unvaccinated participants (adjusted odds ratios, 0.44; [95% confidence interval, .27–.71] and 0.59; [.35–.98], respectively). Conclusions. The prevalence of vaccine-type HPV decreased >90% in vaccinated women, demonstrating high effectiveness in a community setting, and >30% in unvaccinated women, providing evidence of herd protection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".