Non–Vaccine-Type Human Papillomavirus Prevalence After Vaccine Introduction: No Evidence for Type Replacement but Evidence for Cross-Protection
Bibliographic record
Abstract
BACKGROUND: We examined non-vaccine-type human papillomavirus (HPV) prevalence in a community before and during the first 8 years after vaccine introduction, to assess for (1) type replacement with any non-vaccine-type HPV and (2) cross-protection with non-vaccine types genetically related to vaccine-type HPV. METHODS: Sexually experienced 13- to- 26-year-old women were recruited for 3 cross-sectional studies from 2006 to 2014 (N = 1180). Outcome variables were as follows: (1) prevalence of at least 1 of 32 anogenital non-vaccine-type HPVs and (2) prevalence of at least 1 HPV type genetically related to HPV-16 and HPV-18. We determined changes in proportions of non-vaccine-type HPV prevalence across the study waves using logistic regression with propensity score inverse probability weighting. RESULTS: Vaccine initiation rates increased from 0% to 71.3%. Logistic regression demonstrated that from 2006 to 2014, there was no increase in non-vaccine-type HPV among vaccinated women (adjusted odds ratio [AOR], 1.02; 95% confidence interval [CI], 0.73-1.42), but an increase among unvaccinated women (AOR, 1.88; 95% CI, 1.16-3.04). Conversely, there was a decrease in types genetically related to HPV-16 among vaccinated (AOR, 0.57; 95% CI, 0.38-0.88) but not unvaccinated women (AOR, 1.33; 95% CI, 0.81-2.17). CONCLUSIONS: We did not find evidence of type replacement, but did find evidence of cross-protection against types genetically related to HPV-16. These findings have implications for cost-effectiveness analyses, which may impact vaccine-related policies, and provide information to assess the differential risk for cervical cancer in unvaccinated and vaccinated women, which may influence clinical screening recommendations. The findings also have implications for public health programs, such as health messaging for adolescents, parents, and clinicians about HPV vaccination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".