Vaccination of Young Women Decreases Human Papillomavirus Transmission in Heterosexual Couples: Findings from the HITCH Cohort Study
Bibliographic record
Abstract
Abstract Background: Vaccination against human papillomaviruses (HPV) prevents HPV infections and, consequently, cervical lesions. However, the effect of vaccination on HPV transmission within couples is unknown. Methods: We used data from HITCH, a prospective cohort study of heterosexual couples (women ages 18–24 years) in Montreal, 2005 to 2013. Vaccination history was self-reported. Genital samples were tested for HPV DNA by PCR (linear array). Type-specific viral loads were quantified using real-time PCR. OR and HR were estimated using multilevel mixed-effects logistic regression and a parametric model for interval- censored survival-time data, respectively. Differences in viral loads were evaluated using the Friedman ANOVA test. Results: Among 497 couples, 12, 16, and 35 women received 1, 2, or 3 vaccination doses at baseline, respectively. Median age at vaccination was 18 years. Most women (92.1%) had their first coitus before vaccination. At baseline, partner concordance of persistent HPV6/11/16/18 infections was lower in vaccinated than unvaccinated women [adjusted OR = 0.10; 95% confidence interval (CI), 0.01–0.65] but not for non α7/α9/α10-HPV types (adjusted OR = 1.00; 95% CI, 0.44–2.29). Incidence of persistent α7/α9/α10 HPV types in women was inversely associated with vaccination status at baseline (adjusted HR = 0.12; 95% CI, 0.03–0.47). Likewise, male partners of vaccinated women had a lower incidence of α7/α9/α10 HPV infections (adjusted OR = 0.22; 95% CI, 0.05–0.95). Vaccinated women with HPV 6/11/16/18 infections had lower viral loads (P = 0.001) relative to unvaccinated women. Conclusions: Vaccination of sexually active women significantly reduced transmission of α7/α9/α10 HPV types in heterosexual couples. Impact: These results underscore and quantify the positive effect of HPV vaccination on HPV transmission within heterosexual couples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".