Sex- and Type-specific Genital Human Papillomavirus Transmission Rates Between Heterosexual Partners: A Bayesian Reanalysis of the HITCH Cohort
Bibliographic record
Abstract
BACKGROUND: It is unclear whether sexual transmission rates of human papillomaviruses (HPV) differ between sexes and HPV types. We estimate updated transmission rates from the final HITCH cohort study and propose an estimation method that accounts for interval-censored data and infection clearance. METHODS: We enrolled young women 18-24 years old and their male sex partners ≥18 years old in Montréal, Canada, between 2005 and 2011. We followed women over 24 months and men over 4 months. We tested genital samples with Linear Array for HPV DNA detection and genotyping. We calculated infection transmission rates between partners using a multistate Markov model via a Bayesian approach. We report the posterior median and 2.5%-97.5% percentile intervals (95% PI). RESULTS: We observed 166 type-specific incident HPV transmission events in 447 women and 402 men. The estimated median transmission rate from an HPV-positive to a negative partner was 4.2 (95% PI = 3.1 to 5.3) per 100 person-months. The transmission rate from men-to-women was 3.5 (95% PI = 2.5 to 4.7) and from women-to-men was 5.6 (95% PI = 3.8 to 7.0) per 100 person-months, corresponding to a rate ratio of 1.6 (95% PI = 1.0 to 2.5). Partners reporting always using condoms had a 0.22 (95% PI = 0.05 to 0.61) times lower HPV transmission rate than those reporting never using condoms. HPV16/18 did not have particularly high transmission rates relative to other HPV types. CONCLUSION: Our updated analysis supports previous research suggesting higher women-to-men than men-to-women HPV transmission rates and a protective effect of condoms in heterosexual partnerships. Our results also suggest that crude incidence rates underestimate HPV transmission rates due to interval-censoring. See video abstract at http://links.lww.com/EDE/B794.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".