Proportion of new genital human papillomavirus detections attributable to latent infections: Implications for cervical cancer screening
Bibliographic record
Abstract
Abstract Background Infections with human papillomaviruses (HPV) may enter into a latent state in epithelial basal cells, and eventually become reactivated following loss of immune control. It is unclear what proportion of incident detections of HPV are due to reactivation of previous latent infections versus new transmissions. Methods The HITCH cohort study prospectively followed young newly-formed heterosexual partners recruited between 2005-2011 in Montréal, Canada. We calculated the fraction of incident HPV detections non-attributable to sexual transmission risk factors with a Bayesian Markov state transition model. Results are the median (2·5-95·5 th percentiles) of the estimated posterior distribution. Findings 544 type-specific incident HPV detection events occurred in 849 participants; 32·5% of all incident HPV detections occurred in participants whose HITCH partners were negative for that HPV type and who did not report having sex with anyone else over follow-up. We estimate that 42·7% (38·4-47·2%) of all incident HPV detections in this population might be attributable to reactivation of latent infections, not transmission. Interpretation A positive HPV test result in many cases may be a reactivated past infection, rather than a new infection from recent sexual behaviors or partner infidelity. The potential for reactivation of latent infections in previously HPV-negative women should be considered in the context of cervical cancer screening. Funding Canadian Institutes of Health Research, National Institutes of Health, Merck-Frosst Canada Ltd, Merck & Co Ltd, Fonds de la Recherche en Santé du Québec. Research in Context Evidence before this study Previous studies assessing the proportion of HPV infections attributable to reactivation in women have been conducted in individual-based studies. Determining this estimate using a couple-based study design could account for the partner’s HPV status and rule out sexual transmission. Authors from this current study recently published a systematic review that aimed to identify all published couple-based studies measuring HPV transmission. They searched MEDLINE, EMBASE, Scopus, and Cochrane Library from database inception to December 1, 2019, with no language restrictions using the keywords and MeSH terms “HPV,” “papillomavirus infections,” “papillomaviridae,” “transmission,” “heterosexuality,” “couples,” and “sexual partners”. Studies were included if the study population was heterosexual couples, genital samples were collected from each partner, and HPV transmission rates were reported. The search yielded 834 records, of which seven couple-based studies were eligible to be included in the systematic review. None of the identified studies measured the proportion of HPV infections attributable to reactivation. Added value of this study This study presents the first analysis assessing reactivation of HPV infections using a couple-based study design. We recruited young heterosexual couples and collected genital HPV data from both partners, allowing us to control for the sexual partner’s HPV status. We estimate that 57% of the newly detected incident HPV infections in women could be attributed to sexual transmission while the remaining 43% is most likely due to reactivation of latent infections. Implications of all the available evidence In the context of cervical cancer screening, our findings suggest that women who have previously tested HPV negative may not remain HPV-negative, even with no new sexual partners, due to reactivation of a latent infection. This underscores the importance for HPV-negative women to undergo multiple screenings in their lifetime. In addition, the sizeable proportion of newly detected HPV infections attributable to reactivation suggests that a positive HPV test is not necessarily due to recent sex or partner infidelity, which may help de-stigmatize a positive HPV test result.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".