Age-Specific Prevalence of Anal and Cervical Human Papillomavirus Infection and High-Grade Lesions in 11 177 Women by Human Immunodeficiency Virus Status: A Collaborative Pooled Analysis of 26 Studies
Bibliographic record
Abstract
BACKGROUND: Age-specific data on anal, and corresponding cervical, human papillomavirus (HPV) infection are needed to inform female anal cancer prevention. METHODS: We centrally reanalyzed individual-level data from 26 studies reporting HPV prevalence in paired anal and cervical samples by human immunodeficiency virus (HIV) status and age. For women with HIV (WWH) with anal high-grade squamous intraepithelial lesions or worse (HSIL+), we also investigated concurrent cervical cytopathology. RESULTS: In HIV-negative women, HPV16 prevalence decreased significantly with age, both at anus (4.3% at 15-24 years to 1.0% at ≥55 years; ptrend = 0.0026) and cervix (7.4% to 1.7%; ptrend < 0.0001). In WWH, HPV16 prevalence decreased with age at cervix (18.3% to 7.2%; ptrend = 0.0035) but not anus (11.5% to 13.9%; ptrend = 0.5412). Given anal HPV16 positivity, concurrent cervical HPV16 positivity also decreased with age, both in HIV-negative women (ptrend = 0.0005) and WWH (ptrend = 0.0166). Among 48 WWH with HPV16-positive anal HSIL+, 27 (56%) were cervical high-risk HPV-positive, including 8 with cervical HPV16, and 5 were cervical HSIL+. CONCLUSIONS: Age-specific shifts in HPV16 prevalence from cervix to anus suggest that HPV infections in the anus persist longer, or occur later in life, than in the cervix, particularly in WWH. This is an important consideration when assessing the utility of cervical screening results to stratify anal cancer risk.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".