Prevalence, Genotype Distribution, and Determinants of High-Risk Carcinogenic Human Papillomavirus Infection Among Female Sex Workers in Kilimanjaro Region: A Community-Based Cross-Sectional Study
Bibliographic record
Abstract
ABSTRACT Background High-risk carcinogenic human papillomavirus (hrHPV) is the leading cause of cervical cancer globally. Female sex workers (FSWs) are particularly vulnerable due to high-risk sexual behaviors and low cervical cancer screening uptake. Despite their elevated risk, the prevalence, genotype distribution, and determinants of hrHPV infection among this population in Tanzania remain unknown. Hence this study. Methods A community-based cross-sectional study was conducted in Kilimanjaro region between June and July 2024, among 309 FSWs aged 25–49 years with no history of precancerous lesions or total hysterectomy. The Respondent-Driven Sampling (RDS) technique was used to recruit this hard-to-reach population. Self-collected vaginal samples were tested using the ScreenFire HPV assay, detecting 13 genotypes grouped into four risk channels. Data were analysed using SPSS Version 27.0, and a modified Poisson regression with robust error variance was applied to estimate associations between predictor variables and hrHPV infection. Statistical significance was set at p < 0.05. Results Among the 309 FSWs, the mean age was 36.1 years (SD = 5.243), with nearly half (48.5%) aged 25–34 years. The overall hrHPV prevalence was 57.6%, with higher rates among FSWs aged 40–44 years (58.5%), HIV-positive individuals (90.0%), tobacco smokers (76.8%), and those with prior cervical cancer screening (76.5%). Channel-specific hrHPV prevalence was highest for HPV31/33/35/52/58 (23.3%), followed by HPV39/51/56/59/68 (13.6%), HPV16 (9.7%), and HPV18/45 (5.5%). Mixed infections accounted for 5.5%. Religion (APR: 1.45, 95% CI: 1.13–1.61), occupation (APR: 1.28, 95% CI: 1.15–1.45), HIV status (APR: 1.27, 95% CI: 1.12–1.43), tobacco smoking (APR: 1.15, 95% CI: 1.04–1.28), and cervical cancer screening history (APR: 0.83, 95% CI: 0.73–0.94) were significantly associated with hrHPV infection in this population. Conclusion This study reveals a high prevalence of hrHPV among FSWs in Kilimanjaro, Tanzania, highlighting the urgent need for targeted cervical cancer prevention interventions for this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".