Prevalence and Genotypic Diversity of High-Risk Human Papillomavirus Among Women of Reproductive Age in Kilifi County, Kenya
Bibliographic record
Abstract
Background Human papillomavirus (HPV) is the most common sexually transmitted infection and the primary cause of cervical cancer, a leading cause of cancer-related deaths among women in Kenya. Although many HPV infections resolve on their own, some high-risk types may persist and gradually develop into cervical cancer over several years, providing opportunities for early detection and intervention. However, in low-resource settings like Kilifi County, HPV testing is limited, and alternative screening methods like visual inspection with acetic acid (VIA) are commonly used despite their limitations. Objective This study aimed to assess the prevalence and genotype distribution of high-risk HPV (HR-HPV) among women of reproductive age in Kilifi County, Kenya, to inform targeted public health interventions. Methodology This study was nested within a more extensive cross-sectional study on female genital schistosomiasis and human immunodeficiency virus (HIV). We focus on a stratified sample of 320 women aged 15-50 from Rabai and Magarini sub-counties, Kilifi, Kenya, identified as Schistosoma haematobium hotspots. Participants provided informed consent, and pregnant women were excluded. Clinical data was collected and sociodemographic data collected via questionnaires, while high vaginal and cervical swabs were self-collected for HPV testing, screening for 24 HR-HPV genotypes. Results Data from 261 women were analyzed. The overall HR-HPV prevalence was 48.7%, with the Magarini sub-county showing a higher prevalence (31.4%) compared to Rabai (17.2%). The most prevalent HPV genotypes were HPV 18 (25.3%), HPV 45 (22.6%), and HPV 16 (12.6%). Co-infections were common, particularly with HPV 18 and 45. HPV 16 was more prevalent in the Rabai subcounty, while HPV 18 and 45 were more common in the Magarini subcounty. Significant associations were found between sexual partnership type, leukocyte levels, and HPV positivity. Conclusion Kilifi County exhibits a high prevalence of HR-HPV, with genotype variations across sub-counties, suggesting differences in risk factors and access to preventive measures. Self-sampling and community-based screening effectively increased participation and diversity in the study population, highlighting the need for targeted, age-specific screening programs and comprehensive HPV genotyping to enhance cervical cancer prevention strategies in the region.
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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 | 0.000 | 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".