Human papillomavirus genotype distribution and factors associated among female sex workers in West Africa
Bibliographic record
Abstract
OBJECTIVES: This study aimed to: (1) Estimate HPV prevalence and genotype distribution among female sex workers (FSWs) in Mali and Benin as well as the prevalence of multiple HPV type infections in this group, and (2) Identify potential risk factors associated with high-risk (HR) HPV infections. METHODS: We analyzed baseline data of 665 FSWs aged ≥ 18 years recruited during a prospective cohort of cervical cancer screening in Cotonou (Benin) and Bamako (Mali) from 2017 to 2018. The Linear Array HPV genotyping test was used to identify HPV genotypes. Descriptive statistics and multivariate log-binomial regression were used. Adjusted prevalence ratios (APR) with 95% confidence intervals (95%CI) were estimated to identify risk factors associated with HR-HPV infections. RESULTS: HPV data were available for 659 FSWs (Benin: 309; Mali: 350). The mean age was 35.0 years (± 10.7) in Benin and 26.8 years (± 7.6) in Mali. The overall HPV prevalence rates were 95.5% in Benin and 81.4% in Mali. About 87.7% and 63.4% of FSWs harbored ≥ 2 HPV types in Benin and Mali, respectively. The top three prevalent HR-HPV among FSWs in Benin were: HPV58 (37.5%), HPV16 (36.6%) and HPV52 (28.8%). Corresponding patterns in Mali were HPV16 (15.7%), HPV51 (14.3%) and HPV52 (12.9%). In Benin, the main factors associated with HR-HPV were vaginal douching (APR = 1.17; 95%CI:1.02-1.34) and gonococcal infection (APR = 1.16; 95%CI:1.04-1.28), while in Mali they were sex work duration ≤ 1 year (APR = 1.35; 95%CI:1.10-1.65) and HIV infection (APR = 1.26; 95%CI: 1.06-1.51). CONCLUSION: Our study found a very high prevalence of HPV infection as well as high frequency of multiple HPV type infections in FSWs in two countries in West Africa. These findings suggest the necessity to emphasize cervical cancer prevention in this high-risk group.
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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 | 0.000 | 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) | 0.001 | 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".