Cervical human papillomavirus infection among young women engaged in sex work in Phnom Penh, Cambodia: prevalence, genotypes, risk factors and association with HIV infection
Bibliographic record
Abstract
BACKGROUND: Although cervical cancer is the leading cancer in Cambodia, most women receive no routine screening for cervical cancer and few treatment options exist. Moreover, nothing is known regarding the prevalence of cervical HPV or the genotypes present among women in the country. Young sexually active women, especially those with multiple sex partners are at highest risk of HPV infection. We examine the prevalence and genotypes of cervical HPV, as well as the associated risk factors among young women engaged in sex work in Phnom Penh, Cambodia. METHODS: We conducted a cross-sectional study among 220 young women (15-29 years) engaged in sex work in different venues including brothels or entertainment establishments, and on a freelance basis in streets, parks and private apartments. Cervical specimens were collected using standard cytobrush technique. HPV DNA was tested for by polymerase chain reaction (PCR) and genotyping using type-specific probes for 29 individual HPV types, as well as for a mixture of 10 less common HPV types. All participants were also screened for HIV status using blood samples. Multivariate logistic regression analyses were conducted to assess risk factors for any or multiple HPV infection. RESULTS: The prevalence of cervical HPV 41.1%. HPV 51 and 70 were the most common (5.0%), followed by 16 (4.6%), 71 (4.1%) and 81 (3.7%). Thirty-six women (16.4%) were infected with multiple genotypes and 23.3% were infected with at least one oncogenic HPV type. In multivariate analyses, having HIV infection and a higher number of sexual partners were associated with cervical HPV infection. Risk factors for infection with multiple genotypes included working as freelance female sex workers (FSW) or in brothels, recent binge use of drugs, high number of sexual partners, and HIV infection. CONCLUSIONS: This is the first Cambodian study on cervical HPV prevalence and genotypes. We found that HPV infection was common among young FSW, especially among women infected with HIV. These results underscore the urgent need for accessible cervical cancer screening and treatment, as well as for a prophylactic vaccine that covers the HPV subtypes present in Cambodia.
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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) | 0.002 | 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".