Prevalence of high-risk human papillomavirus infection and cervical lesions among female migrant head porters (kayayei) in Accra, Ghana: a pilot cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Little attention has been given to the risk of high-risk human papillomavirus (hr-HPV) infection and cervical precancerous lesions among female migrant head porters (kayayei) in Ghana, as a vulnerable group, and to promote cervical screening in these women. This pilot study aimed to determine the prevalence of hr-HPV infection and cervical lesions among kayayei in Accra, the capital of the Greater Accra Region of Ghana and to describe our approach to triaging and treating these women. METHODS: This descriptive cross-sectional cohort study involved the screening of 63 kayayei aged ≥ 18 years at the Tema Station and Agbogbloshie markets in March 2022 and May 2022. Concurrent hr-HPV DNA testing (with the MA-6000 platform) and visual inspection with acetic acid (VIA) was performed. We present prevalence estimates for hr-HPV DNA positivity and VIA 'positivity' as rates, together with their 95% confidence intervals (CIs). We performed univariate and multivariable nominal logistic regression to explore factors associated with hr-HPV infection. RESULTS: Gross vulvovaginal inspection revealed vulval warts in 3 (5.0%) and vaginal warts in 2 (3.3%) women. Overall, the rate of hr-HPV positivity was 33.3% (95% CI, 21.7-46.7), whereas the VIA 'positivity' rate was 8.3% (95% CI, 2.8-18.4). In the univariate logistic regression analysis, none of the sociodemographic and clinical variables assessed, including age, number of prior pregnancies, parity, past contraceptive use, or the presence of abnormal vaginal discharge showed statistically significant association with hr-HPV positivity. After controlling for age and past contraceptive use, only having fewer than two prior pregnancies (compared to having ≥ 2) was independently associated with reduced odds of hr-HPV infection (adjusted odds ratio, 0.11; 95% CI, 0.02-0.69). CONCLUSION: In this relatively young cohort with a high hr-HPV positivity rate of 33.3% and 8.3% of women showing cervical lesions on visual inspection, we posit that kayayei may have an increased risk of developing cervical cancer if their accessibility to cervical precancer screening services is not increased.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".