Cervical precancer screening using self-sampling, HPV DNA testing, and mobile colposcopy in a hard-to-reach community in Ghana: a pilot study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization has set ambitious goals to eliminate cervical cancer, necessitating evidence on increasing coverage and access to screening and treatment in high-burden areas. We implemented a pilot program to assess the feasibility of obtaining self-collected specimens for high-risk human papillomavirus (hr-HPV) testing in Nzulezo stilt village, a hard-to-reach community in Ghana, and inviting only hr-HPV-positive women to a central location for colposcopy and possible treatment. Subsequently, this study aimed to investigate the prevalence of hr-HPV infection and cervical lesions among the women and to explore factors potentially associated with hr-HPV infection among them. METHODS: This pilot community-based cross-sectional study utilized data from screening sessions held from 2 to 20 November 2021 with specimens collected by participants using Evalyn brushes. HPV DNA testing was performed using the Sansure MA-6000 platform, while visual inspection utilized the Enhanced Visual Assessment (EVA) mobile colposcope. Univariate and multivariable nominal logistic regression was employed to explore factors associated with hr-HPV positivity. RESULTS: Among 100 women screened (mean age, 43.6 ± 14.5 years), the overall hr-HPV prevalence rate was 39.0% (95% CI, 29.4-49.3). The prevalence rates of hr-HPV genotypes were stratified as follows: HPV16-8.0% (95% CI, 3.5-15.2), HPV18-5.0% (95% CI, 1.6-11.2), and other genotype(s) - 31.0% (95% CI, 22.1-41.0). Single-genotype infections with HPV16 and HPV18 were found in 4.0% (95% CI, 1.1-9.9) and 3.0% (95% CI, 0.6-8.5) of women, respectively. Mixed infections were observed in 1.0% (95% CI, 0.0-5.4) for HPV16 + 18, 3.0% (95% CI, 0.6-8.5) for HPV16 + other type(s), and 1.0% (95% CI, 0.0-5.4) for HPV18 + other type(s). The prevalence of cervical lesions among hr-HPV-positive women screened via colposcopy was 11.4% (95% CI, 3.2-26.7). In the multivariable model, reliance on other sources for medical bill payment was associated with hr-HPV infection (aOR, 0.20; 95% CI, 0.04-0.93), whereas age was not (aOR, 1.02; 95% CI, 0.99-1.05). CONCLUSIONS: A high hr-HPV infection prevalence was recorded among the women. Utilizing technologies such as self-sampling, HPV DNA testing, and mobile colposcopy enables screening and treatment in remote and hard-to-reach communities where access to cervical cancer screening and treatment would otherwise be limited. Further research is warranted to assess the value and scalability of this approach in similar remote areas and its potential implementation in future programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".