Nationwide cervical precancer screening in Ghana: concurrent HPV DNA testing and visual inspection under an expanded hub-and-spoke model
Bibliographic record
Abstract
Cervical cancer continues to disproportionately burden women in sub-Saharan Africa, and is the commonest gynecological cancer in Ghana. The Cervical Cancer Prevention and Training Centre (CCPTC), Battor, Ghana spearheaded the Ghana arm of the mPharma 10,000 Women Initiative (mTTWI) between September 2021 and October 2022. The aim of this study was to examine the outcomes of nationwide concurrent screening using high-risk human papillomavirus (hr-HPV) DNA testing and visual inspection methods, as well as factors associated with the screening outcomes. We conducted a descriptive retrospective cross-sectional study to estimate the prevalence of hr-HPV infection (nationally and regionally) and cervical lesions among women screened by graduates of our training center in their own settings (spokes) with remote supervision and mentoring by CCPTC trainers (hub). We modeled factors associated with hr-HPV infection using nominal logistic regression. Among 5217 women screened (mean age, 40.1 years), the overall prevalence of hr-HPV infection and cervical lesions were 29.1% (95% confidence interval [CI] 27.9-30.3) and 3.7% (95% CI 3.2-4.2), respectively. The prevalence distribution varied widely among regions, with the highest recorded in the Oti Region (32.8%) and the lowest recorded in the Upper West (20.7%) and North-East (20.7%) regions. The most frequently detected HPV genotype was other hr-HPV type(s) only (non-HPV16, non-HPV18) (23.5%; 95% CI 22.3-24.7), followed by HPV16 only (1.5%; 95% CI 1.2-1.8), and mixed infection with HPV18 + other hr-HPV type(s) (1.2%; 95% CI 0.9-1.5). Factors found to be significantly associated with hr-HPV infection among women screened included age < 35 years, having a relationship status apart from married/cohabitation, nulliparity, and HIV positivity. Drawing from our implementation of this model within the mTTWI, we posit that while Ghana prepares for organized screening, coordinating opportunistic screening could enable a phased expansion of cervical precancer screening with the help of international and local partners. This approach, combined with concurrent testing (hr-HPV DNA testing and visual inspection), holds promise for mitigating loss to follow-up among women requiring additional evaluation and lesion management.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".