Antenatal and postnatal cervical precancer screening to increase coverage: experience from Battor, Ghana
Bibliographic record
Abstract
Background: Cervical precancer screening in low-resource settings is largely opportunistic with low coverage.Many women in these settings, where the burden of cervical cancer is highest, only visit health institutions when pregnant or after delivery.We explored screening during antenatal and postnatal visits aimed at increasing coverage.Methods: Pregnant women (in any trimester) attending antenatal care (ANC) and women attending postnatal care (PNC; 6-10 weeks) clinics were screened at Catholic Hospital, Battor and at outreach clinics from February to August 2022 (08/02/2022 to 02/08/2022).At the same visit, cervical specimens were obtained for high-risk human papillomavirus (hr-HPV) DNA testing (with the Sansure MA-6000 PCR platform) followed by either visual inspection with acetic acid (VIA) or mobile colposcopy with the enhanced visual assessment system.Results: Two hundred and seventy and 107 women were screened in the antenatal and postnatal groups, respectively.The mean ages were 29.4 (SD, 5.4) in the ANC group and 28.6 (SD, 6.4) years in the PNC group.The overall hr-HPV prevalence rate was 25.5% (95% confidence interval (CI), 21.1-29.9)disaggregated as 26.7% (95% CI, 21.4-31.9) in the ANC group and 22.4% (95% CI, 14.5-30.3) in the PNC group (p = 0.3946).Overall, 58.9% of pregnant women (28.3% hr-HPV+) and 66.4% of postnatal women (22.5% hr-HPV+) only visited a health facility when pregnant or after delivery (at Child Welfare Clinics).The VIA 'positivity' rate for all screened women was 5.3% (95% CI, 3.1-7.6),disaggregated into 5.2% (95% CI, 2.5-7.8) in the ANC group and 5.7% (95% CI, 1.3-10.1) in the PNC group (p-value = 0.853).Conclusion: A significant number of women in Ghana only visit a health facility during pregnancy or after delivery.ANC and PNC clinics would offer opportunities to increase coverage in cervical precancer screening in low-resource settings.Relying on community nurses ensures that such programmes are readily integrated into routine care of women and no opportunity is missed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".