Prevalence of High-risk Human Papillomavirus Infection Among Prisoners in the Nsawam Medium Security Prison, Ghana
Bibliographic record
Abstract
Abstract Background: The Ghana Prisons Service has a Health Directorate that ensures delivery of healthcare to people in their custody under the Ghana Association of Quasi Health Institution (GAQHI) which is one of the agencies under the Ministry of Health. Across Ghana, females comprise 1.2% of the entire prison population (n=15,463). Cervical cancer screening services are however nonexistent and the prevalence of cervical precancer is undocumented. Our aim was to determine the prevalence of high-risk HPV infection and associated cervical abnormalities during a cervical cancer screening outreach. Methods: After informed consent and counselling, women underwent a structured questionnaire-based interview. Responses were recorded directly into a Microsoft Excel spreadsheet. Inmates were co-tested for cervical pre-cancer by two trained nurses using dry brush cervical samples for 15 high risk HPV types using the AmpFire HPV test after which mobile colposcopy with the EVA system was performed. EVA images were reviewed by a gynecologist. Frequencies and percentages were used to describe categorical data while means and standard deviations or medians and interquartile ranges were used to describe continuous data. Results: The majority (75%) of the inmates were convicts with a median sentence of 5 years. Their mean age was 41.1 years (range, 19–97). Out of the 14% that had ever been screened for cervical cancer, 75% had only been screened once. The self-reported prevalence rate of HIV among the inmates was 13.1%, all of who were receiving treatment. The high-risk HPV prevalence rate was 47.6% in the general population of inmates and 63.6% among HIV positive inmates. Using the EVA system, 5(6%) had lesions on the cervix of which 3(3.6%) were treated with thermal coagulation and 2(2.4%) were treated with LEEP. The average age of high-risk HPV positive inmates was 37.8 years. These inmates were also more likely to have been in prison for a shorter duration. Conclusion: There is a high prevalence of high-risk HPV infection among women in custody. These women will benefit from structured cervical cancer prevention services, including treatment for abnormalities that are picked up during such screening.
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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".