A community-focused cervical and breast cancer screening program using a sustainable funding model in a training center in Ghana
Bibliographic record
Abstract
BACKGROUND: While Ghana prepares to roll out a nationwide breast and cervical (pre)cancer screening policy, it is necessary to continuously document high-impact and scalable models. Over the years, the Cervical Cancer Prevention and Training Centre (CCPTC), Battor, has utilized a sustainable funding model in which each trainee pays for 15 women to be screened with visual inspection with acetic acid. This paper details the framework of community-focused trainer-led coordinated cervical and breast screening outreaches carried out under this model. The paper further reports the outcomes of screening over a 5-year period and discusses the advantages and shortcomings of the model in an effort to make recommendations for the development and scale-up of combined cervical and breast screening in a largely opportunistic setting. METHODS: This descriptive retrospective cross-sectional study investigated women who underwent cervical precancer screening using visual inspection with acetic acid or mobile colposcopy and/or high-risk human papillomavirus (hr-HPV) DNA testing between September 2017 and July 2022 (n = 2,273) and clinical breast examination between June 2021 and March 2023 (n = 622) by trainees of the CCPTC on outreaches conducted primarily to solidify their practical skills. For women screened using HPV DNA testing and visual inspection, respectively, the study explored factors associated with HPV infection or visual inspection 'positivity' using nominal logistic regression. RESULTS: The overall prevalence of hr-HPV infection was 14.3% (95% CI, 10.0-19.6) among women with valid results for hr-HPV DNA testing, while the overall visual inspection 'positivity' rate was 2.8% (95% CI, 2.2-3.6). After controlling for age, earning an income was the only factor associated with hr-HPV infection (aOR = 3.00; 95% CI, 1.35 - 6.64; p-value = 0.007). Factors associated with visual inspection 'positivity' after adjusting for age were: number of births (aOR = 0.71; 95% CI, 0.52 - 0.97; p-value = 0.029), number of lifetime pregnancies (aOR = 0.79; 95% CI, 0.67 - 0.93; p-value = 0.004), being single (aOR = 2.42; 95% CI, 1.19 - 4.90; p-value = 0.014), and earning an income (aOR = 0.44; 95% CI, 0.26 - 0.74; p-value = 0.002). Breast examination showed clinically significant masses in 20 women (3.2%), lymphadenopathy in 13 (2.1%), and nipple discharge in 37 women (6.0%) and only n = 3/67 women (4.5%) requiring referral followed up for further management. CONCLUSION: While the outreach approach adopted by the CCPTC has myriad benefits, further evidence-based studies and structured program evaluations are needed to assess if this approach can be adopted on a large scale, especially without the backing of a training institution with the needed resources and capacity to investigate and manage screen positives.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".