Applying the Effective Programme Coverage framework to assess gaps in HIV prevention programmes for female sex workers and men who have sex with men in Nairobi, Kenya: findings from an expanded Polling Booth Survey
Bibliographic record
Abstract
INTRODUCTION: Measuring the coverage of HIV prevention services for key populations (KPs) has consistently been a challenge for national HIV programmes. The current frameworks and measurement methods lack emphasis on effective coverage, occur infrequently, lack timeliness and limit the participation of KPs. The Effective Programme Coverage framework, which utilizes a programme science approach, provides an opportunity to assess gaps in various coverage domains and explore the underlying reasons for these gaps, in order to develop targeted solutions. We have demonstrated the application of this framework in partnership with the KP community in Nairobi, Kenya, using an expanded Polling Booth Survey (ePBS) method. METHODS: Data were collected between April and May 2023 among female sex workers (FSWs) and men who have sex with men (MSM) using (a) PBS, (b) bio-behavioural survey and (c) focus group discussions. Data collection and analysis involved both KP community and non-community researchers. Descriptive analysis was performed, and proportions were used to assess the programme coverage gaps. The data were weighted to account for the sampling design and unequal selection probabilities. Thematic analysis was conducted on the qualitative data. RESULTS: The condom programme for FSW and MSM had low availability (60.2% and 50.9%), contact (68.8% and 65.9%) and utilization (52.1% and 43.9%) coverages. The pre-exposure prophylaxis (PrEP) programme had very low utilization coverage for FSW and MSM (4.4% and 2.8%), while antiretroviral therapy utilization coverage was higher (86.6% and 87.7%). Reasons for coverage gaps included a low peer educator-to-peer ratio, longer distance to the clinics, shortage of free condoms supplied by the government, experienced and anticipated side effects related to PrEP, and stigma and discrimination experienced in the facilities. CONCLUSIONS: The Effective Programme Coverage framework allows programmes to assess coverage gaps and develop solutions and a research agenda targeted at specific domains of coverage with large gaps. The ePBS method works well in collecting data to understand coverage gaps rapidly and allows for the engagement of the KP community.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".