Evaluation of community-based HIV self-testing delivery strategies on reducing undiagnosed HIV infection, and improving linkage to prevention and treatment services, among men who have sex with men in Kenya: a programme science study protocol
Bibliographic record
Abstract
BACKGROUND: HIV prevalence among men having sex with men (MSM) in Kenya is 18.2%. Despite scale-up of HIV testing services, many MSM remain unaware of their HIV status and thus do not benefit from accessing HIV treatment or prevention services. HIV self-testing (HIVST) may help address this gap. However, evidence is limited on how, when, and in what contexts the delivery of HIVST to MSM could increase awareness of HIV status and lead to early linkage to HIV treatment and prevention. METHODS: The study will be embedded within existing MSM-focused community-based HIV prevention and treatment programmes in 3 counties in Kenya (Kisumu, Mombasa, Kiambu). The study is designed to assess three HIV testing outcomes among MSM, namely a) coverage b) frequency of testing and c) early uptake of testing. The study will adopt a mixed methods programme science approach to the implementation and evaluation of HIVST strategies via: (i) a baseline and endline bio-behavioural survey with 1400 MSM; (ii) a socio-sexual network study with 351 MSM; (iii) a longitudinal qualitative cohort study with 72 MSM; (iv) routine programme monitoring in three sites; (v) a programme-specific costing exercise; and (vi) mathematical modelling. This protocol evaluates the impact of community-based implementation of HIV self-testing delivery strategies among MSM in Kenya on reducing the undiagnosed MSM population, and time for linkage to prevention, treatment and care following HIV self-testing. Baseline data collection started in April 2019 and the endline data collection will start in July 2020. DISCUSSION: This study is one of the first programme science studies in Sub-Saharan Africa exploring the effectiveness of integrating HIVST interventions within already existing HIV prevention and treatment programmes for MSM in Kenya at scale. Findings from this study will inform national best approaches to scale up HIVST among MSM in Kenya.
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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 | 0.036 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".