HIV phylogenetic clusters point to unmet hiv prevention, testing and treatment needs among men who have sex with men in kenya
Bibliographic record
Abstract
BACKGROUND: The HIV epidemic in Kenya remains a significant public health concern, particularly among gay, bisexual, and other men who have sex with men (GBMSM), who continue to bear a disproportionate burden of the epidemic. This study's objective is to describe HIV phylogenetic clusters among different subgroups of Kenyan GBMSM, including those who use physical hotspots, virtual spaces, or a combination of both to find male sexual partners. METHODS: Dried blood spots (DBS) were collected from GBMSM in Kisumu, Mombasa, and Kiambu counties, Kenya, in 2019 (baseline) and 2020 (endline). HIV pol sequencing was attempted on all seropositive DBS. HIV phylogenetic clusters were inferred using a patristic distance cutoff of ≤ 0.02 nucleotide substitutions per site. We used descriptive statistics to analyze sociodemographic characteristics and risk behaviors stratified by clustering status. RESULTS: Of the 2,450 participants (baseline and endline), 453 (18.5%) were living with HIV. Only a small proportion of seropositive DBS specimens were successfully sequenced (n = 36/453; 7.9%), likely due to most study participants being virally suppressed (87.4%). Among these sequences, 13 (36.1%) formed eight distinct clusters comprised of seven dyads and one triad. The clusters mainly consisted of GBMSM seeking partners online (n = 10/13; 76.9%) and who tested less frequently than recommended by Kenyan guidelines (n = 11/13; 84.6%). CONCLUSIONS: Our study identified HIV phylogenetic clusters among Kenyan GBMSM who predominantly seek sexual partners online and test infrequently. These findings highlight potential unmet HIV prevention, testing, and treatment needs within this population. Furthermore, these results underscore the importance of tailoring HIV programs to address the diverse needs of GBMSM in Kenya across different venues, including both physical hotspots and online platforms, to ensure comprehensive prevention and care strategies.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".