Temporal trends and transmission dynamics of pre-treatment HIV-1 drug resistance within and between risk groups in Kenya, 1986–2020
Bibliographic record
Abstract
BACKGROUND: Evidence on the distribution of pre-treatment HIV-1 drug resistance (HIVDR) among risk groups is limited in Africa. We assessed the prevalence, trends and transmission dynamics of pre-treatment HIVDR within and between MSM, people who inject drugs (PWID), female sex workers (FSWs), heterosexuals (HETs) and perinatally infected children in Kenya. METHODS: HIV-1 partial pol sequences from antiretroviral-naive individuals collected from multiple sources between 1986 and 2020 were used. Pre-treatment reverse transcriptase inhibitor (RTI), PI and integrase inhibitor (INSTI) mutations were assessed using the Stanford HIVDR database. Phylogenetic methods were used to determine and date transmission clusters. RESULTS: Of 3567 sequences analysed, 550 (15.4%, 95% CI: 14.2-16.6) had at least one pre-treatment HIVDR mutation, which was most prevalent amongst children (41.3%), followed by PWID (31.0%), MSM (19.9%), FSWs (15.1%) and HETs (13.9%). Overall, pre-treatment HIVDR increased consistently, from 6.9% (before 2005) to 24.2% (2016-20). Among HETs, pre-treatment HIVDR increased from 6.6% (before 2005) to 20.2% (2011-15), but dropped to 6.5% (2016-20). Additionally, 32 clusters with shared pre-treatment HIVDR mutations were identified. The majority of clusters had R0 ≥ 1.0, indicating ongoing transmissions. The largest was a K103N cluster involving 16 MSM sequences sampled between 2010 and 2017, with an estimated time to the most recent common ancestor (tMRCA) of 2005 [95% higher posterior density (HPD), 2000-08], indicating propagation over 12 years. CONCLUSIONS: Compared to HETs, children and key populations had higher levels of pre-treatment HIVDR. Introduction of INSTIs after 2017 may have abrogated the increase in pre-treatment RTI mutations, albeit in the HET population only. Taken together, our findings underscore the need for targeted efforts towards equitable access to ART for children and key populations 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".