HIV Drug Resistance Among Key Populations in Nigeria: Insights From the 2020 Integrated Biological and Behavioral Surveillance Survey
Bibliographic record
Abstract
OBJECTIVES: HIV drug resistance mutations (DRMs) undermine the effectiveness of antiretroviral therapy (ART) and can lead to treatment failure. This study aimed to characterize HIV drug resistance among key populations in Nigeria. DESIGN: A cross-sectional integrated biological and behavioral surveillance survey was conducted across 6 Nigerian geopolitical zones among female sex workers, men who have sex with men, people who inject drugs, and transgender individuals (August-December 2020). METHODS: Dried blood spot specimens were collected from 2309 participants, of whom 719 (31.1%) were HIV viremic (>1000 copies/mL). Partial HIV pol genes were sequenced using an in-house genotyping assay. DRMs were identified from MiSeq reads using HyDRA Web and interpreted with Stanford HIVdb. Pearson χ 2 tests assessed associations between sociodemographic factors and DRMs. RESULTS: Among 414 HIV genotyped specimens, 16.7% contained at least 1 DRM. Common DRMs included K103N, M41L, and M184V, with 9.2% showing high-level resistance to efavirenz and nevirapine. DRM prevalence was highest among people who inject drugs (21.6%) and residents of the North Central zone (25.8%). Age was significantly associated with DRMs ( P < 0.001). Notably, 58.7% of participants were unaware of their HIV-positive status and had never received ART. CONCLUSIONS: The presence of DRMs among ART-naive participants suggests transmitted drug resistance. The association between older age and DRMs may reflect suboptimal adherence, prior regimen exposure, or longer treatment. High-level resistance to efavirenz supports transitioning to dolutegravir-based first-line regimens and highlights the importance of supporting key populations in accessing and adhering to ART to prevent the spread of drug-resistant HIV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".