Increasing Prevalence of HIV Pretreatment Drug Resistance in Women But Not Men in Rural Uganda During 2005–2013
Bibliographic record
Abstract
The prevalence of HIV pretreatment drug resistance (PDR) is increasing in sub-Saharan Africa. We sought to describe correlates of PDR and evaluate effects of PDR on clinical outcomes in rural Uganda. We analyzed data from the Uganda AIDS Rural Treatment Outcomes study, a cohort of antiretroviral therapy (ART)-naive adults with HIV (2005–2015). We performed resistance testing on pre-ART specimens. We defined PDR as any World Health Organization (WHO) 2009 surveillance drug resistance mutation and classified PDR level using the Stanford algorithm. We fit unadjusted and sex-stratified log binomial regression and Cox proportional hazard models to identify correlates of PDR and the impact of PDR on viral suppression, loss to follow-up (LTFU), and death. We analyzed data from 738 participants (median age 33 years, 69% female). Overall, prevalence of PDR was 3.5% (n = 26), owing mostly to resistance to non-nucleoside reverse transcriptase inhibitors. PDR increased over time in women (1.8% in those enrolling in clinic in 2001–2006, vs. 7.0% in 2007–2013; p = 0.006), but not in men (1.15% vs. 0.72%, p = 0.737). Lower pre-ART log10 HIV RNA was also associated with higher prevalence of PDR. We identified longer time to viral suppression among those with PDR compared with without PDR (0.5 and 0.3 years, respectively, p = 0.023), but there was no significant relationship with mortality or LTFU (p = 0.139). We observed increasing rates of PDR in women in southwestern Uganda. Implications of this trend, particularly to prevention of mother-to-child transmission programs in the region, require attention due to delayed viral suppression among those with PDR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".