Social and Environmental Predictors of Plasma HIV RNA Rebound Among Injection Drug Users Treated With Antiretroviral Therapy
Bibliographic record
Abstract
INTRODUCTION: Evidence is needed to improve HIV treatment outcomes for individuals who use injection drugs (IDU). Although studies have suggested higher rates of plasma viral load (PVL) rebound among IDU on antiretroviral therapy (ART), risk factors for rebound have not been thoroughly investigated. METHODS: We used data from a long-running community-recruited prospective cohort of IDU in Vancouver, Canada, linked to comprehensive ART and clinical monitoring records. Using proportional hazards methods, we modeled the time to confirmed PVL rebound above 1000 copies per milliliter among IDU on ART with sustained viral suppression, defined as 2 consecutive undetectable PVL measures. RESULTS: Between 1996 and 2009, 277 individuals had sustained viral suppression. Over a median follow-up of 32 months, 125 participants (45.1%) experienced at least 1 episode of virologic failure for an incidence of 12.6 [95% confidence interval (CI): 10.5 to 15.0] per 100 person-years. In a multivariate model, PVL rebound was independently associated with sex-trade involvement [adjusted hazard ratio (AHR) = 1.40, 95% CI: 1.08 to 1.82) and recent incarceration (AHR = 1.83, 95% CI: 1.33 to 2.52). Methadone maintenance therapy (AHR = 0.79, 95% CI: 0.66 to 0.94) was protective. No measure of illicit drug use was predictive. CONCLUSIONS: In this setting of free ART, several social and environmental factors predicted higher risks of viral rebound among IDU, including sex-trade involvement and incarceration. These findings should help inform efforts to identify individuals at risk of viral rebound and targeted interventions to treat and retain individuals in effective ART.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".