Substance use Patterns and HIV-1 RNA Viral Load Rebound among HIV-Positive Illicit Drug users in a Canadian Setting
Bibliographic record
Abstract
BACKGROUND: Active illicit drug use can present a barrier to the medical management of HIV infection by complicating adherence to antiretroviral therapy (ART). Plasma HIV-1 RNA viral load (VL) rebound, defined as a period of detectable HIV VL following ART and VL suppression, can lead to the generation of viral resistance and potential treatment failure. We sought to investigate the contribution of substance use patterns on rates of VL rebound. METHODS: We used data from the ACCESS study, a long-running community-recruited prospective cohort of HIV-positive people who use illicit drugs in Vancouver, Canada, a setting of universal no-cost HIV treatment. We analysed time to VL rebound (that is, two consecutive observations ≥1,000 copies/ml) after ART initiation and sustained viral suppression (that is, two consecutive observations <50 copies/ml) using extended Cox regression models with a recurrent events framework. RESULTS: Between May 1996 and November 2013, 564 ART-exposed participants achieved at least one instance of VL suppression and contributed 1,893.8 person-years of observation. Over follow-up, 198 (35.1%) participants experienced ≥ one instance of VL rebound. In adjusted analyses, VL rebound was associated with younger age (adjusted hazard ratio [AHR] =0.97, 95% CI: 0.95, 0.98), heroin injection (≥ daily versus < daily, AHR =1.52, 95% CI: 1.01, 2.30), crack use (≥ daily versus < daily, AHR = 1.73, 95% CI: 1.08, 1.92) and heavy alcohol use (≥ four versus < four drinks/day, AHR =1.97, 95% CI: 1.17, 3.31). CONCLUSIONS: The present study suggests that in addition to heavy alcohol use, high-intensity illicit drug use, particularly ≥ daily heroin injection and ≥ daily crack smoking are risk factors for VL rebound. In addition to the impact of high-intensity drug use on health-care engagement and ART adherence, some evidence exists on the direct impact of psychoactive substances on ART metabolism and the natural progression of HIV disease. At-risk individuals should be provided additional supports to preserve virological control and maintain the benefits of 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".