Ongoing drug use and outcomes from highly active antiretroviral therapy among injection drug users in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: The effect of ongoing illicit drug use on HIV treatment remains controversial, especially in countries where access to HIV treatment for active injection drug users (IDUs) is limited because of presumed non-adherence. We sought to investigate the influence of drug use patterns on adherence to antiretroviral therapy and virological suppression among IDUs. METHODS: Using generalized estimating equation logistic regression, we explored the effect of abstinence versus ongoing drug use on adherence and virological suppression using data from a community-recruited cohort of IDUs in Vancouver (BC, Canada). RESULTS: A total of 381 HIV-positive IDUs were included in this analysis, among whom the median follow-up time was 30 months. In a multivariate model, no relationship was found between abstinence (reference) and active injection (adjusted odds ratio [AOR] 0.88, 95% confidence interval [CI] 0.65-1.17) and non-injection (AOR 0.97, 95% CI 0.67-1.41) drug use with adherence. In subanalyses, ongoing injection drug use was associated with a lower odds of virological suppression in comparison to abstinence (AOR 0.74, 95% CI 0.57-0.97; P=0.026) and both active IDUs and active non-IDUs had lower odds of virological suppression compared with abstinent participants when longer periods of virological suppression were considered. CONCLUSIONS: Given the absence of a strong relationship between abstinence and ongoing drug use and adherence among HIV-positive IDUs, programmes that restrict antiretrovirals to abstinent individuals should be re-examined. The lower rates of virological suppression associated with ongoing drug use nevertheless highlight the importance of comprehensive systems of care and addiction treatment for active drug users.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".