Drug use patterns associated with risk of non-adherence to antiretroviral therapy among HIV-positive illicit drug users in a Canadian setting: a longitudinal analysis
Bibliographic record
Abstract
BACKGROUND: Among people living with HIV/AIDS, illicit drug use is a risk for sub-optimal treatment outcomes. However, few studies have examined the relative contributions of different patterns of drug use on adherence to antiretroviral therapy (ART). We sought to estimate the effect of different types of illicit drug use on adherence in a setting of universal free HIV/AIDS treatment and care. METHODS: Using data from ongoing prospective cohorts of HIV-positive illicit drug users linked to comprehensive pharmacy dispensation records in Vancouver, Canada, we examined factors associated with ≥95% prescription refill adherence using generalized estimating equations (GEE) logistic regression. RESULTS: Between 1996 and 2013, 692 ART-exposed individuals were followed for a median of 42.7 months (Interquartile Range: 29.1-71.7). In multivariable GEE analyses, heroin injection (Adjusted Odds Ratio [AOR] = 0.75, 95% Confidence Interval [CI]: 0.66-0.85) as well as cocaine injection (AOR = 0.80, 95% CI: 0.72-0.90) were associated with lower likelihoods of optimal adherence. Methadone maintenance therapy (AOR = 1.88, 95% CI: 1.68-2.11) was associated with a greater likelihood of adherence. CONCLUSIONS: Periods of heroin and cocaine injection appeared to have the most deleterious impact upon antiretroviral adherence. The findings point to the need for improved access to treatment for heroin use disorder, particularly methadone, and highlight the need to identify strategies to support ART adherence among cocaine injectors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| 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".