Factors linked to transitions in adherence to antiretroviral therapy among HIV-infected illicit drug users in a Canadian setting
Bibliographic record
Abstract
HIV-positive people who use illicit drugs typically achieve lower levels of adherence to antiretroviral therapy and experience higher rates of sub-optimal HIV/AIDS treatment outcomes. Given the dearth of longitudinal research into ART adherence dynamics, we sought to identify factors associated with transitioning into and out of optimal adherence to ART in a longitudinal study of HIV-infected people who use illicit drugs (PWUD) in a setting of universal no-cost HIV/AIDS treatment. Using data from a prospective cohort of community-recruited HIV-positive illicit drug users confidentially linked to comprehensive HIV/AIDS treatment records, we estimated longitudinal factors associated with losing or gaining ≥95% adherence in the previous six months using two generalized linear mixed-effects models. Among 703 HIV-infected ART-exposed PWUD, becoming non-adherent was associated with periods of homelessness (adjusted odds ratio [AOR] = 2.52, 95% confidence interval [95% CI]: 1.56-4.07), active injection drug use (AOR = 1.25, 95% CI: 1.01-1.56) and incarceration (AOR = 1.54, 95% CI: 1.10-2.17). Periods of sex work (AOR = 0.51, 95% CI: 0.34-0.75) and injection drug use (AOR = 0.62, 95% CI: 0.50-0.77) were barriers to becoming optimally adherent. Methadone maintenance therapy was associated with becoming optimally adherent (AOR = 1.87, 95% CI: 1.50-2.33) and was protective against becoming non-adherent (AOR = 0.52, 95% CI: 0.41-0.65). In conclusion, we identified several behavioural, social and structural factors that shape adherence patterns among PWUD. Our findings highlight the need to consider these contextual factors in interventions that support the effective delivery of ART to this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".