A Longitudinal Analysis of Daily Pill Burden and Likelihood of Optimal Adherence to Antiretroviral Therapy Among People Living With HIV Who Use Drugs
Bibliographic record
Abstract
OBJECTIVES: Among people living with HIV (PLWH), high levels of adherence to prescribed antiretroviral therapy (ART) is required to achieve optimal treatment outcomes. However, little is known about the effects of daily pill burden on adherence amongst PLWH who use drugs. We sought to investigate the association between daily pill burden and adherence to ART among members of this key population in Vancouver, Canada. METHODS: We used data from the AIDS Care Cohort to Evaluate Exposure to Survival Services study, a long-running community-recruited cohort of PLWH who use illicit drugs linked to comprehensive HIV clinical records. The longitudinal relationship between daily pill burden and the odds of ≥95% adherence to ART among ART-exposed individuals was analyzed using multivariable generalized linear mixed-effects modeling, adjusting for sociodemographic, behavioural, and structural factors linked to adherence. RESULTS: Between December 2005 and May 2014, the study enrolled 770 ART-exposed participants, including 257 (34%) women, with a median age of 43 years. At baseline, 437 (56.7%) participants achieved ≥95% adherence in the previous 180 days. Among all interview periods, the median adherence was 100% (interquartile range 71%-100%). In a multivariable model, a greater number of pills per day was negatively associated with ≥95% adherence (adjusted odds ratio [AOR] 0.87 per pill, 95% confidence interval [CI] 0.84-0.91). Further analysis showed that once-a-day ART regimens were positively associated with optimal adherence (AOR 1.39, 95% CI 1.07-1.80). CONCLUSIONS: In conclusion, simpler dosing demands (ie, fewer pills and once-a-day single tablet regimens) promoted optimal adherence among PLWH who use drugs. Our findings highlight the need for simpler dosing to be encouraged explicitly for PWUD with multiple adherence barriers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".