Factors Associated with Antiretroviral Medication Adherence among HIV-Positive Adults Accessing Highly Active Antiretroviral Therapy (HAART) in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Among those accessing treatment, highly active antiretroviral therapy (HAART) has transformed HIV into a chronic and manageable condition. However, high levels of adherence are required to derive a sustained, long-term clinical benefit. The aim of this study was to examine the predictors of adherence based on prescription refill among persons on HAART in British Columbia, Canada. METHODS: This study utilizes data collected between July 2007 and January 2010, as part of the Longitudinal Investigations into Supportive and Ancillary health services (LISA) cohort, which is a study of HIV-positive persons who have accessed antiretroviral therapy (ART) in British Columbia. Participants were considered optimally adherent if they were dispensed ≥95% of their prescribed antiretrovirals. RESULTS: Of a total of 566 participants, only 316 (55.8%) were optimally adherent to HAART. Independent predictors of optimal adherence were increasing age (adjusted odds ratio [AOR] = 1.84, 95% confidence interval [CI]: 1.44-2.33), male gender (AOR = 1.68, 95% CI: 1.07-2.64), and being enrolled in a comprehensive adherence assistance program (AOR = 4.26, 95% CI: 2.12-8.54). Having an annual income <$15 000 (AOR = 0.47, 95% CI: 0.31-0.72) and both former and current injection drug use (AOR = 0.46, 95% CI: 0.29-0.73 and AOR = 0.35, 95% CI: 0.20-0.58, respectively) were independently associated with suboptimal (<95%) adherence. CONCLUSIONS: We found that women and people who inject drugs are at increased risk of being suboptimally adherent to HAART. Optimal adherence remains a significant public health and clinical goal in the context of rapidly expanding access to HAART.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".