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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".