Young Age Predicts Poor Antiretroviral Adherence and Viral Load Suppression Among Injection Drug Users
Bibliographic record
Abstract
Previous studies of adherence to antiretroviral therapy (ART) for HIV among young injection drug users (IDU) have been limited because financial barriers to care disproportionately affect youth, thus confounding results. This study examines adherence among IDU in a unique setting where all medical care is provided free-of-charge. From May 1996 to April 2008, we followed a prospective cohort of 545 HIV-positive IDU of 18 years of age or older in Vancouver, Canada. Using generalized estimating equations (GEE), we studied the association between age and adherence (obtaining ART≥95% of the prescribed time), controlling for potential confounders. Using Cox proportional hazards regression, we also studied the effect of age on time to viral load suppression (<500 copies per milliliter), and examined adherence as a mediating variable. Five hundred forty-five participants were followed for a median of 23.8 months (interquartile range [IQR]=8.5-91.6 months). Odds of adherence were significantly lower among younger IDU (adjusted odds ratio [AOR]=0.76 per 10 years younger; 95% confidence interval [CI], 0.65-0.89). Younger IDU were also less likely to achieve viral load suppression (adjusted hazard ratio [AHR]=0.75 per 10 years younger; 95% CI, 0.64-0.88). Adding adherence to the model eliminated this association with age, supporting the role of adherence as a mediating variable. Despite absence of financial barriers, younger IDU remain less likely to adhere to ART, resulting in inferior viral load suppression. Interventions should carefully address the unique needs of young HIV-positive IDU.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".