The Effects of Opioid Substitution Treatment and Highly Active Antiretroviral Therapy on the Cause-Specific Risk of Mortality Among HIV-Positive People Who Inject Drugs
Bibliographic record
Abstract
BACKGROUND: Prior studies indicated opioid substitution treatment (OST) reduces mortality risk and improves the odds of accessing highly active antiretroviral therapy (HAART); however, the relative effects of these treatments for human immunodeficiency virus (HIV)-positive people who inject drugs (PWID) are unclear. We determine the independent and joint effects of OST and HAART on mortality, by cause, within a population of HIV-positive PWID initiating HAART. METHODS: Using a linked population-level database for British Columbia, Canada, we used time-to-event analytic methods, including competing risks models, proportional hazards models with time-varying covariates, and marginal structural models, to identify the independent and joint effects of OST and HAART on all-cause as well as drug- and HIV-related mortality, controlling for covariates. RESULTS: Among 1727 HIV-positive PWID, 493 (28.5%) died during a median 5.1 years (interquartile range, 2.1-9.1) of follow-up: 18.7% due to drug-related causes, 55.8% due to HIV-related causes, and 25.6% due to other causes. Standardized mortality ratios were 12.2 (95% confidence interval [CI], 9.8, 15.0) during OST and 30.0 (27.1, 33.1) during periods out of OST. Both OST (adjusted hazard, 0.34; 95% CI, .23, .49) and HAART (0.39 [0.31, 0.48]) decreased the hazard of all-cause mortality; however, individuals were at lowest risk of death when these medications were used jointly (0.16 [0.10, 0.26]). Both OST and HAART independently protected against HIV-related death, drug-related death and death due to other causes. CONCLUSIONS: While both OST and HAART are life-saving treatments, joint administration is urgently needed to protect against both drug- and HIV-related mortality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".