Multilevel Predictors of Concurrent Opioid Use during Methadone Maintenance Treatment among Drug Users with HIV/AIDS
Bibliographic record
Abstract
BACKGROUND: Ongoing drug use during methadone maintenance treatment (MMT) negatively affects outcomes of HIV/AIDS care and treatment for drug users. This study assessed changes in opioid use, and longitudinal predictors of continued opioid use during MMT among HIV-positive drug users in Vietnam, with the aim of identifying changes that might enhance program efficacy. METHODS: We analyze data of 370 HIV-positive drug users (mean age 29.5; 95.7% male) taking MMT at multi-sites. Opioid use was assessed at baseline, 3, 6, and 9 months using interviews and heroin confirmatory urine tests. A social ecological model was applied to explore multilevel predictors of continued opioid use, including individual, interpersonal, community and service influences. Generalized estimating equations (GEE) statistical models were constructed to adjust for intra-individual correlations. RESULTS: Over 9 month follow-up, self-reported opioid use and positive heroin urine test substantially decreased to 14.6% and 14.4%. MMT helped improve referrals and access to health care and social services. However, utilization of social integration services was small. GEE models determined that participants who were older (Adjusted Odd Ratio - AOR = 0.97 for 1 year increase), had economic dependents (AOR = 0.33), or were referred to TB treatment (AOR = 0.53) were less likely to continue opioid use. Significant positive predictors of ongoing opioid use included frequency of opioid use prior to MMT, peer pressure, living with sexual partners, taking antiretroviral treatment, other health concerns and TB treatment. CONCLUSION: These findings show that MMT in the Vietnamese context can dramatically reduce opioid use, which is known to be associated with reduced antiretroviral (ART) adherence. Disease stage and drug interactions between antiretrovirals or TB drugs and MMT could explain some of the observed predictors of ongoing drug use; these findings could inform changes in MMT program design and implementation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".