Impact of Illicit Drug Use on Health-Related Quality of Life in Opioid-Dependent Patients Undergoing HIV Treatment
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of illicit drug use on health-related quality of life (health utility) among opioid-dependent HIV-infected patients. DESIGN: Secondary analyses of data from the Buprenorphine-HIV Evaluation and Support cohort of HIV-infected patients with opioid dependence in 9 US HIV clinics between 2004 and 2009. Health status [short form-12 (SF-12)], combination antiretroviral treatment (ART) status, CD4 cell count, hepatitis C virus antibody status, current drug use, and demographics were assessed at the initial visit and quarterly follow-up visits until 1 year. The SF-6D health utility scores were derived from the SF-12. Multivariate mixed-effects regression models were used to assess the impact of illicit drug use on health utility controlling for demographic, clinical, and social characteristics. RESULTS: Health utility was assessed among 307 participants, 67% male, with a median age of 46 years at 1089 quarterly assessments. In multivariate analyses, illicit opioid use, nonopioid illicit drug use, not being on ART, and being on ART with poor adherence were associated with lower health utility. The observed decrement in health utility associated with illicit opioid use was larger for those on ART with good adherence (beta = -0.067; P < 0.01) or poor adherence (-0.049; P < 0.01) than for those not on ART. CONCLUSIONS: Illicit opioid and nonopioid drug use are negatively associated with health utility in patients with HIV; however, the relative effect of illicit opioid use is smaller than that of not being on ART. Postponing ART until initiation of opioid substitution therapy or abstinence may have limited benefits from the perspective of maximizing health utility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".