Homelessness and Adherence to Antiretroviral Therapy among a Cohort of HIV-Infected Injection Drug Users
Bibliographic record
Abstract
Homelessness is prevalent among HIV-infected injection drug users (IDU) and may adversely affect access and adherence to antiretroviral therapy (ART). There are limited descriptions of the effect of homelessness on adherence to ART in long-term cohorts of HIV-infected IDU. We used data from a community-recruited prospective cohort of HIV-infected IDU, including comprehensive ART dispensation records, in a setting where HIV care is free. We examined the relationship between the homelessness measured longitudinally, and the odds of ≥95% adherence to ART using generalized estimating equations logistic regression modeling adjusting for sociodemographics, drug use, and clinical variables. Between May 1996 and September 2008, 545 HIV-infected IDU were recruited and eligible for the present study. The median follow-up duration was 23.8 months (IQR 8.5-91.6 months) contributing 2,197 person-years of follow-up. At baseline, homeless participants were slightly younger (35.8 vs. 37.9 years, p = 0.01) and more likely to inject heroin at least daily (37.1% vs. 24.6%. p = 0.004) than participants who had housing. The multivariate model revealed that homelessness (adjusted odds ratio [AOR] 0.66; 95% CI: 0.53-0.84) and frequent heroin use (AOR 0.40; 95% CI: 0.30-0.53) were significantly and negatively associated with ART adherence, whereas methadone maintenance was positively associated (AOR 2.33; 95% CI: 1.86-2.92). Sub-optimal ART adherence was associated with homelessness and daily injection heroin use among HIV-infected IDU. Given the survival benefit of ART, it is critical to develop and evaluate innovative strategies such as supportive housing and methadone maintenance to address these risk factors to improve adherence.
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 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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".