Homelessness as a Structural Barrier to Effective Antiretroviral Therapy Among HIV-Seropositive Illicit Drug Users in a Canadian Setting
Bibliographic record
Abstract
Despite the advent of effective antiretroviral therapy (ART), HIV-seropositive injection drug users (IDU) continue to suffer from elevated levels of morbidity and mortality. Evidence is needed to identify social- and structural-level barriers to effective ART. We investigated the impact of homelessness on plasma HIV RNA response among illicit drug users initiating ART in a setting with free and universal access to HIV care. We accessed data from a long-running prospective cohort of community-recruited IDU linked to comprehensive HIV clinical monitoring and ART dispensation records. Using Cox proportional hazards with recurrent events modeling, we estimated the independent effect of homelessness on time to plasma HIV viral load suppression. Between May 1996 and September 2009, 247 antiretroviral naïve individuals initiated ART and contributed 1755 person-years of follow-up. Among these individuals, the incidence density of plasma HIV RNA suppression less than 500 copies/mm(3) was 56.7 (95% confidence interval [CI]: 46.9-66.0) per 100 person-years. In unadjusted analyses, homelessness was strongly associated with lower rates suppression (hazard ratio = 0.56, 95% CI: 0.40-0.78, p = 0.001), however, after adjustment for adherence this association was no longer significant (adjusted hazard ratio = 0.79, 95% CI: 0.56-1.11, p = 0.177). Homelessness poses a significant structural barrier to effective HIV treatment. However, since this relationship appears to be mediated by lower levels of ART adherence, interventions to improve adherence among members of this vulnerable population are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".