The population impact of eliminating homelessness on HIV viral suppression among people who use drugs
Bibliographic record
Abstract
Objective: We sought to estimate the change in viral suppression prevalence if homelessness were eliminated from a population of HIV-infected people who use drugs (PWUD). Design: Community-recruited prospective cohort of HIV-infected PWUD in Vancouver, Canada. Behavioral information was collected at baseline and linked to a province-wide HIV/AIDS treatment database. The primary outcome was viral suppression (<50 copies/mL) measured during subsequent routine clinical care. Methods: We employed an imputation-based marginal modelling approach. First, we used modified Poisson regression to obtain effect estimates (adjusting for sociodemographics, substance use, addiction treatment, and other confounders). Then, we imputed an outcome probability for each individual while manipulating the exposure (homelessness). Population viral suppression prevalence under realized and “housed” scenarios were obtained by averaging these probabilities across the population. Bootstrapping was conducted to calculate 95% confidence limits. Results: Of 706 individuals interviewed between January 2005 and December 2015, the majority was male (66.0%), of Caucasian race/ethnicity (55.1%), and had a history of injection (93.6%). At first study visit, 223 (31.6%) reported recent homelessness, and 37.8% were subsequently identified as virally suppressed. Adjusted marginal models estimated a 15.1% relative increase (95%CI: 9.0%, 21.7%) in viral suppression in the entire population—to 43.5% (95%CI: 39.4%, 48.2%)—if all homeless individuals were housed. Among those homeless, eliminating this exposure would increase viral suppression from 22.0% to 40.1% (95%CI: 35.1%, 46.1%), an 82.3% relative increase. Conclusions: Interventions to house homeless, HIV-positive individuals who use drugs could significantly increase population viral suppression. Such interventions should be implemented as a part of renewed HIV/AIDS prevention and treatment efforts.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".