Hepatitis C treatment outcomes among people who inject drugs experiencing unstable versus stable housing: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of hepatitis C virus (HCV) among people who inject drugs (PWID) is between 50-70%. Prior systematic reviews demonstrated that PWID have similar direct acting antiviral treatment outcomes compared to non-PWID; however, reviews have not examined treatment outcomes by housing status. Given the links between housing and health, identifying gaps in HCV treatment can guide future interventions. METHODS: We conducted a systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched six databases for articles from 2014 onward. Two reviewers conducted title/abstract screenings, full-text review, and data extraction. We extracted effect measures for treatment initiation, adherence, completion, success, and reinfection by housing status. Studies underwent quality and certainty assessments, and we performed meta-analyses as appropriate. RESULTS: Our search yielded 473 studies, eight of which met inclusion criteria. Only the treatment initiation outcome had sufficient measures for meta-analysis. Using a random-effects model, we found those with unstable housing had 0.40 (0.26, 0.62) times the odds of initiating treatment compared to those with stable housing. Other outcomes were not amenable for meta-analysis due to a limited number of studies or differing outcome definitions. CONCLUSIONS: Among PWID, unstable housing appears to be a barrier to HCV treatment initiation; however, the existing data is limited for treatment initiation and the other outcomes we examined. There is a need for more informative studies to better understand HCV treatment among those with unstable housing. Specifically, future studies should better define housing status beyond a binary, static measure to capture the nuances and complexity of housing and its subsequent impact on HCV treatment. Additionally, researchers should meaningfully consider whether the outcome(s) of interest are being accurately measured for individuals experiencing unstable housing.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".