Hepatitis C Testing, Status and Treatment among Marginalized People Who Use Drugs in an Inner City Setting: An Observational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Chronic hepatitis C virus (HCV) infection is common among people who inject drugs (PWID) and is associated with morbidity and premature death. Although HCV can be cured, treatment may be inaccessible. We studied HCV testing, status and treatment among marginalized people who use drugs in Ottawa, Canada, a setting with universal insurance coverage for physician services. METHODS: We analyzed data from the Participatory Research in Ottawa: Understanding Drugs study, a cross-sectional, peer-administered survey of people who use drugs from 2012 to 2013. We linked responses to population-based health administrative databases and used multivariable Poisson regression to identify factors independently associated with self-reported HCV testing, self-reported positive HCV status, and database-determined engagement in HCV treatment. RESULTS: Among 663 participants, 562 (84.8%) reported testing for HCV and 258 (45.9%) reported HCV-positive status. In multivariable analysis, HCV-positive status was associated with female gender (RR 1.27; 95%CI 1.04 to 1.55), advancing age (RR 1.03/year; 95%CI 1.02 to 1.04), receiving disability payments (RR 1.42; 95%CI 1.06 to 1.91), injecting drugs (RR 5.11; 95%CI 2.64 to 9.91), ever injecting with a used needle (RR 1.30; 95%CI 1.12 to 1.52), and ever having taken methadone (RR 1.26; 95%CI 1.05 to 1.52). Of HCV positive participants, 196 (76%) were engaged in primary care but only 23 (8.9%) had received HCV therapy. Conclusions/Importance: Although HCV testing and positive status rates are high among PWID in our study, few have received HCV treatment. Innovative initiatives to increase access to HCV treatment for PWID are urgently 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".