Hepatitis C Cascade of Care among People who Inject Drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) have high rates of hepatitis C virus (HCV) infection. Little is known about the rates of diagnosis and treatment for HCV among PWID. Therefore, this study aims to characterize the cascade of care in Vancouver, Canada, to improve HCV treatment access and delivery for PWID. METHODS: Data were derived from 3 prospective cohort studies of PWID in Vancouver, Canada, between December 2005 and May 2015. The progression of participants was identified through 5 steps in the cascade of care: (1) chronic HCV; (2) linkage to HCV care; (3) liver disease assessment; (4) initiation of treatment; and (5) completion of treatment. Predictors of undergoing liver disease assessment for HCV treatment were identified using a multivariable extended Cox regression model. RESULTS: Among 1571 participants with chronic HCV, 1359 (86.5%) had ever been linked to care, 1257 (80.0%) had undergone liver disease assessment, 163 (10.4%) had ever started HCV treatment, and 71 (4.5%) had ever completed treatment. In multivariable analyses, human immunodeficiency virus (HIV) seropositivity, use of methadone maintenance therapy, and hospitalization in the past 6 months were independently and positively associated with undergoing liver disease assessment (all P < .001), whereas daily heroin injection was independently and negatively associated with undergoing liver disease assessment (P < .001). CONCLUSIONS: Among this cohort of PWID, few had been started on or completed treatment for HCV. These findings highlight the need to improve the prescribing of HCV treatment among PWID with active substance use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".