Integrated supervised consumption services and hepatitis C testing and treatment among people who inject drugs in Toronto, Canada: A cross‐sectional analysis
Bibliographic record
Abstract
Despite the availability of publicly funded hepatitis C (HCV) treatment in Canada, treatment gaps persist, particularly among people who inject drugs. We estimate correlates of HCV care cascade engagement (testing, diagnosis, and treatment) among people who inject drugs in Toronto, Canada and examine the effect of accessing differing supervised consumption service (SCS) models on self-reported HCV testing and treatment. This is a cross-sectional baseline analysis of 701 people who inject drugs surveyed in the Toronto, Ontario integrated Supervised Injection Services (OiSIS-Toronto) study between November 2018 and March 2020. We examine correlates of self-reported HCV care cascade outcomes including SCS model, demographic, socio-structural, drug use, and harm reduction characteristics. Overall, 647 participants (92%) reported ever receiving HCV testing, of whom 336 (52%) had been diagnosed with HCV. Among participants who reported ever being diagnosed with HCV, 281 (84%) reported chronic HCV, of whom 130 (46%) reported HCV treatment uptake and 151 (54%) remained untreated. Compared to those with no SCS use, participants who had ever injected at an integrated SCS model with co-located HCV care had greater prevalence of both ever receiving HCV testing (adjusted prevalence ratio [aPR]: 1.12, 95% confidence interval [CI]: 1.02-1.24) and ever receiving HCV treatment (aPR: 1.67, 95% CI: 1.04-2.69). Over half of participants diagnosed with chronic HCV reported remaining untreated. Our findings suggest that integrated SCS models with co-located HCV care represent key strategies for linkage to HCV care, but that more is needed to support scale-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".