Gaps in hepatitis C virus prevention and care for HIV-hepatitis C virus co-infected people who inject drugs in Canada
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) living with HIV are a priority population for eliminating hepatitis C virus (HCV) as a public health threat. Maximizing access to HCV prevention and treatment strategies are key steps towards elimination. We aimed to evaluate engagement in harm reduction programs and HCV treatment, and to describe injection practices among HIV-HCV co-infected PWID in Canada from 2003 to 2019. METHODS: We included Canadian Coinfection Cohort study participants who reported injecting drugs between 2003 and 2019 in Quebec, Ontario, Saskatchewan, and British Columbia, Canada. We investigated temporal trends in HCV treatment uptake, efficacy, and effectiveness; injection practices; and engagement in harm reduction programs in three time periods based on HCV treatment availability: 1) interferon/ribavirin (2003-2010); 2) first-generation direct acting antivirals (DAAs) (2011-2013); 3) second-generation DAAs (2014-2019). Harm reduction services assessed included needle and syringe programs (NSP), opioid agonist therapy (OAT), and supervised injection sites (SIS). RESULTS: Median age of participants (N = 1,077) at cohort entry was 44 years; 69% were males. Province-specific HCV treatment rates increased among HCV RNA-positive PWID, reaching 16 to 31 per 100 person-years in 2014-2019. Treatment efficacy improved from a 50 to 70% range in 2003-2010 to >90% across provinces in 2014-2019. Drug injecting patterns among active PWID varied by province, with an overall decrease in cocaine injection frequency and increasing opioid injections. In the most recent time period (2014-2019), needle/syringe sharing was reported at 8-22% of visits. Gaps remained in engagement in harm reduction programs: NSP use decreased (58-70% of visits), OAT engagement among opioid users was low (8-26% of visits), and participants rarely used SIS (1-15% of visits). CONCLUSION: HCV treatment uptake and outcomes have improved among HIV-HCV coinfected PWID. Yet, this population remains exposed to drug-related harms, highlighting the need to tie HCV elimination strategies with enhanced harm reduction programs to improve overall health for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".