Risk of hepatitis C virus transmission among people who inject prescription opioids: examining the contribution of patterns and contexts
Bibliographic record
Abstract
Introduction: In developed countries, Hepatitis C Virus (HCV) is mainly transmitted through illicit drug injection. Within the last decade, a new population of People Who Inject Drugs (PWID), namely Prescription Opioids (PO) injectors, has emerged both in Canada and worldwide. However, this new group of PWID has not been sufficiently studied. Characterizing PO injectors, and defining the association between PO injection and the risk for HCV seroconversion may be useful for developing specific HCV prevention and treatment programmes. Objectives: The study objectives were: 1) to examine sociodemographic characteristics, drug use patterns, injecting behaviors, and living contexts associated with PO injection in a cohort of PWID in Montreal, 2) to compare HCV incidence rates between PO injectors and non-PO injectors, 3) to examine the association between PO injection, with or without co-use of other drugs, and HCV seroincidence.Methods: Data collected from PWID, who participated in a prospective cohort study (HEPCO Cohort of Montreal), were examined. Eligibility criteria were age of 18 years old or over, drug injection in the previous 6 months, and current residence in Montreal. Data were procured in 2004-2013 by means of a validated, interviewer-administered questionnaire and blood testing for HCV and HIV antibody. Baseline data were assessed in a cross-sectional analysis by Chi-square and Student’s t tests. Logistic regression modeling was applied to identify the correlates of PO injection. HCV negative at baseline PWID were included in the longitudinal analysis. Kaplan-Meier survival and time-varying Cox regression analyses were conducted to calculate the relative rates of HCV seroincidence and examine the association between PO injection and time-to-HCV seroconversion. Results: Cross-sectional analysis. Of 1243 PWID who participated between 2004 and 2011 (83.8% males; mean age: 38.2 years), 380 (30.6%) reported PO injection in the past month. In a multivariate regression analysis, characteristics and behaviors independently associated with PO injection were age (adjusted odds ratio (aOR) by 5-year increment: 0.79; 95% confidence interval [95%CI]: 0.7,0.9), co-use of heroin injection (aOR:3.03, [95%CI: 2.2,4.2]) or non-injection amphetamines (aOR: 1.84; [95%CI: 1.2,2.9]) or non-injection tranquilizers (aOR:2.38, [95%CI: 1.8,3.2]), unstable housing conditions (aOR: 1.78, [95% CI: 1.3,2.4]), injecting in a public place (aOR:2.03, [95%CI: 1.5,2.8]), and HCV seropositivity (aOR: 1.56, [95%CI:1.1,2.2]). Longitudinal analysis. Of 356 participants (81.5% males; mean age: 34.7 years) who were HCV-negative at baseline and were followed up between 2004 and 2013, 123 (34.6%) reported PO injection, and 115 (32%) seroconverted to HCV. Co-use dyads associated with HCV were: PO injection with cocaine injection (crude hazard ratio (cHR): 10.86, [95%CI: 5.8,20.3] vs. use of neither drug), PO injection with crack/cocaine smoking (cHR:4.08, [95%CI: 2.5,6.7]), and PO injection with non-injection tranquilizers (cHR: 4.09, [95%CI: 2.5,6.6]). In a multivariate analysis, PO injection was independently associated with HCV infection. PWID who co-used all three drugs with PO injection had the highest risk of HCV acquisition. Interpretation: Comprehensive harm reduction and HCV prevention strategies for PO injectors in Montreal should address young PWID. In addition, addressing co-use of PO injection with cocaine injection, crack/cocaine smoking, or non-injection use of tranquilizers may also be beneficial. PO injectors who co-use all three drugs should be identified as high priority targets for interventions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".