The rising prevalence of prescription opioid injection and its association with hepatitis C incidence among street‐drug users
Bibliographic record
Abstract
AIMS: To examine trends in prescription opioid (PO) injection and to assess its association with hepatitis C virus (HCV) seroconversion among injection drug users (IDUs). DESIGN: Prospective cohort study. SETTING: Montreal, Canada. PARTICIPANTS: HCV-negative IDUs at baseline, reporting injection in the past month. MEASUREMENTS: Semi-annual visits included HCV antibody testing and an interview-administered questionnaire assessing risk behaviours. HCV incidence rate was calculated using the person-time method. Time-updated Cox regression models were conducted to examine predictors of HCV incidence. FINDINGS: The proportion of IDUs reporting PO injection increased from 21% to 75% between 2004 and 2009 (P < 0.001). Of the 246 participants (81.6% male; mean age 34.5 years; mean follow-up time 23 months), 83 seroconverted to HCV [incidence rate: 17.9 per 100 person-years; 95% confidence interval (CI) 14.3, 22.1]. Compared to non-PO injectors, PO injectors were more likely to become infected [adjusted hazard ratio (AHR): 1.87; 95%CI:1.16, 3.03]. An effect modification was also found: PO injectors who did not inject heroin were more likely to become infected (AHR: 2.88; 95%CI: 1.52, 5.45) whereas no association was found for participants using both drugs (AHR: 1.19; 95% CI: 0.61, 2.30). Other independent predictors of HCV incidence were: cocaine injection, recent incarceration and >30 injections per month. CONCLUSIONS: Prescription opioid injectors who do not inject heroin are at greater risk for HCV seroconversion than are those injecting both heroin and prescription opioids. Important differences in age, behaviour and social context suggest a need for targeted outreach strategies to this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".