Opportunities for prevention: Hepatitis C prevalence and incidence in a cohort of young injection drug users
Bibliographic record
Abstract
The objective of this study was to compare sociodemographic, drug, and sexual risk characteristics between hepatitis C virus (HCV) baseline positive and negative young (13-24 years) injection drug users (IDUs) and to determine prospective risk factors for HCV seroconversion among the youth. Data were collected through the Vancouver Injection Drug Users Study (VIDUS). To date, more than 1,400 Vancouver-area IDUs have been enrolled and followed up; 234 were aged 24 years and younger. Semiannually, participants have completed an interviewer-administered questionnaire and have undergone serologic testing for human immunodeficiency virus (HIV) and HCV. Univariate and multivariate logistic regression analyses were undertaken to investigate predictors of baseline HCV positivity. In the multivariate analyses, Cox regression models with time-dependent covariates were used to identify predictors of HCV seroconversion. Of the 232 young injectors, 107 (46%) were HCV positive at baseline and a further 37 HCV seroconverted during the study period for an incidence rate of 37.3 per 100 person-years. Baseline positivity was associated with Aboriginal ancestry, older age, greater number of years injecting drugs, recent incarceration, sex trade work, more than 100 lifetime sexual partners, a previous sexually transmitted disease, living in the IDU epicenter, and injection more than once per day of heroin, cocaine, and speedball. Factors independently associated with HCV seroconversion were having a partner who uses injection drugs, requiring help to inject, and injection of cocaine more than once daily. In conclusion, unlike older IDUs, more than one half of young injectors were HCV negative at recruitment. Thus, there is a window of opportunity for prevention. However, the incidence rate of HCV among these young IDUs is alarming, suggesting that the opportunity to intervene is exceedingly small.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".