Incidence of hepatitis C virus infection among injection drug users during an outbreak of HIV infection.
Bibliographic record
Abstract
BACKGROUND: Beginning in 1994, Vancouver experienced an explosive outbreak of HIV infection among injection drug users (IDUs). The objectives of this study were to measure the prevalence and incidence of hepatitis C virus (HCV) infection in this context and to examine factors associated with HCV seroconversion among IDUs. METHODS: IDUs recruited through a study site and street outreach completed interviewer-administered questionnaires covering subjects' characteristics, behaviour, health status and service utilization and underwent serologic testing for HIV and HCV at baseline and semiannually thereafter. A Cox proportional hazards model was used to identify independent correlates of HCV seroconversion. RESULTS: As of Nov. 30, 1999, 1345 subjects had been recruited into the study cohort. The prevalence of anti-HCV antibodies was 81.6% (95% confidence interval [CI] 79.6% to 83.6%) at enrollment. Sixty-two HCV seroconversions occurred among 155 IDUs who were initially HCV negative and who returned for follow-up, for an overall incidence density rate of 29.1 per 100 person-years (95% CI 22.3 to 37.3). The HCV incidence remained above 16 per 100 person-years over 3 years of observation (December 1996 to November 1999), whereas HIV incidence declined from more than 19 to less than 5 per 100 person-years. Independent correlates of HCV seroconversion included female sex, cocaine use, injecting at least daily and frequent attendance at a needle exchange program. INTERPRETATION: Because of high transmissibility of HCV among those injecting frequently and using cocaine, the harm reduction initiatives deployed in Vancouver during the study period proved insufficient to eliminate hepatitis C transmission in 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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".