Dual sexual and drug-related predictors of hepatitis C incidence among sex workers in a Canadian setting: gaps and opportunities for scale-up of hepatitis C virus prevention, treatment, and care
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) represents a significant cause of morbidity and mortality globally. While sex workers may face elevated HCV risks through both drug and sexual pathways, incidence data among sex workers are severely lacking. HCV incidence and predictors of HCV seroconversion among women sex workers in Vancouver, BC were characterized in this study. METHODS: Questionnaire and serological data were drawn from a community-based cohort of women sex workers (2010-2014). Kaplan-Meier methods and Cox regression were used to model HCV incidence and predictors of time to HCV seroconversion. RESULTS: Among 759 sex workers, HCV prevalence was 42.7%. Among 292 baseline-seronegative sex workers, HCV incidence density was 3.84/100 person-years (PY), with higher rates among women using injection drugs (23.30/100 PY) and non-injection crack (6.27/100 PY), and those living with HIV (13.27/100 PY) or acute sexually transmitted infections (STIs) (5.10/100 PY). In Cox analyses adjusted for injection drug use, age (hazard ratio (HR) 0.94, 95% confidence interval (CI) 0.86-1.01), acute STI (HR 2.49, 95% CI 1.02-6.06), and non-injection crack use (HR 2.71, 95% CI 1.18-6.25) predicted time to HCV seroconversion. DISCUSSION: While HCV incidence was highest among women who inject drugs, STIs and the use of non-injection stimulants appear to be pathways to HCV infection, suggesting potential dual sexual/drug transmission. Integrated HCV services within sexual health and HIV/STI programs are recommended.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".