Geographic Differences in Temporal Incidence Trends of Hepatitis C Virus Infection Among People Who Inject Drugs: The InC3 Collaboration
Bibliographic record
Abstract
BACKGROUND: We determined temporal trends (1985-2011) in hepatitis C virus (HCV) incidence and associated behavioral exposures for people who inject drugs (PWID) from the United States (Boston, Baltimore, and San Francisco), Canada (Montreal), the Netherlands (Amsterdam), and Australia (Sydney and Melbourne). METHODS: Using population-based cohort data from HCV-negative PWID, we calculated overall and within-city HCV incidence trends, HCV rates by study enrollment period (1985-2011), and temporal trends in exposure behaviors. Poisson regression models estimated trends in HCV incidence over calendar-time. Survival models identified risk factors for HCV incidence across cities and estimated independent effects of city and calendar period on HCV infection risk. RESULTS: Among 1391 initially HCV-negative participants followed prospectively (1644.5 person-years of observation [PYO]), 371 HCV incident infections resulted in an overall incidence of 22.6 per 100 PYO (95% confidence interval [CI], 20.4-25.0). Incidence was highest and remained elevated in Baltimore (32.6/100 PYO), San Francisco (24.7/100 PYO), and Montreal (23.5/100 PYO), lowest in Melbourne and Amsterdam (7.5/100 PYO and 13.1/100 PYO, respectively), and moderate (21.4/100 PYO) in Sydney. Higher rates of syringe and equipment sharing and lower prevalence of opioid agonist therapy were associated with HCV incidence in cities with the highest incidence. Risk for infection dropped by 18% for every 3-year increase in calendar-time (adjusted hazard ratio, 0.8 [95% CI, .8-.9]) in the multivariable model. CONCLUSIONS: Differences in prevention strategies and injecting contexts may explain the ongoing high HCV incidence in these North American cities and emphasize the need for scale-up of opioid agonist therapy and increased coverage of needle and syringe programs in North America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".