O09.5 Hepatitis C incidence rate among people who inject drug (PWID) in british columbia from 2000 to 2015
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>Background</h3> Global Health Sector Strategy on Viral Hepatitis aims to reduce new hepatitis C virus (HCV) infections by 80% by 2030. However, countries lack systems to monitor incidence of HCV. We estimated the HCV incidence over time among people who inject drugs (PWID) at population level to provide proof of concept for incidence monitoring. <h3>Methods</h3> This study utilized data from the BC Hepatitis Testers Cohort (BC-HTC). Incidence was defined as a positive anti-HCV, RNA, or genotype test following a negative anti-HCV test among PWID, assessed based on a previously validated algorithm using administrative data. Annual incidence rates for HCV primary infection from 2000 to 2015 were estimated using a log-binomial regression model and were stratified by birth cohorts (<1965, 1965–1974, >1974) to observe change in risk over time. Adjusted incidence rates (aIR) were calculated controlling for risk factors. <h3>Results</h3> Of the 42,568 participants identified, 4,066 HCV seroconversions occurred over 318,613 person-years (PY) of follow-up. The overall incidence rate was 1.28/100PY. Between 2000 and 2011, the annual aIR decreased steadily from 4.01 to 1.00/100PY. The aIR then rose to 1.49/100PY in 2015. Factors associated with elevated risk of infection include: younger birth cohort (1965–1974: RR:1.9, 95%CI: 1.02,3.6), history of illicit opioid use (RR:2.5, 95%CI: 2.3,2.7), stimulant misuse (RR:1.77, 95%CI: 1.7,1.9), HIV coinfection (RR:3.6, 95%CI: 3.1,4.1), HBV coinfection (RR:1.9, 95%CI: 1.6,2.2), material deprivation (RR:1.5, 95%CI: 1.4,1.7) and social deprivation (RR:1.6, 95%CI: 1.4,1.8). <h3>Conclusion</h3> A slight increase in HCV incidence rate since 2011 was mainly driven by the younger birth cohort and introduction of enhanced testing in 2010. People with HIV or HBV coinfection, opioid and stimulant misuse, social and material deprivation are at higher risk of HCV infection. HCV treatment and prevention programs need to address comorbidities and include harm reduction strategies like opioid substitution therapy and access to social services to achieve HCV elimination goals. <h3>Disclosure</h3> No significant relationships.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 it