P2‐055: Screening for hepatitis C in Ontario, Canada: exploring antibody positivity in the federal and provincial correctional systems
Bibliographic record
Abstract
Introduction: Opioid misuse is a public health crisis in many populations.In Canada, the province of Ontario has more than 50,000 opioid-dependent persons who are engaged in opioid substitution therapy (OST), using mainly methadone and suboxone.Hepatitis C Virus (HCV) infection, with an estimated prevalence of 0.3%>0.9%among all Canadians, is more common in this population.Many experts advocate for testing all OST patients for chronic HCV infection.To date, the impact of HCV infection diagnosis on the substance use behaviors of OST patients is unknown and we aim to explore that here. Methods:We conducted a retrospective cohort analysis using the electronic health data, urine toxicology and antibody-based HCV infection screening information from a network of 47 addiction treatment clinics in Ontario from 2007 to 2013.We used a logistic regression analysis to determine the impact of HCV infection testing and diagnosis on substance-use behaviors for patients engaged in OST.Results: Out of 12386 individuals identified, 8856 patients were screened for HCV infection.1920 (21.7%) individuals tested positive for anti-HCV Ab.Patients were followed for a mean of 9.7 months before and 21.2 months after HCV-Ab test.A significant decline in opioids/benzodiazepines consumption was seen after testing for HCV-Ab.(opioids declined from 35% to 17%, p <0.001; benzodiazepines declined from 8.5% to 6.4%, p <0.001).However, there was no significant decline in overall cocaine use before and after testing for HCV-Ab (16.7% before HCV-Ab test to 16.5% after, p =0.40).For opioid users, there was no difference between those who tested positive for HCV-Ab versus those tested negative.For benzodiazepines, a significant decrease in use was more often observed among those with positive test result for HCV-Ab (aOR=1.39,CI 95% 1.24-1.56)after adjustment for age, sex and geographical location.Even though cocaine users did not change their consumption habit overall, patients who tested positive for HCV-Ab did decrease their use.(aOR=1.48,CI 95% 1.33-1.66). Conclusion:We have demonstrated that HCV infection screening can have a positive impact on substance-use among patients engaged in OST.Expansion and universal screening of OST clients for HCV infection should be encouraged.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".