New hepatitis C diagnoses in Ontario, Canada are associated with the local prescription patterns of a controlled‐release opioid
Bibliographic record
Abstract
Increases in acute hepatitis C virus (HCV) incidence may be a result of the rising prevalence of injection drug use and the opioid epidemic. Among persons who inject drugs, sharing of needles/syringes is less common and leads to a smaller proportion of incident cases than does sharing of injection drug preparation equipment. In Canada and Europe, hydromorphone controlled release has been associated with frequent reuse and sharing of IDPE. Drug excipients within HCR have been shown to preserve virus survival within IDPE. We hypothesized that regional differences in HCV incidence would mirror regional differences in HCR prescribing. We reviewed HCV incidence data across Ontario, Canada for 2016. Opioid prescribing patterns in each Health Unit were reviewed. Multivariable Poisson regression analyses were performed to test the strength of hydromorphone controlled release dispensing patterns in explaining HCV incidence compared to all opioids. Less vehicle access, lack of education, lower income, less population density, higher white race/ethnicity and more opioid substitution therapy recipients remained significant positive predictors of hepatitis C incidence in the Ontario model. Higher hydromorphone controlled release dispensing rate was a stronger predictor of HCV incidence than all opioid prescriptions (standardized risk ratio = 1.17, P < .0001 vs sRR = 1.11, P = .02). When hydromorphone controlled release was excluded from the opioid prescription variable, dispensing patterns of all other opioids no longer remained a significant predictor (sRR = 1.042, P = .34). The observed relationship between HCV incidence and hydromorphone controlled release dispensing suggests that the type of opioid prescribed locally may contribute to variations in HCV incidence. These data add support to evidence that hydromorphone controlled release use is contributing to HCV spread in Ontario.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".