Impact of hepatitis C serostatus on health service utilization for opioid-related harms among individuals prescribed opioid agonist therapy: A longitudinal prospective cohort study
Bibliographic record
Abstract
Varied substance use outcomes have been reported among individuals with a hepatitis C viral (HCV) infection on opioid agonist treatment (OAT) for opioid use disorder. Accordingly, the current study sought to evaluate the association between HCV serostatus, among other factors, and opioid-related acute health service utilization (e.g., emergency department [ED] visits and hospitalizations) among individuals prescribed OAT. Multi-site prospective cohort study data were used to characterize demographic characteristics, substance use patterns, and physical health amongst individuals prescribed OAT. Logistic regression models were built to estimate the association between HCV-seropositivity and opioid-related ED visits and hospitalizations over a three-year follow up period. Among 3430 participants, 10.6 % ( n = 365) were HCV-seropositive. In the follow-up period, 21.3 % ( n = 730) attended the ED and 8.7 % ( n = 298) were hospitalized for opioid related-harms. HCV-seropositivity was associated with an increased incidence of ED visits for opioid poisoning (9.0 % vs 4.9 % for participants who were HCV-seronegative, p < 0.01) and other opioid-related harms (22.5 % vs. 20.8 % for seronegative participants, p = 0.03). However, multiple logistical regression models showed no association between HCV serostatus and opioid-related health service utilization; rather, injection drug use was a significant predictor of opioid-related ED visits (OR 3.39, p < 0.01) and hospitalizations (OR 1.21, p = 0.01). Among individuals prescribed OAT, those with seropositive HCV have increased incidence of ED visits and hospitalizations for opioid-related harms, an association which may be driven by injection use practices. These findings highlight the importance of screening for injection use practices and health symptoms, as well as the potential role for targeting resources (e.g., harm reduction supplies, education regarding transmission) within this vulnerable subgroup. • Among individuals on opioid agonist therapy, those with hepatitis-C have more opioid-related hospital presentations. • However, hepatitis-C is not a prognostic factor for acute opioid-related presentations. • Injection drug use is the strongest predictor of acute opioid-related presentations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".