Sex Discrepancies in the Protective Effect of Opioid Agonist Therapy on Incident Hepatitis C Infection
Bibliographic record
Abstract
BACKGROUND: While opioid agonist therapy (OAT) reduces the risk of hepatitis C virus (HCV) acquisition among people who inject drugs (PWID), protective effects may be attenuated in females. We used pooled data from an international collaboration of prospective cohorts to assess sex disparities in HCV incidence among PWID exposed to OAT. METHODS: Independent predictors of HCV infection were identified using Cox regression models with random effects after accounting for the clustering effect of study sites. Unadjusted and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) are presented in sex-specific analyses. RESULTS: Among 701 participants exposed to OAT, HCV incidence was 16.5/100 person-years of observation (PYO) (95% CI, 13.1-20.7) in females and 7.6/100 PYO (95% CI, 6.0-9.5) in males (female:male adjusted HR [aHR], 1.80 [95% CI, 1.37-2.22]; P < .001). Factors associated with HCV acquisition among females exposed to OAT included nonwhite race (aHR, 1.79 [95% CI, 1.25-2.56]; P = .001), unstable housing (aHR, 4.00 [95% CI, 3.62-4.41]; P < .001), daily or more frequent injection (aHR, 1.45 [95% CI, 1.01-2.08]; P = .042), and receptive syringe sharing (aHR, 1.43 [95% CI, 1.33-1.53]; P < .001). CONCLUSIONS: Female PWID exposed to OAT are twice as likely as their male counterparts to acquire HCV. While there is a need for better understanding of sex differences in immune function and opioid pharmacokinetic and pharmacodynamic parameters, structural and behavioral interventions that target women are required to bolster the efficacy of OAT in preventing HCV transmission.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".