Opportunities to Enhance Linkage to Hepatitis C Care Among Hospitalized People With Recent Drug Dependence in New South Wales, Australia: A Population-based Linkage Study
Bibliographic record
Abstract
BACKGROUND: People who inject drugs are at greater risk of hepatitis C virus (HCV) infection and hospitalization, yet admissions are not utilized for HCV treatment initiation. We aimed to assess the extent to which people with HCV notification, including those with evidence of recent drug dependence, are hospitalized while eligible for direct-acting antiviral (DAA) therapy, and treatment uptake according to hospitalization in the DAA era. METHODS: We conducted a longitudinal, population-based cohort study of people living with HCV in the DAA era (March 2016-December 2018) through analysis of linked databases in New South Wales, Australia. Kaplan-Meier estimates were used to report HCV treatment uptake by frequency, length, and cause-specific hospitalization. RESULTS: Among 57 467 people, 14 938 (26%) had evidence of recent drug dependence, 50% (n = 7506) of whom were hospitalized while DAA eligible. Incidence of selected cause-specific hospitalization was highest for mental health-related (15.84 per 100 person-years [PY]), drug-related (15.20 per 100 PY), and injection-related infectious disease (9.15 per 100 PY) hospitalizations, and lowest for alcohol use disorder (4.58 per 100 PY) and liver-related (3.13 per 100 PY). In total, 65% (n = 4898) of those who were hospitalized had been admitted ≥2 times, and 46% (n = 3437) were hospitalized ≥7 days. By the end of 2018, DAA therapy was lowest for those hospitalized ≥2 times, for ≥7 days, and those whose first admission was for injection-related infectious disease, mental health disorders, and drug-related complications. CONCLUSIONS: Among people who have evidence of recent drug dependence, frequent hospitalization-particularly mental health, drug, and alcohol admissions-presents an opportunity for engagement in HCV care.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".