Impact of Policy Changes Expanding Access to Direct-Acting Antivirals on Hepatitis C Virus–Related Hospitalizations in People With HIV: A Population-Based Study
Bibliographic record
Abstract
Background: The burden of hepatitis C virus (HCV)-related hospitalizations is substantial, particularly among people with HIV and HCV. In Ontario, Canada, use of direct-acting antivirals (DAAs) increased following policies removing fibrosis-stage restrictions and approving of pangenotypic agents in 2017 and 2018, respectively. We examined the impact of expanded DAA access on HCV-related hospitalizations in people with HIV. Methods: We conducted a population-based study using administrative databases between April 2003 and December 2022. We used segmented negative binomial regression to examine changes in level and trend of quarterly HCV-related hospitalization rates in people with HIV following the policy changes and compared predicted rates in the absence of expanded DAA access with observed rates during this period. Results: We identified 2943 HCV-related hospitalizations among people with HIV during our study period. Rates of HCV-related hospitalizations were substantially higher among people with HIV than individuals without HIV. In the postintervention period, there was an immediate level increase in the rate of HCV-related hospitalizations (rate ratio, 1.23; 95% CI, 1.18-1.29), followed by a decrease in trend (rate ratio, 0.94 per quarter; 95% CI, .93-.94). We estimated that expanding DAA access was associated with 192 fewer hospitalizations in people with HIV between 2019 and 2022. Conclusions: Policies expanding DAA access have reduced HCV-related hospitalizations in people with HIV. However, rates were higher relative to those in people without HIV. Further research is needed to identify and address disparities in clinical outcomes among people with HIV and HCV.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".