Impact of direct‐acting antiviral treatment on health utility in patients with chronic hepatitis C in hospital and community settings
Bibliographic record
Abstract
BACKGROUND: Direct-acting antiviral agents (DAAs) have transformed chronic hepatitis C (CHC) treatment. Continued affordable access to DAAs requires updated cost-effectiveness analyses (CEA). Utility is a preference-based measure of health-related quality of life (HRQoL) used in CEA. This study evaluated the impact of DAAs on utilities for patients with CHC in two clinical settings. METHODS: This prospective longitudinal study included patients aged ≥18 years, diagnosed with CHC and scheduled to begin DAA treatment, from two tertiary care hospital clinics and four community clinics in Toronto, Calgary, and Montreal. Patients completed two utility instruments (EQ-5D-5L and Health Utilities Index 2/3 (HUI2/3)) before treatment, 6 weeks after treatment initiation, and 12 weeks and 1 year after treatment completion. We measured utilities for all patients, and for hospital-based and community-based groups. RESULTS: Between 2017 and 2020, 209 patients (126 hospital-based, 83 community-based; average age 53 years; 65% male) were recruited, and 143 completed the 1-year post-treatment assessment. Pre-treatment, utilities were (mean ± standard deviation) 0.77 ± 0.21 (EQ-5D-5L), 0.69 ± 0.24 (HUI2) and 0.58 ± 0.34 (HUI3). The mean changes at 1-year post-treatment were 0.035, 0.038 and 0.071, respectively. While utilities for hospital-based patients steadily improved, utilities for the community-based cohort improved between baseline and 12-weeks post-treatment, but decreased thereafter. DISCUSSION: This study suggests that utilities improve after DAA treatment in patients with CHC in a variety of settings. However, community-based patients may face challenges related to comorbid health and social conditions that are not meaningfully addressed by treatment. Our study is essential for valuing health outcomes in CHC-related CEA.
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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 | 0.003 | 0.011 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".