Cost‐effectiveness analysis of hepatitis C virus (HCV) point‐of‐care assay for HCV screening
Bibliographic record
Abstract
BACKGROUND & AIMS: Hepatitis C virus (HCV) continues to pose significant public health concerns with approximately 44% of chronically infected Canadians undiagnosed. The current HCV screening in Canada is a two-step diagnosis pathway consisting of anti-HCV testing and HCV ribonucleic acid (RNA) testing. The introduction of HCV point-of-care assays, such as the Xpert HCV viral load finger-stick assay, can facilitate HCV RNA diagnosis during a single visit and provide quick linkage to care. We evaluated the cost-effectiveness of HCV point-of-care testing compared with current HCV screening strategies for injection drug users (IDUs) from a Canadian provincial Ministry of Health perspective. METHODS: A state-transition model based on published literature was developed to compare HCV point-of-care assay with the standard-of-care blood screening for a one-time HCV screening and treatment program. It adopted a lifetime time horizon and included health states related to treatment, fibrosis stages, and advanced liver disease clinical states. Outcomes were expressed in costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios. Sensitivity analyses were conducted to assess the robustness of the model. RESULTS: HCV point-of-care assay generated an additional 0.035 QALYs/person at a cost reduction of $21.15 compared with the standard-of-care screening. The results were the most sensitive to the specificity of HCV point-of-care assay. CONCLUSIONS: The implementation of HCV point-of-care screening in Canada is likely to be cost-saving for IDUs. Early detection and treatment of undiagnosed individuals can prolong people's life span and save healthcare costs associated with HCV-related complications.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".