The Cost-Effectiveness of Nucleic Acid Amplification Testing in the Detection of Acute Hepatitis C Virus Infection
Bibliographic record
Abstract
Background and Objectives: Hepatitis C virus (HCV) infection has a high morbidity, mortality, and societal costs. We evaluated the cost-effectiveness of adding pooled nucleic acid amplification testing (pooled NAT) to conventional testing with enzyme immunosorbent assay (EIA) in detecting acute HCV cases in an adult population screened for HCV from a Canadian health care payer's perspective. Methods: A Markov cohort model was constructed to compare two screening strategies (conventional versus pooled NAT inclusive), taking into account treatment of new infections in hypothetical cohorts of 35-year old adults: i) who previously tested negative for HCV antibody; and ii) who had no previous HCV test. Model parameter estimates, including HCV epidemiological data, performance of HCV diagnostic tests, and HCV-related disease progression and costs were obtained from the British Columbia (BC) provincial laboratory, which performs all HCV tests for the BC population, as well as from the published literature. The main outcome measures were annual number of acute HCV cases detected, life years, Quality-Adjusted Life Years (QALYs), and incremental cost-effectiveness ratio (ICER) expressed as cost per additional QALY. Results: Compared to conventional testing, our model predicted that the pooled NAT inclusive strategy would identify six cases of acute HCV per 100,000 adults screened for HCV. The marginal QALY gains of the pooled NAT strategy over conventional strategy, was negligible. The pooled NAT strategy was more costly than the conventional strategy resulting in an ICER of about 44 million Canadian dollars per QALY gained. Conclusions: Our findings suggest that the addition of pooled NAT to the current conventional testing strategy has value in detecting acute HCV infections but is not cost-effective. Effective risk reduction management including both counseling and treatment for acute HCV cases are required to maximize the HCV-related health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".