Is Adding HCV Screening to the Antenatal National Screening Program in Amsterdam, The Netherlands, Cost-Effective?
Bibliographic record
Abstract
INTRODUCTION: Hepatitis C virus (HCV) infection can lead to severe liver disease. Pregnant women are already routinely screened for several infectious diseases, but not yet for HCV infection. Here we examine whether adding HCV screening to routine screening is cost-effective. METHODS: To estimate the cost-effectiveness of implementing HCV screening of all pregnant women and HCV screening of first-generation non-Western pregnant women as compared to no screening, we developed a Markov model. For the parameters of the model, we used prevalence data from pregnant women retrospectively tested for HCV in Amsterdam, the Netherlands, and from literature sources. In addition, we estimated the effect of possible treatment improvement in the future. RESULTS: The incremental costs per woman screened was €41 and 0.0008 life-years were gained. The incremental cost-effectiveness ratio (ICER) was €52,473 which is above the cost-effectiveness threshold of €50,000. For screening first-generation non-Western migrants, the ICER was €47,113. Best-case analysis for both scenarios showed ICERs of respectively €19,505 and €17,533. We estimated that if costs per treatment were to decline to €3,750 (a reduction in price of €31,000), screening all pregnant women would be cost-effective. CONCLUSIONS: Currently, adding HCV screening to the already existing screening program for pregnant women is not cost-effective for women in general. However, adding HCV screening for first-generation non-Western women shows a modest cost-effective outcome. Yet, best case analysis shows potentials for an ICER below €20,000 per life-year gained. Treatment options will improve further in the coming years, enhancing cost-effectiveness even more.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".