Cost‐effectiveness of nucleic acid test screening of volunteer blood donations for hepatitis B, hepatitis C and human immunodeficiency virus in the United States
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aim of this study was to examine the cost-effectiveness of adding nucleic acid testing (NAT) to serological (antibody and antigen) screening protocols for donated blood in the United States (US) with the purpose of reducing the risks of transfusion-transmission of hepatitis B virus (HBV), hepatitis C virus (HCV) and human immunodeficiency virus (HIV). MATERIALS AND METHODS: The costs, health consequences and cost-effectiveness of adding either minipool or individual-donor NAT to serological screening (SS) testing were estimated using a decision-analysis model. RESULTS: With the given modelling assumptions, adding minipool NAT would avoid an estimated 37, 128 and eight cases of HBV, HCV and HIV, respectively, and save approximately 53 additional years of life and 102 additional quality adjusted life years (QALYs) compared with SS, at a net cost of $154 million. SS + minipool NAT - p24 compared with SS alone resulted in an incremental cost-effectiveness ratio of $1.5 million per QALY gained (range in sensitivity analysis $1.0-2.1 million per QALY gained) in this US analysis. CONCLUSIONS: The cost effectiveness of adding NAT screening is outside the typical range for most healthcare interventions, but not for established blood safety measures.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".