An assessment of differences in costs and health benefits of serology and NAT screening of donations for blood transfusion in different Western countries
Bibliographic record
Abstract
Background and Objectives: The cost-utility of safety interventions is becoming increasingly important as a driver of implementation decisions. The aim of this study was to compare the cost-utility of different blood screening strategies in various settings, and to analyse the extent and cause of differences in health economic results. Materials and Methods: For eight Western countries (Australia, Canada, Denmark, Finland, France, The Netherlands, UK and the United States of America), data were collected on donor and recipient populations, blood products, screening tests, and on patient treatment practices and costs. An existing ISBT web-tool model was used to assess the cost-utility of various strategies for HIV, HCV and HBV screening. Results: The cost-utility ratio of serology screening for these eight countries ranges between −11 000 and 92 000 US$ per QALY, and for NAT between −12 000 and 113 000 US$ per QALY when compared to no screening. Combined serology and NAT ranges between 600 and 217 000 US$ per QALY. The incremental cost-utility of NAT after implementation of serology screening ranges from 2 231 000 to 15 778 000 US$ per QALY. Conclusion: There are substantial differences in costs per QALY between countries for various HIV, HBV and HCV screening strategies. These differences are primarily caused by costs of screening tests and infection rates in the donor population. Within each country, similar cost per QALY results for serology and NAT compared to no screening, coupled with evidence of limited value of serology and NAT together prompts the need for further discussion on the acceptability of parallel testing by serology and NAT.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".