The cost‐effectiveness of pathogen reduction technology as assessed using a multiple risk reduction model
Bibliographic record
Abstract
BACKGROUND: Pathogen reduction technology (PRT) for labile blood components has the potential to reduce the risk of many adverse events associated with transfusion. Because of the potential broad-spectrum risk reduction capability of PRT, the health economics of PRT could be an important consideration in decision making for this technology. STUDY DESIGN AND METHODS: Decision analytic models comparing current blood safety screens and interventions to riboflavin-based whole blood PRT (currently in development) and separately to platelets (PLTs)-and-plasma PRT from the health care system perspective in Canada were used to assess the cost-utility of PRT in reducing the following adverse events: human immunodeficiency virus, hepatitis B virus, hepatitis C virus, human T-lymphotropic virus, syphilis, West Nile virus, bacteria, Chikungunya virus, cytomegalovirus, Trypanosoma cruzi, graft-versus-host disease, febrile nonhemolytic transfusion reactions, and transfusion-related immunomodulation. PRT was modeled as an addition to rather than a replacement for current interventions. The potential of PRT to reduce the risk of an unknown pathogen was not assessed. RESULTS: Whole blood PRT was estimated to have a cost-effectiveness of $1,276,000/quality-adjusted life-year (QALY; 95% confidence interval [CI] approximation, 600,000-3,313,000) compared to current screens and interventions. PLTs-and-plasma PRT was estimated to have a cost-effectiveness of $1,423,000/QALY (95% CI approximation, 834,000-2,818,000) on an all-transfusions basis. CONCLUSIONS: Because of the complexity of transfusion risks and practices, the cost-effectiveness of whole blood or PLTs-and-plasma PRT can be modeled provided that assumptions and simplifications are made. Uncertainty remains with respect to the risk reduction that can be achieved for some adverse events. Nevertheless, the results of this cost-effectiveness analysis can be used to inform policy decisions regarding PRT technology in the context of other initiatives designed to improve transfusion safety.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".