Cost-effectiveness of additional blood screening tests in the Netherlands.

BACKGROUND: During the past decade, blood screening tests such as triplex nucleic acid amplification testing (NAT) and human T-cell lymphotropic virus type I or I (HTLV-I/II) antibody testing were added to existing serologic testing for hepatitis B virus (HBV), human immunodeficiency virus (HIV), and hepatitis C virus (HCV). In some low-prevalence regions these additional tests yielded disputable benefits that can be valuated by cost-effectiveness analyses (CEAs). CEAs are used to support decision making on implementation of medical technology. We present CEAs of selected additional screening tests that are not uniformly implemented in the EU. STUDY DESIGN AND METHODS: Cost-effectiveness was analyzed of: 1) HBV, HCV, and HIV triplex NAT in addition to serologic testing; 2) HTLV-I/II antibody test for all donors, for first-time donors only, and for pediatric recipients only; and 3) hepatitis A virus (HAV) for all donations. Disease progression of the studied viral infections was described in five Markov models. RESULTS: In the Netherlands, the incremental cost-effectiveness ratio (ICER) of triplex NAT is €5.20 million per quality-adjusted life-year (QALY) for testing minipools of six donation samples and €4.65 million/QALY for individual donation testing. The ICER for anti-HTLV-I/II is €45.2 million/QALY if testing all donations, €2.23 million/QALY if testing new donors only, and €27.0 million/QALY if testing blood products for pediatric patients only. The ICER of HAV NAT is €18.6 million/QALY. CONCLUSION: The resulting ICERs are very high, especially when compared to other health care interventions. Nevertheless, these screening tests are implemented in the Netherlands and elsewhere. Policy makers should reflect more explicit on the acceptability of costs and effects whenever additional blood screening tests are implemented.

Storage time of blood products and transfusion-related acute lung injury.

BACKGROUND: Besides white blood cell antibodies in plasma-rich products, another cause of transfusion-related acute lung injury (TRALI) could be release of biologically active substances during storage of cellular blood products. We aimed to investigate the association of storage time and risk of TRALI for different product types. STUDY DESIGN AND METHODS: We compared storage time of blood products transfused within 6 hours before the onset of TRALI to storage time of a representative sample of all blood products transfused in the Netherlands. Generalized linear models were used to correct for confounding variables. RESULTS: Platelets (PLTs) in plasma transfused to TRALI patients were stored for 0.7 (95% confidence interval [CI], 0.073 to 1.3) days longer than those transfused to controls. The relative risk of TRALI, after receiving PLTs stored for 4 or 5 days, compared to 3 days or less, was 5.8 (95% CI, 0.99 to 110) and increased to 6.3 (95% CI, 1.1 to 118) after more than 5 days (i.e., 6 or 7 days). CONCLUSIONS: While longer storage of buffy coat-derived PLTs was associated with an increased risk of TRALI, storage of plasma for up to 2 years and red blood cells for up to 35 days was not associated with the risk of TRALI.

Demographic changes and predicting blood supply and demand in the Netherlands.

BACKGROUND: Concerns have been raised that aging of the general population will increase the demand for blood products. Modeling can be applied to assess trends in blood demand and supply and predict how these will develop over time. STUDY DESIGN AND METHODS: We developed mathematical models to describe and predict the national demand of red blood cell (RBC) products. The first demand model assumes that the mean numbers of transfusions per inhabitant per age and sex are constant. A second demand model incorporates observed changes in clinical blood use over time. Further, a donation model is developed to predict future RBC supply. To estimate the supply of whole blood donations, we used annual donor retention rates, donor recruitment rates, and mean numbers of donations per donor year. RESULTS: The model based on demography only predicts an increase of 23% in RBC demand over 2008 to 2015. The second model, incorporating both demographic changes and trends in clinical RBC use, predicts a decrease of RBC demand by 8% over the same period. The predicted RBC supply closely follows the demand as predicted by the second model. CONCLUSIONS: Despite an aging population, RBC demand may not increase as much as predicted in other studies. This depends on the extent to which other effects, like that of optimal blood use, will neutralize the effects of aging of the transfusion recipient population. Still, the observed downward trend in donor recruitment in the Netherlands must be stopped to maintain a sufficient RBC supply.