BLOOD DONORS AND BLOOD COLLECTION: Web interface–supported transmission risk assessment and cost‐effectiveness analysis of postdonation screening: a global model applied to Ghana, Thailand, and the Netherlands

Abstract

The young transfusion recipient population of Ghana in combination with a high risk of viral transmission yields better cost-effectiveness for additional tests. The advanced age of the transfused population of the Netherlands and a small risk of viral transmission gives poor cost-effectiveness for more sensitive screening techniques. It was demonstrated that a global health economic model combined with a Web interface can provide easy access to risk assessment and cost-effectiveness analysis.

Human immunodeficiency virus prevalence, incidence, and residual transmission risk in first‐time and repeat blood donations in Zimbabwe: implications on blood safety

“…HIV transmission risk of first‐time donation was estimated from the prevalence (expressed as a proportion), window period (22 days, 2002‐2006; 17 days, 2007‐2010), and duration in HIV Stage 1 and 2 (5 years, expressed in days) using the method published by van Hulst et al …”

Factors in enhancing blood safety by nucleic acid technology testing for human immunodeficiency virus, hepatitis C virus and hepatitis B virus

“…The treatment costs for hepatocellular carcinoma and decompensated cirrhosis are the highest in the health-care management. [57] Through health economics it appears to be more cost beneficial to screen blood with ID NAT to interdict HBV and HCV.…”

Going beyond “same-for-all” testing of infectious agents in donated blood

“…However, it may be violated when tests measure similar disease markers (e.g., HIV antigens), as they will have similar immunological window periods; i.e., the period between infection and immune system activation and hence will have positively correlated outcomes. For example, the window periods of a generic HIV antibody test and a generic HIV combo test (antibody plus P24) are 20.3 days and 15 days, respectively Van Hulst et al, 2009), indicating that during the first 15 days of an HIV infection, both tests should provide a "-" result. Relaxing Assumption 3 requires extensive data on joint sensitivity and specificity of related tests, which is not completely provided (and understood) in the medical literature.…”

“…3 NAT also has pooling flexibility (in pool sizes of 6-24), but pooling reduces the test's efficacy (Hwang, 1976;Burns and Mauro, 1987;Kline et al, 1989;Behets et al, 1990;Leiby, 2001;Weusten et al, 2002;Jackson et al, 2003;. In the absence of an FDA mandate on NAT testing, the decision of whether or not to use this costly technology falls upon the Blood Center and depends on their testing budget (Jackson et al, 2003;Marshall et al, 2004;Dzik, 2005;Van Hulst et al, 2009). For example, the American Red Cross has recently incorporated the pooled NAT into their routine screening for HIV and HCV (in pool sizes of 16; Stramer (2007); American Red Cross (2008)), and not all Blood Centers in the United States use NAT for their blood screening.…”

“…As a result, resource allocation problems in healthcare have received significant attention in the medical literature and OR literature; see, e.g., Brandeau (2004), for an excellent review and references. Many studies examine the relationship between screening and risk reduction for a certain disease through the use of cost-effectiveness analysis (Leiby, 2001;Sendi et al, 2003;Busch et al, 2009), Markov processes (Schwartz et al, 1990;Jackson et al, 2003;Van Hulst et al, 2009), simulation models (Lefrere et al, 1998;Custer et al, 2005), decision trees (Marshall et al, 2004), or empirical studies (Lander et al, 2009). These methodologies usually compare a small number of specific interventions and rely on quite restrictive assumptions, including that the interventions are perfectly divisible, have constant returns to scale, and are independent (Sendi et al, 2003;Brandeau, 2004;Van Hulst et al, 2010).…”