No abstract

Blood products, derived from donated blood, are essential for many medical treatments, and their safety, in terms of being free of Transfusion-Transmitted Infections (TTIs)-i.e., infectious agents that can be spread through their use-is crucial. However, blood screening tests are not perfectly reliable and may produce false negative or false-positive results. Currently, blood donations are tested using a same-for-all testing scheme, where a single test set is used on all blood donations. This article studies differential testing schemes, which may involve multiple test sets, each applied to a randomly selected fraction of the donated blood. Thus, although each blood donation is still tested by a single test set, multiple test sets may be used by the Blood Center. This problem is modeled within an optimization framework and a novel solution methodology is provided that allows important structural properties of such testing schemes to be characterized. It is shown that an optimal differential testing scheme consists of at most two test sets, and such a dual-test scheme can significantly reduce the TTI risk over the current same-for-all testing. The presented analysis leads to an efficient greedy algorithm that generates the optimal differential test sets for a range of budgets to inform the decision-maker (e.g., Blood Center). The differential model is extended to the case where different test sets can be used on sub-sets of donations defined by donation characteristics (e.g., donor demographics, seasonality, or region) that differentiate the sub-set's TTI prevalence rates. The risk reduction potential of differential testing is quantified through two case studies that use published data from Sub-Saharan Africa and the United States. The study generates key insight into public policy decision making on the design of blood screening schemes.