Background: Switching to new infectious disease blood donor screening assays can precipitate an initial decrease in specificity in an established donor population followed by an increase of specificity, referred to as a “cleaning effect”. We developed a mathematical model to simulate this and to measure the stabilization of specificity.
Methods: A modified exponential distribution curve was created to show the impact of donation frequency on the cleaning of the donor pool. Other parameters (e.g., number of donations from repeat donors/donations per month, average and minimum times between donations, retention of regular repeat donors, ratio of false positives for regular repeat donors/first-time donors and specificity of newly introduced assays) were also used to simulate the rise and fall in number of additional false positives. The mathematical model created was compared with real-world data from a South African blood donation center.
Results: In the mathematical model, the degree and duration of the cleaning effect were influenced by certain parameters. A longer time interval between donations resulted in a higher number of deferred blood donations than a shorter time interval, if deferred after a 1st, 2nd or 3rd false positive result prior to a stable plateau of specificity. Real-world data on false positive, discarded donations from a South African blood donation center were consistent with numbers from the mathematical model.
Conclusions: The mathematical model can identify and describe any “cleaning effect” observed upon switching to a new infectious disease blood screening assay, allowing affected blood donation centers to prepare and adjust, while specificity is stabilized.