A probabilistic method for the estimation of residual risk in donated blood.
The residual risk (RR) of transfusion-transmitted infections, including the human immunodeficiency virus and hepatitis B and C viruses, is typically estimated by the incidence[Formula: see text]window period model, which relies on the following restrictive assumptions: Each screening test, with probability 1, (1) detects an infected unit outside of the test's window period; (2) fails to detect an infected unit within the window period; and (3) correctly identifies an infection-free unit. These assumptions need not hold in practice due to random or systemic errors and individual variations in the window period. We develop a probability model that accurately estimates the RR by relaxing these assumptions, and quantify their impact using a published cost-effectiveness study and also within an optimization model. These assumptions lead to inaccurate estimates in cost-effectiveness studies and to sub-optimal solutions in the optimization model. The testing solution generated by the optimization model translates into fewer expected infections without an increase in the testing cost.
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA ebru@vt.edu.
Ebru K
EK
Bish
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
Prasanna K
PK
Ragavan
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
Douglas R
DR
Bish
Barnabas Health and New Jersey Medical School, University of Medicine and Dentistry of New Jersey, West Orange, NJ 07052, Newark, NJ 07103, USA.
Anthony D
AD
Slonim
American Red Cross, 9315 Gaither Rd., Gaithersburg, MD 20877, USA.
Susan L
SL
Stramer
D001782:Blood Donors / Q000706:statistics & numerical data*; D058731:Blood Safety / Q000191:economics / Q000706:statistics & numerical data*; D003362:Cost-Benefit Analysis; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D011336:Probability; D018570:Risk Assessment
Blood donation; Incidence/window period model; Optimization; Risk estimation