Testing of pooled samples has been proposed as a low-cost alternative for diagnostic screening and surveillance for infectious agents in situations where the prevalence of infection is low and most samples can be expected to test negative. The present study extends our previous work in pooled-sample testing (PST) to evaluate effects of the following factors on the overall PST sensitivity (SEk) and specificity (SPk): dilution (pool size), cross-contamination, and cross-reaction. A probabilistic model, in conjunction with Monte Carlo simulations, was used to calculate SEk and SPk, as applied to detection of bovine viral diarrhea virus (BVDV) persistently infected (PI) animals using RT-PCR. For an average prevalence of BVDV PI of 0.01 and viremia in each animal between 102 and 107 virus particles/mL, the pool size associated with the lowest number of tests, and lowest cost, corresponded to eight samples/pool. However, the least-cost pool size (lowest number of tests) was associated with a SEk of 0.90 (0.75-1), which corresponded to a decrease of 0.04, relative to the assay sensitivity for a single sample. The SPk for the same pool size, considering the effect of detection of BVDV acutely infected animals and cross-contamination as source of false positive results, was 0.90 (0.85-0.95). The effect of a hypothetical cross-reacting agent was to markedly decrease SPk, especially as the prevalence of the cross-reacting agent increased. For a pool size of eight samples and a prevalence of the cross-reacting agent of 0.3, SPk ranged from 0.67 to 0.86, depending on the probability that the assay would detect the cross-reacting agent. The methods presented offer a means of evaluating and understanding the various factors that can influence overall accuracy of PST procedures.
Bibliographical noteFunding Information:
Funded in part by USDA NRI grant no. 980-2517, USDA Formula Funds, the Graduate Group in Epidemiology at UC Davis, and the Eugene Lyons scholarship. The authors thank the reviewers and editor for their helpful suggestions.
- Diagnostic screening
- Monte Carlo simulation
- Pooled sample testing