A statistical model for assessing performance standards for quantitative and semi-quantitative disinfectant test methods

Abstract

A performance standard for a disinfectant test method can be evaluated by quantifying the (Type I) pass-error rate for ineffective products and the (Type II) fail-error rate for highly effective products. This paper shows how to calculate these error rates for test methods where the log reduction in a microbial population is used as a measure of antimicrobial efficacy. The calculations can be used to assess performance standards that may require multiple tests of multiple microbes at multiple laboratories. Notably, the error rates account for among-laboratory variance of the log reductions estimated from a multilaboratory data set and the correlation among tests of different microbes conducted in the same laboratory. Performance standards that require that a disinfectant product pass all tests or multiple tests on average, are considered. The proposed statistical methodology is flexible and allows for a different acceptable outcome for each microbe tested, since, for example, variability may be different for different microbes. The approach can also be applied to semiquantitative methods for which product efficacy is reported as the number of positive carriers out of a treated set and the density of the microbes on control carriers is quantified, thereby allowing a log reduction to be calculated. Therefore, using the approach described in this paper, the error rates can also be calculated for semiquantitative method performance standards specified solely in terms of the maximum allowable number of positive carriers per test. The calculations are demonstrated in a case study of the current performance standard for the semiquantitative AOAC Use-Dilution Methods for Pseudomonas aeruginosa (964.02) and Staphylococcus aureus (955.15), which allow up to one positive carrier out of a set of 60 inoculated and treated carriers in each test. A simulation study was also conducted to verify the validity of the model's assumptions and accuracy. Our approach, easily implemented using the computer code provided, offers a quantitative decision-making tool for assessing a performance standard for any disinfectant test method for which log reductions can be calculated.