Potential Information Loss Due to Categorization of Minimum Inhibitory Concentration Frequency Distributions

Abstract

A bacterial isolate's susceptibility to antimicrobial is expressed as the lowest drug concentration inhibiting its visible growth, termed minimum inhibitory concentration (MIC). The susceptibilities of isolates from a host population at a particular time vary, with isolates with specific MICs present at different frequencies. Currently, for either clinical or monitoring purposes, an isolate is most often categorized as Susceptible, Intermediate, or Resistant to the antimicrobial by comparing its MIC to a breakpoint value. Such data categorizations are known in statistics to cause information loss compared to analyzing the underlying frequency distributions. The U.S. National Antimicrobial Resistance Monitoring System (NARMS) includes foodborne bacteria at the food animal processing and retail product points. The breakpoints used to interpret the MIC values for foodborne bacteria are those relevant to clinical treatments by the antimicrobials in humans in whom the isolates were to cause infection. However, conceptually different objectives arise when inference is sought concerning changes in susceptibility/resistance across isolates of a bacterial species in host populations among different sampling points or times. For the NARMS 1996–2013 data for animal processing and retail, we determined the fraction of comparisons of susceptibility/resistance to 44 antimicrobial drugs of twelve classes of a bacterial species in a given animal host or product population where there was a significant change in the MIC frequency distributions between consecutive years or the two sampling points, while the categorization-based analyses concluded no change. The categorization-based analyses missed significant changes in 54% of the year-to-year comparisons and in 71% of the slaughter-to-retail within-year comparisons. Hence, analyses using the breakpoint-based categorizations of the MIC data may miss significant developments in the resistance distributions between the sampling points or times. Methods considering the MIC frequency distributions in their entirety may be superior for epidemiological analyses of resistance dynamics in populations.