Estimation of the wild‐type minimum inhibitory concentration value distribution

Abstract

Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non-wild-type isolates has therefore gained increased interest. Monitoring is performed based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. In order to account for the unobserved population heterogeneity of wild-type and non-wild-type isolates, mixture models are extremely useful. Instead of estimating the entire mixture globally, it was our major aim to provide an estimate for the wild-type first component only. The characteristics of this first component are not expected to change over time, once the wild-type population has been confidently identified for a given antimicrobial. With this purpose, we developed a new method based on the multinomial distribution, and we carry out a simulation study to study the properties of the new estimator. Because the new approach fits within the likelihood framework, we can compare distinct distributional assumptions in order to determine the most suitable distribution for the wild-type population. We determine the optimal parameters based on the AIC criterion, and attention is also paid to the model-averaged approach using the Akaike weights. The latter is thought to be very suitable to derive specific characteristics of the wild-type distribution and to determine limits for the wild-type MIC range. In this way, the new method provides an elegant means to compare distinct distributional assumptions and to quantify the wild-type MIC distribution of specific antibiotic-bacterium combinations.

Aeromonas Diversity and Antimicrobial Susceptibility in Freshwater—An Attempt to Set Generic Epidemiological Cut-Off Values

“…Results from these methods were in accordance, with frequent full agreement or one dilution step difference. Values could also be computed using Jaspers method (Jaspers et al, 2014- data not shown ) for three antimicrobials (colistin, erythromycin, and temocillin) and were in full agreement with those obtained with other methods. Erythromycin CO WT here computed (32 mg/L) should be interpreted cautiously considering the fact that MICs of the supposed WT population are distributed over eight dilution steps instead of three to five usually.…”

“…Numerous methods were proposed to determine CO WT , from “eye-ball” determination to statistically oriented ones (Turnidge et al, 2006; Turnidge and Paterson, 2007; Kronvall, 2010; Hombach et al, 2014; Jaspers et al, 2014). These methods were applied in the present study according to their authors' recommendation, computing CO WT for 99 and 97.7% of the population level inclusion in the wild type population.…”

Variation in the Antimicrobial Susceptibility ofActinobacillus pleuropneumoniaeIsolates in a Pig, Within a Batch of Pigs, and Among Batches of Pigs from One Farm

“…16 These models were fitted by maximum likelihood, using the loglikelihood function for the mixture of two Gaussian distributions. 30 The single-population model was compared to the mixture model (with five parameters) using the Akaike Information Criterion (AIC) to determine which was most supported by the data.…”

A Bayesian approach to the semiparametric estimation of a minimum inhibitory concentration distribution

“…As will become clear in Section 3.1, priors were taken to be relatively informative, with their means corresponding to the estimates of the multinomial-based method [Jaspers et al (2014a)]. …”

“…The latter approach aims at providing an estimate for the wild-type density function (f 1 ), from which the ECOFF is derived as the 99.9th percentile. In a similar fashion, Jaspers et al (2014a) also adopt a local view, focussing on the wild-type first component only. They proposed an improved likelihood-based procedure, called the multinomial-based method (MBM) to identify the most suitable distribution of the first component and to estimate its parameters.…”