A multicategory logit model detecting temporal changes in antimicrobial resistance
PLoS ONE publish this investigation article
December 1st, 2022
Monitoring and investigating temporal trends in antimicrobial data is a high priority for human and animal health authorities. Timely detection of temporal changes in antimicrobial resistance (AMR) can rely not only on monitoring and analyzing the proportion of resistant isolates based on the use of a clinical or epidemiological cut-off value, but also on more subtle changes and trends in the full distribution of minimum inhibitory concentration (MIC) values. The nature of the MIC distribution is categorical and ordinal (discrete). In this contribution, we developed a particular family of multicategory logit models for estimating and modelling MIC distributions over time. It allows the detection of a multitude of temporal trends in the full discrete distribution, without any assumption on the underlying continuous distribution for the MIC values. The experimental ranges of the serial dilution experiments may vary across laboratories and over time. The proposed categorical model allows to estimate the MIC distribution over the maximal range of the observed experiments, and allows the observed ranges to vary across labs and over time. The use and performance of the model is illustrated with two datasets on AMR in Salmonella
Aerts M., Teng KT., Jaspers S. and Alvarez J..
 | Interuniversity Institute for Biostatistics and Statistical Bioinformatics. Hasselt University. |
| Data Science Institute, Hasselt University. Hasselt University. |
| Department of Veterinary Medicine. College of Veterinary Medicine. National Chung Hsing University. | |
 | Departamento de Sanidad Animal. Facultad de Veterinaria. Universidad Complutense (UCM). |
| Servicio de Zoonosis de Transmisión Alimentaria y Resistencia a Antimicrobianos (ZTA). Centro de Vigilancia Sanitaria Veterinaria (VISAVET). Universidad Complutense (UCM). |
