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A multicategory logit model detecting temporal changes in antimicrobial resistance

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  • Marc Aerts
  • Kendy Tzu-yun Teng
  • Stijn Jaspers
  • Julio Alvarez Sanchez

Abstract

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.

Suggested Citation

  • Marc Aerts & Kendy Tzu-yun Teng & Stijn Jaspers & Julio Alvarez Sanchez, 2022. "A multicategory logit model detecting temporal changes in antimicrobial resistance," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-22, December.
    • Handle: RePEc:plo:pone00:0277866
    DOI: 10.1371/journal.pone.0277866
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    References listed on IDEAS

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    1. Komárek, Arnost, 2009. "A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3932-3947, October.
    2. Min Zhang & Chong Wang & Annette O’Connor, 2020. "A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
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    1. Min Zhang & Chong Wang & Annette O’Connor, 2020. "A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.

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