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A multicategory logit model detecting temporal changes in antimicrobial resistance.

Marc Aerts1,2, Kendy Tzu-Yun Teng3,4, Stijn Jaspers1,2

  • 1Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.

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This summary is machine-generated.

This study introduces a novel statistical model to track changes in antimicrobial resistance (AMR) over time by analyzing minimum inhibitory concentration (MIC) distributions. The model enhances public health surveillance by detecting subtle trends in drug resistance patterns.

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Area of Science:

  • Microbiology
  • Biostatistics
  • Public Health

Background:

  • Monitoring antimicrobial resistance (AMR) is crucial for human and animal health.
  • Traditional AMR trend analysis often relies on proportions above cut-off values, potentially missing subtle shifts.
  • Minimum Inhibitory Concentration (MIC) distributions offer a more detailed view of resistance trends.

Purpose of the Study:

  • To develop and present a statistical modeling approach for estimating and analyzing temporal trends in MIC distributions.
  • To provide a method for detecting multiple temporal trends within the full discrete MIC distribution.
  • To accommodate variations in experimental ranges across different laboratories and time points.

Main Methods:

  • Development of a specific family of multicategory logit models tailored for discrete MIC data.
  • Modeling MIC distributions over time without assuming an underlying continuous distribution.
  • Estimation of MIC distributions across the maximal observed experimental range, allowing for variability.

Main Results:

  • The proposed categorical model effectively estimates MIC distributions over time.
  • The model successfully detects various temporal trends in AMR data.
  • Demonstrated utility with two real-world datasets concerning Salmonella AMR.

Conclusions:

  • The developed multicategory logit model offers a robust method for analyzing temporal AMR trends using MIC distributions.
  • This approach enhances the ability to detect subtle shifts in resistance, improving public health surveillance.
  • The model's flexibility in handling varying experimental ranges makes it broadly applicable in AMR research.