Modelling considerations in the analysis of associations between antimicrobial use and resistance in beef feedlot cattle

N. R. Noyes, K. M. Benedict, S. P. Gow, C. L. Waldner, R. J. Reid-Smith, C. W. Booker, T. A. McAllister, P. S. Morley

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

A number of sophisticated modelling approaches are available to investigate potential associations between antimicrobial use (AMU) and resistance (AMR) in animal health settings. All have their advantages and disadvantages, making it unclear as to which model is most appropriate. We used advanced regression modelling to investigate AMU-AMR associations in faecal non-type-specific Escherichia coli (NTSEC) isolates recovered from 275 pens of feedlot cattle. Ten modelling strategies were employed to investigate AMU associations with resistance to chloramphenicol, ampicillin, sulfisoxazole, tetracycline and streptomycin. Goodness-of-fit statistics did not show a consistent advantage for any one model type. Three AMU-AMR associations were significant in all models. Recent parenteral tetracycline use increased the odds of finding tetracycline-resistant NTSEC [odds ratios (OR) 1·1-3·2]; recent parenteral sulfonamide use increased the odds of finding sulfisoxazole-resistant NTSEC (OR 1·4-2·5); and recent parenteral macrolide use decreased the odds of recovering ampicillin-resistant NTSEC (OR 0·03-0·2). Other results varied markedly depending on the modelling approach, emphasizing the importance of exploring and reporting multiple modelling methods based on a balanced consideration of important factors such as study design, mathematical appropriateness, research question and target audience.

Original languageEnglish (US)
Pages (from-to)1313-1329
Number of pages17
JournalEpidemiology and infection
Volume144
Issue number6
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© Copyright Cambridge University Press 2015.

Keywords

  • Antimicrobial drugs
  • Escherichia coli
  • antimicrobial resistance in agricultural settings
  • mathematical modelling
  • surveillance

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