Considerations When Using Discriminant Function Analysis of Antimicrobial Resistance Profiles To Identify Sources of Fecal Contamination of Surface Water in Michigan

The goals of this study were to (i) identify issues that affect the ability of discriminant function analysis (DA) of antimicrobial resistance profiles to differentiate sources of fecal contamination, (ii) test the accuracy of DA from a known-source library of fecal Escherichia coli isolates with is...

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Veröffentlicht in:Applied and Environmental Microbiology 2007-05, Vol.73 (9), p.2878-2890
Hauptverfasser: Kaneene, John B, Miller, RoseAnn, Sayah, Raida, Johnson, Yvette J, Gilliland, Dennis, Gardiner, Joseph C
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container_end_page 2890
container_issue 9
container_start_page 2878
container_title Applied and Environmental Microbiology
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creator Kaneene, John B
Miller, RoseAnn
Sayah, Raida
Johnson, Yvette J
Gilliland, Dennis
Gardiner, Joseph C
description The goals of this study were to (i) identify issues that affect the ability of discriminant function analysis (DA) of antimicrobial resistance profiles to differentiate sources of fecal contamination, (ii) test the accuracy of DA from a known-source library of fecal Escherichia coli isolates with isolates from environmental samples, and (iii) apply this DA to classify E. coli from surface water. A repeated cross-sectional study was used to collect fecal and environmental samples from Michigan livestock, wild geese, and surface water for bacterial isolation, identification, and antimicrobial susceptibility testing using disk diffusion for 12 agents chosen for their importance in treating E. coli infections or for their use as animal feed additives. Nonparametric DA was used to classify E. coli by source species individually and by groups according to antimicrobial exposure. A modified backwards model-building approach was applied to create the best decision rules for isolate differentiation with the smallest number of antimicrobial agents. Decision rules were generated from fecal isolates and applied to environmental isolates to determine the effectiveness of DA for identifying sources of contamination. Principal component analysis was applied to describe differences in resistance patterns between species groups. The average rate of correct classification by DA was improved by reducing the numbers of species classifications and antimicrobial agents. DA was able to correctly classify environmental isolates when fewer than four classifications were used. Water sample isolates were classified by livestock type. An evaluation of the performance of DA must take into consideration relative contributions of random chance and the true discriminatory power of the decision rules.
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The average rate of correct classification by DA was improved by reducing the numbers of species classifications and antimicrobial agents. DA was able to correctly classify environmental isolates when fewer than four classifications were used. Water sample isolates were classified by livestock type. 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source American Society for Microbiology; MEDLINE; PubMed Central; Alma/SFX Local Collection
subjects Animals
Animals, Domestic - microbiology
Anti-Infective Agents - toxicity
Biological and medical sciences
Contamination
Discriminant Analysis
Drug resistance
Drug Resistance, Bacterial - genetics
E coli
Escherichia coli - drug effects
Escherichia coli - isolation & purification
Feces - microbiology
Fundamental and applied biological sciences. Psychology
Geese - microbiology
Michigan
Microbial Sensitivity Tests
Microbiology
Principal Component Analysis
Public Health Microbiology
Species Specificity
Surface water
Water Microbiology
title Considerations When Using Discriminant Function Analysis of Antimicrobial Resistance Profiles To Identify Sources of Fecal Contamination of Surface Water in Michigan
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