A repeated cross-sectional study was conducted to determine the patterns of antimicrobial resistance in 1,286 Escherichia coli strains isolated from human septage, wildlife, domestic animals, farm environments, and surface water in the Red Cedar watershed in Michigan. Isolation and identification of E. coli were done by using enrichment media, selective media, and biochemical tests. Antimicrobial susceptibility testing by the disk diffusion method was conducted for neomycin, gentamicin, streptomycin, chloramphenicol, ofloxacin, trimethoprim-sulfamethoxazole, tetracycline, ampicillin, nalidixic acid, nitrofurantoin, cephalothin, and sulfisoxazole. Resistance to at least one antimicrobial agent was demonstrated in isolates from livestock, companion animals, human septage, wildlife, and surface water. In general, E. coli isolates from domestic species showed resistance to the largest number of antimicrobial agents compared to isolates from human septage, wildlife, and surface water. The agents to which resistance was demonstrated most frequently were tetracycline, cephalothin, sulfisoxazole, and streptomycin. There were similarities in the patterns of resistance in fecal samples and farm environment samples by animal, and the levels of cephalothin-resistant isolates were higher in farm environment samples than in fecal samples. Multidrug resistance was seen in a variety of sources, and the highest levels of multidrug-resistant E. coli were observed for swine fecal samples. The fact that water sample isolates were resistant only to cephalothin may suggest that the resistance patterns for farm environment samples may be more representative of the risk of contamination of surface waters with antimicrobial agent-resistant bacteria.
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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