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Use of a simulation model to evaluate sampling strategies for characterization of antimicrobial resistance in non–type-specific Escherichia coli isolated from dairy cows

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  • 1 Animal Population Health Institute, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523-1681.
  • | 2 Animal Population Health Institute, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523-1681.
  • | 3 Department of Veterinary Preventive Medicine, College of Veterinary Medicine, The Ohio State University, Columbus, OH 43210-1092.
  • | 4 Animal Population Health Institute, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523-1681.

Abstract

Objective—To evaluate various sampling strategies for potential use in measuring prevalence of antimicrobial susceptibility in cattle.

Sample Population—500 isolates of non–type-specific Escherichia coli (NTSEC) isolated from the feces of 50 cows from 2 dairy farms (25 cows/farm and 10 isolates/cow).

Procedures—Diameters of inhibition zones for 12 antimicrobials were analyzed to estimate variation among isolates, cows, and farms and then used to determine sampling distributions for a stochastic simulation model to evaluate 4 sampling strategies. These theoretic sampling strategies used a total of 100 isolates in 4 allocations (1 isolate from 100 cows, 2 isolates from 50 cows, 3 isolates from 33 cows, or 4 isolates from 25 cows).

Results—Analysis of variance composition revealed that 74.2% of variation was attributable to isolates, 18.5% to cows, and 7.3% to farms. Analysis of results of simulations suggested that when most of the variance was attributable to differences among isolates within a cow, culturing 1 isolate from each of 100 cows underestimated overall prevalence, compared with results for culturing more isolates per cow from fewer cows. When variance was not primarily attributable to differences among isolates, all 4 sampling strategies yielded similar results.

Conclusions and Clinical Relevance—It is not always possible to predict the hierarchical level at which clustering will have its greatest impact on observed susceptibility distributions. Results suggested that sampling strategies that use testing of 3 or 4 isolates/cow from a representative sample of all animals better characterize herd prevalence of antimicrobial resistance when impacted by clustering.

Abstract

Objective—To evaluate various sampling strategies for potential use in measuring prevalence of antimicrobial susceptibility in cattle.

Sample Population—500 isolates of non–type-specific Escherichia coli (NTSEC) isolated from the feces of 50 cows from 2 dairy farms (25 cows/farm and 10 isolates/cow).

Procedures—Diameters of inhibition zones for 12 antimicrobials were analyzed to estimate variation among isolates, cows, and farms and then used to determine sampling distributions for a stochastic simulation model to evaluate 4 sampling strategies. These theoretic sampling strategies used a total of 100 isolates in 4 allocations (1 isolate from 100 cows, 2 isolates from 50 cows, 3 isolates from 33 cows, or 4 isolates from 25 cows).

Results—Analysis of variance composition revealed that 74.2% of variation was attributable to isolates, 18.5% to cows, and 7.3% to farms. Analysis of results of simulations suggested that when most of the variance was attributable to differences among isolates within a cow, culturing 1 isolate from each of 100 cows underestimated overall prevalence, compared with results for culturing more isolates per cow from fewer cows. When variance was not primarily attributable to differences among isolates, all 4 sampling strategies yielded similar results.

Conclusions and Clinical Relevance—It is not always possible to predict the hierarchical level at which clustering will have its greatest impact on observed susceptibility distributions. Results suggested that sampling strategies that use testing of 3 or 4 isolates/cow from a representative sample of all animals better characterize herd prevalence of antimicrobial resistance when impacted by clustering.

Contributor Notes

Supported by the Animal Population Health Institute at Colorado State University through a grant from the USDA Cooperative State Research, Education, and Extension Service and by the College of Veterinary Medicine and Biomedical Sciences at Colorado State University.

Address correspondence to Dr. Morley.