JAC Advance Access originally published online on May 19, 2008
Journal of Antimicrobial Chemotherapy 2008 62(3):535-538; doi:10.1093/jac/dkn210
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Original research |
Using data on resistance prevalence per sample in the surveillance of antimicrobial resistance
1 Department of Microbiology and Risk Assessment, National Food Institute, Technical University of Denmark, Søborg, Denmark 2 Faculty of Life Science, University of Copenhagen, Frederiksberg, Denmark 3 Department of Food Safety, Zoonoses and Foodborne Diseases, WHO, Geneva, Switzerland
Received 31 January 2008; returned 1 April 2008; revised 22 April 2008; accepted 23 April 2008
* Correspondence address. Department of Microbiology and Risk Assessment, National Food Institute, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark. Tel: +45-72-34-70-75; Fax: +45-72-34-70-01; E-mail: avixx{at}food.dtu.dk
Objectives: In most existing antimicrobial resistance monitoring programmes, one single bacterial colony from each collected sample is susceptibility tested against a panel of antimicrobials. Detecting the proportion of colonies resistant to different antimicrobials in each sample can provide quantitative data on antimicrobial resistance (resistance prevalence per sample).
Methods: In this study, a total of 98 faecal samples from slaughter pigs were tested for tetracycline and sulphonamide resistance in Escherichia coli using the single colony method, and these results were compared with the results obtained using the resistance prevalence per sample method.
Results: The results obtained by the resistance prevalence per sample method showed a lower occurrence of resistance. Tetracycline resistance in E. coli was found in 36.7% of the samples using the single colony method, while the mean tetracycline resistance prevalence was 22.5% using the resistance prevalence per sample method. Similarly, sulphonamide resistance was 32.7% using the single colony method and 19.6% when using the resistance prevalence per sample method. Although different estimates were obtained by each method, the correlation test and the regression model demonstrated that there is a significant association between the results obtained using both methods (P value <0.01) for both antimicrobials tested.
Conclusions: To support risk assessment and analysis of the association between consumption of antimicrobials and occurrence of resistance, there is a need to move towards a more quantitative approach when dealing with antimicrobial resistance in a population, and the resistance prevalence per sample method can provide some of this additional information.
Keywords: E. coli , quantitative , method