Analysis of isolate based data on antimicrobial resistance collected from volunteer Member States for the year 2010
Meta data
The present document has been produced and adopted by the bodies identified above as authors. In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the authors in the context of a grant agreement between the European Food Safety Authority and the authors. The present document is published complying with the transparency principle to which the European Food Safety Authority is subject. It may not be considered as an output adopted by EFSA. EFSA reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.
Abstract
The European Food Safety Authority (EFSA) is charged with coordinating the annual reporting of zoonoses, zoonotic agents, antimicrobial resistance (AMR) and food-borne outbreaks in the European Union under the Directive 2003/99/EC as well as analysing and summarising the data collected. Until the year 2010, data on antimicrobial resistance (AMR) in animals and food were submitted by the reporting countries and analysed by EFSA on an aggregated level (i.e. as numbers of isolates resistant to a given antimicrobial substance out of the total number of isolates tested). However, data at bacterial isolate level enable more in-depth analysis of resistance, including multi-resistance patterns of isolates and description of clonal spreading of resistant strains. A pilot project for the collection of isolate based AMR data from volunteering reporting countries was carried out in 2011. Data were successfully collected from eleven Member States and one reporting country. This report focuses on the analysis of multi-resistance, co-resistance and complete susceptibility patterns in these isolate-based AMR data, containing information on Salmonella, Campylobacter, indicator E. coli and indicator enterococci isolates from food and animal samples for the year 2010. In addition, attention is paid to the study of the quantitative association between resistance to different antimicrobials in the dataset. Finally, several methods to identify possible clusters of similar multi-resistance patterns are described and exemplified based on selected subsets. Results are shown through specific summary tables as well as graphical representations.