Cluster analysis of resistance combinations in Escherichia coli from different human and animal populations in Germany 2014-2017 [post]

Beneditta Suwono, Tim Eckmanns, Heike Kaspar, Roswitha Merle, Benedikt Zacher, Chris Kollas, Armin A. Weiser, Ines Noll, Marcel Feig, Bernd-Alois Tenhagen
2020 unpublished
Background Recent findings on Antibiotic Resistance (AR) have brought renewed attention to the comparison of data on AR from human and animal sectors. This is however, a major challenge since the data is not harmonized. This study performs a comparative analysis of phenotypical AR data from different routine surveillance and monitoring systems in Germany. Escherichia coli data were used as a model to describe the similarities based on the resistance patterns in human and different animal
more » ... erent animal populations in Germany. Method: Data on E. coli isolates were collected from 2014 to 2017 from human clinical isolates, non-clinical isolates from food-producing animals and food, and clinical isolates from food-producing and companion animals from national routine surveillance and monitoring for AR in Germany. Four antibiotics - ampicillin, cefotaxime, ciprofloxacin and gentamicin - were chosen for the analysis. Resistant isolates were defined according to EUCAST clinical breakpoints for humans. Based on the 16 possible resistance combinations to these four antibiotics, cluster analysis was performed using hierarchical clustering with Euclidian and average distance. All analyses were performed with the software "R". Result Data of 333,496 E. coli isolates were included in this study. Forty-one different human and animal populations were included in the cluster analysis. Three main clusters were detected. Within these three clusters, all human populations (intensive care unit (ICU), general ward and outpatient care) showed similar relative frequencies of the resistance combinations and clustered together. They demonstrated similarities with clinical isolates from different animal populations and most isolates from pigs from both non-clinical and clinical isolates. Isolates from healthy poultry demonstrated similarities in relative frequencies of resistance combinations and clustered together. However, they clustered separately from the human isolates. All isolates from different animal populations with low relative frequencies of resistance combinations clustered together and likewise separately from the human populations. Conclusion Cluster analysis facilitated the comparison of phenotypical AR data across human and animal sectors. It indicated linkage among human isolates and with isolates from various animal populations based on the resistance combinations in E. coli. Further analyses based on these findings might promote a better one-health approach for AR in Germany.
doi:10.21203/ fatcat:hncjsyu5one73hp3zgoczgduiu