Antimicrobial resistance monitoring in Escherichia coli from livestock : Evaluation & interpretation

Effective antimicrobials are essential for adequate healthcare, but unfortunately, worldwide antimicrobial resistance (AMR) threatens this effectiveness, caused by antimicrobial use (AMU). The possibilities for development of antimicrobials are limited, and new antimicrobials will not become widely available. This leaves prudent AMU and other interventions to limit existing AMR as an important strategy and therefore, AMR must be monitored. Production animals are a relevant reservoir to monitor, because AMR may be transmitted to humans directly, or indirectly via food or the environment. This thesis is about monitoring of AMR in livestock as public health hazard in indicator organism Escherichia coli. In the European Union, monitoring of AMR in animals as public health hazard is performed by European legislation in commensal E. coli and food-borne pathogens Salmonella and Campylobacter. The international legislation has led to harmonisation and standardisation of the sampling and the microbiological methods. Elements not prescribed create room for improvement. The evaluation and interpretation by statistical analysis of AMR monitoring results is not prescribed, but is challenging and will be more complex when the amount of data increases. The updated EU legislation in 2020 has allowed whole-genome sequencing (WGS) as alternative method to culture-based antimicrobial susceptibility testing in AMR monitoring. So far, no statistical approaches were described to evaluate WGS versus culture-based methods. Analyses can be improved for optimal evaluation and interpretation of AMR monitoring data. Therefore, the first aim of this thesis is to evaluate AMR monitoring results with statistical methods. The second aim is to improve the interpretation of AMR monitoring in commensal E. coli. The third aim is to assess WGS versus culture-based methods to monitor AMR. The conclusions from this thesis are that E. coli is a useful indicator to monitor AMR in livestock, provided that bias in the sampling is prevented, and that proper statistical methods are used for the evaluation and interpretation. As we show, the randomized sample from healthy animals is well suited to analyse AMR trends over time. Other types of samples such as risk-based sampling (for example from diseased animals) are useful to detect rare or emerging resistance, but should be used next to a random sample to ensure representativeness for the whole animal population. To improve interpretation of AMR monitoring data, quantitative analyses should be incorporated in routine monitoring, because in the future the amount and complexity of data will further increase. The validity of the statistical approaches in this thesis should be further investigated in data with more variation, from different countries. We promote further evaluation of AMR surveillance systems, and the analysis of AMR monitoring outcomes should be harmonized, next to already existing harmonization of laboratory methods