Introduction: Antimicrobial resistance (AMR), associated with antimicrobial use (AMU), is a major public concern. Surveillance and monitoring systems are essential to assess and control the trends in AMU and AMR. However, differences in the surveillance and monitoring systems between countries and sectors make comparisons challenging. The purpose of this article is to describe all surveillance and monitoring systems for AMU and AMR in the human and livestock sectors, as well as national surveillance and monitoring systems for AMR in food, in six European countries (Spain, Germany, France, the Netherlands, the United Kingdom and Norway) as a baseline for developing suggestions to overcome current limitations in comparing AMU and AMR data. Methods: A literature search in 2018 was performed to identify relevant peer-reviewed articles and national and European grey reports as well as AMU/AMR databases. Results: Comparison of AMU and AMR systems across the six countries showed a lack of standardization and harmonization with different AMU data sources (prescription vs sales data) and units of AMU and AMR being used. The AMR data varied by sample type (clinical/non-clinical), laboratory method (disk diffusion, microdilution, and VITEK, among others), data type, ie quantitative (minimum inhibition concentration (MIC) in mg/L/inhibition zone (IZ) in mm) vs qualitative data (susceptible-intermediate-resistant (SIR)), the standards used (EUCAST/CLSI among others), and/or the evaluation criteria adopted (epidemiological or clinical). Discussion: A One Health approach for AMU and AMR requires harmonization in various aspects between human, animal and food systems at national and international levels. Additionally, some overlap between systems of AMU and AMR has been encountered. Efforts should be made to improve standardization and harmonization and allow more meaningful analyses of AMR and AMU surveillance data under a One Health approach.

Antimicrobial resistance (AMR) is a global threat in humans and animals, and antimicrobial usage (AMU) has been identified as a main trigger of AMR. The purpose of this work was to compare data on AMR in clinical and non-clinical isolates of Escherichia coli in German broilers and turkeys between 2014 and 2017. Furthermore, we investigated AMR changes over time and the association of changes in AMU with changes in AMR. Data on clinical and non-clinical isolates together with data on therapy frequency of broilers and turkeys were collected from German monitoring systems. Logistic regression analyses were performed to assess the association between the explanatory factors (AMU, year and isolate type) and the dependent variable (AMR). In broilers, the analysis showed lower resistance proportions of clinical isolates of E. coli to ampicillin and colistin (ampicillin: Odds ratio (OR) and 95% confidence interval (CI) = 0.44 (0.3–0.64), p<0.001; colistin: OR and 95% CI = 0.75 (0.73–0.76), p<0.001) but higher proportions for cefotaxime (OR and 95% CI = 4.58 (1.56–15.1), p = 0.007). Resistance to ampicillin, gentamicin and tetracycline was less frequent in clinical isolates in turkeys (ampicillin: OR and 95% CI = 0.4 (0.29–0.53), p<0.001; gentamicin: OR and 95% CI = 0.5 (0.26–0.94), p = 0.035; tetracycline: OR and 95% CI = 0.4 (0.29–0.55), p<0.001). The analysis found decreasing associations of AMU with resistance to tetracycline in turkeys and to colistin in broilers. Year was associated with a decrease in resistance to colistin in broilers and to tetracycline in turkeys. Differences in resistance found in this study between clinical and non-clinical isolates might play an important role in resistance prevalence. This study indicated that further data analyses over longer time intervals are required to clarify the differences found between clinical and non-clinical isolates and to assess the long-term effects of changes in AMU on the prevalence of AMR.

Human exposure to bacteria carrying antimicrobial resistance (AMR) genes through the consumption of food of animal origin is a topic which has gained increasing attention in recent years. Bacterial transmission can be enhanced, particularly in situations in which the consumer pays less attention to hygiene practices, and consumer exposure to foodborne resistant bacteria through ready-to-eat foods could be increased. It has been demonstrated that even methicillin-resistant Staphylococcus aureus (MRSA) bacteria, which have low prevalence and concentration in raw chicken meat in Germany, may reach the consumer during barbecue events after failures in hygiene practices. This study aimed to quantify the consumer exposure to extended-spectrum beta-lactamase- (ESBL) or ampicillinase class C (AmpC) beta-lactamase-producing E. coli in Germany through the consumption of chicken meat and bread during household barbecues. The study considered cross-contamination and recontamination processes from raw chicken meat by using a previously-developed probabilistic consumer exposure model. In addition, a comparative analysis of consumer exposure was carried out between ESBL-/AmpC-producing E. coli and MRSA. Our results demonstrated that the probability of ESBL-/AmpC-producing E. coli reaching the consumer was 1.85 × 10⁻⁵ with the number of bacteria in the final serving averaging 332. Given the higher prevalence and concentration of ESBL-/AmpC-producing E. coli in raw chicken meat at retail compared to MRSA, comparative exposure assessment showed that the likelihood and extent of exposure were significantly higher for ESBL-/AmpC-producing E. coli than for MRSA. ESBL-/AmpC-producing E. coli was determined to be 7.6 times likelier (p-value < 0.01) than MRSA to reach the consumer, with five times the concentration of bacteria in the final serving (p-value < 0.01).

Livestock data on antimicrobial resistance (AMR) are commonly collected from bacterial populations of clinical and non-clinical isolates. In contrast to data on non-clinical isolates from livestock, data on clinical isolates are not harmonized in Europe. The Normalized Resistance Interpretation (NRI) method was applied to overcome the lack of harmonization of laboratory methods and interpretation rules between monitoring systems. Statistical analyses were performed to identify associations between the isolate type (clinical vs. non-clinical) and resistance to four antimicrobials (ampicillin, tetracycline, gentamicin, and nalidixic acid) per animal category in Germany and France. Additional statistical analyses comparing clinical and non-clinical isolates were performed with the available data on the same antimicrobial panel and animal categories from the UK and Norway. Higher resistance prevalence was found in clinical isolates compared to non-clinical isolates from calves to all antimicrobials included in Germany and France. It was also found for gentamicin in broilers from France. In contrast, in broilers and turkeys from Germany and France and in broilers from the UK, a higher resistance level to ampicillin and tetracycline in non-clinical isolates was encountered. This was also found in resistance to gentamicin in isolates from turkeys in Germany. Resistance differed within countries and across years, which was partially in line with differences in antimicrobial use patterns. Differences in AMR between clinical and non-clinical isolates of Escherichia coli are associated with animal category (broiler, calf, and turkey) and specific antimicrobials. The NRI method allowed comparing results of non-harmonized AMR systems and might be useful until international harmonization is achieved.

In the last decades, mathematical models and model-based simulations became important elements not only in the area of risk assessment concerning microbiological and chemical hazards but also in modelling biological phenomena in general. Unfortunately, many of the developed models are published in non-standardized ways, which hinders efficient exchange, re-use and continuous improvement of models within the risk assessment domain. The establishment of guidelines for model annotation is an important pre-condition to overcome these obstacles. Additionally, implementation of annotation guidelines can improve transparency, quality control and even aid the clarification of intellectual property rights. Here, we address the question of "What is the minimum set of metadata that should be provided for a model in the risk assessment domain?". The proposed guideline focuses on food safety risk assessment models and is called "Minimum Information Required to Annotate food safety Risk Assessment Models (MIRARAM)". MIRARAM supports the model creator during the model documentation step and could also be used as a checklist by scientific journal editors or database curators. Software developers could take up MIRARAM and develop easy-to-use software tools or new features in existing programs that can help model creators to provide proposed model annotations in harmonized file formats. Based on experiences from similar guidelines in related scientific disciplines (like systems biology), it is expected that MIRARAM could contribute to the promotion of application and re-use of models as well as to implementing more standardized quality control in the food safety modelling domain.