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.

Tattooing is a globally growing trend. Overall prevalence among adults in industrialized countries is around 10-20%. Given the high and increasing numbers of tattooed people worldwide, medical and public health implications emerging from tattooing trends require greater attention not only by the public, but also by medical professionals and health policy makers. We performed a systematic review of the literature on tattooassociated bacterial infections and bacterial contamination of tattoo inks. Furthermore, we surveyed tattoo inks sampled during an international tattoo convention in Germany to study their microbial status. Our systematic review identified 67 cases published between 1984 and 2015, mainly documenting serious bacterial infectious complications after intradermal deposition of tattoo inks. Both local skin infections (e.g. abscesses, necrotizing fasciitis) and systemic infections (e.g. endocarditis, septic shock) were reported. Published bacteriological surveys showed that opened as well as unopened tattoo ink bottles frequently contained clinically relevant levels of bacteria indicating that the manufactured tattoo product itself may be a source of infection. In our bacteriological survey, two of 39 colorants were contaminated with aerobic mesophilic bacteria. Inappropriate hygiene measures in tattoo parlors and non-medical wound care are major risk factors for tattoo-related infections. In addition, facultative pathogenic bacterial species can be isolated from tattoo inks in use, which may pose a serious health risk.

The Numeral Unit Spread Assessment Pedigree (NUSAP) system was implemented to evaluate assumptions in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. This QMRA model allows the testing of mitigation strategies for the reduction of human salmonellosis and aims to serve as a basis for science‐based policy making. The NUSAP method was used to assess the subjective component of assumptions in the QMRA model by a set of four pedigree criteria: ‘the influence of situational limitations’, ‘plausibility’, ‘choice space’ and ‘the agreement among peers’. After identifying 13 key assumptions relevant for the QMRA model, a workshop was organized to assess the importance of these assumptions on the output of the QMRA. The quality of the assumptions was visualized using diagnostic and kite diagrams. The diagnostic diagram pinpointed assumptions with a high degree of subjectivity and a high ‘expected influence on the model results’ score. Examples of those assumptions that should be dealt with care are the assumptions regarding the concentration of Salmonella on the pig carcass at the beginning of the slaughter process and the assumptions related to the Salmonella prevalence in the slaughter process. The kite diagrams allowed a clear overview of the pedigree scores for each assumption as well as a representation of expert (dis)agreement. The evaluation of the assumptions using the NUSAP system enhanced the debate on the uncertainty and its communication in the results of a QMRA model. It highlighted the model’s strong and weak points and was helpful for redesigning critical modules. Since the evaluation of assumptions allows a more critical approach of the QMRA process, it is useful for policy makers as it aims to increase the transparency and acceptance of management decisions based on a QMRA model.