Comparison of MICs in Escherichia coli isolates from human health surveillance with MICs obtained for the same isolates by broth microdilution

Objectives Human health surveillance and food safety monitoring systems use different antimicrobial susceptibility testing (AST) methods. In this study, we compared the MICs of Escherichia coli isolates provided by these methods. Methods E. coli isolates (n = 120) from human urine samples and their MICs were collected from six medical laboratories that used automated AST methods based on bacterial growth kinetic analyses. These isolates were retested using broth microdilution, which is used by the food safety monitoring system. The essential and categorical agreements (EA and CA), very major errors (VME), major errors (ME) and minor errors (mE) for these two methods were calculated for 11 antibiotics using broth microdilution as a reference. For statistical analysis, clinical breakpoints provided by EUCAST were used. Results Five study laboratories used VITEK®2 and one MicroScan (Walkaway Combo Panel). Out of 120 isolates, 118 isolates (98.3%) were confirmed as E. coli. The 99 E. coli isolates from five study laboratories that used VITEK®2 showed high proportions of EA and CA with full agreements for gentamicin, meropenem, imipenem and ertapenem. Additionally, 100% CA was also observed in cefepime. Few VME (0.5%), ME (1.9%) and mE (1.5%) were observed across all antibiotics. One VME for ceftazidime (7.1%) and 12 MEs for ampicillin (29.4%), cefotaxime (2.4%), ciprofloxacin (3.2%), tigecycline (1.5%) and trimethoprim (22.2%) were detected. Conclusions MICs from E. coli isolates produced by VITEK®2 were similar to those determined by broth microdilution. These results will be valuable for comparative analyses of resistance data from human health surveillance and food safety monitoring systems

SARS-CoV-2 outbreaks in hospitals and long-term care facilities in Germany: a national observational study

Background: Outbreaks of coronavirus disease (COVID-19) in hospitals and long-term care facilities (LTCFs) pose serious public health threats. We analysed how frequency and size of SARS-CoV-2 outbreaks in hospitals and LTCFs have altered since the beginning of the pandemic, in particular since the start of the vaccination campaign. Methods: We used mandatory notification data on SARS-CoV-2 cases in Germany and stratified by outbreak cases in hospitals and LTCFs. German vaccination coverage data were analysed. We studied the association of the occurrence of SARS-CoV-2 outbreaks and outbreak cases with SARS-CoV-2 cases in Germany throughout the four pandemic waves. We built also counterfactual scenarios with the first pandemic wave as the baseline. Findings: By 21 September 2021, there were 4,147,387 SARS-CoV-2 notified cases since March 2020. About 20% of these cases were reported as being related to an outbreak, with 1% of the cases in hospitals and 4% in LTCFs. The median number of outbreak cases in the different phases was smaller (≤5) in hospitals than in LTCFs (>10). In the first and second pandemic waves, we observed strong associations in both facility types between SARS-CoV-2 outbreak cases and total number of notified SARS-CoV-2 cases. However, during the third pandemic wave we observed a decline in outbreak cases in both facility types and only a weak association between outbreak cases and all cases. Interpretation: The vaccination campaign and non-pharmaceutical interventions have been able to protect vulnerable risk groups in hospitals and LTCFs. Funding: No specific fundingPeer Reviewe

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

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 data on resistance combinations in Escherichia coli (E. coli) from different routine surveillance and monitoring systems for human and different animal populations in Germany. Data on E. coli isolates were collected between 2014 and 2017 from human clinical isolates, non-clinical animal isolates from food-producing animals and food, and clinical animal isolates from food-producing and companion animals from national routine surveillance and monitoring for AR in Germany. Sixteen possible resistance combinations to four antibiotics—ampicillin, cefotaxime, ciprofloxacin and gentamicin–for these populations were used for hierarchical clustering (Euclidian and average distance). All analyses were performed with the software R 3.5.1 (Rstudio 1.1.442). Data of 333,496 E. coli isolates and 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. They also clustered separately from the human populations. Cluster analysis has been able to demonstrate the linkage among human isolates and isolates from various animal populations based on the resistance combinations. Further analyses based on these findings might support a better one-health approach for AR in Germany.Peer Reviewe

COSIK – COVID-19-Surveillance in Krankenhäusern

Um die Belastung der Krankenhäuser durch die COVID-19-Pandemie darzustellen und auch nosokomiale SARS-CoV-2-Infektionen zu erfassen, hat das RKI in Zusammenarbeit mit dem Nationalen Referenzzentrum für die Surveillance von nosokomialen Infektionen an der Charité Berlin eine systematische Krankenhaus-Surveillance von SARS-CoV-2-Infektionen in Deutschland, COSIK, entwickelt

Bakterielle Zoonosen mit Bedeutung für den öffentlichen Gesundheitsschutz in Deutschland – Vorkommen, Verbreitung und Übertragungswege

Bakterielle zoonotische Erreger sind häufig Auslöser von Erkrankungen mit teilweise schweren Verläufen. Sie sind wechselseitig zwischen Tieren (sowohl Wild- als auch Haustieren) und Menschen übertragbar. Die Transmissionswege sind sehr variabel, so kann die Übertragung u. a. durch orale Aufnahme über Lebensmittel, respiratorische Aufnahme über Tröpfchen und Aerosole sowie über Vektoren wie Zeckenstiche oder Nagerkontakte stattfinden. In diesem Zusammenhang sind auch das Auftreten und die Verbreitung von antibiotikaresistenten bakteriellen Erregern von zunehmender Bedeutung für den öffentlichen Gesundheitsschutz. Die Ausbreitung zoonotischer Erreger wird aktuell durch zahlreiche Faktoren verstärkt. Dazu gehören die Zunahme des internationalen Warenverkehrs, die Einengung der Lebensräume von Tieren und der dadurch zunehmend engere Kontakt zwischen Menschen und Wildtieren. Aber auch eine veränderte Tierhaltung in der Landwirtschaft und Klimaveränderungen können zur Ausbreitung beitragen. Der öffentliche Gesundheitsschutz und die Erforschung von Zoonosen sind deshalb von besonderer krankheitspräventiver, aber auch gesellschaftlicher, politischer und wirtschaftlicher Bedeutung. Ziel dieses Übersichtsartikels ist es, anhand von Beispielen die Spannbreite von Infektionskrankheiten darzustellen, die durch bakterielle zoonotische Erreger ausgelöst werden. Die unterschiedlichen Transmissionswege, epidemischen Potenziale und epidemiologischen Maßzahlen der beispielhaft gewählten Krankheiten sind Herausforderungen für den öffentlichen Gesundheitsdienst, den Tiergesundheitsdienst und die Lebensmittelüberwachung, deren Aufgabe es ist, die Bevölkerung vor diesen Infektionskrankheiten zu schützen.Bacterial zoonotic pathogens are often the cause of diseases, sometimes with severe outcomes. They are mutually transferable between animals (both wild and domestic) and humans. The transmission paths are very variable and include oral intake via food, respiratory infection via droplets and aerosols, or infections via vectors such as tick bites or rodent contact. Furthermore, the emergence and spread of antibiotic-resistant bacterial pathogens is of paramount public health concern. The likelihood of further spread is influenced by various factors. These include the increase in international trade, the endangerment of animal habitats, and the increasingly closer contact between humans and wild animals. Additionally, changes in livestock and climate change may also contribute. Therefore, research into zoonoses serves to protect human and animal health and is of particular social, political, and economic importance. The aim of this review article is to present the range of infectious diseases caused by bacterial zoonotic pathogens in order to provide a better understanding of the important work in public health services, animal health services, and food safety control. The different transmission routes, epidemic potentials, and epidemiological measures of the exemplary selected diseases show the challenges for the public health system to monitor and control the spread of these bacterial pathogens in order to protect the population from disease.Peer Reviewe

Bakterielle Antibiotikaresistenzen in Menschen und unterschiedlichen Tierpopulationen: ein Vergleich von phänotypischen Daten aus den nationalen Surveillance- und Monitoringsystemen in Deutschland

Antibiotikaresistenzen bei Bakterien kommen häufig bei Menschen, unterschiedlichen Tierpopulationen und in der Umwelt vor. Es ist daher wichtig diesen Problemen mit einem sektorübergreifenden Ansatz, wie dem „One Health-Ansatz“, zu untersuchen. Im Rahmen dieses One Health Ansatzes war das Ziel dieser Arbeit eine sektorübergreifende Harmonisierung der Daten von Antibiotikaresistenzen zu untersuchen, um eine verbesserte Einschätzung der derzeitigen Lage und Erkennung von Trends von Antibiotikaresistenzen zu erhalten und verbesserte Maßnahmen dafür abzuleiten. Die Resistenzdaten wurden aus unterschiedlichen Surveillance- und Monitoring Systemen für Menschen und verschiedene Tierpopulationen in Deutschland gewonnen: Antibiotikaresistenz Surveillance System (ARS) für die Resistenzdaten bei Menschen, Zoonosen Monitoring für die Resistenzdaten aus gesunden Nutztieren und Lebensmitteln und GERM-Vet für die Resistenzdaten von Tierpathogenen. Als Modellorganismus wurde die Daten von Escherichia coli (E. coli) aus den drei Systemen verwendet. Zunächst wurde die Vergleichbarkeit der Resistenzdaten anhand der Übereinstimmung der Resistenzdaten aus unterschiedlichen Labormethoden für Routinediagnostik in der Human- und Veterinärmedizinbereiche sowie Lebensmittelsicherheit analysiert. Zweitens, vier Antibiotika, die routinemäßig in den drei Systemen getestet sind, und Ergebnissen zu den Empfindlichkeitstestungen, wurden ausgewählt und weiterhin benutzt um die Ähnlichkeiten der Resistenzkombinationen zwischen den unterschiedlichen Human- und Tierpopulationen zu untersuchen. Aus den unterschiedlichen Analysen konnten es festgestellt werden, dass die Routinediagnostik sowie die davon erhaltenen Resistenzdaten für E. coli aus den drei Surveillance- und Monitoringsysteme vergleichbar sind. Darüber hinaus konnten die ähnlichen Resistenzkombinationen von E. coli zwischen den unterschiedlichen Human- und Tierpopulationen erkannt werden, wie z.B. die Ähnlichkeiten der Resistenzkombinationen innerhalb der klinischen E. coli-Isolate aus unterschiedlichen Stationen und zwischen Humanpopulationen und erkrankten Tieren. Somit leistet diese Arbeit einen wichtigen Beitrag zum One-Health-Konzept für integrierte Datenanalyse der Resistenzlage in Deutschland und ist eine Grundlage für die Weiterentwicklung der Strategien zu harmonisierten und standardisierten Surveillance- und Monitoringsystemen für Antibiotikaresistenzen in Deutschland wie im Bericht zu Deutsche Antibiotika-Resistenzstrategie 2030 (DART 2030) beschrieben wurde.Bacterial antibiotic resistance occurs frequently in humans, different animal populations and in the environment. It is therefore important to investigate these problems using a multi-sectoral approach, such as the "One Health". Within this framework, the aim of this thesis was to investigate a joint analysis of antibiotic resistance data in order to obtain an improved assessment of the current situation and detection of trends in antibiotic resistance. The resistance data originated from different surveillance and monitoring systems for humans and different animal populations in Germany: Antibiotic Resistance Surveillance System (ARS) for the resistance data in humans, Zoonoses Monitoring for the resistance data from healthy livestock and food and GERM-Vet for the resistance data from animal pathogens. Escherichia coli (E. coli) data from the three systems was used as a model organism. First, the comparability of the resistance data was analysed based on the agreement of the resistance data from different laboratory methods for routine diagnostics in human and veterinary medicine as well as food safety. Second, four antibiotics routinely tested in the three systems and susceptibility testing results, were selected and further used to analyse the similarities of resistance combinations between the different human and animal populations. The different analyses highlighted the comparability of routine diagnostics and the resistance data for E. coli from the three surveillance and monitoring systems. In addition, the similar resistance combinations of E. coli between the different human and animal populations could be recognised, such as the similarities of the resistance combinations within the clinical E. coli isolates from different stations and between human populations and diseased animals. Thus, this work makes an important contribution to the One Health concept for integrated data analysis of the resistance situation in Germany and is a basis for the further development of strategies for harmonised and standardised surveillance and monitoring systems for antibiotic resistance in Germany as described in the report on the German Antibiotic Resistance Strategy 2030 (DART 2030)

Analyse der Datenqualität nach Einführung der elektronischen Labormeldung über DEMIS

Die Einführung automatisierter, elektronischer Meldungen von Infektionskrankheiten ist insbesondere in großen Ländern und bei dezentraler Organisation des Gesundheitssystems herausfordernd. In Deutschland wurde dafür das Deutsche Elektronische Melde- und Informationssystem für den Infektionsschutz (DEMIS) entwickelt. Seit dem 1.1.2022 sollen alle Erregernachweismeldungen über DEMIS erfolgen. Das Ziel der vorgestellten Analyse, war es zu untersuchen inwieweit die Einführung der elektronischen Labormeldung einzelne Qualitätsaspekte der Meldedaten im Jahr 2022 beeinflusste. Dafür wurden die meldepflichtigen Nachweise von sechs Erregern anhand ihrer Häufigkeit und diagnostischen Komplexität ausgewählt - auch mit dem Ziel, eine möglichst große Bandbreite von Meldeszenarien abzudecken. Eine Weiterführung der Auswertung mit den Meldedaten des Jahres 2023 ist bereits geplant

Recombinant production of A1S_0222 from Acinetobacter baumannii ATCC 17978 and confirmation of its DNA-(adenine N6)-methyltransferase activity

Acinetobacter baumannii appears as an often multidrug-resistant nosocomial pathogen in hospitals worldwide. Its remarkable persistence in the hospital environment is probably due to intrinsic and acquired resistance to disinfectants and antibiotics, tolerance to desiccation stress, capability to form biofilms, and is possibly facilitated by surface-associated motility. Our attempts to elucidate surface-associated motility in A. baumannii revealed a mutant inactivated in a putative DNA-(adenine N6)-methyltransferase, designated A1S_0222 in strain ATCC 17978. We recombinantly produced A1S_0222 as a glutathione S-transferase (GST) fusion protein and purified it to near homogeneity through a combination of GST affinity chromatography, cation exchange chromatography and PD-10 desalting column. Furthermore we demonstrate A1S_0222-dependent adenine methylation at a GAATTC site. We propose the name AamA (Acinetobacteradenine methyltransferase A) in addition to the formal names M.AbaBGORF222P/M.Aba17978ORF8565P. Small angle X-ray scattering (SAXS) revealed that the protein is monomeric and has an extended and likely two-domain shape in solution