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Background and Objective: The essential role of infection prevention in long-term care facilities has become evident during the current SARS-CoV‑2 pandemic. In order to obtain a reliable database on nosocomial infections and antibiotic use, the European Centre for Disease Prevention and Control (ECDC) initiated the third point prevalence survey in European long-term care facilities from 2016 to 2017 (HALT-3)., Material and Methods: In Germany, 131 facilities with 10,565 residents participated voluntarily. On a single day in 2016, the number of nosocomial infections and/or uses of antibiotics as well as care characteristics and risk factors of the residents were recorded. Infections were documented based on symptoms using an algorithm in accordance with the McGeer surveillance criteria for long-term care facilities., Results: A nosocomial infection was documented in 177 residents, which corresponds to a prevalence of 1.7% (95% CI: 1.3-2.1), still low in comparison with the European prevalence (mean value 3.9%). Urinary tract infections were the most common infections at almost 50%, followed by respiratory, skin, and soft-tissue infections. The type of infection was consistent with the most common indications for antibiotic use. Antibiotic use was documented in 143 residents (prevalence of 1.4%, 95% CI: 1.1-1.7). The frequent use of fluoroquinolones with over 20% of all prescriptions was noticeable., Conclusions: The establishment of facility-based surveillance of the most common infections and antibiotic consumption, together with the creation of guidelines specifically tailored to the geriatric population, could contribute to improving infection prevention and control as well as a more rational use of antibiotics, thus increasing the quality and safety of care., (© 2022. The Author(s).)

German national surveillance data analysis shows that hospitalisation odds associated with Omicron lineage BA.1 or BA.2 infections are up to 80% lower than with Delta infection, primarily in ≥ 35-year-olds. Hospitalised vaccinated Omicron cases' proportions (2.3% for both lineages) seemed lower than those of the unvaccinated (4.4% for both lineages). Independent of vaccination status, the hospitalisation frequency among cases with Delta seemed nearly threefold higher (8.3%) than with Omicron (3.0% for both lineages), suggesting that Omicron inherently causes less severe disease.

The early detection of infectious disease outbreaks is a crucial task to protect population health. To this end, public health surveillance systems have been established to systematically collect and analyse infectious disease data. A variety of statistical tools are available, which detect potential outbreaks as abberations from an expected endemic level using these data. Here, we present supervised hidden Markov models for disease outbreak detection, which use reported outbreaks that are routinely collected in the German infectious disease surveillance system and have not been leveraged so far. This allows to directly integrate labeled outbreak data in a statistical time series model for outbreak detection. We evaluate our model using real Salmonella and Campylobacter data, as well as simulations. The proposed supervised learning approach performs substantially better than unsupervised learning and on par with or better than a state-of-the-art approach, which is applied in multiple European countries including Germany., Competing Interests: The authors have declared that no competing interests exist.

The collection of data on SARS-CoV‑2 tests is central to the assessment of the infection rate in the context of the COVID-19 pandemic. At the Robert Koch Institute (RKI), data collected from various laboratory data recording systems are consolidated. First, this article aims to exemplify significant aspects regarding test procedures. Subsequently the different systems for recording laboratory tests are described and test numbers from the RKI test laboratory query and the laboratory-based SARS-CoV‑2 surveillance as well as accounting data from the Association of Statutory Health Insurance Physicians for SARS-CoV‑2 laboratory tests are shown.Early in the pandemic, the RKI test laboratory query and the laboratory-based SARS-CoV‑2 surveillance became available and able to evaluate data on performed tests and test capacities. By recording the positive and negative test results, statements about the total number of tests and the proportion of positive test rates can be made. While the aggregate test numbers are largely representative nationwide, they are not always representative at the state and district level. The billing data of the Association of Statutory Health Insurance Physicians can complement the laboratory data afterwards. In addition, it can provide a retrospective assessment of the total number of SARS-CoV‑2 numbers in Germany, because the services provided by statutory health insurers (around 85% of the population in Germany) are included. The various laboratory data recording systems complement one another and the evaluations flow into the recommended measures for the pandemic response.

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., Competing Interests: The authors have declared that no competing interests exist.

BackgroundHealthcare-associated infections (HAIs) pose a major challenge to health systems. Burden of disease estimations in disability-adjusted life years (DALYs) are useful for comparing and ranking HAIs.AimTo estimate the number of five common HAIs, their attributable number of deaths and burden for Germany.MethodsWe developed a new method and R package that builds on the approach used by the Burden of Communicable Diseases in Europe (BCoDE) project to estimate the burden of HAIs for individual countries. We used data on healthcare-associated Clostridioides difficile infection, healthcare-associated pneumonia, healthcare-associated primary bloodstream infection, healthcare-associated urinary tract infection and surgical-site infection, which were collected during the point prevalence survey of HAIs in European acute-care hospitals between 2011 and 2012.ResultsWe estimated 478,222 (95% uncertainty interval (UI): 421,350-537,787) cases for Germany, resulting in 16,245 (95% UI: 10,863-22,756) attributable deaths and 248,920 (95% UI: 178,693-336,239) DALYs. Despite the fact that Germany has a relatively low hospital prevalence of HAIs compared with the European Union/European Economic Area (EU/EEA) average, the burden of HAIs in Germany (308.2 DALYs/100,000 population; 95% UI: 221.2-416.3) was higher than the EU/EEA average (290.0 DALYs/100,000 population; 95% UI: 214.9-376.9). Our methodology is applicable to other countries in or outside of the EU/EEA. An R package is available from https://CRAN.R-project.org/package=BHAI.ConclusionThis is the first study to estimate the burden of HAIs in DALYs for Germany. The large number of hospital beds may be a contributing factor for a relatively high burden of HAIs in Germany. Further focus on infection prevention control, paired with reduction of avoidable hospital stays, is needed to reduce the burden of HAIs in Germany.