No abstract

The nares represent an important bacterial reservoir for endogenous infections. This study aimed to assess the prevalence of nasal colonization by different important pathogens, the associated antimicrobial susceptibility and risk factors. We performed a prospective cohort study among 1878 nonhospitalized volunteers recruited from the general population in Germany. Participants provided nasal swabs at three time points (each separated by 4–6 months). Staphylococcus aureus, Enterobacteriaceae and important nonfermenters were cultured and subjected to susceptibility testing. Factors potentially influencing bacterial colonization patterns were assessed. The overall prevalence of S. aureus, Enterobacteriaceae and nonfermenters was 41.0, 33.4 and 3.7%, respectively. Thirteen participants (0.7%) were colonized with methicillin-resistant S. aureus. Enterobacteriaceae were mostly (>99%) susceptible against ciprofloxacin and carbapenems (100%). Extended-spectrum β-lactamase–producing isolates were not detected among Klebsiella oxytoca, Klebsiella pneumoniae and Escherichia coli. Several lifestyle- and health-related factors (e.g. household size, travel, livestock density of the residential area or occupational livestock contact, atopic dermatitis, antidepressant or anti-infective drugs) were associated with colonization by different microorganisms. This study unexpectedly demonstrated high nasal colonization rates with Enterobacteriaceae in the German general population, but rates of antibiotic resistance were low. Methicillin-resistant S. aureus carriage was rare but highly associated with occupational livestock contact.

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.

Several standards and frameworks have been described in existing literature and technical manuals that contribute to solving the interoperability problem. Their data models usually focus on clinical data and only support healthcare delivery processes. Research processes including cross organizational cohort size estimation, approvals and reviews of research proposals, consent checks, record linkage and pseudonymization need to be supported within the HiGHmed medical informatics consortium. The open source HiGHmed Data Sharing Framework implements a distributed business process engine for executing arbitrary biomedical research and healthcare processes modeled and executed using BPMN 2.0 while exchanging information using FHIR R4 resources. The proposed reference implementation is currently being rolled out to eight university hospitals in Germany as well as a trusted third party and available open source under the Apache 2.0 license.

Medical routine data promises to add value for research. However, the transfer of this data into a research context is difficult. Therefore, Medical Data Integration Centers are being set up to merge data from primary information systems in a central repository. But, data from one organization is rarely sufficient to answer a research question. The data must be merged beyond institutional boundaries. In order to use this data in a specific research project, a researcher must have the possibility to query available cohort sizes across institutions. A possible solution for this requirement is presented in this paper, using a process for fully automated and distributed feasibility queries (i.e. cohort size estimations). This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4 resources. The proposed solution is currently being deployed at eight university hospitals and one trusted third party across Germany.