http://edoc.rki.de/176904/44 2026-04-13T15:44:50Z 2026-04-13T15:44:50Z Bleischwitz, Sinja Winkelmann, Tristan Salomon Pfeifer, Yvonne Fischer, Martin Alexander Pfennigwerth, Niels Hammerl, Jens André Binsker, Ulrike Hans, Jörg B. Gatermann, Sören Käsbohrer, Annemarie Werner, Guido Kreienbrock, Lothar http://edoc.rki.de/176904/13616 2026-04-10T12:27:24Z 2024-07-16T00:00:00Z

Antimicrobial Resistance Surveillance: Data Harmonisation and Data Selection within Secondary Data Use Bleischwitz, Sinja; Winkelmann, Tristan Salomon; Pfeifer, Yvonne; Fischer, Martin Alexander; Pfennigwerth, Niels; Hammerl, Jens André; Binsker, Ulrike; Hans, Jörg B.; Gatermann, Sören; Käsbohrer, Annemarie; Werner, Guido; Kreienbrock, Lothar Resistance to last-resort antibiotics is a global threat to public health. Therefore, surveillance and monitoring systems for antimicrobial resistance should be established on a national and international scale. For the development of a One Health surveillance system, we collected exemplary data on carbapenem and colistin-resistant bacterial isolates from human, animal, food, and environmental sources. We pooled secondary data from routine screenings, hospital outbreak investigations, and studies on antimicrobial resistance. For a joint One Health evaluation, this study incorporates epidemiological metadata with phenotypic resistance information and molecular data on the isolate level. To harmonise the heterogeneous original information for the intended use, we developed a generic strategy. By defining and categorising variables, followed by plausibility checks, we created a catalogue for prospective data collections and applied it to our dataset, enabling us to perform preliminary descriptive statistical analyses. This study shows the complexity of data management using heterogeneous secondary data pools and gives an insight into the early stages of the development of an AMR surveillance programme using secondary data.

2024-07-16T00:00:00Z Blume, Miriam Bartig, Susanne Wollgast, Lina Koschollek, Carmen Kajikhina, Katja Bug, Marleen Hapke, Ulfert Hövener, Claudia http://edoc.rki.de/176904/13613 2026-04-10T09:27:25Z 2024-08-27T00:00:00Z

Determinants of Mental Health Inequalities Among People With Selected Citizenships in Germany Blume, Miriam; Bartig, Susanne; Wollgast, Lina; Koschollek, Carmen; Kajikhina, Katja; Bug, Marleen; Hapke, Ulfert; Hövener, Claudia Objectives: Mental health is essential for overall health and is influenced by different social determinants. The aim of this paper was to examine which determinants are associated with mental health inequalities among people with selected citizenships in Germany. Methods: Data were derived from the multilingual interview survey “German Health Update: Fokus (GEDA Fokus)” among adults with Croatian, Italian, Polish, Syrian, or Turkish citizenship (11/2021–05/2022). Poisson regressions were used to calculate prevalence ratios for symptoms of depression (PHQ-9) and anxiety disorder (GAD-7). Results: Sociodemographic (sex, income, age, household size) and psychosocial (social support and self-reported discrimination) determinants were associated with symptoms of depression and/or anxiety disorder. The prevalence of mental disorders varied most by self-reported discrimination. Conclusion: Our findings suggest mental health inequalities among people with selected citizenships living in Germany. To reduce these, social inequities and everyday discrimination need to be addressed in structural prevention measures as well as in interventions on the communal level. Protective factors (e.g., social support) are also important to reduce mental health inequalities on the individual and community level.

2024-08-27T00:00:00Z Bokelmann, Marcel Riesle-Sbarbaro, Silke A. Lander, Angelika Wahlbrink, Annette Groschup, Martin H. Balkema-Buschmann, Anne Couacy-Hymann, Emmanuel Prescott, Joseph Kurth, Andreas http://edoc.rki.de/176904/13612 2026-04-10T09:27:24Z 2024-08-09T00:00:00Z

Temperature as a potential regulator for Ebola virus replication in primary cells from Mops condylurus Bokelmann, Marcel; Riesle-Sbarbaro, Silke A.; Lander, Angelika; Wahlbrink, Annette; Groschup, Martin H.; Balkema-Buschmann, Anne; Couacy-Hymann, Emmanuel; Prescott, Joseph; Kurth, Andreas Introduction: For more than 40 years, outbreaks of ebolavirus disease have been documented, but the natural reservoir(s) of ebolaviruses remain unknown. However, recent studies provide evidence that the Angolan free-tailed bat (Mops condylurus), an insectivorous bat belonging to the family Molossidae, is a likely ebolavirus reservoir. Being a heterothermic species, M. condylurus bats are highly tolerant to variations in ambient temperatures, and therefore are capable of living under a broad range of climatic and environmental conditions by using adaptive thermoregulation. Body core temperatures as low as 12.0°C have been measured during winter, while increased body temperatures were observed in their hot roosts or during flight, reaching temperatures typical of fever in most other mammalian species. Methods: Here, we investigated the impact of temperature fluctuations between 27°C and 42°C on Ebola virus (EBOV) survival and replication kinetics in cells from M. condylurus using qRT-PCR. Results: We found that primary cells derived from M. condylurus, similar to the bats in their natural environment, were highly tolerant to temperature variations. EBOV replication was temperature-dependent, showing a strong reduction of replication efficiency at low temperature. Discussion: We therefore conclude, that heterothermy might be involved in balancing the level of EBOV replication and thereby be a key factor for tolerating EBOV infections in vivo.

2024-08-09T00:00:00Z Dubey, Akshat Yang, Zewen Hattab, Georges http://edoc.rki.de/176904/13600 2026-03-27T08:57:24Z 2024-10-24T00:00:00Z

AI Readiness in Healthcare through Storytelling XAI Dubey, Akshat; Yang, Zewen; Hattab, Georges Artificial Intelligence is rapidly advancing and radically impacting everyday life, driven by the increasing availability of computing power. Despite this trend, the adoption of AI in real-world healthcare is still limited. One of the main reasons is the trustworthiness of AI models and the potential hesitation of domain experts with model predictions. Explainable Artificial Intelligence (XAI) techniques aim to address these issues. However, explainability can mean different things to people with different backgrounds, expertise, and goals. To address the target audience with diverse needs, we develop storytelling XAI. In this research, we have developed an approach that combines multi-task distillation with interpretability techniques to enable audience-centric explainability. Using multi-task distillation allows the model to exploit the relationships between tasks, potentially improving interpretability as each task supports the other leading to an enhanced interpretability from the perspective of a domain expert. The distillation process allows us to extend this research to large deep models that are highly complex. We focus on both model-agnostic and model-specific methods of interpretability, supported by textual justification of the results in healthcare through our use case. Our methods increase the trust of both the domain experts and the machine learning experts to enable a responsible AI.

2024-10-24T00:00:00Z