Background The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) affect healthcare seeking behaviour, access to healthcare, test strategies, disease notification and workload at public health authorities, but may also lead to a true change in transmission dynamics. We aimed to assess the impact of the pandemic and NPIs on other notifiable infectious diseases under surveillance in Germany. Methods We included 32 nationally notifiable disease categories with case numbers >100/year in 2016–2019. We used quasi-Poisson regression analysis on a weekly aggregated time-series incorporating trend and seasonality, to compute the relative change in case numbers during week 2020–10 to 2020–32 (pandemic/NPIs), in comparison to week 2016–01 to 2020–09. Findings During week 2020–10 to 2020–32, 216,825 COVID-19 cases, and 162,942 (-35%) cases of other diseases, were notified. Case numbers decreased across all ages and notification categories (all p <0•05), except for tick-borne encephalitis, which increased (+58%). The number of cases decreased most for respiratory diseases (from -86% for measles, to -12% for tuberculosis), gastro-intestinal diseases (from -83% for rotavirus gastroenteritis, to -7% for yersiniosis) and imported vector-borne diseases (-75% dengue fever, -73% malaria). The less affected infections were healthcare associated pathogens (from -43% infection/colonisation with carbapenem-non-susceptible Acinetobacter , to -28% for Methicillin-resistant Staphylococcus aureus invasive infection) and sexually transmitted and blood-borne diseases (from -28% for hepatitis B, to -12% for syphilis). Interpretation During the COVID-19 pandemic a drastic decrease of notifications for most infectious diseases and pathogens was observed. Our findings suggest effects of NPIs on overall disease transmission that require further investigation. Funding The Robert Koch Institute is the National Public Health Institute of Germany, and is an institute within the portfolio of the Federal Ministry of Health.
Background The COVID-19 pandemic expanded the need for timely information on acute respiratory illness at population level. Aim We explored the potential of routine emergency department data for syndromic surveillance of acute respiratory illness in Germany. Methods We used routine attendance data from emergency departments, which continuously transferred data between week 10 2017 and 10 2021, with ICD-10 codes available for > 75% of attendances. Case definitions for acute respiratory infection (ARI), severe acute respiratory infection (SARI), influenza-like illness (ILI), respiratory syncytial virus infection (RSV) and COVID-19 were based on a combination of ICD-10 codes, and/or chief complaints, sometimes combined with information on hospitalisation and age. Results We included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020 during the first COVID-19 pandemic wave, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/21. A pattern of seasonality of respiratory infections could be observed. By using different case definitions (i.e. for ARI, SARI, ILI, RSV) both the annual influenza seasons in the years 2017–2020 and the dynamics of the COVID-19 pandemic in 2020/21 were apparent. The absence of the 2020/21 influenza season was visible, parallel to the resurge of COVID-19 cases. SARI among ARI cases peaked in April–May 2020 (17%) and November 2020–January 2021 (14%). Conclusion Syndromic surveillance using routine emergency department data can potentially be used to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza viruses and SARS-CoV-2.
ZusammenfassungEchtzeitdaten aus der medizinischen Versorgung spielen für die Handlungsteuerung in Public Health eine entscheidende Rolle. Dies zeigt die COVID-19-Pandemie besonders deutlich: Viele Public-Health-Akteure sind auf die aktuellen Daten aus dem klinischen und Versorgungsgeschehen angewiesen, um wichtige Entscheidungen treffen und Empfehlungen geben zu können. Die Automatisierung der Verarbeitungs- und Kommunikationsprozesse ist essenziell, damit eine Kontinuität des Datenflusses gewährleistet wird und Ressourcen geschont werden. Bisher standen der Entwicklung digital automatisierter Echtzeitsysteme mit wissenschaftlichem Qualitätsanspruch verschiedene technische, fachliche und organisatorische Herausforderungen im Weg. Die COVID-19-Pandemie dient seit ihrem Beginn Anfang 2020 als Motor für zukunftsfähige Systementwicklungen.In diesem Beitrag wird zunächst beschrieben, wie ein Echtzeitdatensystem aufgebaut sein muss, damit eine automatisierte Datenverarbeitung möglich ist. Wichtige Aspekte bei der Zusammenführung der Daten, ihrer Aufbereitung, Bereitstellung und Kommunikation werden dargestellt. Als Beispiel dient ein System, das Routinedaten aus Notaufnahmen in Echtzeit verarbeitet und diese Public-Health-Akteuren bereitstellt. Es setzt sich zusammen aus dem Notaufnahmeregister des Aktionsbündnisses für Informations- und Kommunikationstechnologie in Intensiv- und Notfallmedizin (AKTIN) der Universität Magdeburg und der RWTH Aachen und dem Surveillance Monitor (SUMO) des Robert Koch-Instituts.Die Entwicklung zukunftsfähiger Echtzeitsysteme zur Verarbeitung von Forschungsdaten aus der medizinischen Versorgung kann nur durch die Zusammenarbeit verschiedenster Akteure gelingen. Eine wichtige Grundlage für den langfristigen Erfolg ist die Entwicklung eines gesetzlichen Rahmens.
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