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In the early phase of the COVID-19 pandemic, many local collections of clinical data on patients infected with SARS-CoV-2 were initiated in Germany. As part of the National Pandemic Cohort Network (NAPKON) of the University Medicine Network, the "Integration Core" was established to design the legal, technical and organisational requirements for the integration of inventory data into ongoing prospective data collections and to test the feasibility of the newly developed solutions using use cases (UCs). Detailed study documents of the data collections were obtained. After structured document analysis, a review board evaluated the integrability of the data in NAPKON according to defined criteria. Of 30 university hospitals contacted, 20 responded to the request. Patient information and consent showed a heterogeneous picture with regard to the pseudonymised transfer of data to third parties and re-contact. The majority of the data collections (n=13) met the criteria for integration into NAPKON; four studies would require adjustments to the regulatory documents. Three cohorts were not suitable for inclusion in NAPKON. The legal framework for retrospective data integration and consent-free data use via research clauses (§27 BDSG) was elaborated by a legal opinion by TMF - Technology, Methods and Infrastructure for Networked Medical Research, Berlin. Two UCs selected by the NAPKON steering committee (CORKUM, LMU Munich; Pa-COVID-19, Charité- Universitätsmedizin Berlin) were used to demonstrate the feasibility of data integration in NAPKON by the end of 2021. Quality assurance and performance-based reimbursement of the cases were carried out according to the specifications. Based on the results, recommendations can be formulated for various contexts in order to create technical-operational prerequisites such as interoperability, interfaces and data models for data integration and to fulfil regulatory requirements on ethics, data protection, medical confidentiality and data access when integrating existing cohort data. The possible integration of data into research networks and their secondary use should be taken into account as early as the planning phase of a study - particularly with regard to informed consent - in order to maximise the benefits of the data collected.
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http://dx.doi.org/10.1055/a-2346-9680 | DOI Listing |
Public Health
September 2025
Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Objectives: Participation rates in fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening differ across socio-demographic subgroups. The largest health gains could be achieved in subgroups with low participation rates and high risk of CRC. We investigated the CRC risk within different socio-demographic subgroups with low participation in the Dutch CRC screening program.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFWater Res
September 2025
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address:
Groundwater overextraction presents persistent challenges due to strategic interdependence among decentralized users. While game-theoretic models have advanced the analysis of individual incentives and collective outcomes, most frameworks assume fully rational agents and neglect the role of cognitive and social factors. This study proposes a coupled model that integrates opinion dynamics with a differential game of groundwater extraction, capturing the interaction between institutional authority and evolving stakeholder preferences.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
Anim Reprod Sci
September 2025
Department of Biomedical & Clinical Sciences (BKV), BKH/Obstetrics & Gynecology, Faculty of Medicine and Health Sciences, Linköping University, Linköping SE-58185, Sweden.
Embryo transfer (ET) is a valuable reproductive technology in pigs, albeit its efficiency remains significantly lower than that of natural mating or artificial insemination (AI), owing to high embryonic death rates. Critical for embryo survival and pregnancy success is the placenta, which supports conceptus development through nutrient exchange, hormone production, and immune modulation. Alterations in placental development and function may therefore underlie the reduced efficiency of ET.
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