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Fair allocation of funding in multi-centre clinical studies is challenging. Models commonly used in Germany - the case fees (“fixed-rate model”, FRM) and up-front staffing and consumables (“up-front allocation model”, UFAM) lack transparency and fail to suitably accommodate variations in centre performance. We developed a performance-based reimbursement model (PBRM) with automated calculation of conducted activities and applied it to the cohorts of the National Pandemic Cohort Network (NAPKON) within the Network of University Medicine (NUM). The study protocol activities, which were derived from data management systems, underwent validation through standardized quality checks by multiple stakeholders. The PBRM output (first funding period) was compared among centres and cohorts, and the cost-efficiency of the models was evaluated. Cases per centre varied from one to 164. The mean case reimbursement differed among the cohorts (1173.21€ [95% CI 645.68–1700.73] to 3863.43€ [95% CI 1468.89–6257.96]) and centres and mostly fell short of the expected amount. Model comparisons revealed higher cost-efficiency of the PBRM compared to FRM and UFAM, especially for low recruitment outliers. In conclusion, we have developed a reimbursement model that is transparent, accurate, and flexible. In multi-centre collaborations where heterogeneity between centres is expected, a PBRM could be used as a model to address performance discrepancies. Trial registration: ; ; . [ABSTRACT FROM AUTHOR]

Background: Depending on the underlying etiology and epilepsy type, the burden of disease for patients with seizures can vary significantly. This analysis aimed to compare direct and indirect costs and quality of life (QoL) among adults with tuberous sclerosis complex (TSC) related with epilepsy, idiopathic generalized epilepsy (IGE), and focal epilepsy (FE) in Germany. Methods: Questionnaire responses from 92 patients with TSC and epilepsy were matched by age and gender, with responses from 92 patients with IGE and 92 patients with FE collected in independent studies. Comparisons were made across the main QoL components, direct costs (patient visits, medication usage, medical equipment, diagnostic procedures, ancillary treatments, and transport costs), indirect costs (employment, reduced working hours, missed days), and care level costs. Results: Across all three cohorts, mean total direct costs (TSC: €7602 [median €2620]; IGE: €1919 [median €446], P < 0.001; FE: €2598 [median €892], P < 0.001) and mean total indirect costs due to lost productivity over 3 months (TSC: €7185 [median €11,925]; IGE: €3599 [median €0], P < 0.001; FE: €5082 [median €2981], P = 0.03) were highest among patients with TSC. The proportion of patients with TSC who were unemployed (60%) was significantly larger than the proportions of patients with IGE (23%, P < 0.001) or FE (34%, P = P < 0.001) who were unemployed. Index scores for the EuroQuol Scale with 5 dimensions and 3 levels were significantly lower for patients with TSC (time-trade-off [TTO]: 0.705, visual analog scale [VAS]: 0.577) than for patients with IGE (TTO: 0.897, VAS: 0.813; P < 0.001) or FE (TTO: 0.879, VAS: 0.769; P < 0.001). Revised Epilepsy Stigma Scale scores were also significantly higher for patients with TSC (3.97) than for patients with IGE (1.48, P < 0.001) or FE (2.45, P < 0.001). Overall Quality of Life in Epilepsy Inventory-31 items scores was significantly lower among patients with TSC (57.7) and FE (57.6) than among patients with IGE (66.6, P = 0.004 in both comparisons). Significant differences between patients with TSC and IGE were also determined for Neurological Disorder Depression Inventory for Epilepsy (TSC: 13.1; IGE: 11.2, P = 0.009) and Liverpool Adverse Events Profile scores (TSC: 42.7; IGE: 37.5, P = 0.017) with higher score and worse results for TSC patients in both questionnaires. Conclusions: This study is the first to compare patients with TSC, IGE, and FE in Germany and underlines the excessive QoL burden and both direct and indirect cost burdens experienced by patients with TSC. Key points: • This is the first analysis to compare QoL and direct/indirect cost burden among patients with TSC, IGE, and FE in Germany. • Generic QoL for patients with TSC was significantly lower than for patients with IGE and FE. • Care grade and disability cards were more frequently obtained by patients with TSC than by patients in the other cohorts. • Drug treatment costs were the highest direct cost component for patients with TSC. • More patients with TSC than with IGE or FE were unemployed, and the mean indirect productivity costs were highest for the TSC cohort. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Prof. Heuschmann berichtet über Zuschüsse vom deutschen Bundesministerium für Forschung und Bildung, während der Durchführung der Studie; weiterhin erhielt er Forschungszuschüsse des Bundesministeriums für Forschung und Bildung, Europäischen Union, Charité-Universitätsmedizin Berlin, Ärztekammer Berlin Ärztekammer Berlin, Deutsche Parkinson Gesellschaft, Universitätsklinikum Würzburg, Robert-Koch-Institut, Deutsche Herzstiftung, Gemeinsamer Bundesausschuss Gemeinsamer Bundesausschuss (G-BA) im Innovationsfond, Deutsche Forschungsgemeinschaft Forschungsgemeinschaft, Bayerischer Staat (Ministerium für Wissenschaft und Kunst), Deutsche Krebshilfe, Charité-Universitätsmedizin Berlin (im Rahmen von Mondafis; unterstützt durch einen uneingeschränkten Forschungszuschuss von Bayer an die Charité), Universität Göttingen (im Rahmen von FIND-AF randomisiert; unterstützt durch eine Universität Göttingen von Boehringer-Ingelheim), Universitätsklinikum Heidelberg (im Rahmen von RASUNOAprime; unterstützt durch einen uneingeschränkten Forschungszuschuss an die Universität Universitätsklinikum Heidelberg von Bayer, BMS, Boehringer-Ingelheim, Daiichi Sankyo), außerhalb der eingereichten Arbeit. Dr. Scherer berichtet ein Referentenhonorar von AstraZeneca, unabhängig von dieser Arbeit. Prof. Jens-Peter Reese berichtet über Forschungszuschüsse des Bundesministeriums für Forschung und Bildung, des Bayerischen Staates (Ministerium für Wissenschaft und Kunst), des Gemeinsamen Bundesausschusses (G-BA) im Rahmen des Innovationsfonds, des Deutschen Zentrums für Lungenforschung; Honorar für Gutachten des Bundesministeriums für Gesundheit (BMG); Honorar für EBM-Fortbildungsvorlesung durch die Landesärztekammer Hessen, außerhalb der eingereichten Arbeit. Alle anderen Ko-Autor:innen geben an, keine Interessenskonflikte zu haben., (Thieme. All rights reserved.)