A precise performance-based reimbursement model for the multi-centre NAPKON cohorts - development and evaluation.

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: https://clinicaltrials.gov/ct2/show/NCT04768998 ; https://clinicaltrials.gov/ct2/show/NCT04747366 ; https://clinicaltrials.gov/ct2/show/NCT04679584 .

Comparison of post-COVID-19 symptoms in patients infected with the SARS-CoV-2 variants delta and omicron-results of the Cross-Sectoral Platform of the German National Pandemic Cohort Network (NAPKON-SUEP).

PURPOSE: The influence of new SARS-CoV-2 variants on the post-COVID-19 condition (PCC) remains unanswered. Therefore, we examined the prevalence and predictors of PCC-related symptoms in patients infected with the SARS-CoV-2 variants delta or omicron. METHODS: We compared prevalences and risk factors of acute and PCC-related symptoms three months after primary infection (3MFU) between delta- and omicron-infected patients from the Cross-Sectoral Platform of the German National Pandemic Cohort Network. Health-related quality of life (HrQoL) was determined by the EQ-5D-5L index score and trend groups were calculated to describe changes of HrQoL between different time points. RESULTS: We considered 758 patients for our analysis (delta: n = 341; omicron: n = 417). Compared with omicron patients, delta patients had a similar prevalence of PCC at the 3MFU (p = 0.354), whereby fatigue occurred most frequently (n = 256, 34%). HrQoL was comparable between the groups with the lowest EQ-5D-5L index score (0.75, 95% CI 0.73-0.78) at disease onset. While most patients (69%, n = 348) never showed a declined HrQoL, it deteriorated substantially in 37 patients (7%) from the acute phase to the 3MFU of which 27 were infected with omicron. CONCLUSION: With quality-controlled data from a multicenter cohort, we showed that PCC is an equally common challenge for patients infected with the SARS-CoV-2 variants delta and omicron at least for the German population. Developing the EQ-5D-5L index score trend groups showed that over two thirds of patients did not experience any restrictions in their HrQoL due to or after the SARS-CoV-2 infection at the 3MFU. CLINICAL TRAIL REGISTRATION: The cohort is registered at ClinicalTrials.gov since February 24, 2021 (Identifier: NCT04768998).

Definition of the Post-COVID syndrome using a symptom-based Post-COVID score in a prospective, multi-center, cross-sectoral cohort of the German National Pandemic Cohort Network (NAPKON).

PURPOSE: The objective examination of the Post-COVID syndrome (PCS) remains difficult due to heterogeneous definitions and clinical phenotypes. The aim of the study was to verify the functionality and correlates of a recently developed PCS score. METHODS: The PCS score was applied to the prospective, multi-center cross-sectoral cohort (in- and outpatients with SARS-CoV-2 infection) of the "National Pandemic Cohort Network (NAPKON, Germany)". Symptom assessment and patient-reported outcome measure questionnaires were analyzed at 3 and 12 months (3/12MFU) after diagnosis. Scores indicative of PCS severity were compared and correlated to demographic and clinical characteristics as well as quality of life (QoL, EQ-5D-5L). RESULTS: Six hundred three patients (mean 54.0 years, 60.6% male, 82.0% hospitalized) were included. Among those, 35.7% (215) had no and 64.3% (388) had mild, moderate, or severe PCS. PCS severity groups differed considering sex and pre-existing respiratory diseases. 3MFU PCS worsened with clinical severity of acute infection (p = .011), and number of comorbidities (p = .004). PCS severity was associated with poor QoL at the 3MFU and 12MFU (p < .001). CONCLUSION: The PCS score correlated with patients' QoL and demonstrated to be instructive for clinical characterization and stratification across health care settings. Further studies should critically address the high prevalence, clinical relevance, and the role of comorbidities. TRAIL REGISTRATION NUMBER: The cohort is registered at www. CLINICALTRIALS: gov under NCT04768998.

Prevalence of infectious diseases, immunity to vaccine-preventable diseases and chronic medical conditions among Ukrainian refugees in Germany - A cross sectional study from the German Network University Medicine (NUM).

BACKGROUND: Vulnerability to infectious diseases in refugees is dependent on country of origin, flight routes, and conditions. Information on specific medical needs of different groups of refugees is lacking. We assessed the prevalence of infectious diseases, immunity to vaccine-preventable diseases, and chronic medical conditions in children, adolescents, and adult refugees from Ukraine who arrived in Germany in 2022. METHODS: Using different media, we recruited Ukrainian refugees at 13 sites between 9-12/2022. An antigen test for acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection, serologies for a range of vaccine-preventable diseases, as well as interferon gamma release assays (IGRAs) for tuberculosis (TB), and SARS-CoV-2 were performed. We assessed personal and family history of chronic medical conditions, infectious diseases, vaccination status, and conditions during migration. RESULTS: Overall, 1793 refugees (1401 adults and 392 children/adolescents) were included. Most participants were females (n = 1307; 72·3%) and from Eastern or Southern Ukraine. TB IGRA was positive in 13% (n = 184) of the adults and in 2% (n = 7) of the children. Serology-based immunological response was insufficient in approximately 21% (360/1793) of the participants for measles, 32% (572/1793) for diphtheria, and 74% (1289/1793) for hepatitis B. CONCLUSIONS: We show evidence of low serological response to vaccine-preventable infections and increased LTBI prevalence in Ukrainian refugees. These findings should be integrated into guidelines for screening and treatment of infectious diseases in migrants and refugees in Germany and Europe. Furthermore, low immunity for vaccine-preventable diseases in Ukrainians independent of their refugee status, calls for tailor-made communication efforts.

Ethical and coordinative challenges in setting up a national cohort study during the COVID-19 pandemic in Germany.

With the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), global researchers were confronted with major challenges. The German National Pandemic Cohort Network (NAPKON) was launched in fall 2020 to effectively leverage resources and bundle research activities in the fight against the coronavirus disease 2019 (COVID-19) pandemic. We analyzed the setup phase of NAPKON as an example for multicenter studies in Germany, highlighting challenges and optimization potential in connecting 59 university and nonuniversity study sites. We examined the ethics application process of 121 ethics submissions considering durations, annotations, and outcomes. Study site activation and recruitment processes were investigated and related to the incidence of SARS-CoV-2 infections. For all initial ethics applications, the median time to a positive ethics vote was less than two weeks and 30 of these study sites (65%) joined NAPKON within less than three weeks each. Electronic instead of postal ethics submission (9.5 days (Q1: 5.75, Q3: 17) vs. 14 days (Q1: 11, Q3: 26), p value = 0.01) and adoption of the primary ethics vote significantly accelerated the ethics application process. Each study center enrolled a median of 37 patients during the 14-month observation period, with large differences depending on the health sector. We found a positive correlation between recruitment performance and COVID-19 incidence as well as hospitalization incidence. Our analysis highlighted the challenges and opportunities of the federated system in Germany. Digital ethics application tools, adoption of a primary ethics vote and standardized formal requirements lead to harmonized and thus faster study initiation processes during a pandemic.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients.

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.