Social inequalities and long-term health impact of COVID-19 in Belgium: protocol of the HELICON population data linkage.

INTRODUCTION: Data linkage systems have proven to be a powerful tool in support of combating and managing the COVID-19 pandemic. However, the interoperability and the reuse of different data sources may pose a number of technical, administrative and data security challenges. METHODS AND ANALYSIS: This protocol aims to provide a case study for linking highly sensitive individual-level information. We describe the data linkages between health surveillance records and administrative data sources necessary to investigate social health inequalities and the long-term health impact of COVID-19 in Belgium. Data at the national institute for public health, Statistics Belgium and InterMutualistic Agency are used to develop a representative case-cohort study of 1.2 million randomly selected Belgians and 4.5 million Belgians with a confirmed COVID-19 diagnosis (PCR or antigen test), of which 108 211 are COVID-19 hospitalised patients (PCR or antigen test). Yearly updates are scheduled over a period of 4 years. The data set covers inpandemic and postpandemic health information between July 2020 and January 2026, as well as sociodemographic characteristics, socioeconomic indicators, healthcare use and related costs. Two main research questions will be addressed. First, can we identify socioeconomic and sociodemographic risk factors in COVID-19 testing, infection, hospitalisations and mortality? Second, what is the medium-term and long-term health impact of COVID-19 infections and hospitalisations? More specific objectives are (2a) To compare healthcare expenditure during and after a COVID-19 infection or hospitalisation; (2b) To investigate long-term health complications or premature mortality after a COVID-19 infection or hospitalisation; and (2c) To validate the administrative COVID-19 reimbursement nomenclature. The analysis plan includes the calculation of absolute and relative risks using survival analysis methods. ETHICS AND DISSEMINATION: This study involves human participants and was approved by Ghent University hospital ethics committee: reference B.U.N. 1432020000371 and the Belgian Information Security Committee: reference Beraadslaging nr. 22/014 van 11 January 2022, available via https://www.ehealth.fgov.be/ehealthplatform/file/view/AX54CWc4Fbc33iE1rY5a?filename=22-014-n034-HELICON-project.pdf. Dissemination activities include peer-reviewed publications, a webinar series and a project website.The pseudonymised data are derived from administrative and health sources. Acquiring informed consent would require extra information on the subjects. The research team is prohibited from gaining additional knowledge on the study subjects by the Belgian Information Security Committee's interpretation of the Belgian privacy framework.

Evidence-Based Diagnostic Algorithm for Glioma: Analysis of the Results of Pathology Panel Review and Molecular Parameters of EORTC 26951 and 26882 Trials.

PURPOSE: With the rapid discovery of prognostic and predictive molecular parameters for glioma, the status of histopathology in the diagnostic process should be scrutinized. Our project aimed to construct a diagnostic algorithm for gliomas based on molecular and histologic parameters with independent prognostic values. METHODS: The pathology slides of 636 patients with gliomas who had been included in EORTC 26951 and 26882 trials were reviewed using virtual microscopy by a panel of six neuropathologists who independently scored 18 histologic features and provided an overall diagnosis. The molecular data for IDH1, 1p/19q loss, EGFR amplification, loss of chromosome 10 and chromosome arm 10q, gain of chromosome 7, and hypermethylation of the promoter of MGMT were available for some of the cases. The slides were divided in discovery (n = 426) and validation sets (n = 210). The diagnostic algorithm resulting from analysis of the discovery set was validated in the latter. RESULTS: In 66% of cases, consensus of overall diagnosis was present. A diagnostic algorithm consisting of two molecular markers and one consensus histologic feature was created by conditional inference tree analysis. The order of prognostic significance was: 1p/19q loss, EGFR amplification, and astrocytic morphology, which resulted in the identification of four diagnostic nodes. Validation of the nodes in the validation set confirmed the prognostic value (P < .001). CONCLUSION: We succeeded in the creation of a timely diagnostic algorithm for anaplastic glioma based on multivariable analysis of consensus histopathology and molecular parameters.