Background rates of Adverse Events of Special Interest for monitoring COVID-19 vaccines
Corinne Willame, Caitlin Dodd, Rosa Gini, Carlos E. Durán, RM Thomsen, L Wang, Anne Gedebjerg, Johnny Kahlert, Ehrenstein, Claudia Bartolini, C. Droz‐Perroteau, Nathan Moore, Ulrike Haug, Tania Schink, Javier Díez‐Domingo, Ainara Mira‐Iglesias, Clara Vergara-Hernández, JJ Carreras, Felipe Villalobos, Meritxell Pallejà, Mónica Hernández Aragón, Susana Perez‐Gutthann, Alba Regina Azevedo Arana, Carlo Giaquinto, Elisa Barbieri, Luca Stona, Claudia Huerta, Pallejà, M, Aragón, M, García Poza, P, Álvaro Burgos, Mayra A. Martínez-González, Patrick C. Souverein, Helga Gardarsdóttir, SJ Siiskonen, Douglas B. Weibel, Petra Mahy, Olaf H. Klungel, Miriam Sturkenboom
Abstract
<strong>Rationale and background</strong>: The global rapid spread of COVID-19 caused by the SARS-CoV-2 triggered the need for developing vaccines to control for this pandemic. This study aimed to generate background incidence rates of adverse events of special interest (AESI) that may be used to monitor benefit-risk profile of COVID-19 vaccines. <strong>Data sources</strong>: This study included 10 data sources from 7 European countries (Denmark, Germany, France, Italy, Netherlands, Spain, United Kingdom). Data sources contain health insurance data (GePaRD, SNDS), hospitalisation record linkage data (PHARMO, Danish registries (DCE-AU), SIDIAP, ARS) or data from general practitioners (CPRD, PEDIANET, BIFAP, FISABIO). For this final report data from 9 data sources were included. <strong>Study size</strong>: The study population for the total study comprised approximately 141.6 million individuals. In this final report, a total number of 45 million individuals were included. An update including French data is expected later this year <strong>Results</strong> This report comprises background rate data on AESI from 6 countries (UK, ES, IT, DK, NL, DE) and 9 data sources(BIFAP, Pedianet (children only), CPRD, ARS, Danish registries, FISABIO, SIDIAP, PHARMO, GeParD). Data from France (SNDS) could not be generated in a timely manner due to administrative constraints in data release. Data sources included different subpopulations based on the availability of numerator data of the observed persontime (Hosp= hospital based, PC= primary care, HOSP-PC= overlap between hospitalization and primary care). This entry also includes the results in excel format and also the links to the codes and event definitions