Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients

Archives of Public Health

Lize Cuypers¹  Emmanuel André²  Herman Van Oyen³  Mathil Vandromme³  Ruben Brondeel³  Marjan Meurisse³  Chloé Wyndham-Thomas³  Ben Serrien³  Lucy Catteau³  Sofieke Klamer³  Koen Blot³  Nina Van Goethem⁴ 

[1] Department of Laboratory Medicine, National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Herestraat 49, BE-3000, Leuven, Belgium;Department of Laboratory Medicine, National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Herestraat 49, BE-3000, Leuven, Belgium;KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory Clinical Bacteriology and Mycology, box 1040, Herestraat 49, BE-3000, Leuven, Belgium;Scientific Directorate of Epidemiology and public health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium;Scientific Directorate of Epidemiology and public health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium;Department of Epidemiology and Biostatistics, Institut de recherche expérimentale et clinique, Faculty of Public Health, Université catholique de Louvain, Clos Chapelle-aux-champs 30, 1200, Woluwe-Saint-Lambert, Belgium;

关键词: COVID-19;    SARS-CoV-2 variants;    Hospitals;    Causality;   

DOI  :  10.1186/s13690-021-00709-x

来源: Springer

【 摘 要 】

BackgroundSARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients.MethodsA causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants.DiscussionA well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries.Trial registrationEach individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29). OSF project created on 18 May 2021.

【 授权许可】

CC BY