Nina Van Goethem, Ben Serrien, Mathil Vandromme, Chloé Wyndham-Thomas, Lucy Catteau, Ruben Brondeel, Sofieke Klamer, Marjan Meurisse, Lize Cuypers, Emmanuel André, Koen Blot & Herman Van Oyen
Archives of Public Health, Volume 79, Article number: 185 (2021)
SARS-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.
A 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.
A 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.
Each 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.