TY - JOUR
T1 - Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients
AU - van Goethem, Nina
AU - Serrien, Ben
AU - Vandromme, Mathil
AU - Wyndham-Thomas, Chloé
AU - Catteau, Lucy
AU - Brondeel, Ruben
AU - Klamer, Sofieke
AU - Meurisse, Marjan
AU - Cuypers, Lize
AU - André, Emmanuel
AU - Blot, Koen
AU - van Oyen, Herman
N1 - Funding Information: We would like to sincerely thank all hospitals taking part in the surveillance and providing valuable information about hospitalized COVID-19 patients, greatly contributing to the management of COVID-19 in Belgium. We would like to thank all people in charge of the clinical microbiology laboratories for their collaboration and transfer of data. We would like to thank Johan Van Bussel, Kurt Vanbrabant, Andreas Gryncewicz, and other colleagues of Healthdata.be to set up the data infrastructure. Finally, we would like to thank Dieter Van Cauteren, Freek Haarhuis, and other colleagues at Sciensano for their valuable insights and feedback. Publisher Copyright: © 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Background: 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. Methods: 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. Discussion: 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. Trial registration: 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.
AB - Background: 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. Methods: 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. Discussion: 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. Trial registration: 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.
KW - COVID-19
KW - Causality
KW - Hospitals
KW - SARS-CoV-2 variants
UR - http://www.scopus.com/inward/record.url?scp=85117769632&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13690-021-00709-x
DO - https://doi.org/10.1186/s13690-021-00709-x
M3 - Article
C2 - 34696806
SN - 0778-7367
VL - 79
JO - Archives of public health = Archives belges de santé publique
JF - Archives of public health = Archives belges de santé publique
IS - 1
M1 - 185
ER -