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. 2021 Oct 25;79(1):185.
doi: 10.1186/s13690-021-00709-x.

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

Nina Van Goethem #  1   2 , Ben Serrien #  3 , Mathil Vandromme  3 , Chloé Wyndham-Thomas  3 , Lucy Catteau  3 , Ruben Brondeel  3 , Sofieke Klamer  3 , Marjan Meurisse  3 , Lize Cuypers  4 , Emmanuel André  4   5 , Koen Blot  3 , Herman Van Oyen  3
Affiliations

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

Nina Van Goethem et al. Arch Public Health. .

Abstract

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.

Keywords: COVID-19; Causality; Hospitals; SARS-CoV-2 variants.

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Conflict of interest statement

Herman Van Oyen is editor of Archives of Public Health. The other authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Conceptual framework to assess the causal relationship between SARS-CoV-2 variants and COVID-19 disease severity among hospitalized patients presented in a Directed Acyclic Graph (DAG). VARIANT = infection with a particular SARS-CoV-2 variant. SEVERITY = developing severe complications following SARS-CoV-2 infection. SES = socio-economic status
Fig. 2
Fig. 2
Blocking non-causal associations between SARS-CoV-2 variants and COVID-19 disease severity among hospitalized patients by the use of a matched cohort design presented in a Directed Acyclic Graph (DAG). S = selection into the study
Fig. 3
Fig. 3
Conceptual framework to assess the causal relationship between SARS-CoV-2 variants and COVID-19 disease severity among hospitalized patients presented in a Directed Acyclic Graph (DAG) in the scenario of selection bias. CONFOUNDERS = all other confounders as listed in the DAG in Fig. 1. E = error term. U = unmeasured confounders
Fig. 4
Fig. 4
Data linkage of existing COVID-19 surveillance registries within the context of the LINK-VACC project, Belgium
Fig. 5
Fig. 5
Flow chart of the study population selection to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. CHS = Clinical Hospital Survey
See this image and copyright information in PMC

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