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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Van Goethem, Nina Serrien, Ben Vandromme, Mathil Wyndham-Thomas, Chloé Catteau, Lucy Brondeel, Ruben Klamer, Sofieke Meurisse, Marjan Cuypers, Lize André, Emmanuel Blot, Koen Van Oyen, Herman |
| 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. |
| Related Links | https://archpublichealth.biomedcentral.com/counter/pdf/10.1186/s13690-021-00709-x.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 20493258 |
| DOI | 10.1186/s13690-021-00709-x |
| Journal | Archives of Public Health |
| Issue Number | 1 |
| Volume Number | 79 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2021-10-25 |
| Access Restriction | Open |
| Subject Keyword | Public Health Medicine Health Policy Health Services Research Health Informatics COVID-19 SARS-CoV-2 variants Hospitals Causality Medicine/Public Health |
| Content Type | Text |
| Resource Type | Article |
| Subject | Public Health, Environmental and Occupational Health |
| Journal Impact Factor | 3.2/2023 |
| 5-Year Journal Impact Factor | 3.3/2023 |
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