Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?

Stephen Clark, Mark Birkin, Nik Lomax and Michelle Morris

No 2568p, OSF Preprints from Center for Open Science

Abstract: In this short communication we demonstrate how an individual level classification built using a Whole Systems approach to an understanding of obesity can be used to profile individual’s exposure, treatment and mortality for COVID-19. The cohort is the UK Biobank and the information on COVID-19 test outcomes, hospitalisations and mortality are provided as part of this research initiative. We find that the cohort profiles accurately against the understood heightened risk factors for COVID-19, namely age, gender, ethnicity, obesity and deprivation. This confidence in these data then allows us to profile the participants in each of the classification clusters for these COVID-19 outcomes. We see that there is a large degree of differentiation between the classes. The article finishes by highlighting how this classification can help in prioritising care, treatments and vaccine delivery.

Date: 2020-11-13
New Economics Papers: this item is included in nep-hea
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:2568p

DOI: 10.31219/osf.io/2568p

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