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

Abstract

COVID-19 is a disease that has been shown to have outcomes that vary by certain socio-demographic and socio-economic groups. It is increasingly important that an understanding of these outcomes should be derived not from the consideration of one aspect, but by a more multi-faceted understanding of the individual. In this study use is made of a recent obesity driven classification of participants in the United Kingdom Biobank (UKB) to identify trends in COVID-19 outcomes. This classification is informed by a recently created obesity systems map, and the COVID-19 outcomes are: undertaking a test, a positive test, hospitalisation and mortality. It is demonstrated that the classification is able to identify meaningful differentials in these outcomes. This more holistic approach is recommended for identification and prioritisation of COVID-19 risk and possible long-COVID determination.

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Item Type:	Article
Authors/Creators:	Clark, S https://orcid.org/0000-0003-4090-6002 Morris, M https://orcid.org/0000-0002-9325-619X Lomax, N https://orcid.org/0000-0001-9504-7570 Birkin, M
Copyright, Publisher and Additional Information:	© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Dates:	Published: October 2021 Published (online): 6 July 2021 Accepted: 23 June 2021
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
Depositing User:	Symplectic Publications
Date Deposited:	27 May 2021 13:38
Last Modified:	25 Jun 2023 22:40
Status:	Published
Publisher:	Springer Nature
Identification Number:	10.1038/s41366-021-00891-6
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:174616

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

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