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

Stephen Clark1, Michelle Morris2, Nik Lomax3

  • 1Consumer Data Research Centre and School of Geography, University of Leeds, LEEDS, LS2 9JT, UK. tra6sdc@leeds.ac.uk.

International Journal of Obesity (2005)
|July 7, 2021
PubMed

Insights

Understanding COVID-19 outcomes requires a multi-faceted approach. A new obesity classification in UK Biobank data reveals significant differences in testing, positive results, hospitalization, and mortality among diverse groups.

Area of Science:

  • Public Health
  • Epidemiology
  • Obesity Research

Background:

  • COVID-19 outcomes exhibit significant disparities across socio-demographic and economic groups.
  • A holistic, multi-faceted individual assessment is crucial for understanding these varied outcomes.
  • Existing research often overlooks the complex interplay of factors influencing disease severity.

Purpose of the Study:

  • To investigate COVID-19 outcomes using a novel, obesity-driven classification system.
  • To identify trends in COVID-19 testing, positive results, hospitalization, and mortality.
  • To assess the utility of a multi-faceted approach for risk stratification and long-COVID determination.

Main Methods:

  • Utilized the United Kingdom Biobank (UKB) participant data.
  • Employed a recent obesity systems map to classify participants.
  • Analyzed COVID-19 outcomes including testing, positive test rates, hospitalization, and mortality.

Main Results:

  • The obesity-driven classification identified meaningful differentials in COVID-19 outcomes.
  • Significant variations in disease outcomes were observed across different obesity classifications.
  • The holistic approach proved effective in distinguishing risk levels.

Conclusions:

  • A multi-faceted understanding, incorporating obesity classification, enhances COVID-19 risk assessment.
  • This approach aids in the identification and prioritization of individuals at higher risk.
  • The methodology shows promise for determining long-COVID risk and informing public health strategies.

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