A population-based cohort study of obesity, ethnicity and COVID-19 mortality in 12.6 million adults in England

  1. Thomas Yates
  2. Annabel Summerfield
  3. Cameron Razieh
  4. Amitava Banerjee
  5. Yogini Chudasama
  6. Melanie J. Davies
  7. Clare Gillies
  8. Nazrul Islam
  9. Claire Lawson
  10. Evgeny Mirkes
  11. Francesco Zaccardi
  12. Kamlesh Khunti
  13. Vahé Nafilyan

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Abstract

Obesity and ethnicity are known risk factors for COVID-19 outcomes, but their combination has not been extensively examined. We investigate the association between body mass index (BMI) and COVID-19 mortality across different ethnic groups using linked national Census, electronic health records and mortality data for adults in England from the start of pandemic (January 2020) to December 2020. There were 30,067 (0.27%), 1,208 (0.29%), 1,831 (0.29%), 845 (0.18%) COVID-19 deaths in white, Black, South Asian and other ethnic minority groups, respectively. Here we show that BMI was more strongly associated with COVID-19 mortality in ethnic minority groups, resulting in an ethnic risk of COVID-19 mortality that was dependant on BMI. The estimated risk of COVID-19 mortality at a BMI of 40 kg/m 2 in white ethnicities was equivalent to the risk observed at a BMI of 30.1 kg/m 2 , 27.0 kg/m 2 , and 32.2 kg/m 2 in Black, South Asian and other ethnic minority groups, respectively.

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  1. Version published to 10.1038/s41467-022-28248-1
  2. ScreenIT

    SciScore for 10.1101/2021.07.22.21260416: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    RandomizationWhen fitting the Cox models, we included all individuals who died during the analysis period and a weighted random sample of those who did not, with a sampling rate of 1% for those of white British ethnicity and 10% for adults from ethnic minority groups.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Covariates: Our analysis included key sociodemographic data including measures of deprivation, household composition, occupation and key worker status, educational attainment and exposure to others, defined within Supplementary eTable 1.
    Covariates
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, there were limitations. Most notably, this analysis is generalisable to the 52.4% of the English population with coded BMI data within their health care records in the 10 years preceding the pandemic. In England, height and weight are collected as part of routine care by trained staff using medical grade equipment. Nevertheless, family practice incentivisation schemes and differential take-up rates to population level vascular screening programmes means that data is not missing at random [20]. Previous analysis has shown that women, those who attend their family doctor more often, who come from more deprived areas, who have a high or low BMI and have a greater number of comorbidities are more likely to have a coded BMI value [20]. Nevertheless, it has been demonstrated that missing data within clinical records can provide unbiased estimates of adjusted exposure-outcome associations under a wide range of missing data assumptions [25]. In addition, primary care data in England provide some of the most detailed electronic health-care records internationally and are routinely used to identify individuals at risk of chronic and infectious diseases, including COVID-19 mortality [19, 26], giving this study real world utility. This study utilized data from the 2011 Census, therefore any sociodemographic changes within the last decade will not be reflected in the analysis. Although we adjusted for factors related to the risk of SARS-CoV-2 exposure, including household composi...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.07.22.21260416v1 on medRxiv