Ethnicity, comorbidity, socioeconomic status, and their associations with COVID-19 infection in England: a cohort analysis of UK Biobank data

  1. Albert Prats-Uribe
  2. Roger Paredes
  3. Daniel Prieto-Alhambra

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Abstract

Objectives

Recent data suggest higher COVID-19 rates and severity in Black, Asian, and minority ethnic (BAME) communities. The mechanisms underlying such associations remain unclear. We aimed to study the association between ethnicity and risk of COVID-19 infection and disentangle any correlation with socioeconomic deprivation or previous comorbidity.

Design

Prospective cohort.

Setting

UK Biobank linked to Hospital Episode Statistics (HES) and COVID-19 tests until 14 April 2020.

Participants

UK Biobank participants from England, excluding drop-outs and deaths.

Main measures

COVID-19 infection based on a positive PCR test. Ethnicity was self-reported and classified using Office of National Statistics groups. Socioeconomic status was based on index of multiple deprivation quintiles. Comorbidities were self-reported and completed from HES.

Analyses

Multivariable Poisson analysis to estimate incidence rate ratios of COVID-19 infection according to ethnicity, adjusted for socioeconomic status, alcohol drinking, smoking, body mass index, age, sex, and comorbidity.

Results

415,582 participants were included, with 1,416 tested and 651 positive for COVID-19. The incidence of COVID-19 was 0.61% (95% CI: 0.46%-0.82%) in Black/Black British participants, 0.32% (0.19%-0.56%) in ‘other’ ethnicities, 0.32% (0.23%-0.47%) in Asian/Asian British, 0.30% (0.11%-0.80%) in Chinese, 0.16% (0.06%-0.41%) in mixed, and 0.14% (0.13%-0.15%) in White. Compared with White participants, Black/Black British participants had an adjusted relative risk (RR) of 3.30 (2.39-4.55), Chinese participants 3.00 (1.11-8.06), Asian/Asian British participants 2.04 (1.36-3.07), ‘other’ ethnicities 1.93 (1.08-3.45), and mixed ethnicities 1.07 (0.40-2.86). Socioeconomic status (adjusted RR 1.93 (1.51-2.46) for the most deprived), obesity (adjusted RR 1.04 (1.02-1.05) per kg/m2) and comorbid hypertension, chronic obstructive pulmonary disease, asthma, and specific renal diseases were also associated with increased risk of COVID-19.

Conclusions

COVID-19 rates in the UK are higher in BAME communities, those living in deprived areas, obese patients, and patients with previous comorbidity. Public health strategies are needed to reduce COVID-19 infections among the most susceptible groups.

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  1. Version published to 10.1101/2020.05.06.20092676v3 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.05.06.20092676: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Demographics, lifestyle, disease history, and physiological measurements were collected via questionnaires, physical measurements and interviews in baseline assessments (2006-10). [10,11] Participants gave informed consent for data linkage to medical records.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data management was performed in Python 3.7.6.[
    Python
    suggested: (IPython, RRID:SCR_001658)
    17] All analyses were performed in STATA version 15.1.[18]
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    [15] Our study has several limitations. We did not have data on region of residence. According to ONS, [1] there are geographic differences in mortality attributable to COVID-19 in the UK, with the highest rates currently in London and the West Midlands. Heterogeneous distribution of ethnic groups and socioeconomic status across the country could partially explain the observed effect. Misclassification of COVID-19 infection due to differential testing across ethnic or socioeconomic strata may have affected the accuracy of our results. However, our data were collected when only severe cases were tested, and our findings were similar when restricted to inpatient testing, suggesting that even with these potential misclassifications, the observed associations remain clinically relevant. There was also potential for misclassification of comorbidity. However, we used linked electronic medical records to minimise recall bias and collect the most recent comorbidities. The observational nature of this study makes the results susceptible to confounding and prevents us from inferring causality. We also lacked power to explore interactions or perform stratified analyses between the studied socioeconomic factors. There is a need to further explore the synergistic effects between these and with other health determinants on COVID-19 risk. This study also has strengths, particularly surrounding the data used. We used a large sample with little missing data, links to hospital data, and self-r...

    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.

    About SciScore

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  3. Version published to 10.1101/2020.05.06.20092676v2 on medRxiv
  4. Version published to 10.1101/2020.05.06.20092676v1 on medRxiv