Ethnicity and outcomes in patients hospitalised with COVID-19 infection in East London: an observational cohort study

  1. Vanessa J Apea
  2. Yize I Wan
  3. Rageshri Dhairyawan
  4. Zudin A Puthucheary
  5. Rupert M Pearse
  6. Chloe M Orkin
  7. John R Prowle

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

To describe outcomes within different ethnic groups of a cohort of hospitalised patients with confirmed COVID-19 infection. To quantify and describe the impact of a number of prognostic factors, including frailty and inflammatory markers.

Setting

Five acute National Health Service Hospitals in east London.

Design

Prospectively defined observational study using registry data.

Participants

1737 patients aged 16 years or over admitted to hospital with confirmed COVID-19 infection between 1 January and 13 May 2020.

Main outcome measures

The primary outcome was 30-day mortality from time of first hospital admission with COVID-19 diagnosis during or prior to admission. Secondary outcomes were 90-day mortality, intensive care unit (ICU) admission, ICU and hospital length of stay and type and duration of organ support. Multivariable survival analyses were adjusted for potential confounders.

Results

1737 were included in our analysis of whom 511 had died by day 30 (29%). 538 (31%) were from Asian, 340 (20%) black and 707 (40%) white backgrounds. Compared with white patients, those from minority ethnic backgrounds were younger, with differing comorbidity profiles and less frailty. Asian and black patients were more likely to be admitted to ICU and to receive invasive ventilation (OR 1.54, (95% CI 1.06 to 2.23); p=0.023 and OR 1.80 (95% CI 1.20 to 2.71); p=0.005, respectively). After adjustment for age and sex, patients from Asian (HR 1.49 (95% CI 1.19 to 1.86); p<0.001) and black (HR 1.30 (95% CI 1.02 to 1.65); p=0.036) backgrounds were more likely to die. These findings persisted across a range of risk factor-adjusted analyses accounting for major comorbidities, obesity, smoking, frailty and ABO blood group.

Conclusions

Patients from Asian and black backgrounds had higher mortality from COVID-19 infection despite controlling for all previously identified confounders and frailty. Higher rates of invasive ventilation indicate greater acute disease severity. Our analyses suggest that patients of Asian and black backgrounds suffered disproportionate rates of premature death from COVID-19.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.10.20127621: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection: Clinical and demographic data, blood results and coding data from current and prior clinical encounters, were collated from the Barts Health Cerner Millennium Electronic Medical Record (EMR) data warehouse and locally held ICNARC databases by members of the direct clinical care team.
    ICNARC
    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:
    Strengths and Limitations: We believe this study is both one of the largest and most detailed of studies exploring COVID-19 outcomes in BAME populations so far reported. In contrast to previous studies examining ethnicity and COVID-19 outcomes we were able to address the contributions of socio-economic deprivation, comorbid disease, pre-morbid function, lifestyle and demographic factors to ethnic disparities in COVID-19 outcomes, including ICU interventions. Our analysis was strengthened by the inclusion of measures of frailty which is a critical determinant of outcomes in acute disease as well as a potential driver of clinician decision-making. It should be acknowledged, however, that frailty has social and biological dimensions and measures have not been extensively validated in BAME groups. Importantly, this study was conducted in a single region where COVID-19 has had significant impact and thus is not confounded by differences in incidence of COVID-19 disease across the UK, regional concentration of minority ethnic groups and regional differences in the time-course of the epidemic. In addition, we employed a pre-specified statistical analysis plan and performed multiple sensitivity analyses to test the robustness of our findings. Limitations in our analyses must also be considered. Importantly, SARS-CoV-2 testing has an appreciable false negative rate and suspected, but not proven, cases are an important group. Nevertheless, given that clinical suspicion varied both betw...

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1136/bmjopen-2020-042140
  3. ScreenIT

    SciScore for 10.1101/2020.06.10.20127621: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableIn this model older age, male sex, smoking, BMI

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Our analyses suggest that patients of Asian and Black backgrounds suffered disproportionate rates of premature death from COVID-19. Funding: None Research in context Evidence before this study: We searched PubMed, Google Scholar, Medrxiv, Trip Medical Database and internet search engines from inception to May 10th 2020, using the terms “(COVID-19 or 2019-nCoV or SARS-CoV-2) AND (ethnicity)”, with no language restrictions, for research articles, editorials and commentaries.
    PubMed
    suggested: (PubMed, SCR_004846)
          <div style="margin-bottom:8px">
        <div><b>Google Scholar</b></div>
        <div>suggested: (Google Scholar, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008878">SCR_008878</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data collection Clinical and demographic data, blood results and coding data from current and prior clinical encounters, were collated from the Barts Health Cerner Millennium Electronic Medical Record (EMR) data warehouse and locally held ICNARC databases by members of the direct clinical care team.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>ICNARC</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr></table>

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

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.06.10.20127621v1 on medRxiv