Risk of COVID-19 hospital admission and COVID-19 mortality during the first COVID-19 wave with a special emphasis on ethnic minorities: an observational study of a single, deprived, multiethnic UK health economy

  1. Baldev M Singh
  2. James Bateman
  3. Ananth Viswanath
  4. Vijay Klaire
  5. Sultan Mahmud
  6. Alan Nevill
  7. Simon J Dunmore

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The objective of this study was to describe variations in COVID-19 outcomes in relation to local risks within a well-defined but diverse single-city area.

Design

Observational study of COVID-19 outcomes using quality-assured integrated data from a single UK hospital contextualised to its feeder population and associated factors (comorbidities, ethnicity, age, deprivation).

Setting/participants

Single-city hospital with a feeder population of 228 632 adults in Wolverhampton.

Main outcome measures

Hospital admissions (defined as COVID-19 admissions (CA) or non-COVID-19 admissions (NCA)) and mortality (defined as COVID-19 deaths or non-COVID-19 deaths).

Results

Of the 5558 patients admitted, 686 died (556 in hospital); 930 were CA, of which 270 were hospital COVID-19 deaths, 47 non-COVID-19 deaths and 36 deaths after discharge; of the 4628 NCA, there were 239 in-hospital deaths (2 COVID-19) and 94 deaths after discharge. Of the 223 074 adults not admitted, 407 died. Age, gender, multimorbidity and black ethnicity (OR 2.1 (95% CI 1.5 to 3.2), p<0.001, compared with white ethnicity, absolute excess risk of <1/1000) were associated with CA and mortality. The South Asian cohort had lower CA and NCA, lower mortality compared with the white group (CA, 0.5 (0.3 to 0.8), p<0.01; NCA, 0.4 (0.3 to 0.6), p<0.001) and community deaths (0.5 (0.3 to 0.7), p<0.001). Despite many common risk factors for CA and NCA, ethnic groups had different admission rates and within-group differing association of risk factors. Deprivation impacted only the white ethnicity, in the oldest age bracket and in a lesser (not most) deprived quintile.

Conclusions

Wolverhampton’s results, reflecting high ethnic diversity and deprivation, are similar to other studies of black ethnicity, age and comorbidity risk in COVID-19 but strikingly different in South Asians and for deprivation. Sequentially considering population and then hospital-based NCA and CA outcomes, we present a complete single health economy picture. Risk factors may differ within ethnic groups; our data may be more representative of communities with high Black, Asian and minority ethnic populations, highlighting the need for locally focused public health strategies. We emphasise the need for a more comprehensible and nuanced conveyance of risk.

Article activity feed

  1. Version published to 10.1136/bmjopen-2020-046556
  2. ScreenIT

    SciScore for 10.1101/2020.11.20.20224691: (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
    Statistical Analysis: This was undertaken in SPSS v26.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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 Weaknesses: Combining Wolverhampton’s health data evaluated our local population’s heterogeneous demographic and its associations with community or hospital non-COVID and COVID hospital admission and mortality, uniquely approaching these outcomes simultaneously. This local nuance complements larger studies, informing appraisal of risk from an urban, and multi-ethnic and deprived setting, highlighting concerns of extrapolation from larger datasets to UK localities. An example of a particular strength of the data quality was the cross check ascertainment of COVID admission, without sole reliance on COVID testing, permitting specific categorisation of deaths (COVID, non-COVID and post discharge) rather than less accurately into global mortality. Limitations of the study: This is a twelve-week evaluation spanning the pandemic’s upsurge and peak; the population and event number were comparatively small; cause of death in the community was unknown and it is likely that people died away from the hospital undiagnosed with COVID 19 ; some data were missing but this was mitigated; whilst being aligned to the population at 99.5% concordance, hospital data were not totally drawn from the City population, which varied by GP registration, residency, or admission from immediately surrounding areas and a small proportion of admissions were non-resident or non-registered, so this is not strictly an epidemiological study but an observational study comparing defined cohorts in tie...

    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.
    • Thank you for including a protocol registration statement.

    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.

  3. Version published to 10.1101/2020.11.20.20224691v1 on medRxiv