Prevalence of suspected COVID-19 infection in patients from ethnic minority populations: a cross-sectional study in primary care

  1. Sally A Hull
  2. Crystal Williams
  3. Mark Ashworth
  4. Chris Carvalho
  5. Kambiz Boomla

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Abstract

The first wave of the London COVID-19 epidemic peaked in April 2020. Attention initially focused on severe presentations, intensive care capacity, and the timely supply of equipment. While general practice has seen a rapid uptake of technology to allow for virtual consultations, little is known about the pattern of suspected COVID-19 presentations in primary care.

Aim

To quantify the prevalence and time course of clinically suspected COVID-19 presenting to general practices, to report the risk of suspected COVID-19 by ethnic group, and to identify whether differences by ethnicity can be explained by clinical data in the GP record.

Design and setting

Cross-sectional study using anonymised data from the primary care records of approximately 1.2 million adults registered with 157 practices in four adjacent east London clinical commissioning groups. The study population includes 55% of people from ethnic minorities and is in the top decile of social deprivation in England.

Method

Suspected COVID-19 cases were identified clinically and recorded using SNOMED codes. Explanatory variables included age, sex, self-reported ethnicity, and measures of social deprivation. Clinical factors included data on 16 long-term conditions, body mass index, and smoking status.

Results

GPs recorded 8985 suspected COVID-19 cases between 10 February and 30 April 2020.Univariate analysis showed a two-fold increase in the odds of suspected COVID-19 for South Asian and black adults compared with white adults. In a fully adjusted analysis that included clinical factors, South Asian patients had nearly twice the odds of suspected infection (odds ratio [OR] = 1.93, 95% confidence interval [CI] = 1.83 to 2.04). The OR for black patients was 1.47 (95% CI = 1.38 to 1.57).

Conclusion

Using data from GP records, black and South Asian ethnicity remain as predictors of suspected COVID-19, with levels of risk similar to hospital admission reports. Further understanding of these differences requires social and occupational data.

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  1. Version published to 10.3399/bjgp20x712601
  2. ScreenIT

    SciScore for 10.1101/2020.05.23.20101741: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: CEG has the written consent of all practices in the study area to use pseudonymised patient data for audit and research for patient benefit.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Ethnic categories were based on the 18 categories of the UK 2011 census and were combined into four groups reflecting the study population: White (British, Irish, other White), Black (Black African, Black Caribbean, Black British, other Black and mixed Black), South Asian (Bangladeshi, Pakistani, Indian, Sri Lankan, British Asian, other Asian or mixed Asian), and Other (Chinese, Arab, any other ethnic group).
    population: White
    suggested: None
    Software and Algorithms
    SentencesResources
    : StataCorp LP.) We fitted logistic, mixed effect models, nesting patients within practices.
    StataCorp
    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:
    Strengths and limitations: The strength of this study is based on the use of primary care data for the entire population registered at 157 general practices in adjacent CCGs in east London. The high level of ethnicity recording, coupled with the accurate recording of co-morbidities associated with the QOF, provides a unique opportunity to explore how clinical factors and demography affect the prevalence of suspected COVID-19 by ethnicity. Using UK government data on test-confirmed cases by London borough, (22) we confirm that GP coded data for suspected COVID-19 follows the same time course as the London epidemic (Figures 1,2). The inclusion of all episodes of URTI and LRTI from January suggest good separation of these clinical syndromes in east London practices. Data from RCGP surveillance practices suggest BAME populations present to GPs with URTI at similar rates to the white population. (24) Limitations common to studies using routinely collected clinical data include potential diagnostic inaccuracies, and under-recording of some conditions. General practitioners did not have access to COVID-19 viral testing, hence the majority of recorded cases reflect suspected disease. It is likely that this report underestimates the effect size, there will be many asymptomatic, mildly ill, or patients who contacted NHS 111 (but not their practice) in the population not coded for suspected COVID-19. In contrast to studies which use an extended list of co-morbidities or weighted co-morb...

    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 10.1101/2020.05.23.20101741v1 on medRxiv