Associations with covid-19 hospitalisation amongst 406,793 adults: the UK Biobank prospective cohort study

  1. Anthony P Khawaja
  2. Alasdair N Warwick
  3. Pirro G Hysi
  4. Alan Kastner
  5. Andrew Dick
  6. Peng T Khaw
  7. Adnan Tufail
  8. Paul J Foster
  9. Kay-Tee Khaw

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Abstract

OBJECTIVES

To identify the sociodemographic, lifestyle, comorbidity and antihypertensive medication associations with the development of hospitalisation with covid-19 in an English population.

DESIGN

Prospective cohort study

SETTING

The population-based UK Biobank study was linked to English covid-19 test results.

PARTICIPANTS

Individuals resident in England and alive in 2020.

MAIN OUTCOME MEASURES

Cases (n=605) were defined by a positive covid-19 test result conducted between 16 th March and 16 th April 2020, during a restricted testing policy for hospitalised individuals with severe disease.

RESULTS

A total of 406,793 participants were included. Mean age on 1 st January 2020 was 68 years (range 48 to 85 years). 55% were women. In multivariable models, major independent risk factors for hospitalisation with covid-19 were male sex (odds ratio 1.52; 95% confidence interval 1.28 to 1.81; P<0.001), South Asian ethnicity (2.02; 1.28 to 3.17; P=0.002) or black ethnicity (3.09; 2.18 to 4.38; P<0.001) compared to white ethnicity, greater residential deprivation (1.92 for most deprived quartile compared to least deprived quartile; 1.50 to 2.47; P<0.001), higher BMI (2.04 for BMI >35 compared to <25 Kg/m 2 ; 1.50 to 2.77; P<0.001), former smoking (1.39 compared to never smoked; 1.16 to 1.66; P<0.001), and comorbidities hypertension (1.28; 1.06 to 1.53; P=0.009) and chronic obstructive pulmonary disease (1.81; 1.34 to 2.44; P<0.001). Increased risk was observed with increasing number of antihypertensive medications used rather than any individual class.

CONCLUSION

Understanding why these factors confer increased risk of severe covid-19 in the population may help elucidate the underlying mechanisms as well as inform strategy and policy to prevent this disease and its consequences. We found no evidence of increased risk with specific classes of antihypertensive medication.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.06.20092957: (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
    All analyses were conducted with Stata software, version 15.1 (StataCorp).
    Stata
    suggested: (Stata, RRID:SCR_012763)
    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:
    Limitations: A major issue is the classification of cases and controls. The ascertainment of severe covid-19 cases in the UK Biobank cohort was almost certainly an underascertainment. We identified cases through the national Public Health database which collects data for all covid-19 tests, both positive and negative. After the initial contact tracing policy, from 16th March 2020, testing was reserved for individuals hospitalised with covid-19. This was due to limited test availability and to protect services from being overwhelmed by people with mild disease. Individuals identified with positive covid-19 tests from the UK Biobank cohort therefore represent the hospitalised and most severe cases. There will be individuals in the comparison control population who are susceptible to severe covid-19 infection but who have been uninfected to date. These numbers are likely to be relatively small compared to the overall denominator. In any case, misclassification of controls is likely only to attenuate any associations observed and would not explain our significant findings. Furthermore, our results are in keeping with hospital-based cohort reports. While this is one of the first studies to prospectively examine community-level associations with severe infection, we did not have data to assess subsequent mortality; future linkage to hospital outcome data will enable these analyses. While the diversity of the UK population permits adequately powered analyses of ethnicity, our findin...

    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 10.1101/2020.05.06.20092957v1 on medRxiv