Socioeconomic inequality in SARS-CoV-2 testing and COVID-19 outcomes in UK Biobank over the first year of the pandemic: can inequalities be explained by selection bias?

  1. Alice R Carter
  2. Gemma L Clayton
  3. M Carolina Borges
  4. Laura D Howe
  5. Rachael A Hughes
  6. George Davey Smith
  7. Deborah A Lawlor
  8. Kate Tilling
  9. Gareth J Griffith

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Abstract

Background

Structural barriers to testing may introduce selection bias in COVID-19 research. We explore whether changes to testing and lockdown restrictions introduce time-specific selection bias into analyses of socioeconomic position (SEP) and SARS-CoV-2 infection.

Methods

Using UK Biobank (N = 420 231; 55 % female; mean age = 56·3 [SD=8·01]) we estimated the association between SEP and i) being tested for SARS-CoV-2 infection versus not being tested ii) testing positive for SARS-CoV-2 infection versus testing negative and iii) testing negative for SARS-CoV-2 infection versus not being tested, at four distinct time-periods between March 2020 and March 2021. We explored potential selection bias by examining the same associations with hypothesised positive (ABO blood type) and negative (hair colour) control exposures. Finally, we conducted a hypothesis-free phenome-wide association study to investigate how individual characteristics associated with testing changed over time.

Findings

The association between low SEP and SARS-CoV-2 testing attenuated across time-periods. Compared to individuals with a degree, individuals who left school with GCSEs or less had an OR of 1·05 (95% CI: 0·95 to 1·16) in March-May 2020 and 0·98 (95% CI: 0·94 to 1·02) in January-March 2021. The magnitude of the association between low SEP and testing positive for SARS-CoV-2 infection increased over the same time-period. For the same comparisons, the OR for testing positive increased from 1·27 (95% CI: 1·08 to 1·50), to 1·73 (95% CI: 1·59 to 1·87). We found little evidence of an association between both control exposures and all outcomes considered. Our phenome-wide analysis highlighted a broad range of individual traits were associated with testing, which were distinct across time-periods.

Interpretation

The association between SEP (and indeed many individual traits) and SARS-CoV-2 testing changed over time, indicating time-specific selection pressures in COVID-19. However, positive, and negative control analyses suggest that changes in the magnitude of the association between SEP and SARS-CoV-2 infection over time were unlikely to be explained by selection bias and reflect true increases in socioeconomic inequalities.

Funding

University of Bristol; UK Medical Research Council; British Heart Foundation; European Union Horizon 2020; Wellcome Trust and The Royal Society; National Institute of Health Research; UK Economic and Social Research Council

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  1. ScreenIT

    SciScore for 10.1101/2022.05.05.22274721: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    RandomizationThe latter is a negative control outcome, where if selection were random, there should be no difference in risk factor estimates between tested (true) negative participants and untested (assumed) negative participants.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are limitations of these measures. These data were measured at UK Biobank baseline, up to 14 years before the pandemic began (years 2006-2010). Whilst factors such as education (highest qualification) are unlikely to have changed in adults during this time, other measures (income and household size for example) may have changed. In sensitivity analyses removing retired individuals from income analyses, we could only exclude individuals retired at baseline. Whilst this will be correlated with income at baseline, we could not unpick how current employment status was associated with COVID-19. Further, we could not examine how associations with occupation, which has been shown to be associated with COVID-19, have changed over time. Whilst some occupation data are available, we do not have access to i) self-employment status or ii) The National Statistics Socio-economic classification codes which can be used to proxy SEP. UK Biobank is healthier and wealthier than the general population,19 and as such, the point estimates obtained here may not be transportable to other populations. Whilst UK Biobank has relatively little missing data, some variables (e.g., income) experience high amounts of missingness, which we did not account for. Multiple imputation offers an opportunity to account for missing data in analyses, however, this method is only valid where data are missing at random. Here, it is plausible to assume that missingness in the reporting of income is missing not at ...

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.05.05.22274721v1 on medRxiv