Several ethnic minority groups had a higher risk of both being diagnosed and testing positive in a hospital setting with laboratory-confirmed SARS-CoV-2 infection in the UK Biobank study. The black, south Asian and white Irish ethnic groups were found to be at greatest risk. Similarly, measures of socioeconomic disadvantage (area-based deprivation and lower education) were also associated with an increased risk of having confirmed infection and being a hospital case. For both ethnicity and socioeconomic position, we did not find evidence that these patterns were likely to be due to differential ascertainment, since although the likelihood of testing was increased, the likelihood of a positive test was, if anything, higher among ethnic minorities who had been tested. Ethnic differences in infection risk did not appear to be fully accounted for by differences in pre-existing health, behavioural risk factors or country of birth measured at baseline. Furthermore, socioeconomic differences appeared to make a modest contribution to these ethnic differences.

Our study has several important strengths. First, by using a well characterised cohort study, we can identify a clearly defined population at risk of experiencing SARS-CoV-2 infection. By combining data linkage with a large sample size, this has allowed us to provide empirical data from this pandemic in a timely fashion. Ethnicity was collected using self-report which is widely considered to be a gold-standard approach¹⁷ and the availability of a large dataset has allowed us to provide empirical data on this crucial policy priority in a timely fashion, including a more nuanced appreciation of the risks of infection within different members of the white majority population, as well as minority ethnic populations. Our investigation of socioeconomic position has similarly benefited from being able to study different measures and assess the pattern of findings across these. The detailed data collected in this cohort has also allowed us to investigate the extent to which observed inequalities are potentially mediated by a wide range of factors, including behavioural risk factors, pre-existing health status and other social variables.

However, several potential limitations should be noted. Ascertainment bias is potentially problematic and could arise in several ways, including differential healthcare seeking, differential testing and differential prognosis. Even so, we have been unable to find any evidence to suggest that differential healthcare seeking or testing would explain the observed pattern of findings. Increased ascertainment amongst ethnic minorities would be expected to result in a lower proportion of confirmed cases amongst those tested whereas we observed the opposite. One possibility that remains is that some ethnic and socioeconomic groups have a poorer prognosis and are therefore more likely to be admitted to hospital and therefore to be tested. However, if this were the case, the issue of more adverse outcomes among these groups remains concerning. Other limitations include the non-representativeness of the UK Biobank study population, with those who were more advantaged being more likely to participate and ethnic minorities less well represented. There is therefore the potential that the findings in our study may not reflect the broader UK population.¹⁸ However, empirical research has found that this does not result in substantial bias in measures of association in the UK Biobank study.¹⁹ We have also been unable to fully exclude all deaths that occurred prior to the pandemic, due to lack of up-to-date linkage to mortality records at present. Our exposure data were collected some years ago and it is therefore likely that pre-existing health, risk factors and some social variables have changed, although generally most risk factors track throughout life. Being a healthcare worker was also ascertained at baseline, although many who stopped employment in this area have now returned to work. Lastly, we have not explored the role of specific health conditions such as asthma, diabetes and high blood pressure, which have been shown to be associated with a higher risk of severe outcomes^3,20 and are more prevalent amongst socioeconomically disadvantaged groups and some ethnic minority groups.^21,22 However, these are likely to operate as mediators rather than confounders.

Administrative data from health services has recently suggested an increased risk of severe COVID-19 disease within ethnic minority groups. The UK’s Intensive Care National Audit & Research Centre (ICNARC) analysed data on 5,578 patients admitted to critical care up to 16^th April 2020 and found black and Asian people comprised a high proportion of total patients (11.2% and 14.9% respectively), although it was unclear whether these higher percentages were biased by most cases being initially seen in areas with high BME proportions.²³ Similarly, data from the US Centers for Disease Control and Prevention also suggest a higher risk amongst Black or African American people, but information on race was missing for approximately two-thirds of those diagnosed.²⁴ Academic research on this topic has been limited to date. An ecological study of US counties has suggested that more socially vulnerable areas (which included greater numbers of people with socioeconomic disadvantage and ethnic minorities) were associated with higher COVID-19 case fatality rates.²⁵ Our study adds substantially to the evidence by finding that ethnicity appears to be an important predictor of laboratory-confirmed SARS-CoV-2 infection that is only partly attenuated by a large range of potential mediators (such as socioeconomic position), as well as addressing concerns about numerator-denominator bias.

Our results suggest there is an urgent need for further research on how SARS-CoV-2 infection affects different ethnic and socioeconomic groups. Our findings warrant replication in other datasets, ideally including representative samples and across different countries. As the pandemic evolves, there is a need to monitor infection and disease outcomes by ethnicity and socioeconomic position. However, data to allow this disaggregation is often not available – record linkage could potentially help address this gap, particularly in settings where administrative register data are available. Given the differences in health risks across occupational groups²⁶, understanding the risks that the full range of key workers experience is also required. Lastly, other social groups, such as homeless people, prisoners and undocumented migrants, experience severe disadvantage and research is necessary to study these highly vulnerable populations too.^27,28