Ethnic and socioeconomic differences in SARS-CoV-2 infection: prospective cohort study using UK Biobank

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its resulting disease (COVID-19) are spreading rapidly worldwide [1]. A better understanding of the predictors of developing infection is essential for health service planning (e.g. ensuring adequate facilities for those most at risk), targeting prevention efforts (e.g. targeted shielding or surveillance) and informing future modelling efforts. Age, male sex and pre-existing medical conditions are established predictors of adverse COVID-19 outcomes, as is excess adiposity [2], but the role of social determinants is poorly understood [3, 4].

Ethnicity and socioeconomic position strongly influence health outcomes for both infectious and non-communicable diseases. Previous pandemics have often disproportionately impacted ethnic minorities and socioeconomically disadvantaged populations [5, 6]. Early evidence suggests that the same may be occurring in the current SARS-CoV-2 pandemic but empirical research remains highly limited [7]. It is highly plausible that infection risk will vary across these social groups. For example, socioeconomic disadvantage is linked to living in overcrowded housing. Similarly, Bangladeshi, Indian and Chinese households are more likely to live in intergenerational households (e.g. with children, parents and grandparents) [8], which has been hypothesised to increase transmission [9].

Establishing the risk of developing infection across different social groups is challenging. A major issue is that information about ethnicity and socioeconomic position are often not well collected within routine health data. Furthermore, the size of the different social groups in the general population is also often not accurately known [10]. The ideal approach to estimating infection risk across different social groups is to analyse data from a cohort study, but most existing cohort studies which include detailed information about ethnicity and socioeconomic position are subject to long delays in data being available for analysis and are too small to provide useful estimates of infection risk.

The UK Biobank study has carried out data linkage between its study participants and SARS-CoV-2 test results held by Public Health England. We therefore aimed to investigate the relationship between ethnicity, socioeconomic position and the risk of having confirmed SARS-CoV-2 infection in the population-based UK Biobank study.

A total of 392,116 participants were included in the study (after excluding 36,109 (8.4%) people with missing data, Additional file Figure S1 for flowchart and Table S1 for patterns of missing data by ethnicity and socioeconomic position). Most of the baseline UK Biobank sample in England was white British, with the next largest groups being other white, white Irish and then south Asian and black (Table 1 and Additional file Table S2). Approximately one-third (32.9%) of the sample had a degree and 16.2% had no formal qualifications. In our sample, 2658 people had been tested for SARS-CoV-2 and 948 had at least one positive test (726 received a positive test in a hospital setting suggesting more severe illness) (see Additional file Table S3 for outcomes by ethnicity, socioeconomic deprivation and education level). The geometric mean number of tests performed per participant tested was 1.53 (95% CI 1.50–1.56).

In comparison to the white British majority ethnic group, several ethnic minority groups had a higher risk of testing positive for SARS-CoV-2 infection and also testing positive while attending hospital (Fig. 1 and Additional file: Tables S4 and S5). Black participants had the highest risk (RR 3.35 (95% CI 2.48–4.53)), with adjustment for the country of birth resulting in little attenuation (RR 3.13 (95% CI 2.18–4.48)); adjustment for a history of being a healthcare worker (RR 2.66 (95% CI 1.83–3.84)) and for social factors (including measures of socioeconomic position) did additionally attenuate the risk (RR 2.05 (95% CI 1.39–3.03)). South Asians also had an elevated risk of testing positive (RR 2.42 (95% CI 1.75–3.36) in model 1), with a broadly similar pattern of attenuation as for the black ethnic group. The white Irish group also had a marginally elevated risk of having a positive test (RR 1.42 (95% CI 1.00–2.03)) which attenuated with adjustment for social variables (RR 1.23 (95% CI 0.86–1.75). The Chinese group had imprecisely estimated risk ratios due to smaller numbers. The pattern of findings for hospital cases was similar (Additional file S5), suggesting that the higher testing rates amongst certain ethnic groups in the community were not skewing the results. Similarly, analyses of the likelihood of testing positive amongst those who had been tested were often higher or the same in these ethnic groups (Table 2 and Additional file S16), whereas a lower risk would have suggested differentially high testing.

When using a more detailed ethnicity classification within the south Asian and black groups, we observed important heterogeneity in the pattern of findings between the Indian group and other south Asian groups (Fig. 2 and Additional file Tables S7-S9). Compared to white British, risks were largest in the Pakistani group (RR 3.24 (95% CI 1.73–6.07)), followed by other south Asians (RR 3.00 (95% CI 1.64–5.49)) and were more modestly increased in the Indian group (RR 1.98 (95% CI 1.26–3.09)). There were less clear differences in the estimates for black Caribbeans and black Africans: RR 3.51 (95% CI 2.39–5.15) and RR 3.11 (95% CI 1.97–4.91) in initial models and RR 2.18 (95% CI 1.43–3.32) and RR 1.53 (95% CI 0.87–2.69) in fully adjusted models respectively.

In comparison to the most socioeconomically advantaged quartile, living in a disadvantaged area (according to the Townsend deprivation score) was associated with a higher risk of confirmed infection, particularly for the most disadvantaged quartile (RR 2.19 (95% CI 1.80–2.66)) (Fig. 3 and Additional file: Table S10). Differences in ethnicity and country of birth, social factors, baseline health and behavioural risk factors all moderately attenuated the association in the most disadvantaged quartile. Socioeconomic deprivation was also associated with hospital cases (Additonal file: Table S11). While testing was again more likely, the risk of being diagnosed positive amongst those tested also tended to be higher, rather than lower (Table 2 and Additional file: Table S17).

Analyses by education level also showed a higher risk of confirmed SARS-CoV-2 infection with the lowest level of education (RR 2.00 (95% CI 1.66–2.42) for no qualifications compared to degree level educated) (Fig. 4 and Additional file: Table S13). While adjustment for ethnicity and country of birth made little difference to the association, adjustment for social factors, baseline health and behavioural risk factors all attenuated the association somewhat (RR 1.46 (95% CI 1.19–1.79) in fully adjusted model). We again observed a similar pattern in hospital cases and found little evidence of increased testing amongst the less educated groups (Fig. 4 and Additional file Tables S14 and S18).

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 and south Asian groups were found to be at greatest risk, with Pakistani ethnicity at greatest risk within the south Asian group. 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 amongst 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 moderate 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 approach [20], 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 drilling down into more specific minority ethnic groups [21]. 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 [7]. However, if this were the case, the issue of more adverse outcomes amongst these groups remains concerning. Other limitations include the non-representativeness of the UK Biobank study population, potentially exacerbated by missing data, 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 [22, 23]. However, empirical research has found that this may not result in substantial bias in measures of association in the UK Biobank study [24]. Furthermore, estimates from other sources of inequalities in COVID-19 mortality show similar patterns of associations to our results [25, 26]. 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 [27]. However, it is possible that management for chronic health conditions could have been differential across ethnic and socioeconomic groups [28] between baseline data collection and the pandemic period. Being a healthcare worker was also ascertained at baseline, although many who stopped employment in this area have now returned to work [29]. Lastly, due to sparse data, 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, 30] and are more prevalent amongst socioeconomically disadvantaged groups and some ethnic minority groups [31, 32]. 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 5578 patients admitted to critical care up to 16th 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 proportions of ethnic minority groups [33]. 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 [34]. Analyses of administrative UK data have also suggested increased COVID-19 mortality in black and south Asian ethnic groups [26], which was only partly accounted for by socioeconomic differences [25]. However, the role of prior health and risk factors was not accounted for. 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 [35]. 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 groups [36], 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 [37, 38].

The limited evidence available suggests that some ethnic minority groups, particularly black and south Asian people, are particularly vulnerable to the adverse consequences of COVID-19. Socioeconomic disadvantage and poorer pre-existing health do not explain all of this elevated risk. There is therefore a need to determine why this increased risk occurs. An immediate policy response is required to ensure the health system is responsive to the needs of ethnic minority groups. This should include ensuring that health and care workforces, which often rely on workers from minority ethnic populations, have access to the necessary personal protective equipment (PPE) to ensure they can work safely. Timely communication of guidelines to reduce the risk of being exposed to the virus is also required in a range of languages [39]. Previous evidence suggests ethnic minorities in the UK tend to receive reasonably equitable care in many, but not all, areas [40]. However, this is not the case in many other countries (such as the USA) where the adverse consequences of SARS-CoV-2 infection may be even worse. SARS-CoV-2 therefore has the potential to substantially exacerbate ethnic and socioeconomic inequalities in health [41], unless steps are taken to mitigate these inequalities. The data from this study may be helpful to inform allocation of more aggressive therapies in people with severe disease, or targeting preventative vaccination to at-risk groups, once evidence for such approaches becomes available.