The linked paper by Niedzwiedz et al.  adds much-needed new information. This study links participants in the UK Biobank to COVID-19 testing data from Public Health England (PHE). The study recruited 40–70-year-olds and recorded their baseline characteristics between 2006 and 2010 . Associations between the baseline data, in particular, self-reported ethnicity and socioeconomic status (deprivation and highest education), were explored and compared in COVID-19 testing, testing positive and testing positive in a hospital setting. Results were adjusted for potential confounding and mediating factors that reflected the health and social status of the participants.
Of 392,116 participants in the cohort, 2658 were tested for COVID-19, of whom 948 tested positive. Compared to the White British population, positive tests were more likely in Black (relative risk (RR) 3.35 (95% CI 2.48–4.53)), South Asian (RR 2.42 (95% CI 1.75–3.36) and White Irish (RR 1.42 (95% CI 1.00–2.03)) people. When breaking down into more detailed categories, Black Caribbean (RR 3.51 (95% CI 2.39–5.15) and Pakistani (RR 3.24 (95% CI 1.73–6.07)) ethnic groups had respectively the highest risk ratio in the Black and South Asian ethnic backgrounds. The risk of a positive test was also linked to socioeconomic status and education, where those living in the most disadvantaged quartile (RR 2.19 (95% CI 1.80–2.66)) and those with lower levels of education (RR 2.00 (95% CI 1.66–2.42)) having the highest risk of confirmed COVID-19 infection. Adjusting for potential risk factors, including country of birth, whether they are a healthcare worker, socioeconomic status and pre-existing health, did not fully explain these ethnic differences. Socioeconomic differences, however, appeared to make the most substantial contribution to these ethnic variations.
Collaborations such as these are needed to address public health emergencies, and all parties (the authors, Biobank UK and PHE) should be commended for enabling and conducting a linked data study of this scale so quickly. Insights are extremely useful and further evidence of what has been previously seen in ethnic groups and socioeconomic backgrounds in relation to COVID-19.
Nonetheless, there are potential limitations to cohort studies using UK Biobank. Firstly, the cohort consists of volunteers meaning results may not reflect the whole UK with complete accuracy. In particular, this study only uses participants from English assessment centres who were aged 40–70 years when recruited and therefore cannot be generalised nationally. Secondly, the baseline characteristics of participants were recorded over 10 years ago and may have changed since they were enrolled. In addition, the models only adjust for risk factors and do not explore potential interactions between the exposures and risk factors. Investigating these may add extra value to insights to evaluate whether ethnic differences occur differently in the potential confounding factors. Alongside this analysis, further data from people in hospitals and care homes would help identify where along the patient pathways the problems lie.