Study population and setting
This study aimed to describe the potential socioeconomic, lifestyle, mental, and physical health factors associated with ethnic disparities in COVID-19 hospitalization in the UK. Data for the UK Biobank was collected at 22 research sites across the UK from 2006 to 2010 among people aged 40 to 69 years. Using the UK Biobank, which includes 340,966 men and women, 640 cases of COVID-19 were identified. These cases included 571 white participants; 31 Black participants; 21 South Asian participants; and 17 participants, who were grouped into their own category, of Chinese background, a mix of backgrounds, or a background not listed on the survey.
Summary of Main Findings
Black participants had a 4.32 increased odds (95% CI: 3.00, 6.23) of COVID-19 compared to White participants, after adjustment for age and sex. Increased odds were also seen among Asian (OR: 2.12, 95% CI: 1.37, 3.28) and other participants (1.84, 95% CI: 1.13, 2.99) compared to White participants. After adjustment for socioeconomic factors, including education, income, occupation, index of area deprivation, and similar factors, there was a 25% attenuation in odds among Black participants, a 32% attenuation among Asian participants, and a 30% attenuation among other participants. After adjusting for lifestyle factors (physical activity, smoking, alcohol consumption, etc.), comorbid clinical conditions (cardiovascular disease, chronic bronchitis, diabetes, etc.), and metabolic biomarkers (lipoprotein cholesterol, C-reactive protein, etc.), there remained a significant association between Black race/ethnicity (OR: 2.66, 95% CI: 1.82, 3.91) compared to White participants. When disaggregated by sex, the association was higher among Black men (OR: 3.51, 95% CI: 2.11, 5.81) than for Black women (OR: 1.93, 95% CI: 1.07, 3.48).
The study’s major strength was that it was able to draw upon a range of both sociodemographic and clinical variables in order to test for associations, including both individual socioeconomic status and neighborhood deprivation index. By using a large biobank repository, the study was able to examine exposures before COVID-19 infection, and benefits from many Black, South Asian, Chinese, and ethnically mixed participants from the UK, in a setting where research teams have struggled to recruit minority participants in the past. The UK only recently began reporting COVID-19 cases by race and ethnicity, so this biobank cohort represented a unique opportunity to study racial disparities in hospitalizations for COVID-19.
This study described the attenuation of the effect size between race/ethnicity and COVID-19, however, it did not use a formal mediation analysis framework. Such a framework would help discern whether the attenuation was simply due to confounding from lifestyle, comorbidity, and metabolic biomarker factors (with race/ethnicity considered causal factors for increased risk of hospitalization due to COVID-19) or if these factors were in the causal pathway and mediated the response. The study also likely has selection bias. In other words, since the researchers depended on hospital records, only patients who had severe disease that required inpatient hospitalization were included in this study. Similarly, the UK Biobank is not intended to be representative of the general population and skews older (minimum age 40). Only Biobank participants without any missing values for their measures of interest were included. Therefore, these results may not be generalizable to the entire UK.
This is one of the first studies examining the effect of numerous sociodemographic, clinical, and biological factors that may partially explain the relationship of race/ethnicity to COVID-19 in the UK.
This review was posted on: 4 July 2020