Journal Article

Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform

Curtis, Helen J, Evans, David, Grieve, Richard, Mehrkar, Amir, Hester, Frank, Cockburn, Jonathan, Khunti, Kamlesh, Williamson, Elizabeth J, Evans, Stephen J W, Rowan, Kathy, Inglesby, Peter, Mathur, Rohini, Tomlinson, Laurie, Wong, Angel Y S, DeVito, Nicholas J, Douglas, Ian J, Bhaskaran, Krishnan, MacKenna, Brian, McDonald, Helen I, Chaturvedi, Nishi, Forbes, Harriet, Goldacre, Ben, Croker, Richard, Morton, Caroline E, Bacon, Seb, Bates, Chris, Wing, Kevin, Smeeth, Liam, Eggo, Rosalind M, Hulme, William J, Schultze, Anna, Walker, Alex J, Harper, Sam, Harrison, David, Drysdale, Henry, Rentsch, Christopher T, Parry, John

2021

Overview

COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England. We conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region. Of 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07–1·09]), Black group (1·08 [1·06–1·09]), and mixed ethnicity group (1·04 [1·02–1·05]) and was decreased in the other ethnicity group (0·77 [0·76–0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94–2·04]), Black group (1·69 [1·62–1·77]), mixed ethnicity group (1·49 [1·39–1·59]), and other ethnicity group (1·20 [1·14–1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41–1·55], Black group 1·78 [1·67–1·90], mixed ethnicity group 1·63 [1·45–1·83], other ethnicity group 1·54 [1·41–1·69]), COVID-19-related ICU admission (2·18 [1·92–2·48], 3·12 [2·65–3·67], 2·96 [2·26–3·87], 3·18 [2·58–3·93]), and death (1·26 [1·15–1·37], 1·51 [1·31–1·71], 1·41 [1·11–1·81], 1·22 [1·00–1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories. Some minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination. Medical Research Council.

• Publisher: Elsevier Ltd,Elsevier Limited,Elsevier
• Subject: Adult / Adults / Biobanks / Blood pressure / Categories / Cohort analysis / Cohort Studies / Coronaviruses / COVID-19 / COVID-19 - epidemiology / COVID-19 - ethnology / COVID-19 - mortality / Cultural differences / Death / Deprivation / Disaggregation / Disease transmission / Electronic health records / Electronic medical records / England / Ethnicity / Ethnicity - statistics & numerical data / Health care / Heterogeneity / Hospitalization / Hospitalization - statistics & numerical data / Hospitals / Households / Humans / Inequalities / Infections / Intensive care / Intensive Care Units - statistics & numerical data / Medical research / Minority & ethnic groups / Mortality / Observational studies / Observational Studies as Topic / Pandemics / Patient Admission - statistics & numerical data / Population / Populations / Primary care / Severe acute respiratory syndrome coronavirus 2 / Sociodemographics / Survival Analysis / Vaccination / Viral diseases