Ethnic differences in Long COVID diagnosed in primary care in England (2020–2022): an observational cohort study using OpenSAFELY

Year: 2026

Type: article

Abstract: Background: Long COVID continues to affect millions of adults and contribute to substantial economic burden across Europe. Ethnic inequalities in Long COVID, and the reasons underlying these, are poorly understood. We aimed to investigate ethnic differences in the incidence of diagnosed Long COVID in England using linked national primary care data. Methods: With approval from NHS England, we used linked health record data from England, 2020-2022, accessed through the OpenSAFELY platform. We applied Cox regression to compare incidence of diagnosed Long COVID in primary care across self-reported ethnicity in five groups. We explored potential explanations for these differences by 1) adjusting for sociodemographic and health-related factors, 2) restricting to those tested or hospitalised with COVID-19, 3) stratifying into 16 ethnic sub-groups. Findings: Our sample comprised 17,848,825 adults, of whom 16,970 (0.1%) had a diagnosis of Long COVID recorded in primary care. Hazard ratios (95% confidence intervals) for Long COVID compared with the white group were 1.04 (0.98-1.11) for the South Asian group, 0.84 (0.75-0.94) for the Black group, 0.97 (0.84-1.13) for the Mixed Ethnicity group, and 0.63 (0.55-0.72) for Other ethnic groups, which remained similar when adjusting for sociodemographic and health-related factors and among those tested or hospitalised for COVID-19. Disaggregating into 16 ethnic sub-groups revealed heterogeneity within groups, for example, compared with the White British group, hazard ratios were 1.21 (1.00-1.47) for the Bangladeshi group and 1.09 (0.99-1.21) for the Pakistani group, but 0.77 (0.70-0.86) for the Indian group; and 1.15 (0.95-1.40) for the Black Caribbean group but 0.61 (0.51-0.72) for the Black African group. Interpretation: Differences in Long COVID diagnoses across broad ethnic groups mask important sub-group inequalities, offering insight into underlying mechanisms and approaches to better target Long COVID services. Funding: The OpenSAFELY platform is principally funded by grants from: NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY and this analysis have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC_PC_20030, MC_PC_20059) [2020-2022] and the Data and Connectivity strand (MC_PC_20058) [2021-2022]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]; NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC_PC_20051) [2021-2024] and MRC (MR/V040235/1) [2021-24].

Source: The Lancet Regional Health - Europe

DOI: 10.1016/j.lanepe.2026.101605

Institutions: London School of Hygiene & Tropical Medicine, University of Glasgow, University of Oxford, Qatar University, National Institute of Nutrition, Queen Mary University of London

Language: English

Topic: Long-Term Effects of COVID-19

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $4,930

Funders: Wellcome Trust, Heart of England NHS Foundation Trust, National Institute for Health and Care Research, Department of Health and Social Care, Medical Research Council

Awards: The OpenSafely Research Platform, G2149548466, G2558559306, Explaining the differential severity of COVID-19 between Indians in India and the UK, The OpenSafely Research Platform, G4069887101, Urgent research and surveillance on Covid-19 using the new OpenSAFELY secure platform across 55 million patients' full linked primary care records, Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care services, G5509661070, G6526062327, G6781659469, G8890197811

OpenAlex ID: W7128080716