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NHS Digital has contributed to a study by academics at the University of California and Cambridge University1 looking at the increased risk to people from ethnic minorities from COVID-19.
The study, published2 by Cambridge University, used NHS Digital’s expertise to safely and accurately link five datasets3 to construct a comprehensive record of the disease outcomes of a cohort of patients diagnosed with COVID-19 in England.
NHS Digital is the national safe haven for health and care data and provided its deep expertise of health data structures to curate information, perform quality assurance and to assist the academic team with their understanding of the data, ensuring that it was used appropriately and to its maximum potential.
The analysis was performed using depersonalised and non-disclosive data.
The unidentifiable data provided a cohort of over 78,000 patients who had been diagnosed with COVID-19, focusing on the 72,000 among this group for whom the data provided reliable information on ethnicity. The academics found that individuals from a BAME background are more likely to be diagnosed with COVID-19 and more likely to be admitted to hospital and intensive care, compared to the general population of England
It also found that the average age of patients from ethnic minorities diagnosed with COVID-19 was significantly lower than white patients.
It found that people from ethnic minorities accounted for disproportionate levels of positive cases, hospitalisation and ITU admissions, particularly amongst younger age groups. Conversely, patients aged over 70 were disproportionately white.
The median age of diagnosed patients in the Asian ethnic group was 51 years, whilst that in the Black and Other groups was 57 years, compared to a median age of 69 years in the White patient population.
Underlying health conditions were different amongst the ethnic groups, with respiratory illnesses more common in white patients, whilst the BAME cohort was more likely to have cardiovascular disease and diabetes.
The younger demographic, prevalence of comorbidities, higher deprivation and the fact that ethnically diverse regions in England exhibited higher infection rates, may explain much of the disproportionate rates of diagnosis, hospitalisation and ICU admissions among younger BAME patients. However, an Asian ethnic background, particularly Indian, Pakistani or Bangladeshi, was still found to be a significant independent risk factor for mortality even after adjusting for age, deprivation and co-morbidities. Diabetes and cardiovascular disease were also strong independent predictors of mortality and more prevalent in the Asian ethnic group.
Jonathan Benger, Chief Medical Officer at NHS Digital said:
“NHS Digital was delighted to use our data expertise to contribute to this hugely important piece of work. This is a comprehensive analysis of a large number of COVID-19 positive patients. Working closely with academic colleagues we have shown that COVID-19 patients from a BAME background are generally younger than those from a white background and have different co-morbidities. It is a complex picture, but there does seem to be an increased risk associated with the BAME population in England. The reasons for this remain unknown.”
Ahmed M. Alaa from the University of California said:
“The potential that COVID-19 might tend to cause a more severe illness in those with a BAME background has understandably caused significant concern, especially among health and care workers, a substantial proportion of whom are from ethnic minority backgrounds. Whilst awaiting further research, we suggest that ethnicity be considered as an independent risk factor when assessing an individual’s COVID-19 risk.”
Mihaela van der Schaar said:
“In many countries worldwide, including Brazil and the USA, there is emerging evidence to suggest that variability in the impact of COVID-19 across ethnicities is particularly pronounced. Our research has confirmed this to be the case in England, as well. At this stage, it is still difficult to pin down specific reasons for these variations, but it is clear that further study is urgently needed in order to determine how best to protect those at risk.”
Notes for editors
The study authors were:
Ahmed M Alaa, University of California, Los Angeles
Zhaozhi Qian, University of Cambridge Centre for Mathematical Sciences
Jem Rashbass, Executive Director of Master Registries and Data, NHS Digital
Keith Gomes Pinto, Clinical Fellow, NHS Digital
Jonathan Benger, Chief Medical Officer, NHS Digital
Mihaela van der Schaar, John Humphrey Plummer Professor of Machine Learning, AI and Medicine at the University of Cambridge
2. The full paper can be found here: https://vanderschaar-lab.com/papers/Ethnicity_COVID19_Cambridge_NHS.pdf
3. The datasets used in this study were:
SGSS data. COVID-19 testing data was obtained from the Second Generation Surveillance System (SGSS), covering the period to 16th April 2020. The data set contains 78,443 people with positive COVID-19 laboratory tests.
CHESS data. Data for COVID19 hospitalizations was obtained from the COVID-19 Hospitalization in England Surveillance System (CHESS) established by Public Health England (PHE). This data covered the period from February 8th to April 14th 2020 and was submitted from 94 of 152 (62%) of acute hospital trusts in England (7,714 hospital admissions, including 3,092 Intensive Care Unit (ICU) admissions).
PDS data. We utilized the Personal Demographics Service (PDS) data from NHS Digital, which recorded 15,090 deaths due to COVID-19 up to April 20th, 2020.
HES data. We used data from Hospital Episode Statistics (HES) in order to obtain information regarding each patient’s self-declared ethnicity. If a patient had multiple HES records with conflicting ethnicity, we chose the one with highest frequency. We divided the population of patients diagnosed with COVID-19 in England into four broad ethnic groups: White, Asian, Black and Other/Mixed ethnic background.
Primary care data. Finally, we used primary care prescription medicine data recorded between July 2019 and January 2020 to extract information on each patient’s likely preexisting medical conditions. Comorbidities were inferred from the British National Formulary (BNF) chapter code for all medications prescribed to each patient
4. This is a preprint describing a research report that has not yet been subject to peer review. It should not be relied on to guide clinical practice, and should not be reported in news media as established information.