van der Schaar Lab

Study shows importance of ethnicity as a COVID-19 risk factor in England

Photo by Gunnar Madlung on Unsplash
Photo by Gunnar Madlung on Unsplash

A statistical study conducted by researchers from the van der Schaar Lab, in conjunction with NHS Digital, has shed light on increased COVID-19 prevalence and higher mortality risks among individuals with a Black, Asian, or minority ethnic (BAME) background in England.

The study was finalized on April 24 and submitted for publication on May 1. Its findings have been echoed in a report issued by PHE on June 2, entitled “Disparities in the risk and outcomes of COVID-19.”

Background of study

In many countries worldwide, there is emerging evidence to suggest that variability in the impact of COVID-19 across ethnicities is particularly pronounced. This is supported, for example, by a study conducted in May using Brazil’s publicly available COVID-19 patient dataset. In this study, the van der Schaar Lab and a team of Brazilian researchers determined conclusively that patients from two of the country’s BAME-equivalent ethnicities exhibited significantly higher risk of mortality.

The study of ethnicity as a COVID-19 risk factor in England was conducted jointly by Dr. Ahmed Alaa, Zhaozhi Qian, and Prof. Mihaela van der Schaar from the van der Schaar Lab, and Prof. Jonathan Benger, Dr. Jem Rashbass, and Dr. Keith Gomes Pinto from NHS Digital. The researchers analyzed observational data for five routine datasets to track hospital admission, intensive care unit admission and death by self-reported ethnicity for a cohort of patients who had been diagnosed with COVID-19.

These datasets were combined into one comprehensive dataset for a cohort of 72,358 COVID-19 patients in England, including information on whether each patient was hospitalized, whether they were admitted to the ICU, their comorbidities, basic demographic information (age, sex, geographical region and postcode), and clinical outcome. A multivariate mixed-effects Cox regression model was fitted to adjust for the effect of age, deprivation (derived from postcode) and comorbidities on outcome.

Overview of findings

The study confirmed that BAME individuals are over-represented in COVID-19 deaths compared to their proportion of the general population of England. However, they are also over-represented in positive tests, hospitalizations and particularly ICU admissions.

The study also found that an Asian ethnic background significantly elevates mortality risk even when adjusting for age, deprivation and comorbidities. Of note, the Indian, Pakistani and Bangladeshi ethnic group was observed to have a higher mortality risk than other Asian individuals. Asian patients displayed an elevated risk in older age groups (more than 70 years old) for both the hospitalized and non-hospitalized subgroups, and for the hospitalized age group between 60 and 70 years old.

Interpretation of findings

The reason that BAME individuals are over-represented in positive tests may relate to geographical variation in the prevalence of COVID-19: major outbreaks have occurred in metropolitan areas where the population is much more ethnically diverse than the country as a whole.

Other factors that may be important are socio-economic and occupational. A further striking difference between BAME and White patients with COVID-19 is the younger age distribution in BAME patients. This age differential can be explained by: (i) the difference in the underlying age distribution within each ethnic group in the population, (ii) differences in social deprivation which tends to be higher in BAME populations, and (iii) differences in the age of onset of chronic diseases and co-morbidities that themselves influence outcome. Differences in the age profile between populations may also explain the higher rates of ICU admission in BAME patients reported previously: individuals with the best chance of benefit are selected for ICU care, and this tends to favour younger patient populations. Conversely, older patients with COVID-19 may not be considered appropriate for hospital admission, with a preference for care to be delivered at or closer to home.

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, socio-economic factors, and co-morbidities. Of note, endocrine diseases (i.e. diabetes) and cardiovascular disease are also strong independent predictors of mortality and more prevalent in the Asian ethnic group. It is therefore possible that some of the excess mortality associated with a BAME background is attributable to diabetes and cardiovascular disease that is present but not diagnosed, or present but not detected in this analysis.

Alternatively, the elevated risk within the Asian ethnic group, and to some extent other BAME individuals, may be attributable to other confounding clinical, social or genetic factors that we have not been able to measure in this study.

Conclusion

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.

At the same time, it should be understood that this study represents an initial statistical analysis exposing a number of issues that warrant further in-depth research. Additionally, there remains a possibility that the observational data analyzed in this study have been influenced by unmeasured confounders.

Whilst awaiting further research, the authors suggest that ethnicity be considered as an independent risk factor when assessing an individual’s COVID-19 risk.

The NHS Digital press release on the study can be found here.

To find out more about the van der Schaar Lab’s work related to the COVID-19 pandemic, visit our dedicated page here.

Ahmed Alaa

Ahmed Alaa

Ahmed M. Alaa is a Postdoctoral Scholar at the ECE Department, University of California, Los Angeles (UCLA), and an affiliated Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

His primary research focus has been on causal inference, automated machine learning, uncertainty quantification and time-series analysis.

He has published papers in several leading machine learning conferences including NeurIPS, ICML, ICLR and AISTATS.

Zhaozhi Qian

Zhaozhi Qian

After obtaining a MSc in Machine Learning at UCL, Zhaozhi Qian started his career as a data scientist in the largest mobile gaming company in Europe. Three years later, he found it might be more fulfilling to apply AI to cure cancer than to make the gamers hit the purchase button 1% more often.

He thus joined the group in 2019 as a PhD student focusing on robust and interpretable learning for longitudinal data. So far, his work has included inferring latent disease interaction networks from Electronic Health Records, uncovering the causal structure between events that unfold over time, and calibrating the predictive uncertainty under domain shift.

Zhaozhi also worked as a contractor in the NHS during the COVID-19 pandemic contributing his analytical skills to the national response to the pandemic.

Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

Nick Maxfield

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.