We used UK Biobank to investigate ethnic differences in SARS-CoV-2, the virus which causes COVID-19. Black and south Asian groups were at highest risk of testing positive and having a positive test while attending hospital, suggesting they were also at greater risk of severe disease from the virus.
These risks remained largely unchanged even when accounting for pre-existing health conditions, health-related behaviours (such as smoking) and the likelihood of working for the health service. However, socioeconomic differences seemed to partly but not wholly explain ethnic differences in COVID-19. Using UK Biobank data, we found that black people were at highest risk of testing positive with SARS-CoV-2, more than three times more likely than white people. South Asian groups also had a higher risk of testing positive, with Pakistani groups having the highest risk among them. Socioeconomic deprivation and having no qualifications were also consistently associated with a higher risk of confirmed infection. The UK Biobank study linked data between its study participants and SARS-CoV-2 test results held by Public Health England. Among 392,116 Biobank participants in England, 2,658 had been tested for SARS-CoV-2 and 948 tested positive (726 in hospital) between 16 March and 3 May 2020.
Age, male sex and pre-existing medical conditions have already been established as predictors of adverse COVID-19 outcomes, however the role of social factors and ethnicity is less well understood. Based on these new findings, we suggest that more work urgently needs done to better understand and address these elevated risks.
Understanding the biological, lifestyle and environmental risks and outcomes for multimorbidity in psychiatric disorders
People with mental health disorders such as depression and anxiety have a higher chance of getting a physical health condition such as heart disease. This is a significant health inequality and contributes to the shortened life expectancy among people with mental health disorders.
The aim of this research project is to add to our understanding of the things that increase people's chance of developing different health conditions (e.g. dementia and cancer) if they have experienced a mental health disorder. The research will use novel techniques derived from computer science (e.g. machine learning) to uncover whether they can help us better understand why people develop multimorbidity (two or more health conditions experienced by one person) and what health conditions increase the chances of being hospitalised and dying. Multimorbidity is a growing public health concern and places significant pressure on the health service. Better understanding of multimorbidity is required to enable us to better prevent the development of multiple health conditions and also develop more effective treatments.
The research will make use of methods from different academic disciplines. Machine learning is a set of tools that can use the vast amounts of information now available to researchers to learn from patterns in the data and help us predict who might develop multimorbidity and what might happen to them afterwards. The application of machine learning to better understand public health and, in particular, multimorbidity is novel and has the potential to identify new factors that heighten people's risk of developing certain health conditions. We will then investigate whether or not these factors are a potential true cause of particular illnesses.
The research will take place over the course of the next three years and will make a significant contribution to our understanding of multimorbidity which can lead to the development of new treatments and prevention efforts.
|Lead investigator:||Dr Claire Niedzwiedz|
|Lead institution:||University of Glasgow|