Inequality, infections and chronic disease in England: the interaction of risk factors and the dynamics of transmission

In England, there are differences between people in how likely they are to catch infections. There are also differences in how ill people get when they are infected. The same groups of people are more likely to get infected and more likely to get very unwell. Those groups are people of ethnic minority backgrounds, and those groups who experience socioeconomic deprivation. There are lots of reasons why some groups are more likely to catch infections, for example because they have more social contacts because of their job, or their behaviour, or they may have different types of contact, for example if they work in a hospital or have to go there frequently for treatment. Other factors include the size of households that people live in, or the different ages of people they live with. This is important because a lot of infectious disease transmission happens in households. The reasons why people are more likely to get very unwell if they are infected are mostly because of their age and if they have an existing health condition, for example, heart disease. Some of these conditions are more common amongst people who live with socioeconomic deprivation, and amongst ethnic minority groups. Differences in health between groups of people are called health disparities, and there are lots of reasons why these differences exist, and how difficult they are to solve. What this study aims to do is understand better the health disparities and the role that infectious diseases play in creating and sustaining them. Ultimately the aim is to develop better interventions to try to decrease the health disparities that people who live with socioeconomic deprivation or ethnic minority groups experience. I will study these problems by analysing electronic health records data for over 20 million people in England. The health records are held securely on an analysis platform called OpenSAFELY, and individual patient health data is never at risk of being exposed. To add to the health records, I will use a smartphone app called Airmid to ask people about how many social contacts they have in a day and the age and occupation of their contacts. I will also use the app to ask people about how many infections they get, how severe they are, and whether they go to the GP or hospital for that infection. Collecting these data will help understand the differences seen between groups. During the project, I will analyse how infections are distributed among different groups in the population and what factors are associated with having more infections. I will look at how infections are associated with different causes of death, and if those associations are different for ethnic minority groups or those experiencing deprivation. Later in the project I will use computer models to try to understand if there are interventions that could decrease the health disparities, such as vaccination or better treatment. The project will have patient advisors involved to share their experiences and participate in the research.