For World Diabetes Day, ARC East of England collaborator Helen Green and Shane Gordon discuss their work in Suffolk and North East Essex, which addresses the inequalities that people face with diabetes prevention and management and the important role data and intelligence might play in addressing them.
Helen Green works as a Consultant in Public Health in the East of England at the Department of Health and Social Care and NHS England. Helen is hosted by the East Suffolk and North Essex NHS Foundation Trust. Shane Gordon is the Director of Strategy, Research and Innovation for East Suffolk and North Essex NHS Foundation Trust. Shane has been a GP since 2002 and has also worked in NHS management for 20 years.
What disease is a leading cause of preventable sight loss in people of working age? People with what condition typically have longer stays in hospitals if admitted? What disease has around 10% of the entire NHS budget spent on it, with 80% of this spent on treating its complications? The answer to this is diabetes.
It has been one hundred years since the Nobel Prize was awarded for discovering insulin, its treatment, meaning that today diabetes is no longer the fatal disease that it was in the early 1920s. However, a century on, it still significantly changes people's lives and the number of people developing type 2 diabetes, which is 90% of diabetes diagnoses, is increasing year on year despite it being largely preventable, including in people under 40.
Diabetes can lead to serious health complications, including chronic kidney disease, heart attacks, strokes, and lower limb amputations. Once diagnosed, individuals are supported to actively manage diabetes, dependent upon the type of diabetes, through a combination of dietary changes, physical activity, medication, and regular monitoring (e.g., of blood sugar levels), with nationally recommended care processes and treatment targets in place to support its management. However, through routinely collected data, we can see inequalities across population groups in the prevention, diagnosis, and management of diabetes.
Are there significant health inequalities with diabetes?
Unfortunately, yes, diabetes does not affect everyone equally. Health inequalities are avoidable differences in health between different groups of people. This can include differences in an individual’s health outcomes, their access to and experiences of healthcare services, and behavioural and environmental risks to health. Groups of people can be defined in different ways, including by geography (e.g., rural), protected characteristics in the Equality Duty (e.g., age), socioeconomic groups (e.g., low income), inclusion health groups (e.g., people who experience homelessness), or a combination of these factors.
“For people with diabetes, inequalities in access to services and their health outcomes have been documented for many years. Inequalities are also seen with the risk of developing type 2 diabetes, where differences are seen by ethnicity and socioeconomic deprivation leading to higher levels of diabetes for different groups of people.”
As well as higher levels of diabetes, we also see higher levels of early-onset diabetes in individuals in these groups, meaning they develop the disease at a younger age. This means it needs to be managed well for a longer period of time and is therefore associated with premature mortality, worse long-term health outcomes, and a higher risk of diabetes-related health complications.
How do we know there are differences between population groups?
Routine statistics are collected through the National Diabetes Audit, one of the largest annual clinical audits in the world. It can help us answer questions like: What proportion of people registered with diabetes are receiving the recommended care? What are the outcomes for people we know have diabetes? And how do they vary by population factors?
This data and additional local data are being used in Suffolk and North East Essex to address a key local aim: “We will reduce health inequalities for patients with diabetes.” Being able to monitor diabetes and see how its prevention, diagnosis, and management vary by population group allows us to review any differences and ensure we are supporting those with the highest need to receive appropriate support. For example, developments in glucose monitoring technologies can help people manage their diabetes better, yet we see variation in who accesses this technology.
How do data and intelligence changes affect diabetes prevention and management?
"Through population health management, we aim to improve health outcomes for diabetes patients by linking insights from the data to the person, finding individuals at risk of developing diabetes and associated complications, and targeting care to support them to stay well."
As discussed in an earlier ARC blog, there have been significant recent advancements in data and intelligence through the development of individual-level linked datasets. Locally, we are reviewing what population health management means in practice and what additional information is available from working with individual-level linked datasets. For example, can it help us answer questions like:
- Who is most at risk of developing type 2 diabetes?
- Who is most likely to develop complications after developing diabetes?
- Who could benefit most from a different approach to diabetes prevention and management?
With this information, we can then look at what care is currently provided and if it can be delivered in a different way to reduce the risk of developing poor health. For example, combined lifestyle interventions can be effective in reducing the risk of developing type 2 diabetes through healthy eating, regular exercise, and achieving a healthy body weight. How can we find individuals who are at risk of developing type 2 diabetes and work with them to help them receive the recommended support?
How will these data and intelligence changes help to address health inequalities?
We want to identify those who are at risk of developing worse health outcomes, reduce this risk, and reduce health inequalities. There are many research opportunities to inform population health management approaches to support this, ranging from developing innovative analytical methodology to identify those at risk through to designing and evaluating targeted approaches against health and wellbeing outcomes. When thinking about how best to use this approach to help address health inequalities, the use of data locally has identified some important considerations.
We must consider who is missing from the data that we are analysing. This includes people who are socially marginalised and often face multiple risk factors for poor health, such as people who experience homelessness, who are not consistently recorded in electronic datasets. They are invisible in the data and unlikely to be identified, meaning services are unlikely to meet their needs despite these groups having significantly poorer health outcomes. Representation in data is only one of the many important dimensions of data quality to support improving identification, monitoring, and addressing health inequalities; improving this is the focus of another ARC project.
"We need to better understand what drives diabetes health inequalities and acknowledge that data collected through healthcare services only forms part of the story. To do this, we must talk to individuals and local communities to help us understand what factors outside of their control are affecting their ability to manage their diabetes and what we can do differently to support this."
Finally, to effectively address health inequalities, we need to consider how to make best use of these data informed targeted approaches collectively across organisations in conjunction with existing work done to improve diabetes prevention and health outcomes. This will include diabetes service improvement work and work to address the unequal social, economic, and environmental factors outside of the health and care system which influence an individual’s risk of developing type 2 diabetes and diabetes patients’ abilities to maintain good health.
For more information about diabetes, please see https://www.nhs.uk/conditions/diabetes/.
We would like to acknowledge Louise Lafortune in the PEDS research theme for inputting into the development of this blog.