Multimorbidity describes two or more long term conditions (MLTC) in one person. One in four people in the UK have MLTC and it is a major challenge currently facing individuals and health services. We will use advanced statistical and computing tools to analyse large national and international datasets, to understand how MLTC develops over a lifetime.
Background
‘Multimorbidity’ describes two or more (multiple) long term conditions (MLTC) in one person. A common example is the presence of diabetes, arthritis and high blood pressure. One in four of the UK population have MLTC. While it is well known that certain conditions often occur together, the reasons for why this is happening are not yet fully understood. This knowledge is crucial for determining effective interventions to limit MLTC progression. Developing an ability to recognise the early origins of later disease clustering through examining long term disease patterns has potential benefits to patients in terms of prediction of future outcomes and for prevention.
We will focus on chronic inflammation which refers to the body’s natural ability to respond to outside threats, such as fighting an infection. This lies at the root of many diseases including heart disease, type 2 diabetes, arthritis, and dementia. Some people are more likely to experience chronic inflammation. Foods and nutrients, for instance vitamin C, can reduce inflammation. People who are less well-off are more likely to eat a poor diet and may struggle to get enough of these nutrients. Additionally, we will consider how people's cultural background (including race) and position in society (such as income and education) combine with inflammation and nutrition to influence the likelihood of developing MLTCs.
The goal is to develop tools for primary care to predict MLTC, enabling timely and effective prevention by GPs and health professionals particularly to support communities most at risk of developing MLTC.
Our focus is on chronic inflammation which refers to the body’s natural ability to respond to outside threats and lies at the root of many diseases. Additionally, we will consider how foods and nutrients alongside people's cultural background and position in society (such as income and education) combine with inflammation to influence the likelihood of developing MLTCs. The goal is to develop tools for primary care to predict MLTC, enabling timely and effective prevention by GPs and health professionals particularly to support communities most at risk of developing MLTC.
Project Aims
- Develop and apply the most effective suite of analytical tools to large scale population research and electronic healthcare datasets to identify longitudinal clustering of MLTC throughout the lifecourse.
- To identify the role of chronic inflammation, nutrition, and malnutrition in the development of MLTC.
- To quantify individual-level variation in risk of MTLC according to age, sex, ethnicity and socio-economic background.
- To develop interventions and policies targeting individuals and populations at risk to prevent the onset and progression of MLTC.
Project Activity
- We have established and secured access to 6 key data sets (including all key data assurances and security).
- We have developed a unified code list for MLTC to be applied across all datasets including ICD-10, SNOMED and Readcodes.
- We are developing a list of key inflammatory biomarkers to be applied across all datasets (where applicable).
- We are developing a list of indicators of multiple disadvantage to be applied across all datasets.
- We are developing core dataset processes, coding and preparatory data cleaning.
- We are establishing appropriate methodological approaches for clustering and temporal phenotyping.
- We have set up a core PPIE group for our programme - engaging with a diverse population across England. This groups meet every other month and members are actively involved with each stage of the programme, including developing the list of MLTC.
Anticipated or actual outputs
- Phase 2 will examine how the insights from Phase 1 might be developed into tools that can influence patient behaviour to reduce their future risk of MLTC.
- We will examine the feasibility of applying these insights to routine data in general practice, and how they might be delivered through policy interventions.
Who is involved?
- PI - Professor Alexander Macgregor (UEA)
- Corresponding researcher - Dr Charlotte Davies (Senior Research Fellow)
- Co-applicants: Professor Elena Kulinskaya (London School of Hygiene and Tropical Medicine)
- Professor Ailsa Welch (UEA)
- Professor Chris Fox (University of Exeter)
- Jackie Chipping (UEA, PPIE lead)
- Ron Brewer (PPIE co-applicant)
- Professor Basma Ellahi (University of Chester)
- Researchers: Professor John Ford (Queen Mary University, London)
- Dr Min Hane Aung (UEA)
- Dr Tahmina Zebin (Brunel University)
- Dr Mizanur Khondoker (UEA)
- Dr Alexander Lewin (London School of Hygiene and Tropical Medicine)
- Professor Caitlin Notley (UEA)
- Professor Wendy Wills (University of Hertfordshire)
- Dr Kathryn Richardson (UEA)
- Dr Jane Skinner (UEA)
- Magdalena Brander-Pye (UEA)
- Dr Daniel Asfaw (UEA)
- Dr Soumya Paris (London School of Hygiene and Tropical Medicine)
- Dr Katherine Bradbury (University of Southampton)
Evergreen Life - The Richmond Group of Charities
Citizens Academy, UEA
