The paper, published in the Lancet Digital Health called Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study, addresses the challenge set out by the Academy of Medical Sciences and the Chief Medical Officer to investigate which diseases co-occur in the same individuals.
The research, led by Aroon Hingorani, Harry Hemingway, Claudia Langenberg, Nish Chaturvedi and Valerie Kuan with co-investigators from the Multimorbidity Mechanism and Therapeutics Research Collaborative at UCL, was supported by HDR UK’s phenomics and multimorbidity research projects with input from Spiros Denaxas and Reecha Sofat of the BHF Data Science Centre.
As part of their research, the team developed online tools to explore multimorbidity and comorbidity patterns by ethnicity, sex, and age.
The findings and online tools could help:
- Policymakers plan resource allocation
- Researchers develop new or use existing medicines to treat several diseases
- Healthcare providers optimise service delivery
- Doctors plan management of patients’ care
- Patients understand their illness better
The research has also led to the discovery of pathways common to multiple diseases as well as opportunities to develop new or existing medicines to treat several conditions together.
Using information from routine health records data from around four million patients in England accessed safely and securely via the UCL Trusted Research Environment (TRE), the research team identified patterns of clustering of 308 common mental and physical health conditions of men and women of different ages and with different ethnicities.
Of these participants, 50.5% were women and girls, 49.5% were men and boys, 68.9% were White, 4.0% were south Asian and 2.6% were Black.
Some patterns found include: heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease.
Hypertension was most strongly associated with kidney disorders in those aged 20–29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older.
Breast cancer was associated with different comorbidities in individuals from different ethnicities, asthma with different comorbidities between the sexes, and bipolar disorder with different comorbidities in younger ages compared with older ages.
Aroon Hingorani, Director of the UCL British Heart Foundation Research Accelerator, said:
“Information from minority ethnic groups and younger people has often been missing from studies of multimorbidity, but by using diverse electronic health records, we present a more inclusive and representative perspective of multimorbidty. This is one area where the NHS, electronic health records and data science can generate important insights.”
Spiros Denaxas, Associate Director for the BHF Data Science Centre, said:
“Millions of people live with multiple diseases, yet our understanding of how and when these transpire is limited. This research project is the first step towards understanding how these diseases co-occur and identifying how to best treat them. Being able to do research that can potentially help millions worldwide and have a positive impact is a very exciting and humbling experience.”
The research was funded by UK Research and Innovation’s Strategic Priority Fund “Tackling multimorbidity at scale” programme (grant number MR/V033867/1), Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, the British Heart Foundation, and The Alan Turing Institute, in collaboration with the Engineering and Physical Sciences Research Council.
Read the full paper of the population study.
View all the opensourced algorithms for defining the diseases included in the paper.
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