Journal article 128 views 31 downloads
Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
,
Alexandra Lee,
Ronan Lyons ,
Ashley Akbari ,
Niels Peek,
Farideh Jalali-najafabadi,
Thamer Ba Dhafari,
Jane Lyons,
Alan Watkins ,
Rowena Bailey,
Alexandra Lee
PLOS ONE, Volume: 18, Issue: 12, Start page: e0295300
Swansea University Authors:
Jim Rafferty , Ronan Lyons , Ashley Akbari , Jane Lyons, Alan Watkins , Rowena Bailey, Alexandra Lee
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DOI (Published version): 10.1371/journal.pone.0295300
Abstract
Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched. The study pop...
| Published in: | PLOS ONE |
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| ISSN: | 1932-6203 |
| Published: |
Public Library of Science (PLoS)
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65335 |
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| Abstract: |
Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched. The study population consisted of a cohort of people living in Wales, UK aged 20 years or older in 2000 who were followed up until the end of 2017. Multimorbidity clusters by prevalence and healthcare resource use (HRU) were modelled using hypergraphs, mathematical objects relating diseases via links which can connect any number of diseases, thus capturing information about sets of diseases of any size. The cohort included 2,178,938 people. The most prevalent diseases were hypertension (13.3%), diabetes (6.9%), depression (6.7%) and chronic obstructive pulmonary disease (5.9%). The most important sets of diseases when considering prevalence generally contained a small number of diseases, while the most important sets of diseases when considering HRU were sets containing many diseases. The most important set of diseases taking prevalence and HRU into account was diabetes & hypertension and this combined measure of importance featured hypertension most often in the most important sets of diseases. We have used a single approach to find the most important sets of diseases based on co-occurrence and HRU measures, demonstrating the flexibility of the hypergraph approach. Hypertension, the most important single disease, is silent, underdiagnosed and increases the risk of life threatening co-morbidities. Co-occurrence of endocrine and cardiovascular diseases was common in the most important sets. Combining measures of prevalence with HRU provides insights which would be helpful for those planning and delivering services. |
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| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This work was supported by the Medical Research Council (MRC), grant no. MR/S027750/1. FJ is supported by a MRC/University of Manchester Skills Development Fellowship (grant number MR/R016615). |
| Issue: |
12 |
| Start Page: |
e0295300 |