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Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study

Jim Rafferty Orcid Logo, Alexandra Lee, Ronan Lyons Orcid Logo, Ashley Akbari Orcid Logo, Niels Peek, Farideh Jalali-najafabadi, Thamer Ba Dhafari, Jane Lyons, Alan Watkins Orcid Logo, Rowena Bailey, Alexandra Lee

PLOS ONE, Volume: 18, Issue: 12, Start page: e0295300

Swansea University Authors: Jim Rafferty Orcid Logo, Ronan Lyons Orcid Logo, Ashley Akbari Orcid Logo, Jane Lyons, Alan Watkins Orcid Logo, 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...

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ISSN: 1932-6203
Published: Public Library of Science (PLoS) 2023
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spelling v2 65335 2023-12-17 Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study 52effe759a718bd36eb12cdd10fe1a09 0000-0002-1667-7265 Jim Rafferty Jim Rafferty true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 1b74fa5125a88451c52c45bcf20e0b47 Jane Lyons Jane Lyons true false 81fc05c9333d9df41b041157437bcc2f 0000-0003-3804-1943 Alan Watkins Alan Watkins true false 455e2c1e6193448f6269b9e72acaf865 Rowena Bailey Rowena Bailey true false 7c6dc217555b0fea264ff0dd7d0aa374 Alexandra Lee Alexandra Lee true false 2023-12-17 HDAT 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. Journal Article PLOS ONE 18 12 e0295300 Public Library of Science (PLoS) 1932-6203 15 12 2023 2023-12-15 10.1371/journal.pone.0295300 http://dx.doi.org/10.1371/journal.pone.0295300 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University SU Library paid the OA fee (TA Institutional Deal) 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). MR/S027750/1, MR/R016615 2024-03-25T18:50:27.2414095 2023-12-17T21:25:26.5594716 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Jim Rafferty 0000-0002-1667-7265 1 Alexandra Lee 2 Ronan Lyons 0000-0001-5225-000X 3 Ashley Akbari 0000-0003-0814-0801 4 Niels Peek 5 Farideh Jalali-najafabadi 6 Thamer Ba Dhafari 7 Jane Lyons 8 Alan Watkins 0000-0003-3804-1943 9 Rowena Bailey 10 Alexandra Lee 11 65335__29382__c30a98e9807845da88c1882c89413709.pdf 65335.pdf 2024-01-04T15:44:43.2325275 Output 1173936 application/pdf Version of Record true This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. false eng https://creativecommons.org/licenses/by/4.0/
title Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
spellingShingle Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
Jim Rafferty
Ronan Lyons
Ashley Akbari
Jane Lyons
Alan Watkins
Rowena Bailey
Alexandra Lee
title_short Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
title_full Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
title_fullStr Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
title_full_unstemmed Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
title_sort Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study
author_id_str_mv 52effe759a718bd36eb12cdd10fe1a09
83efcf2a9dfcf8b55586999d3d152ac6
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author_id_fullname_str_mv 52effe759a718bd36eb12cdd10fe1a09_***_Jim Rafferty
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
1b74fa5125a88451c52c45bcf20e0b47_***_Jane Lyons
81fc05c9333d9df41b041157437bcc2f_***_Alan Watkins
455e2c1e6193448f6269b9e72acaf865_***_Rowena Bailey
7c6dc217555b0fea264ff0dd7d0aa374_***_Alexandra Lee
author Jim Rafferty
Ronan Lyons
Ashley Akbari
Jane Lyons
Alan Watkins
Rowena Bailey
Alexandra Lee
author2 Jim Rafferty
Alexandra Lee
Ronan Lyons
Ashley Akbari
Niels Peek
Farideh Jalali-najafabadi
Thamer Ba Dhafari
Jane Lyons
Alan Watkins
Rowena Bailey
Alexandra Lee
format Journal article
container_title PLOS ONE
container_volume 18
container_issue 12
container_start_page e0295300
publishDate 2023
institution Swansea University
issn 1932-6203
doi_str_mv 10.1371/journal.pone.0295300
publisher Public Library of Science (PLoS)
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
url http://dx.doi.org/10.1371/journal.pone.0295300
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description 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.
published_date 2023-12-15T18:50:22Z
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