Clustering by multiple long-term conditions and social care needs: a cross-sectional study among 10 026 older adults in England
Clustering by multiple long-term conditions and social care needs: a cross-sectional study among 10 026 older adults in England
Background: people with multiple long-term conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care needs (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes.
Methods: a cross-sectional study was conducted using the English Longitudinal Study of Ageing, including people with up to 10 MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, 10 measures of mobility difficulties and whether health status was limiting earning capability. Latent class analysis was performed to identify clusters. Multivariable logistic regression quantified associations between derived MLTC/SCN clusters, all-cause mortality and nursing home admission.
Results: our study included 9171 people at baseline with a mean age of 66.3 years; 44.5% were men. Nearly 70.8% had two or more MLTC, the most frequent being hypertension, arthritis and cardiovascular disease. We identified five distinct clusters classified as high SCN/MLTC through to low SCN/MLTC clusters. The high SCN/MLTC included mainly women aged 70-79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR=8.71; 95% CI: 4.22 to 18). We found no association between clusters and all-cause mortality.
Conclusions: we have highlighted those at risk of worse care outcomes, including nursing home admission. Distinct clusters of individuals with shared sociodemographic characteristics can help identify at-risk individuals with MLTC and SCN at primary care level.
CLUSTER ANALYSIS, EPIDEMIOLOGY, GERIATRICS, PUBLIC HEALTH, Cross-Sectional Studies, Humans, Male, Activities of Daily Living, Aging, Female, Aged, Longitudinal Studies, Cluster Analysis
770-776
Khan, Nusrat
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Chalitsios, Christos V.
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Nartey, Yvonne
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Simpson, Glenn
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Zaccardi, Francesco
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Santer, Miriam
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Roderick, Paul J.
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Stuart, Beth
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Farmer, Andrew J.
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Dambha-Miller, Hajira
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9 November 2023
Khan, Nusrat
0da3cc33-cd6e-4846-a790-ffc05f53d5d1
Chalitsios, Christos V.
a96ff8c1-a547-46a5-b254-224a1764f3d5
Nartey, Yvonne
18b02d48-c668-497a-a1ee-8695b013960a
Simpson, Glenn
802b50d9-aa00-4cca-9eaf-238385f8481c
Zaccardi, Francesco
8d31a980-3db1-4477-9514-c18087cf886a
Santer, Miriam
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Roderick, Paul J.
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Stuart, Beth
626862fc-892b-4f6d-9cbb-7a8d7172b209
Farmer, Andrew J.
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Dambha-Miller, Hajira
58961db5-31aa-460e-9394-08590c4b7ba1
Khan, Nusrat, Chalitsios, Christos V., Nartey, Yvonne, Simpson, Glenn, Zaccardi, Francesco, Santer, Miriam, Roderick, Paul J., Stuart, Beth, Farmer, Andrew J. and Dambha-Miller, Hajira
(2023)
Clustering by multiple long-term conditions and social care needs: a cross-sectional study among 10 026 older adults in England.
Journal of Epidemiology and Community Health, 77 (12), .
(doi:10.1136/jech-2023-220696).
Abstract
Background: people with multiple long-term conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care needs (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes.
Methods: a cross-sectional study was conducted using the English Longitudinal Study of Ageing, including people with up to 10 MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, 10 measures of mobility difficulties and whether health status was limiting earning capability. Latent class analysis was performed to identify clusters. Multivariable logistic regression quantified associations between derived MLTC/SCN clusters, all-cause mortality and nursing home admission.
Results: our study included 9171 people at baseline with a mean age of 66.3 years; 44.5% were men. Nearly 70.8% had two or more MLTC, the most frequent being hypertension, arthritis and cardiovascular disease. We identified five distinct clusters classified as high SCN/MLTC through to low SCN/MLTC clusters. The high SCN/MLTC included mainly women aged 70-79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR=8.71; 95% CI: 4.22 to 18). We found no association between clusters and all-cause mortality.
Conclusions: we have highlighted those at risk of worse care outcomes, including nursing home admission. Distinct clusters of individuals with shared sociodemographic characteristics can help identify at-risk individuals with MLTC and SCN at primary care level.
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More information
Accepted/In Press date: 7 August 2023
e-pub ahead of print date: 23 August 2023
Published date: 9 November 2023
Additional Information:
Funding Information:
This study is independent research funded by the National Institute for Health Research (Artificial Intelligence for Multiple Long-Term Conditions (AIM), (NIHR202637). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
Keywords:
CLUSTER ANALYSIS, EPIDEMIOLOGY, GERIATRICS, PUBLIC HEALTH, Cross-Sectional Studies, Humans, Male, Activities of Daily Living, Aging, Female, Aged, Longitudinal Studies, Cluster Analysis
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Local EPrints ID: 486036
URI: http://eprints.soton.ac.uk/id/eprint/486036
ISSN: 0143-005X
PURE UUID: b9840c58-4791-44b2-b8f1-4819f136b094
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Date deposited: 08 Jan 2024 17:31
Last modified: 18 Mar 2024 03:57
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Contributors
Author:
Nusrat Khan
Author:
Christos V. Chalitsios
Author:
Yvonne Nartey
Author:
Francesco Zaccardi
Author:
Andrew J. Farmer
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