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Clustering by multiple long-term conditions and social care needs: a cohort study amongst 10,025 older adults in England

Clustering by multiple long-term conditions and social care needs: a cohort study amongst 10,025 older adults in England
Clustering by multiple long-term conditions and social care needs: a cohort study amongst 10,025 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 need (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes.

Methods: a cohort study was conducted using the English Longitudinal Study of Ageing (ELSA), including people with up to ten MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, ten 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 SCN/MLTC clusters, all-cause mortality, and nursing home admission.

Results: the cohort included 9171 people at baseline with a mean age of 66·3 years; 44·5% were males. 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 to 79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR = 8·97; 95% CI: 4·36 to 18·45). We found no association between clusters and all-cause mortality.

Conclusions: this results in five clusters with distinct characteristics that permit the identification of high-risk groups who are more likely to have worse care outcomes, including nursing home admission. This can inform targeted preventive action to where it is most needed amongst those with MLTC.
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
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Stuart, Beth
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Farmer, Andrew
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Dambha-Miller, Hajira
58961db5-31aa-460e-9394-08590c4b7ba1
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
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Santer, Miriam
3ce7e832-31eb-4d27-9876-3a1cd7f381dc
Roderick, Paul
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Stuart, Beth
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Farmer, Andrew
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Dambha-Miller, Hajira
58961db5-31aa-460e-9394-08590c4b7ba1

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

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 need (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes.

Methods: a cohort study was conducted using the English Longitudinal Study of Ageing (ELSA), including people with up to ten MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, ten 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 SCN/MLTC clusters, all-cause mortality, and nursing home admission.

Results: the cohort included 9171 people at baseline with a mean age of 66·3 years; 44·5% were males. 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 to 79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR = 8·97; 95% CI: 4·36 to 18·45). We found no association between clusters and all-cause mortality.

Conclusions: this results in five clusters with distinct characteristics that permit the identification of high-risk groups who are more likely to have worse care outcomes, including nursing home admission. This can inform targeted preventive action to where it is most needed amongst those with MLTC.

UNSPECIFIED
2023.05.18.23290064v1.full - Author's Original
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Published date: 26 May 2023

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Local EPrints ID: 478603
URI: http://eprints.soton.ac.uk/id/eprint/478603
PURE UUID: 955e51a6-17a5-41ea-a9a5-ddba8694c787
ORCID for Glenn Simpson: ORCID iD orcid.org/0000-0002-1753-942X
ORCID for Miriam Santer: ORCID iD orcid.org/0000-0001-7264-5260
ORCID for Paul Roderick: ORCID iD orcid.org/0000-0001-9475-6850
ORCID for Beth Stuart: ORCID iD orcid.org/0000-0001-5432-7437
ORCID for Hajira Dambha-Miller: ORCID iD orcid.org/0000-0003-0175-443X

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Date deposited: 05 Jul 2023 17:24
Last modified: 30 Nov 2024 03:04

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Contributors

Author: Nusrat Khan
Author: Christos V. Chalitsios
Author: Yvonne Nartey
Author: Glenn Simpson ORCID iD
Author: Francesco Zaccardi
Author: Miriam Santer ORCID iD
Author: Paul Roderick ORCID iD
Author: Beth Stuart ORCID iD
Author: Andrew Farmer

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