Trajectories in long-term condition accumulation and mortality in older adults: a group-based trajectory modelling approach using the English Longitudinal Study of Ageing
Trajectories in long-term condition accumulation and mortality in older adults: a group-based trajectory modelling approach using the English Longitudinal Study of Ageing
Objectives: to classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.
Design: we conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.
Results: five distinct clusters of accumulating LTC trajectories were identified and characterised as: 'no LTC' (18.57%), 'single LTC' (31.21%), 'evolving multimorbidity' (25.82%), 'moderate multimorbidity' (17.12%) and 'high multimorbidity' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the 'high multimorbidity' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the 'no LTC' cluster.
Conclusions: the development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
Humans, Longitudinal Studies, Aged, Female, Male, Middle Aged, Multimorbidity, England/epidemiology, Chronic Disease/mortality, Aged, 80 and over, Aging, Mortality/trends, Cluster Analysis, Risk Factors
e074902
Chalitsios, Christos V.
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Santoso, Cornelia
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Nartey, Yvonne
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Khan, Nusrat
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Simpson, Glenn
802b50d9-aa00-4cca-9eaf-238385f8481c
Islam, Nazrul
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Stuart, Beth
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Farmer, Andrew
cfd4b749-4fe8-4bcc-879b-a4d9aa7f9b2e
Dambha-Miller, Hajira
58961db5-31aa-460e-9394-08590c4b7ba1
12 July 2024
Chalitsios, Christos V.
a96ff8c1-a547-46a5-b254-224a1764f3d5
Santoso, Cornelia
a10d071e-662f-4324-b84c-fde52f6c5153
Nartey, Yvonne
18b02d48-c668-497a-a1ee-8695b013960a
Khan, Nusrat
0da3cc33-cd6e-4846-a790-ffc05f53d5d1
Simpson, Glenn
802b50d9-aa00-4cca-9eaf-238385f8481c
Islam, Nazrul
e5345196-7479-438f-b4f6-c372d2135586
Stuart, Beth
a51c80d3-5855-4672-b24f-8c65fd2e1444
Farmer, Andrew
cfd4b749-4fe8-4bcc-879b-a4d9aa7f9b2e
Dambha-Miller, Hajira
58961db5-31aa-460e-9394-08590c4b7ba1
Chalitsios, Christos V., Santoso, Cornelia, Nartey, Yvonne, Khan, Nusrat, Simpson, Glenn, Islam, Nazrul, Stuart, Beth, Farmer, Andrew and Dambha-Miller, Hajira
(2024)
Trajectories in long-term condition accumulation and mortality in older adults: a group-based trajectory modelling approach using the English Longitudinal Study of Ageing.
BMJ Open, 14 (7), .
(doi:10.1136/bmjopen-2023-074902).
Abstract
Objectives: to classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.
Design: we conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.
Results: five distinct clusters of accumulating LTC trajectories were identified and characterised as: 'no LTC' (18.57%), 'single LTC' (31.21%), 'evolving multimorbidity' (25.82%), 'moderate multimorbidity' (17.12%) and 'high multimorbidity' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the 'high multimorbidity' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the 'no LTC' cluster.
Conclusions: the development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
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Accepted/In Press date: 12 June 2024
Published date: 12 July 2024
Keywords:
Humans, Longitudinal Studies, Aged, Female, Male, Middle Aged, Multimorbidity, England/epidemiology, Chronic Disease/mortality, Aged, 80 and over, Aging, Mortality/trends, Cluster Analysis, Risk Factors
Identifiers
Local EPrints ID: 492523
URI: http://eprints.soton.ac.uk/id/eprint/492523
ISSN: 2044-6055
PURE UUID: 0828cf2a-76f8-4969-9f29-42e3438a33da
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Date deposited: 30 Jul 2024 16:43
Last modified: 31 Jul 2024 02:05
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Contributors
Author:
Christos V. Chalitsios
Author:
Cornelia Santoso
Author:
Yvonne Nartey
Author:
Nusrat Khan
Author:
Nazrul Islam
Author:
Beth Stuart
Author:
Andrew Farmer
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