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Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
OBJECTIVES: In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023455/ https://www.ncbi.nlm.nih.gov/pubmed/33822803 http://dx.doi.org/10.1371/journal.pone.0248844 |
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author | Nguyen, Hai Moreno-Agostino, Dario Chua, Kia-Chong Vitoratou, Silia Prina, A. Matthew |
author_facet | Nguyen, Hai Moreno-Agostino, Dario Chua, Kia-Chong Vitoratou, Silia Prina, A. Matthew |
author_sort | Nguyen, Hai |
collection | PubMed |
description | OBJECTIVES: In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. SETTING AND PARTICIPANTS: Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). METHODS: Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. RESULTS: Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). CONCLUSION: Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity. |
format | Online Article Text |
id | pubmed-8023455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-80234552021-04-15 Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts Nguyen, Hai Moreno-Agostino, Dario Chua, Kia-Chong Vitoratou, Silia Prina, A. Matthew PLoS One Research Article OBJECTIVES: In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. SETTING AND PARTICIPANTS: Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). METHODS: Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. RESULTS: Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). CONCLUSION: Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity. Public Library of Science 2021-04-06 /pmc/articles/PMC8023455/ /pubmed/33822803 http://dx.doi.org/10.1371/journal.pone.0248844 Text en © 2021 Nguyen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nguyen, Hai Moreno-Agostino, Dario Chua, Kia-Chong Vitoratou, Silia Prina, A. Matthew Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts |
title | Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts |
title_full | Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts |
title_fullStr | Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts |
title_full_unstemmed | Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts |
title_short | Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts |
title_sort | trajectories of healthy ageing among older adults with multimorbidity: a growth mixture model using harmonised data from eight athlos cohorts |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023455/ https://www.ncbi.nlm.nih.gov/pubmed/33822803 http://dx.doi.org/10.1371/journal.pone.0248844 |
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