Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts. Nguyen, H., Moreno-Agostino, D., Chua, K., Vitoratou, S., & Prina, A. M. PLOS ONE, 16(4):e0248844, April, 2021.
Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts [link]Paper  doi  abstract   bibtex   14 downloads  
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.
@article{nguyen_trajectories_2021,
	title = {Trajectories of healthy ageing among older adults with multimorbidity: {A} growth mixture model using harmonised data from eight {ATHLOS} cohorts},
	volume = {16},
	issn = {1932-6203},
	shorttitle = {Trajectories of healthy ageing among older adults with multimorbidity},
	url = {https://dx.plos.org/10.1371/journal.pone.0248844},
	doi = {10.1371/journal.pone.0248844},
	abstract = {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.},
	language = {en},
	number = {4},
	urldate = {2021-04-08},
	journal = {PLOS ONE},
	author = {Nguyen, Hai and Moreno-Agostino, Dario and Chua, Kia-Chong and Vitoratou, Silia and Prina, A. Matthew},
	editor = {Zhan, Y},
	month = apr,
	year = {2021},
	pmcid = {PMC8023455},
	pmid = {33822803},
	keywords = {ATHLOS, Ageing Trajectories of Health – Longitudinal Opportunities and Synergies, Mplus, growth mixture modeling (GMM)},
	pages = {e0248844},
}
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