Javier de la Fuente1, Francisco Félix Caballero1,2,3, Albert Sánchez-Niubó4, Demosthenes B Panagiotakos5, A Matthew Prina6, Holger Arndt7, Josep Maria Haro2,4, Somnath Chatterji8, José Luis Ayuso-Mateos1,2,3. 1. Department of Psychiatry, Universidad Autónoma de Madrid, Spain. 2. CIBER of Mental Health, Madrid, Spain. 3. Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS Princesa), Madrid, Spain. 4. Parc Sanitari Sant Joan de Déu, Barcelona, Spain. 5. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece. 6. Department of Health Service and Population Research, King's College London, Institute of Psychiatry, Psychology and Neuroscience, UK. 7. SPRING TECHNO GMBH & Co. KG, Bremen, Germany. 8. Information, Evidence and Research, World Health Organization, Geneva, Switzerland.
Abstract
Background: Aging is a multidimensional process with a remarkable interindividual variability. This study is focused on identifying groups of population with similar aging patterns, and to define the health trajectories of these groups. Sociodemographic and health determinants of these trajectories are also identified. Methods: Data from the English Longitudinal Study of Aging (ELSA) and the Health and Retirement Study (HRS) were used. A set of self-reported health items and measured tests were used to generate a latent health metric by means of a Bayesian multilevel IRT model, assessing the ability of the metric to predict mortality. Then, a Growth Mixture Model (GMM) was conducted in each study to identify latent classes and assess health trajectories. Kaplan-Meier survival curves were obtained for each class and a multinomial logistic regression was used to identify determinants of these trajectories. Results: The health score generated showed an adequate ability to predict mortality over 10 years in ELSA (AUC = 0.74; 95% CI: 0.72, 0.75) and HRS (AUC = 0.74; 95% CI: 0.73, 0.75). By means of GMM, four latent classes were identified in ELSA and five in HRS. Chronic conditions, no qualification and low level of household wealth were associated to the classes which showed a higher mortality in both studies. Conclusion: The method based on the creation of a common metric of health and the use of GMM to identify similar patterns of aging, allows for the comparison of trajectories of health across longitudinal surveys. Multimorbidity, educational level, and household wealth could be considered as determinants associated to these trajectories.
Background: Aging is a multidimensional process with a remarkable interindividual variability. This study is focused on identifying groups of population with similar aging patterns, and to define the health trajectories of these groups. Sociodemographic and health determinants of these trajectories are also identified. Methods: Data from the English Longitudinal Study of Aging (ELSA) and the Health and Retirement Study (HRS) were used. A set of self-reported health items and measured tests were used to generate a latent health metric by means of a Bayesian multilevel IRT model, assessing the ability of the metric to predict mortality. Then, a Growth Mixture Model (GMM) was conducted in each study to identify latent classes and assess health trajectories. Kaplan-Meier survival curves were obtained for each class and a multinomial logistic regression was used to identify determinants of these trajectories. Results: The health score generated showed an adequate ability to predict mortality over 10 years in ELSA (AUC = 0.74; 95% CI: 0.72, 0.75) and HRS (AUC = 0.74; 95% CI: 0.73, 0.75). By means of GMM, four latent classes were identified in ELSA and five in HRS. Chronic conditions, no qualification and low level of household wealth were associated to the classes which showed a higher mortality in both studies. Conclusion: The method based on the creation of a common metric of health and the use of GMM to identify similar patterns of aging, allows for the comparison of trajectories of health across longitudinal surveys. Multimorbidity, educational level, and household wealth could be considered as determinants associated to these trajectories.
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