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Education and Successful Aging Trajectories: A Longitudinal Population-Based Latent Variable Modelling Analysis

  • Theodore D. Cosco (a1) (a2), Blossom C.M. Stephan (a3), Carol Brayne (a4), Graciela Muniz (a5) and MRC CFAS...
Abstract

As the population ages, interest is increasing in studying aging well. However, more refined means of examining predictors of biopsychosocial conceptualizations of successful aging (SA) are required. Existing evidence of the relationship between early-life education and later-life SA is unclear. The Successful Aging Index (SAI) was mapped onto the Cognitive Function and Aging Study (CFAS), a longitudinal population-based cohort (n = 1,141). SAI scores were examined using growth mixture modelling (GMM) to identify SA trajectories. Unadjusted and adjusted (age, sex, occupational status) ordinal logistic regressions were conducted to examine the association between trajectory membership and education level. GMM identified a three-class model, capturing high, moderate, and low functioning trajectories. Adjusted ordinal logistic regression models indicated that individuals in higher SAI classes were significantly more likely to have higher educational attainment than individuals in the lower SAI classes. These results provide evidence of a life course link between education and SA.

Le vieillissement de la population a non seulement accru l’intérêt pour les études concernant les problèmes de santé, mais aussi pour celles associées au vieillissement en santé. Cependant, il serait nécessaire de définir des mesures plus raffinées liées aux prédicteurs compris dans les conceptualisations biopsychosociales du vieillissement réussi (VR). Les données probantes recueillies à ce jour concernant les liens entre l’éducation à un jeune âge et le VR ne sont pas encore claires. L’indice de vieillissement réussi (IVR) a été cartographié dans le cadre de l’étude Cognitive Function and Aging Study (CFAS), impliquant une cohorte populationnelle étudiée dans une perspective longitudinale (n = 1141). Les scores IVR ont été examinés selon l’approche du growth mixture modelling (GMM) afin d’identifier des trajectoires de VR. Des régressions logistiques ordinales pondérées (selon l’âge, le sexe, le statut professionnel) et non pondérées ont été réalisées pour évaluer l’association entre les types de trajectoires et le niveau d’éducation. Le GMM a permis d’identifier un modèle à trois classes, comprenant des trajectoires de fonctionnement hautes (TFH), modérées (TFM) et basses (TFB). Les régressions logistiques ordinales pondérées ont mis en évidence que les individus des classes d’IVR supérieures présentaient une plus forte probabilité d’avoir atteint un niveau de scolarité plus élevé que les individus à IVR inférieur (TFM, TFB) dans cet échantillon (RR 1.44, IC 95 % 1.14-1.82). L’éducation à un jeune âge est associée de manière indépendante à des trajectoires de VR supérieures plus tard dans la vie. Ces résultats recueillis dans une cohorte de sujets britanniques âgés démontrent l’impact du parcours de vie par des liens entre l’éducation et le VR, et révèlent l’influence bénéfique de l’éducation dans le long terme.

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Corresponding author
*Correspondence and requests for reprints should be sent to / La correspondance et les demandes de tirés à part doivent être adressées à : Theodore D. Cosco, Ph.D. Oxford Institute of Population Ageing 66 Banbury Road University of Oxford Oxford, OX2 6PR UK <tdcosco@cantab.net>
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Canadian Journal on Aging / La Revue canadienne du vieillissement
  • ISSN: 0714-9808
  • EISSN: 1710-1107
  • URL: /core/journals/canadian-journal-on-aging-la-revue-canadienne-du-vieillissement
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