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

被引:19
|
作者
Cosco, Theodore D. [1 ,2 ]
Stephan, Blossom C. M. [4 ]
Brayne, Carol [3 ]
Muniz, Graciela [5 ]
机构
[1] Univ Oxford, Oxford Inst Populat Ageing, 66 Banbury Rd, Oxford OX2 6PR, England
[2] UCL, Unit Lifelong Hlth & Ageing, MRC, London, England
[3] Univ Cambridge, Cambridge Inst Publ Hlth, Cambridge, England
[4] Newcastle Univ, Inst Hlth & Soc, Newcastle Upon Tyne, Tyne & Wear, England
[5] Univ Edinburgh, Ctr Clin Brain Sci, Edinburgh, Midlothian, Scotland
关键词
aging; successful aging; growth mixture modelling; healthy aging; educational status; PREDICTORS; SELECTION; ADULTS;
D O I
10.1017/S0714980817000344
中图分类号
R4 [临床医学]; R592 [老年病学];
学科分类号
1002 ; 100203 ; 100602 ;
摘要
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.
引用
收藏
页码:427 / 434
页数:8
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