Is there a common latent cognitive construct for dementia estimation across two Chinese cohorts?

Publication
Age Ageing

Abstract

Introduction: It is valuable to identify common latent cognitive constructs for dementia prevalence estimation across Chinese aging cohorts.

Methods: Based on cognitive measures of 12015 Chinese Longitudinal Healthy Longevity Survey (CLHLS; 13 items) and 6623 China Health and Retirement Longitudinal Study (CHARLS; 9 items) participants aged 65 to 99 in 2018, confirmatory factor analysis was applied to identify latent cognitive constructs, and to estimate dementia prevalence compared to Mini-Mental State Examination (MMSE) and nationwide estimates of the literature.

Results: A common three-factor cognitive construct of orientation, memory, and executive function and language was found for both cohorts with adequate model fits. Crude dementia prevalence estimated by factor scores was similar to MMSE in CLHLS, and was more reliable in CHARLS. Age-standardized dementia estimates of CLHLS were lower than CHARLS among those aged 70+, which were close to the nationwide prevalence reported by the COAST study and Global Burden of Disease.

Discussion: We verified common three-factor cognitive constructs for both cohorts, providing an approach to estimate dementia prevalence at the national level.

Highlights: Common three-factor cognitive constructs were identified in Chinese Longitudinal Healthy Longevity Survey (CLHLS) and China Health and Retirement Longitudinal Study (CHARLS).Crude dementia estimates using factor scores were reliable in both cohorts.Estimates of CHARLS were close to current evidence, but higher than that of CLHLS.

Keywords: China; cognitive impairment; confirmatory factor analysis; dementia; epidemiology.

Yuntao Chen
Yuntao Chen
Senior Fellow Researcher,Faculty of Brain Science,University College London

dynamic prediction models and longitudinal modelling of modifiable risk factors for dementia, especially air pollution

Yu-Tzu Wu
Yu-Tzu Wu
Royal Statistical Society Chartered Statistician,Fellow of Newcastle University Policy Academy

Ageing research especially the potential impact of environment on health and wellbeing in later life.

Jing Liao
Jing Liao
Associate professor, Department of Medical Statistics & Epidemiology| SYSU Global Health Institute (SGHI), Sun Yat-sen University, China

Healthy ageing dynamics, examining social networks × health behaviors × multidimensional functioning (physical/cognitive/social). Uses longitudinal cohort modeling (global datasets) to pinpoint socio-determinants, with RCT-validated interventions.