P52 Calibrating cognitive tests across two Chinese ageing cohorts for dementia prevalence estimation: a confirmatory factor analysis. (26th August 2022)

• Record Type: Journal Article
• Authors: Liu, Yuyang; Wu, Yanjuan; Cai, Jingheng; Chen, Yuntao; Venkatraman, Tishya; Lobanov-Rostovsky, Sophia; Yang, Yung-Jen; Wu, Yu-Tzu; Liao, Jing; Brunner, Eric; Huang, Yun; Bandosz, Piotr; Hao, Yuantao
• Is Part Of: Journal of epidemiology and community health. Volume 76(2022)Supplement 1
• Page Start: A69
• Page End: A70
• Publication Date: 2022-08-26
• Subjects: Cognitive impairment -- Dementia -- Confirmatory factor analysis; Public health -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1136/jech-2022-SSMabstracts.145
• Languages: English
• ISSNs: 0143-005X

Abstract

Abstract : Background: There is limited evidence on the nationwide estimates and existing epidemiological surveys generally focused on specific regions that might not be representative of the older population across the whole country. To estimate the nationwide prevalence of dementia using existing data, one of the key challenges is variation in the cognitive measures utilized across cohorts. This study aimed to calibrate cognitive tests into common latent cognitive construct for dementia prevalence estimation across these two population-representative Chinese ageing cohorts. Methods: Chinese Longitudinal Healthy Longevity Survey (CLHLS, N=12015) and China Health and Retirement Longitudinal Study (CHARLS, N=6623) are ongoing nationwide surveys on health and its risk factors of older adults, with different cognitive batteries. Based on participants aged 65–99 from the 2018 wave of CLHLS and CHARLS, confirmatory factor analysis was applied to identify latent cognitive construct and to estimate dementia prevalence. Dementia prevalence estimated by factor scores of the latent approach was compared to raw scores (i.e. the sum of original cognitive test scores), and Mini-Mental State Examination scores. Age-specific dementia prevalence estimated by the CFA models was compared with estimates reported by a nationwide survey, the COAST study and by the Global Burden of Disease (GBD) group. Results: Common three-factor cognitive constructs (orientation, memory, executive function and language) were verified in CLHLS (13 items) and CHARLS (9 items). Dementia prevalence estimated by factor scores was more consistent in CLHLS 5.9% (95%CI:5.7%-6.1%) and CHARLS 4.9% (95%CI: 4.7%-5.1%) than by raw scores and MMSE. Dementia estimates of CLHLS and CHARLS largely overlapped with each other and were in line with these of the cross-sectional COAST survey particularly in the age range between 65 to 80. For estimations above 80 years old, CLHLS and CHARLS estimates turned to be lower than the COAST study, and all lower than the GBD estimates. Discussion: Configural invariance established in this study facilitated the calibrating different cognitive measures across the two ageing cohorts in China and provide a potential approach to estimate dementia prevalence across surveys and over time at the national level. Our latent approach may serve further statistical harmonization of cognitive assessments across aging cohorts over time.