Evidence map›Paper›PMID 36177152›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2022

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

Yuyang Liu, Yanjuan Wu, Jingheng Cai, Yun Huang, Yuntao Chen, Tishya M Venkatraman, Sophia Lobanov-Rostovsky, Piotr Bandosz, Yung-Jen Yang, Yu-Tzu Wu and 3 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yuyang LiuDepartment of Medical Statistics School of Public Health Sun Yat-sen University Guangzhou People's Republic of China.
Yanjuan WuDepartment of Medical Statistics School of Public Health Sun Yat-sen University Guangzhou People's Republic of China.
Jingheng CaiDepartment of Statistics Sun Yat-sen University Guangzhou People's Republic of China.
Yun HuangGuangdong Provincial Center for Disease Control and Prevention Guangzhou People's Republic of China.
Yuntao ChenDepartment of Epidemiology & Public Health University College London London UK.
Tishya M VenkatramanDepartment of Epidemiology & Public Health University College London London UK.
Sophia Lobanov-RostovskyDepartment of Epidemiology & Public Health University College London London UK.
Piotr BandoszDepartment of Public Health and Policy University of Liverpool Liverpool UK.
Yung-Jen YangSocial Research Institute Institute of Education University College London London UK.
Yu-Tzu WuPopulation Health Sciences Institute Newcastle University Newcastle UK.
Jing LiaoDepartment of Medical Statistics School of Public Health Sun Yat-sen University Guangzhou People's Republic of China.
Yuantao HaoDepartment of Medical Statistics School of Public Health Sun Yat-sen University Guangzhou People's Republic of China.
Eric J BrunnerDepartment of Epidemiology & Public Health University College London London UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Chinacognitive impairmentconfirmatory factor analysisdementiaepidemiology

Identifiers

PMID36177152
PMCPMC9473486

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.