Evidence map›Paper›PMID 38021459›Full record

ArticleSSM - population health2023

Multidimensional health heterogeneity of Chinese older adults and its determinants.

Yi Hu, Zhenyu Wang, Liqun Wu

Abstract read
In one paragraph

Article in SSM - population health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

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3 · Its place in the literature

Who cites it

7 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

3 authors.

Yi HuShenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, 518028, China.
Zhenyu WangSchool of Government, Sun Yat-sen University, Guangzhou, Guangdong, 510006, China.
Liqun WuShenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, 518028, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nowadays, the "Healthy China" and "Actively Addressing Population Aging" are two important national strategies in China. Promoting high-quality development of demand-driven older adults health services is an important way to achieve these strategies. From the perspective of active ageing, assessing the health status of older adults from multiple dimensions becomes crucial as it helps identify their specific health service needs, intervention measures, and health policies tailored to this population. Methods: Data were derived from the China Health and Retirement Longitudinal Study (CHARLS) wave 4 (2018). A total of 4190 older adults (aged ≥60 years) were included as the analysis sample. Latent class analysis was performed to categorize older adults based on 6 health indicators, including Activities of Daily Living (ADLs), Instrumental Activities of Daily Living (IADLs), doctor diagnosed chronic diseases, depressive symptoms, cognitive function, and social participation. Multinomial logistic model was used to explore determinants associated with the various patterns of multidimensional health of older adults. Results: The multidimensional health of older people was classified into three latent classes: Relatively Healthy (Class 1, n = 2806, 66.97%), Highly Depressed and Relatively Health Risk (Class 2, n = 1189, 28.38%), and Functional Impairment (Class 3, n = 195, 4.65%). Gender, age, education, marital status, number of children, alcohol consumption, physical activity, savings, residence, air quality satisfaction, and medical service satisfaction had significant effects on the attribution of all multidimensional health latent classes. Conclusion: Heterogeneous and multidimensional health classes exist in China's older population, and these classes are influenced by a variety of factors and to varying degrees. Policymakers and healthcare providers can use these evidence to further address the diverse needs of older adults and improve older-care health services, ultimately achieving the goal of Active Ageing and Healthy China.

Indexed as

Active ageingLatent class analysisMultidimensional healthOlder adults

Identifiers

PMID38021459
PMCPMC10661850

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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.