Evidence map›Paper›PMID 42021161›Full record

ArticleBMC geriatrics2026

Multidimensional stress among rural older adults in China: status, typology, and mental health associations.

Xiuliang Dai, Boyue Sun, Lijian Wang

Abstract read
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xiuliang DaiSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28, West Xianning Road, Xi'an, Shaanxi, 710049, China.
Boyue SunSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28, West Xianning Road, Xi'an, Shaanxi, 710049, China.
Lijian WangSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28, West Xianning Road, Xi'an, Shaanxi, 710049, China. wanglijian2@mail.xjtu.edu.cn.

Funding

Ministry of Education's Major Project for Research in Philosophy and Social Sciences in China 24JZD039Postdoctoral Science Foundation of Shaanxi Province 2023BSHEDZZ87Shaanxi Provincial Innovation Capability Support Program - General Soft Science Project 2025KG-YBXM-162
6 · The paper itself

Abstract

backgroundWith the intensification of population aging in China, research on stress among older adults and its associations with mental health has become increasingly important. While previous studies have examined economic and health stress among older adults, research on stress related to caregiving, family relations, and children’s development remains insufficient. Therefore, this study aimed to classify stress and its factors, and further investigate the relationship between stress and mental health.

methodsBased on data of 728 participants from the “Comprehensive Social Survey on the Care Status of Home-based Older Adults” conducted by Xi’an Jiaotong University in 2023, we first performed a descriptive analysis of multidimensional stress among rural older adults in China. Subsequently, Latent Class Analysis was used to categorize the types of stress, multinomial logistic regression was also used to identify factors associated with stress type. Finally, an Analysis of Covariance was used to explore the relationship between stress types and mental health.

resultsSocial adaptation stress remained at a relatively high level among rural older adults in China. Three latent types of stress were identified among older adults: “mild stress” (48.6%), “moderate stress” (40.6%), and “severe stress” (10.8%). Compared to the “Mild Stress Type,” the “Moderate Stress Type” was more likely to be associated with “Income is at an average level” (RRR: 3.254, P = 0.006), “Relatively low income” (RRR: 5.739, P < 0.001), and “Very low income” (RRR: 24.002, P < 0.001). Conversely, the “Mild Stress Type” was more likely to be associated with a “High school/Vocational school” education level (RRR: 0.310, P = 0.008). Compared to the “Severe Stress Type”, the “Mild Stress Type” was more likely to be associated with a “Divorced” marital status (RRR: 0.003, P < 0.001). The relationship between stress and mental health follows a U-shaped pattern. Older adults with the “moderate stress type” exhibit the best mental health, followed by those with the “mild stress type”.

conclusionsThis study classified stress among older adults into three categories, and identified a U-shaped association between stress and mental health. Our findings may help the Chinese government implement measures that are better suited to older adults in China, particularly in addressing stress and mental health.

Indexed as

Mental HealthRural PopulationStress, PsychologicalAgedAged, 80 and overChinaFemaleHumansMaleMental healthMultidimensional stressRural older adults in China

Identifiers

PMID42021161
PMCPMC13238003

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LicenceCC BY-NC-ND
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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.