Evidence map›Paper›PMID 39604986›Full record

ArticleBMC public health2024

Exploring the interplay of living arrangements, social support, and depression among older adults in rural northwest China.

Qiufeng Gao, Cuiyao Lei, Xiaofei Wei, Lanxi Peng, Xintong Wang, Ai Yue, Yaojiang Shi

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Qiufeng GaoCenter of Experimental Economics in Education, Shaanxi Normal University, No. 620 West Chang'an Street, Chang'an District, Xi'an, Shaanxi Province, 710119, China.
Cuiyao LeiCenter of Experimental Economics in Education, Shaanxi Normal University, No. 620 West Chang'an Street, Chang'an District, Xi'an, Shaanxi Province, 710119, China.
Xiaofei WeiCenter of Experimental Economics in Education, Shaanxi Normal University, No. 620 West Chang'an Street, Chang'an District, Xi'an, Shaanxi Province, 710119, China.
Lanxi PengSchool of Public Administration, Southwestern University of Finance and Economics, 555 Liutai Avenue, Wenjiang District, Chengdu, Sichuan Province, 611130, China.
Xintong WangCenter of Experimental Economics in Education, Shaanxi Normal University, No. 620 West Chang'an Street, Chang'an District, Xi'an, Shaanxi Province, 710119, China.
Ai YueCenter of Experimental Economics in Education, Shaanxi Normal University, No. 620 West Chang'an Street, Chang'an District, Xi'an, Shaanxi Province, 710119, China. yueai@163.com.
Yaojiang ShiCenter of Experimental Economics in Education, Shaanxi Normal University, No. 620 West Chang'an Street, Chang'an District, Xi'an, Shaanxi Province, 710119, China.

Funding

111 Project B16031National Natural Science Foundation of China 71933003National Natural Science Foundation of China 72203134National Social Science Foundation of China 22BGL212
6 · The paper itself

Abstract

backgroundWith the intensification of aging, depression among the older adults has become a significant global health challenge, especially in developing countries where the problem is particularly severe. This study aims to explore the association between living arrangements and depression, with a specific focus on examining the mediating role of social support in rural northwest China.

methodsData were collected from 501 individuals aged 60 and above in rural Shaanxi Province, northwest China. Living arrangements were assessed based on co-residing family members. Depression was measured using the Geriatric Depression Scale-30, and social support was evaluated with the Social Support Rating Scale. Ordinary Least Squares Regression, Quantile Regression, Generalized Linear Regression, and Ordered Regression analyzed the association between living arrangements and depression. Hierarchical multiple linear regression examined the mediating role of social support. A heterogeneity analysis by gender and socio-economic status was also conducted.

resultsThe depression rates were 51.84% for non-empty nests, 67.92% for individuals living alone, and 46.80% for individuals only living with a spouse. The regression analysis did not reveal a significant difference in depressive between empty nests compared to non-empty nests. However, older adults living alone exhibited a higher susceptibility to depression symptoms (β = 2.594, P < 0.05). The mediating analysis showed that empty nesters living alone experience higher rates of depression due to receiving less social support compared to those living with a spouse or in non-empty nest arrangements, particularly concerning subjective social support (β = 0.481, P < 0.05). Heterogeneity analysis identified specific vulnerable groups among older adults, emphasizing the higher susceptibility to depressive symptoms among those living alone, especially among male older adults or those from non-poor families.

conclusionsOur findings emphasize a significant prevalence of depression among older adults in rural areas, particularly highlighting the vulnerability of those living alone. This underscores the urgent need for government attention to address the mental health challenges faced by the older adults. Future interventions should focus on improving social support networks to mitigate the impact of living alone on depression symptoms among this population.

Indexed as

DepressionResidence CharacteristicsRural PopulationSocial SupportAgedAged, 80 and overChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedDepressionLiving arrangementsOlder adultsSocial support

Identifiers

PMID39604986
PMCPMC11600826

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Registered trials

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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.