Depressive symptoms among Chinese residents: how are the natural, built, and social environments correlated?


Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
05 Jul 2019
Historique:
received: 03 10 2018
accepted: 17 06 2019
entrez: 7 7 2019
pubmed: 7 7 2019
medline: 24 9 2019
Statut: epublish

Résumé

Depression has become a severe societal problem in China. Although many studies have analyzed how environmental characteristics within neighborhoods affect depression, only a few have dealt with developing countries, and even fewer have considered built, natural, and social environments concurrently. Based on a sample of 20,533 Chinese residents assessed in 2016, the present study examined associations between depressive symptoms and respondents' built, natural, and social environments. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CES-D), and multilevel regression models were fitted accounting for potential covariates. Results indicated that living in neighborhoods with more green spaces and a higher population density were negatively associated with CES-D scores. Living in neighborhoods with more social capital was protective against depression. Furthermore, results showed that the social environment moderated the association between the built environment and depression. Social environments moderate the relationship between the built environment and depression. As environments seem to interact with each other, we advise against relying on a single environment when examining associations with depressive symptoms.

Sections du résumé

BACKGROUND BACKGROUND
Depression has become a severe societal problem in China. Although many studies have analyzed how environmental characteristics within neighborhoods affect depression, only a few have dealt with developing countries, and even fewer have considered built, natural, and social environments concurrently.
METHODS METHODS
Based on a sample of 20,533 Chinese residents assessed in 2016, the present study examined associations between depressive symptoms and respondents' built, natural, and social environments. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CES-D), and multilevel regression models were fitted accounting for potential covariates.
RESULTS RESULTS
Results indicated that living in neighborhoods with more green spaces and a higher population density were negatively associated with CES-D scores. Living in neighborhoods with more social capital was protective against depression. Furthermore, results showed that the social environment moderated the association between the built environment and depression.
CONCLUSIONS CONCLUSIONS
Social environments moderate the relationship between the built environment and depression. As environments seem to interact with each other, we advise against relying on a single environment when examining associations with depressive symptoms.

Identifiants

pubmed: 31277619
doi: 10.1186/s12889-019-7171-9
pii: 10.1186/s12889-019-7171-9
pmc: PMC6611031
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

887

Subventions

Organisme : National Natural Science Foundation of China
ID : 41320104001
Organisme : National Natural Science Foundation of China
ID : 41501151
Organisme : National Natural Science Foundation of China
ID : 41871140
Organisme : The Innovative R&D Team Introduction Program of Guangdong Province
ID : 2017ZT07X355

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Auteurs

Ruoyu Wang (R)

School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China.
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China.

Ye Liu (Y)

School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China. liuye25@mail.sysu.edu.cn.
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China. liuye25@mail.sysu.edu.cn.

Desheng Xue (D)

School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China. eesxds@mail.sysu.edu.cn.
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China. eesxds@mail.sysu.edu.cn.

Marco Helbich (M)

Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands.

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