Urban–rural network differences in depression, sleep and cognition in older Chinese adults
In a nationwide cross-sectional survey of 32,523 community-dwelling adults aged 65 and older in China, depression symptoms were the most connected indicator in both urban and rural networks; urban–rural differences were concentrated in a few sleep–cognition–emotion connections. [1]
Automatic summary · not editorially reviewed
The key findings
The study analysed cross-sectional data from 32,523 community-dwelling adults aged 65 or older in China (mean age 74.2 years; 52.1% female; 62.5% urban). [1]
Depression (PHQ-9) had the highest node strength in the overall, urban and rural networks and the highest bridge strength in both residential groups. [1]
When SF was excluded, the global-strength difference was no longer significant (p=.232), and only the AD8 and ESS centrality differences survived correction. [1]
The source at a glance
- Study type / format
- Nationwide cross-sectional study (abstract).
- Population / context
- Community-dwelling adults aged 65 or older in China (China Ageing and Health Survey; n=32,523).
- Summary based on
- Study abstract
- Publication status
- Preprint · peer-review status not confirmed
- Original publication
- October 5, 2026
Survey of 32,523 older adults in China
The study asked whether patterns of connection among mental health symptoms, sleep problems, cognitive changes and limitations in functioning differ between urban and rural older adults in China. It used cross-sectional data from the China Ageing and Health Survey: 32,523 community-dwelling adults aged 65 or older, mean age 74.2 years, 52.1% female and 62.5% urban. Nine indicators were analysed, including depression (PHQ-9), anxiety (GAD-7), insomnia (ISI), daytime sleepiness (ESS), subjective cognitive decline (SCD), cognitive-change screening (AD8), physical functioning (PF), general health (GH) and social functioning (SF). Because the data are cross-sectional, they describe connections at one point in time, not causes or changes over time.
Supporting source [1]
Depression most connected; some urban–rural edge differences
Depression (PHQ-9) had the highest node strength in the overall, urban and rural networks (overall strength 1.401) and the highest bridge strength in both groups (urban 0.715; rural 0.682). Its connection with anxiety (GAD-7) was the strongest in the network (partial r = 0.699). The urban and rural networks differed in structure (p=.001) and global strength (p=.025), and six connection differences survived correction for multiple testing. After correction across nine node-strength tests, AD8, SF and ESS had higher strength in the urban network, while GAD-7 had higher strength in the rural network. AD8 and ESS also had higher bridge strength in the urban network after correction. These are statistical associations, not evidence of cause.
Supporting source [1]
Sensitivity analyses partly changed the results
Sensitivity analyses excluded the PHQ-9 sleep item, excluded SF, and used an alternative network method. Excluding SF left PHQ-9 as the highest-strength node, showed high agreement with the primary connection weights (Pearson r=.992–.995) and preserved the structural difference (p=.001). However, the global-strength difference was no longer significant (p=.232), and only the AD8 and ESS centrality differences survived correction. The authors conclude that urban–rural differences were concentrated in several sleep–cognition–emotion connections and in the bridge strength of AD8 and ESS, and that these results identify a small set of cross-domain differences for longitudinal testing. This abstract reports a cross-sectional study; the full text was not reviewed.
Supporting source [1]
How to interpret this
Abstract only; the full text was not reviewed. The data are cross-sectional, so the reported connections and urban–rural differences describe associations at one time point, not causes or changes over time. The authors state that the results identify differences for longitudinal testing.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Zhou L, Chen C, Fung HH, Shen J, Jiang Y, Zhao X, Ren J, Liu J, Shi H. · europepmc.org · October 5, 2026