Disparity in Self‐Reported Sleep Quality Between Old Adults in Urban and Rural Areas of China: A Fairlie Decomposition Analysis
OBJECTIVE: Identify drivers of urban-rural sleep quality disparities among Chinese older adults. METHODS: Data were derived from the Chinese Longitudinal Healthy Longevity Survey. Univariate and logistic regression and Fairlie model analyzed correlations and disparity contributors. RESULTS: In urban groups, female (OR = 1.84, 95% CI = 1.59-2.15), poor self-reported health (OR = 2.31, 95% CI = 1.90-2.80), and chronic disease comorbidity (OR = 1.28, 95% CI = 1.09-1.49) were significant predictors of impaired sleep quality. In rural groups, female (OR = 2.30, 95% CI = 1.64-3.21) and poor self-reported health (OR = 2.13, 95% CI = 1.48-3.06) were associated with higher odds, while alcohol consumption was protective (OR = 0.72, 95% CI = 0.52-0.99). Rural older adults had a 2.94% higher probability of impaired sleep quality than urban counterparts. The measured factors explained 67.7% of disparities, primarily driven by self-reported health status (73.4%), gender (15.0%), chronic disease comorbidity (5.7%), and smoking (5.9%), with 32.3% remaining unexplained. CONCLUSION: Self-reported health, gender, chronic disease comorbidity, and smoking are key factors leading to inequality. Findings inform policies bridging urban-rural health gaps and improving elderly sleep health.
Authors
- Changli Liu (ORCID: https://orcid.org/0000-0002-4531-5749)
- Wang Shen (ORCID: https://orcid.org/0000-0001-5779-5471)
- Liu Sun (ORCID: https://orcid.org/0000-0002-9152-9289)
Institutions
- Capital Medical University (CN)
- Peking University (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Geriatrics and gerontology international/Geriatrics & gerontology international
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1111/ggi.70470
- Citations
- 1
- Primary Topic
- Health disparities and outcomes
- Type
- article
- Field-Weighted Citation Impact
- 34.61
Funders
- Beijing Municipal Natural Science Foundation