Abstract

OBJECTIVES: China is currently facing an unprecedented complex health demand from a rapidly aging population. Based on multidimensional health, this study aimed to identify heterogeneous latent health classes for older Chinese people, and assess regional disparities and associated sociodemographic factors.

STUDY DESIGN: Chinese Longitudinal Healthy Longevity Survey in 2014 was adopted.

METHODS: For 2886 participants aged 65 years and more without missing health indicators in physical, psychological, and social dimensions, latent class analysis was used to identify heterogeneous health. For 2128 participants with complete information, logistic regressions were used to examine how regional divisions and sociodemographic factors impact each identified class.

RESULTS: Four classes were identified and labeled as 'Lacking Socialization' (17.4%), 'High Comorbidity' (13.7%), 'Functional Impairment' (7.1%), and 'Relative Health' (61.8%). When the Relative Health class was the reference, the likelihoods of the High Comorbidity and Functional Impairment classes were higher for older adults in eastern and central regions than in western regions. Those in eastern regions also tended to be in the Lacking Socialization class than in western regions. The effects of regional divisions on the different classes were significantly impacted by sociodemographic characteristics.

CONCLUSIONS: Four health classes identified by multidimensional health have enhanced our understanding of heterogeneity among older Chinese people. By examining regional disparities in China, our study provided evidence for health policies addressing the issue of aging with respect to regional disparities.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalPublic Health
Volume178
DOIs
Publication statusPublished - Jan 2020

Keywords

  • Aged
  • Aged, 80 and over
  • China/epidemiology
  • Comorbidity
  • Female
  • Health Status Disparities
  • Health Surveys
  • Humans
  • Logistic Models
  • Longitudinal Studies
  • Male
  • Rural Health/statistics & numerical data
  • Socioeconomic Factors
  • Urban Health/statistics & numerical data

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