Prevalence and correlates of late-life depression compared between urban and rural populations in Korea

Int J Geriatr Psychiatry. 2002 May;17(5):409-15. doi: 10.1002/gps.622.


Background: The aetiology of late-life depression has received relatively little research in developing countries. Urban and rural populations have rarely been sampled in the same study.

Objectives: To investigate demographic factors associated with depression and depressive symptoms in an urban and rural sample of older Korean people.

Methods: A community survey of residents aged 65 or over was conducted in an urban and a rural area within Kwangju, South Korea. The Korean Form of the Geriatric Depression Scale (KGDS) was administered. Associations with demographic, socio-economic factors and cognitive function (MMSE) were investigated for depression categorised according to a previously validated cut-off.

Results: The sample comprised 485 urban-dwelling and 649 rural-dwelling participants. No difference was found between urban and rural samples for prevalence rates of depression. However associations with independent variables varied between the areas. In the urban sample, increased age, low education, manual occupation and current rented accommodation were independently associated with depression. Only low education was associated with depression in the rural sample. The interaction with sample area was strongest for age (p < 0.01) and persisted after further adjustment for cognitive function.

Conclusions: Adverse socio-economic status was strongly associated with depression and appeared to operate across the life-course. While no evidence was found for urban-rural differences in prevalence rates of depression, factors associated with depression differed between these populations.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Analysis of Variance
  • Depressive Disorder / epidemiology*
  • Female
  • Health Surveys
  • Humans
  • Korea / epidemiology
  • Male
  • Multivariate Analysis
  • Regression Analysis
  • Risk Factors
  • Rural Population*
  • Social Environment*
  • Socioeconomic Factors
  • Urban Population*