Correlation Between Depressive Symptoms And Quality Of Life, And Associated Factors For Depressive Symptoms Among Rural Elderly In Anhui, China

Clin Interv Aging. 2019 Nov 4;14:1901-1910. doi: 10.2147/CIA.S225141. eCollection 2019.


Purpose: We aimed to assess the current status of depressive symptoms and quality of life (QoL) among rural elderly in central China (Anhui Province) and explore their correlation and associated factors for depressive symptoms.

Methods: A multi-stage random sampling method was used to obtain 3349 participants (aged ≥60): 1206 poor and 2143 non-poor. The 30-item Geriatric Depression Scale (GDS-30) and five-dimensional European quality of health scale (EQ-5D) were employed to evaluate depressive symptoms and QoL, respectively.

Results: The prevalence of depressive symptoms was 52.9%, and that in the poor group (62.3%) was significantly higher than the non-poor group (47.6%). The GDS-30 score was 12.40 ± 7.089, and the poor group scored significantly higher (14.045 ± 6.929) than the non-poor group (11.472 ± 7.011). The EQ-5D score was 0.713 ± 0.186, and the poor group (0.668 ± 0.192) scored significantly lower than the non-poor group (0.738 ± 0.178). There was a significant negative correlation between depressive symptoms and QoL (r = -0.400, P-value <0.05). The following factors were associated with depressive symptoms: poverty, low EQ-5D score, female gender, older age, illiteracy, unemployed, chronic diseases, and hospitalization in previous year.

Conclusion: Rural elderly in central China have a high prevalence of depressive symptoms and low QoL. Poverty was associated with a higher prevalence of depressive symptoms and lower QoL.

Keywords: central China; depressive symptoms; quality of life; rural elderly.

MeSH terms

  • Aged
  • Aged, 80 and over
  • China / epidemiology
  • Depression / epidemiology*
  • Female
  • Health Status*
  • Humans
  • Male
  • Poverty / statistics & numerical data
  • Prevalence
  • Quality of Life / psychology*
  • Rural Population / statistics & numerical data*
  • Socioeconomic Factors