Prevalence and predictors of depression among an elderly population of Pakistan

Aging Ment Health. 2008 May;12(3):349-56. doi: 10.1080/13607860802121068.

Abstract

Objective: To assess the magnitude and risk factors of the problem of depression in an elderly population of Pakistan.

Method: A cross-sectional study was conducted using a sample of 402 people aged 65 and above visiting the Community Health Center of the Aga Khan University, Karachi. Questionnaire based interviews were conducted for data collection and the 15-Item Geriatric Depression Scale was used to screen for depression. Univariate and multivariate logistic regression analyses were performed to identify factors associated with depression.

Results: Of the 402 participants; 69.7% (95% CI=+/-4.5%) were men, 76.4% (95% CI=+/-4.2%) were currently married, 36.8% (95% CI=+/-5%) had received 11 or more years of education and 24.4% (95% CI=+/-4.2%) were employed. The mean age was 70.57 years (SD=+/-5.414 years). The prevalence of depression was found to be 22.9% (95% CI=+/-4.1%) and multiple logistic regression analysis indicated that higher number of daily medications (p-value=0.03), total number of health problems (p-value=0.002), financial problems (p-value<0.001), urinary incontinence (p-value=0.08) and inadequately fulfilled spiritual needs (p-value = 0.067) were significantly associated with depressive symptoms.

Conclusion: We have identified several risk factors for depression in the elderly which need to be taken into account by practicing family physicians and health care workers.

Publication types

  • Comparative Study

MeSH terms

  • Age Factors
  • Aged
  • Aged, 80 and over
  • Asian People / statistics & numerical data*
  • Comorbidity
  • Cross-Sectional Studies
  • Depressive Disorder / diagnosis
  • Depressive Disorder / epidemiology*
  • Female
  • Geriatric Assessment
  • Health Status
  • Humans
  • Logistic Models
  • Male
  • Pakistan / epidemiology
  • Prevalence
  • Probability
  • Psychiatric Status Rating Scales / statistics & numerical data
  • Quality of Life
  • Risk Factors
  • Socioeconomic Factors
  • Surveys and Questionnaires