Depression and associated factors among older adults in Bahir Dar city administration, Northwest Ethiopia, 2020: Cross-sectional study

Abstract

Background Depression is the most common psychiatric condition among older adults, and it goes unnoticed by individuals themselves and is under-diagnosed by clinicians due to the misconception that these are normal parts of aging. However, the problem is not properly addressed in Ethiopia. This study aimed to determine the prevalence and associated factors of depression among the older adults in Bahir Dar city. Methods A community-based cross-sectional survey was conducted among 423 older adults in Bahir Dar city. A simple random sampling technique was used to select the study participants. Depression was assessed using a 15-item Geriatric Depression Scale (GDS). A multivariable logistic regression analysis was used to explore the potential determinants of depression among the participants. Results The prevalence of depression among older adults was found to be 57.9% (95% CI: 53.2–62.6). This study showed that educational status with grades 5-8th (AOR: 5.72, 95% CI: 2.87–11.34), and 9-12th grade (AOR: 3.44, 95% CI: 1.59–7.41), income <2004 ETB (AOR = 1.89, 95% CI: 1.16–3.07), cognitive impairments (AOR: 3.54, 95% CI: 2.16–5.81), family history of mental illness (AOR:3.06, 95% CI: 1.03–9.04), and poor quality of life (AOR: 2.78, 95% CI: 1.74–4.46) were significantly associated with depression. Conclusion The prevalence of depression among older adults was found to be huge. Having low educational status, low monthly income, cognitive impairments, family history of mental illness, and poor quality of life were associated with depression. Therefore, raising community awareness of mental health, increasing social participation, providing supportive counseling and routine screening of depressive symptoms are essential in combating depression among Bahir-Dar city older adults.

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Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest

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