Analysis on the status of depression and its influencing factors in empty-nest elderly in Shaanxi Province

2018 
Objective To investigate the incidence and influencing factors of depression in empty-nest elderly, so as to provide evidence for reducing depression in empty-nest elderly. Methods During July to August 2016, the elderly over 60 years old in Shaanxi Province, including 504 empty-nest elderly and 194 non-empty-nest, were selected convenience sampling method. The Geriatric Depression Scale (GDS) , Social Support Scale, and self-designed questionnaire about the general condition were used. ANOVA was applied to analyze the depression scores; Pearson correlation analysis was used to analyze the relation between social support and depressive symptoms; and Logistic regression analysis had been used to analyze the influencing factors of depression. Results The detection rate on depression of empty-nest elderly was 45.63%, and that of the non-empty-nest elderly was 32.99%, the difference was statistically significant (χ2=10.447, P<0.05) . The scores of GDS of the empty-nest and non-empty-nest elderly were (9.68±5.26) and (8.51±4.69) respectively, and the difference was statistically significant (t=7.390, P<0.01) . Logistic regression analysis showed that living pattern (OR=0.596) , degree of education (OR=0.799) , age (OR=1.394) , place of abode (OR=1.699) , happiness degree of later life (OR=1.663) and current life satisfaction (OR=1.474) were correlated with depressive symptom (P<0.05) . In addition, Pearson correlation analysis indicated that the social support of the empty-nesters was negatively correlated with depression (r=-0.260, P<0.01) . Conclusions The incidence of depression is high in empty-nest elderly, which seriously affects the physical and mental health of them. Targeted measures should be taken to provide more comprehensive social support for empty-nest elderly, improve their life quality and reduce the incidence of depression. Key words: Aged; Depression; Social support; Empty-nest; Influencing factors
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