Prevalence Of Depression And Correlates Of Depressive Symptoms For Residents In The Urban Part Of Jeju Island, Korea

INTERNATIONAL JOURNAL OF SOCIAL PSYCHIATRY(2007)

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摘要
Alms: This study examined the prevalence of depression and depressive symptoms, and the correlates of depressive symptoms, and proposes some methods for reducing risk of depression in residents of the urban part of Jeju Island in Korea.Methods: In all, 1050 residents were selected using multiphasic cluster sampling to represent each district. Of the 981 respondents, 413 were men and 568 were women. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to evaluate depression (CES-D score over 25) and depressive symptoms (CES-D score over 21). Multiple logistic regression analysis was performed for comparisons.Results: The prevalence of depression in males and females was comparable, at 9.47 and 11.36%, respectively. The prevalence of depressive symptoms in men was 15.01%, while in women the level rose to 18.37%. Those with high self-assessed level of stress scores were significantly more likely to have depressive symptoms than those with low self-assessed level of stress scores (odds ratio (OR) = 5.73 (95% confidence interval (95%Cl), 1.29-25.36)). Residents at high risk of problem drinking (CAGE score over 3) were significantly more likely to have depressive symptoms than those with a CAGE score under 1 (OR = 3.43 95%Cl, 1.77-6.66). Respondents who slept poorly had more depressive symptoms than respondents who slept well (OR = 2.11 95%Cl, 1.37-3.23). Females were significantly more likely to have more depressive symptoms than males (OR = 1.70 95%Cl, 1.08-2.68).Conclusions: The prevalence of depression and depressive symptoms in urban Jeju Island is similar to that in a nation-wide sample. By providing intensive mental health services to those who have high stress levels, problem drinking, and poor health behavior, early detection of depressive symptoms in the community will be important for improving general health status.
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关键词
confidence interval,cluster sampling,odd ratio,logistic regression analysis
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