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KMID : 0379220140340020387
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Journal of Korea Gerontological Society
2014 Volume.34 No. 2 p.387 ~ p.407
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Multiple Trajectories of Depressive Symptoms Among Older Adults
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Kang Eun-Na
Choi Jae-Sung
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Abstract
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This study aims to identify the multiple trajectories of depressive symptoms and the characteristics of each trajectory group among the elderly. This study uses five waves of longitudinal data from the Korean Welfare Panel Study (KWPS, 2006-2010). Subjects were older adults aged 60 and over who had completed at least three waves between 2006 and 2010. A total of 4,181 respondents were analyzed. The latent growth mixture model and the multiple logistic regression model were mainly used for data analysis. The major findings were as follows: After controlling for the variables of gender, age, education, marital status, self-assessed health, and poverty, this study identified four different trajectory classes: stable low depressive symptoms (71.8%), high but decreased depressive symptoms (10.6%), moderate but increased depressive symptoms (7.9%), and an increased, then a decreased pattern of depressive symptoms (9.7%). The characteristics of theses trajectories as compared to previous studies were a lower percentage of ‘stable low depressive symptoms’, no ‘persistently high depressive symptoms’, and higher level of depressive symptoms. Also, the elderly in the stable low trajectory group had better health status, higher self-esteem and a good relationship with family members, having longer working periods, and more living in non-poverty. In addition, chronic health problems, loss of spouse, and household income differentiated the increased and then decreased pattern from the low stable pattern. Also, age and public pension differentiated the moderated but increased pattern from the low stable pattern. Based on the findings of this study, the researchers suggested political and practical implications for reducing depressive symptoms in later life.
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KEYWORD
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Depressive Symptoms, Multiple Trajectories, The Conservation of Resources, Latent Growth Mixture Modeling, Older Adults
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