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Psychiatry Journal
Volume 2015, Article ID 537073, 7 pages
Research Article

Prevalence of Depressive Symptoms and Related Factors in Japanese Employees: A Comparative Study between Surveys from 2007 and 2010

1Akita Prefectural Mental Health and Welfare Center, 2-1-51 Nakadori, Akita City, Akita Prefecture 010-0001, Japan
2Akita Occupational Health Promotion Center, 6-6 Senshukubota-machi, Akita City, Akita Prefecture 010-0874, Japan

Received 18 May 2015; Revised 3 July 2015; Accepted 5 July 2015

Academic Editor: Nicola Magnavita

Copyright © 2015 Masahito Fushimi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aims. The aim of this study was to examine the prevalence of depressive symptoms and their related factors in Japan. The results were analyzed to identify the relationship between high scores on the CES-D, sociodemographic status, and employment-related variables. Methods. Employees in Akita prefecture completed the Center for Epidemiologic Studies Depression Scale (CES-D) during a survey period between November and December 2010. The cutoff point for the CES-D scores was 16 or above (high scorers). Results. Data from 1,476 employees indicated that 44.2% had high scores on the CES-D. Sociodemographic and occupation-related factors associated with a high risk of depression were being female, young age, fewer hours of sleep on weekdays, and working over 8 hours per day, whereas drinking alcohol one to two days per week, albeit only in men, was significantly associated with a low risk of depression. The present results were consistent with the results of a previous survey completed in 2007; however, the present results regarding job categories and smoking behavior were not significantly associated with depression and thus were inconsistent with the 2007 survey data. Conclusions. These results can be useful as benchmark values for the CES-D and might help predict depressive disorders.