(2021) Unemployment associated with major depression disorder and depressive symptoms: a systematic review and meta-analysis. International Journal of Occupational Safety and Ergonomics. p. 13. ISSN 1080-3548
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Abstract
Objectives. This study investigated the association between unemployment and depressive symptoms and major depression disorder worldwide using a systematic review and meta-analysis. Methods. Search time was limited to all articles published in English until December 2020. In the association between unemployment and depression, first, the results of qualified studies were extracted and, then, the results of each study were pooled with each other using the random effects method. Results. The prevalence of depression in the unemployed is 21, 95 confidence interval (CI) 18, 24%. This prevalence for depression symptoms is 24%, 95% CI 20, 28% and for major depressive disorder is 16%, 95% CI 9-24%. The association between unemployment and depressive symptoms was odds ratio (OR) 2.06, 95% CI 1.85, 2.30 and the association for major depressive disorder was OR 1.88, 95% CI 1.57, 2.25. The association between unemployment and depression in men was OR 2.27, 95% CI 1.76, 2.93 and in women was OR 1.62, 95% CI 1.40, 1.87. Conclusions. What is clear from the present study is that unemployment can lead to a higher prevalence of depressive symptoms and major depressive disorder, thereby undermining the mental health of the unemployed.
Item Type: | Article |
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Keywords: | unemployment major depression disorder depression symptoms meta-analysis systematic review generalized anxiety disorder quality-of-life mental-health risk-factors employment status psychological distress follow-up job loss youth unemployment gender differences Engineering Public, Environmental & Occupational Health |
Page Range: | p. 13 |
Journal or Publication Title: | International Journal of Occupational Safety and Ergonomics |
Journal Index: | ISI |
Identification Number: | https://doi.org/10.1080/10803548.2021.1954793 |
ISSN: | 1080-3548 |
Depositing User: | مهندس مهدی شریفی |
URI: | http://eprints.bmsu.ac.ir/id/eprint/9555 |
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