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Combination use of Beck Depression Inventory and two-question case-finding instrument as a screening tool for depression in the workplace.

BMJ OPEN(2012)

引用 16|浏览7
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摘要
Objectives: The present study aimed to validate screening tools that could be used to identify depression among workers. Design: Diagnostic test study. Settings: Workers from three Japanese companies agreed to participate. Participants: Recruitment for the group 1 occurred between January 2001 and February 2004, and 89 participants (81 men and 8 women with a mean age of 38.4+/-6.6 years) (98.8%) took part in the study. Recruitment for the group 2 occurred between July 2000 and February 2004, and 1500 participants (1408 men and 92 women with a mean age of 40.9+/-7.2 years) (94.2%) took part in the study. Demographic data are shown in supplementary table 1. Interventions: Primary and secondary outcome measures: the Beck Depression Inventory (BDI) and a two-question case-finding instrument (TQI) were administered to 89 workers and Mini-International Neuropsychiatric Interview was conducted to verify the diagnosis of depression. A second group of 1500 workers completed the BDI and TQI to detect possible sample bias for the distribution of depression. Specificity, sensitivity and positive predictive value were calculated in order to obtain the optimal cut-off scores for BDI and TQI and receiver operating characteristic curves, and Youden Index were applied to further refine the optimal cut-off scores. Results: When paired together, BDI score >= 10 and TQI score of 2 adequately identified workers who had major depressive disorder and those who had other psychiatric disorders that are frequently comorbid with major depressive disorder. Conclusions: The combination of BDI score >= 10 and TQI score of 2 can adequately screen for current and potential cases of depression among workers. Furthermore, BDI and TQI offer the advantage of being relatively easy to administer to a large number of workers. Early detection of depression could improve treatment outcomes and decrease economic burden.
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bioinformatics,mental health,biomedical research,text mining
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