Assessing the Quick Inventory of Depressive Symptomatology Self-Report scores to predict continuous employment in mood disorder patients.

Objective: Depression significantly impacts the job performance and attendance of workers, leading to increased absenteeism. Predicting occupational engagement for individuals with depression is of paramount importance. This study aims to determine the cut-off score which predicts continuous employment for patients with mood disorders using the Quick Inventory of Depressive Symptomatology, Self-Report (QIDS-SR). Methods: In a prospective observational trial conducted in Tokyo, 111 outpatients diagnosed with either major depressive disorder or bipolar depression were enrolled. Their employment statuses of these participants were tracked over a six-month period after their QIDS-SR scores were recorded. Based on their employment trajectories, participants were categorized into either continuous or non-continuous employment groups. Binary logistic regression was applied to examine the relationship between the QIDS-SR scores and employment outcomes, with adjustments for age, gender, and psychiatric diagnoses. Receiver operating characteristic curves were utilized to identify the optimal QIDS-SR cut-off values for predicting continuous employment. Findings: Binary logistic regression demonstrated that a lower score on the QIDS-SR was linked to an elevated likelihood of continuous employment (adjusted odds ratio 1.15, 95% CI: 1.06-1.26, p=0.001). The optimal cut-off point, determined by the Youden Index, was 10/11, showcasing a 63% sensitivity and 71% specificity. Conclusion: The results emphasize the potential of the QIDS-SR as a prognostic instrument for predicting employment outcomes among individuals with depressive disorders. These findings further underscore the importance of managing depressive symptoms to mild or lower intensities to ensure ongoing employment.