Bi-factor model.

<p>Bi-factor model.</p

Summary of goodness of fit statistics for tested models in multi-group analyses.

<p>Summary of goodness of fit statistics for tested models in multi-group analyses.</p

Standardized factor loadings for the bi-factor model using entire sample.

<p>Standardized factor loadings for the bi-factor model using entire sample.</p

Unidimensional factor model.

<p>Unidimensional factor model.</p

Two-dimensional factor model.

<p>Two-dimensional factor model.</p

Assessing Depression Related Severity and Functional Impairment: The Overall Depression Severity and Impairment Scale (ODSIS)

<div><p>Background</p><p>The Overall Depression Severity and Impairment Scale (ODSIS) is a brief, five-item measure for assessing the frequency and intensity of depressive symptoms, as well as functional impairments in pleasurable activities, work or school, and interpersonal relationships due to depression. Although this scale is expected to be useful in various psychiatric and mental health settings, the reliability, validity, and interpretability have not yet been fully examined. This study was designed to examine the reliability, factorial, convergent, and discriminant validity of a Japanese version of the ODSIS, as well as its ability to distinguish between individuals with and without a major depressive disorder diagnosis.</p><p>Methods</p><p>From a pool of registrants at an internet survey company, 2830 non-clinical and clinical participants were selected randomly (619 with major depressive disorder, 619 with panic disorder, 576 with social anxiety disorder, 645 with obsessive–compulsive disorder, and 371 non-clinical panelists). Participants were asked to respond to the ODSIS and conventional measures of depression, functional impairment, anxiety, neuroticism, satisfaction with life, and emotion regulation.</p><p>Results</p><p>Exploratory and confirmatory factor analysis of three split subsamples indicated the unidimensional factor structure of ODSIS. Multi-group confirmatory factor analysis showed invariance of factor loadings between non-clinical and clinical subsamples. The ODSIS also showed excellent internal consistency and test–retest intraclass correlation coefficients. Convergence and discriminance of the ODSIS with various measures were in line with our expectations. Receiver operating characteristic curve analyses showed that the ODSIS was able to detect a major depressive syndrome accurately.</p><p>Conclusions</p><p>This study supports the reliability and validity of ODSIS in a non-western population, which can be interpreted as demonstrating cross-cultural validity.</p></div

Goodness of fit indices for four types of equivalence restriction on a one-factor model with error theory of the ODSIS.

<p>Model 1 imposed no equivalence restriction.</p><p>Model 2 had equivalence restriction on factor loadings.</p><p>Model 3 had equivalence restriction on factor loadings and variance of latent factor.</p><p>Model 4 included equivalence restriction on factor loadings, variance of latent factor, covariances and variances of error terms.</p><p>Goodness of fit indices for four types of equivalence restriction on a one-factor model with error theory of the ODSIS.</p

ODSIS scores in non-clinical and clinical samples.

<p>MDD, major depressive disorder; PD, panic disorder; SAD, social anxiety disorder; OCD, obsessive–compulsive disorder</p><p>ODSIS scores in non-clinical and clinical samples.</p

ROC curve for ODSIS scores to detect the presence of major depressive syndrome.

<p>ROC curve for ODSIS scores to detect the presence of major depressive syndrome.</p

Comparison of the intervention and control groups at the 4-month follow-up.

<p>The means and their 95% confidence intervals were estimated from maximum likelihood mixed effects models adjusting for stratification variables, age and gender.</p><p>The effect size was calculated from the difference in the means at 4 months divided by their pooled SD.</p><p>P-values compare the two groups and are from 1) a mixed model (using all randomized individuals, including those with missing follow-up data) which adjusted for stratification variables, age, and gender, 2) a t-test, and 3) a permutation test done within the 4 strata defined by baseline BDI and absenteeism. Permutation test is based on Monte-Carlo simulation with 1,000,000 samples (used to get exact distribution of t-statistic).</p>†<p>: Because this variable did not have the baseline measurement, the means at month 4 and their difference were examined by regression models adjusting for stratification variables, age and gender.</p><p>BDI-II: Beck Depression Inventory-II.</p><p>HPQ: World Health Organization Health and Work Performance Questionnaire.</p