Assessing internalizing symptoms and their relation with levels of impairment: Evidence-based cutoffs for interpreting Inventory for Depression and Anxiety Symptoms (IDAS-II) scores

Abstract

Background Tests and scales measuring psychological disorders should provide information about how scores relate to other constructs such as quality of life or functional impairment. Such information is necessary to allow that their scores contribute to clinical decision making. The current study analyzes the clinical utility of the Inventory for Depression and Anxiety Symptoms (IDAS-II) to discriminate between different levels of functional impairment and identify the IDAS-II scales that contribute most to explaining impairment Methods The total sample (N = 1390) consists of two subsamples: a community sample of the general population (n = 1072) selected by random sampling; and a sample of patients (n = 318) from public and private mental health services. The IDAS-II for measuring internalizing symptoms and WHODAS 2.0 for measuring impairment were administered to all participants. Results All scales show statistically significant higher scores in the patient sample, with Cohen's d effect sizes values greater than 0.30, except for well-being (d = 0.19). The cutoff values and their confidence intervals do not overlap with the means of either the community or patient sample. AUC values for most of the scales are above .70, except for appetite gain, ordering, euphoria, cleaning, and well-being. Multiple linear regression model using IDAS-II scales explain 57.1% of the variance of the WHODAS 2.0 (F 12.1377 = 155.305; p < .001). Conclusions Cutoff values provided allow us to reliably differentiate between the patients and community samples. IDAS-II scores show greater sensitivity and specificity in detecting those with greater impairment. General Depression, Lassitude, Panic and Claustrophobia contribute to impairment in a greater extent. Knowledge of which symptoms are most related with impairment, allows healthcare providers to improve treatment planning based on empirical evidence.