Diagnostic Efficiency of Caregiver Report on the SCARED for Identifying Youth Anxiety Disorders in Outpatient Settings

Abstract

Objective This study investigated the diagnostic and clinical utility of the parent-rated Screen for Child Anxiety Related Emotional Disorders (SCARED-P) for detecting youth anxiety disorders. Method Youth aged 6 to 12.9 were recruited from 9 outpatient mental health clinics (N=707). Consensus diagnoses were based on semi-structured interviews (KSADS) with youth and caregivers; 31% were diagnosed with at least one anxiety disorder. Caregivers completed the SCARED-P to describe youth anxiety levels. SCARED-P scores were not considered during the consensus diagnoses. Areas under the curve (AUCs) from receiver operating characteristic analyses and diagnostic likelihood ratios (DLRs) quantified performance of the SCARED-P total score and subscale scores (generalized anxiety disorder and separation anxiety disorder). Results SCARED-P total scores had variable efficiency (AUCs=.69-.88) and GAD and separation anxiety subscale scores were excellent (AUCs=.86-.89) for identifying specific anxiety disorders. Optimal subscale cutoff scores were computed to help rule-in (DLRs=2.7-5.4) or rule-out (DLRs<1.0) anxiety disorders among youth. Conclusion Results suggest that the GAD and separation anxiety SCARED-P subscales accurately identify their respective matched diagnoses. DLRs may aid clinicians in screening for youth anxiety disorders and improve accuracy of diagnosis.

Assessing anxiety disorders in children and adolescents

“…Sensitivity and specificity for detection of an diagnosed anxiety disorder: Low to moderate diagnostic accuracy in identifying children with anxiety disorders in community and clinical contexts‐ SCARED‐ Child – 41 items, community sample) AUC = .50, Sensitivity = .81, Specificity = .52 (DeSousa et al., ) ; SCARED‐Child (Community sample) African Americans, Sensitivity = .60, Specificity = .88, Non‐Hispanic (White) Sensitivity = .68, Specificity = .77 (Gonzalez et al., ) ; SCARED‐parent total score (at risk sample) AUC = .69, Specificity = .56, Sensitivity = .81 (Van Meter et al., ) ; SCARED‐child (community sample) AUC = .80, Sensitivity = .76, Specificity = .68; SCARED‐parent AUC = .69, Sensitivity = .66, Specificity = .60 (Canals et al., ); SCARED‐child (clinical sample) AUC = .67 (Monga et al., ); SCARED‐child (41 items; clinical sample) sensitivity = .71, specificity = .67 (Birmaher et al., )…”

Diagnostic Efficiency of the Child and Adolescent Symptom Inventory (CASI-4R) Depression Subscale for Identifying Youth Mood Disorders

“…Consistent with prior work (Ong et al, 2016; Van Meter et al, 2016) and commonplace in epidemiological work to get more accurate population parameter estimates (Little, Lewitzky, Heeringa, Lepkowski, & Kessler, 1997), we weighted the data used in these analyses to address the unequal probability of selection and sample response. First, a base weight was created by utilizing the inverse of the probability of selection.…”

“…The resulting final weight was calculated by taking the product of the base weight and non-response weight, rescaled so that the sum was equal to the sample size. This weighting process created a sample that more closely matches the overall presenting clinical population, reducing the risk of misleading results, and has been used in previous studies using similar analytic methods (Ong et al, 2016; Van Meter et al, 2016). …”

Comparing the CASI-4R and the PGBI-10 M for Differentiating Bipolar Spectrum Disorders from Other Outpatient Diagnoses in Youth

“…Because the sampling plan followed an a priori design, we weighted primary analyses by site to adjust for the sampling by ESM status (Van Meter et al, in press). Descriptive statistics summarized clinical and demographic characteristics of the sample, while t-tests and chi-square analyses tested for significant differences between youth with BPSD and other youth in the sample.…”

“…When degeneracy occurs, it can result in inaccurate estimates of test accuracy (Zhou et al, 2011). Low scoring cases were undersampled by design, so all subsequent analyses weighted the ESM- cases to adjust for the sampling to produce unbiased results (Van Meter et al, in press). Youngstrom (2014) makes a strong case for representative samples when calculating DLRs to avoid inaccuracies that may result from a biased sample.…”