Depression Screening in Youth: Multi-Informant Algorithms for the Child Welfare Setting

The Achenbach System of Empirically Based Assessment (ASEBA) represents the most widely used protocol for mental and behavioral health screening in the Child Welfare System (CWS). However, because past studies have (a) relied on the self- or parent report, (b) focused on the internalizing subscales, (c) focused solely on current or prospective depression, and (d) not assessed incremental validity, it is difficult to use the ASEBA to address recommended universal depression screening initiatives in the CWS. In response, the present study used an evidence-based medicine (EBM) framework to identify the best combination of subscales that predict adolescent depression outcomes within the CWS. Overall, we found that a combination of self-reported internalizing symptoms, and to a lesser extent, self-reported attention problems, delinquent behavior, and parent-reported social problems best forecasted concurrent depression status. Meanwhile, self-reported anxious/depressed and externalizing symptoms, in addition to parent-reported somatic complaints and withdrawn symptoms, were necessary to adequately forecast prospective depression outcomes. Using these algorithms, we were able to differentiate and classify youth at minimal, moderate, or substantial risk for current and future depression symptoms. Findings are contextualized with past research on the Achenbach scales and clinical implications for more targeted depression screening are discussed. Public Significance Statement The present study demonstrates that assessing the incremental validity of multi-informant and multisymptom inventories can help to create more unbiased and targeted referral processes for children at risk for current and/or future depressive symptoms in the Child Welfare System.