Evaluating psychiatric hospital admission decisions for children in foster care: an optimal classification tree analysis

Abstract

This study explored clinical and nonclinical predictors of inpatient hospital admission decisions across a sample of children in foster care over 4 years (N = 13,245). Forty-eight percent of participants were female and the mean age was 13.4 (SD = 3.5 years). Optimal data analysis (Yarnold & Soltysik, 2005) was used to construct a nonlinear classification tree model for predicting admission decisions. As expected, clinical variables such as suicidality, psychoticism, and dangerousness predicted psychiatric admissions; however, several variables that are not direct indications of acute psychiatric distress, such as the presence of family problems and the location of the hospital screening, impacted decision making in a subsample of cases. Further analyses indicated that the model developed in Year 1 reliably and consistently predicted admission decisions (with 64%-68% overall accuracy) across the next 3 years. Policy, research, and clinical implications are discussed.