Development of a Brief Screener for Crosscutting Patterns of Family Maltreatment and Psychological Health Problems

Prior work established the presence of six crosscutting patterns of clinically significant family maltreatment (FM) and psychological health (PH) problems among active-duty service members. Here, we develop a brief screener for these patterns via Classification and Regression Trees (CART) analyses using a sample of active-duty members of the United States Air Force. CART is a predictive algorithm used in machine learning. It balances prediction accuracy and model parsimony to identify an optimal set of predictors and identifies the thresholds on those predictors in relation to a discrete condition of interest (e.g., diagnosis of pathology). A 22-item screener predicted membership in five of the six classes (sensitivities and specificities > 0.96; positive and negative predictive values > 0.90). However, for service members at extremely high risk of clinically significant externalizing behavior, sensitivity and positive predictive values were much lower. The resulting 22-item brief screener can facilitate feasible, cost-effective detection of five of the six identified FM and PH problem patterns with a small number of items. The sixth pattern can be predicted far better than chance. Researchers and policymakers can use this tool to guide prevention efforts for FM and PH problems in service members.