Caseload factors predictive of family abuse and neglect treatment outcomes

Background: In child welfare, caseloads are frequently far higher than optimal. Not all cases are created equal; however, little is known about which combination and interaction of factors make caseloads more challenging and impact child and family outcomes. Objective: This study aims to identify which case, provider, and organizational factors most strongly differentiate between families with favorable and less-than-positive treatment outcomes. Participants and setting: Participants were 25 family advocacy program providers and 17 supervisors at 11 Department of the Air Force installations. Methods: Following informed consent, participants completed demographic and caseload questionnaires, and we collected information about organizational factors. Providers were sent a weekly case update and burnout questionnaire for seven months. We used linear mixed-effects model tree (LMM tree) algorithms to determine the provider, client, and organizational characteristics that best distinguish between favorable vs. unfavorable outcomes. Results: The LMM tree predicting provider-rated treatment success yielded three significant partitioning variables: (a) commander involvement, (b) case complexity, and (c) % of clients in a high-risk field. The LMM predicting client-rated treatment progress yielded seven significant partitioning variables: (a) command involvement; (b) ease of reaching tenant unit command; (c) # of high-risk cases; (d) % of clients receiving Alcohol and Drug Abuse Prevention and Treatment services; (e) ease of reaching command; (f) % of clients with legal involvement; (g) provider age. Conclusions: This study is a first step toward developing a dynamic caseload management tool. An intelligent, algorithm-informed approach to case assignment could help child welfare agencies operate in their typically resource-scarce contexts in a manner that improves outcomes.