Association for Public Policy Analysis & Management

Each year in the United States, hundreds of thousands of children are reported to the child welfare system due to abuse or neglect. In 2014, 683,487 children were victims of child maltreatment, and 148,262 entered foster care (Administration for Children and Families 2015). These children and their families often have acute need for health, mental health, substance abuse, and other services to ensure a safe and nurturing environment. But often there is a gap between service need and receipt (Maher et al. 2016). As a result, a key concern among child welfare and Medicaid agencies is how to provide services that meet family needs without being unnecessarily restrictive or providing services for a longer duration than needed.

To address this concern, this study examines the types and patterns of high service use, or “superutilization,” among children and families and identifies the factors that predict superutilization. The goal of this study is to help child welfare and Medicaid agencies develop more effective, targeted service delivery to better meet the needs of children and families while reducing service costs. By better understanding the characteristics of superutilization, these findings will allow agencies to identify and serve families before intensive services are required.

To assess and identify predictors of superutilization, the study links and analyzes five years of administrative data from child welfare, Medicaid, and other services for two sites – the state of Tennessee and a three-county region of Florida, comprised of Hillsborough, Pinellas, and Pasco counties. Descriptive analysis is used to better understand service use among children and parents and to define a measure of superutilization. Latent class analysis is used to identify types of superutilization based on patterns of service use or combinations of services. With input from agency site partners on their policy and practice priorities, predictive analytics is used to identify characteristics that lead to superutilization of child welfare, Medicaid, and other services.

Implications of the results and recommendations to improve outcomes for children and families will be developed in collaboration with agency staff and stakeholders. Study results will inform program staff and policymakers about patterns of superutilization of child welfare, Medicaid, and other services as well as the characteristics and needs of families who experience high-levels of service use. The study results will also be used to inform the implementation of more timely, targeted, and effective services, reducing the use of expensive, unnecessarily restrictive, excessive, or ineffective services for children and families engaged in the child welfare system. Additionally, the predictive factors associated with a high risk of superutilization can give child welfare caseworkers, health care practitioners, and other providers an evidenced-based approach to serving children and families who are in the child welfare system. This project serves as a model for data-informed decision making for child welfare, health, and other policy areas by linking administrative data across service systems, applying advanced analytical techniques, and using predictive models.