Child Welfare

Child maltreatment and involvement in child welfare systems are well-known adverse childhood experiences associated with poor health outcomes during childhood and later in life. Very little is known about the health needs of, and services used by families involved in child welfare systems, particularly those services funded by Medicaid.

Increasing availability of linked child welfare and Medicaid data can advance research on the intersections of child welfare and Medicaid. The project, Child and Caregiver Outcomes Using Linked Data (CCOULD), developed a research-use dataset combining child welfare records and Medicaid claims for children and families involved in child welfare systems in Florida and Kentucky.

This brief describes progress in the Child Welfare and Health Infrastructure for Linking and Data Analysis of Resources, Effectiveness, and Needs (CHILDREN) Initiative, which is entering its second year. At this time, four jurisdictions have been selected for participation in the CHILDREN Initiative and are engaging in feasibility studies to determine readiness for linking data.

This research brief uses claims data from the Medicaid and Children's Health Insurance Programs to examine the behavioral health diagnoses and treatment services received by children and youth involved with the child welfare system in 2019.

This report provides information to state, tribal, and local child welfare and behavioral health agencies that are interested in linking their Medicaid and child welfare data.

In this brief, we highlight experiences and practices from substance use treatment providers and their human services partners when serving people of color. We selected providers that focused on serving people of color, and this study was not intended to assess outcomes or effectiveness of any of the practices highlighted.

This brief presents estimates of the number and percentage of children whose parents engage in problematic substance use, including past-year substance use and SUD by substance type and child demographics. Key findings include:

In recent years several researchers and child welfare agencies have begun developing predictive risk models to support child welfare decision-making. Predictive analytics is a sophisticated form of risk modeling that uses historical data to understand relationships between myriad factors to estimate a probability score for the outcome of interest.

This brief summarizes an environmental scan and series of key informant interviews describing the challenges that human services programs face in identifying participants with substance use disorders (SUD), and subsequently referring them to treatment. The review focused on child welfare services, domestic violence services, Head Start, and Temporary Assistance for Needy Families.

This brief summarizes discussions among experts participating in a roundtable. The discussions focused on promising strategies to identify substance use disorder (SUD) among human services participants and refer them to treatment and recovery supports. The roundtable concentrated on four programs: Temporary Assistance for Needy Families, child welfare, domestic violence, and Head Start.