Abstract: Child welfare agency performance: How are child, agency, and county factors related to achieving timely permanency outcomes for children in foster care? (Society for Social Work and Research 14th Annual Conference: Social Work Research: A WORLD OF POSSIBILITIES)

11913 Child welfare agency performance: How are child, agency, and county factors related to achieving timely permanency outcomes for children in foster care?

Sunday, January 17, 2010: 8:45 AM
Pacific Concourse F (Hyatt Regency)
* noted as presenting author
Elizabeth Caplick Weigensberg, PhD , Chapin Hall at the University of Chicago, Researcher, Chicago, IL
Performance measurement and accountability have become increasingly important for state and local child welfare agencies. Despite a great need for understanding factors related to achieving performance outcomes, little research exists evaluating the role of context in regard to child welfare performance measures. Specifically, research is needed to understand what individual characteristics and contextual factors may influence timely achievement of permanency for children in foster care. The study evaluated the research question of how child characteristics, local child welfare agency factors, and county demographics are related to achievement of timely permanency outcomes.

This study used longitudinal child welfare administrative data of 22,316 children who entered foster care for the first time in North Carolina between 2002 and 2005, along with local agency and county demographic data that were obtained from existing administrative and survey datasets. Given that children are served by local child welfare agencies, they share a similar set of contextual factors that shape their experience in foster care. Analytical methods are needed to account for the autocorrelation of child-level data nested within local child welfare agencies, however these methods are rarely used in existing research. Therefore, this study applied a multi-level survival approach using corrective Cox proportional hazard models to assess individual and contextual factors related to timely achievement of permanency. Furthermore, a competing risks analytical framework, which was stratified by age group, was used to simultaneously assess how child, agency, and county factors relate to the speed of achieving several competing permanency outcomes, specifically reunification, adoption, guardianship or custody, and emancipation.

Study results demonstrated that numerous child, agency, and county factors were related to how quickly children in foster care achieved permanency outcomes, yet the strength and direction of these relationships differed by age and type of permanency. In particular, the child characteristics of age, gender, race, ethnicity, and reason for placement into foster care were all shown to have significant relationships with timely achievement of permanency. Local child welfare agency characteristics of caseload size, use of relative placements, agency engagement in alternative response, and agency history of implementing reform efforts were significantly related to timely achievement of several permanency outcomes. Also, county demographics of poverty and unemployment rates were significantly related to timely achievement of selected permanency outcomes.

These findings provide insight into how individual- and macro-level factors are related to timely achievement of permanency outcomes, furthering the understanding of the role of contextual factors when measuring agency performance. This research contributes to a needed evidence base to identify factors for estimating stratified performance measures, allowing agencies to gain an understanding of how well specific subpopulations are achieving outcomes. Ultimately knowing how individual, agency, and county factors are related to permanency can help child welfare agencies better understand their own performance to shape policy and target limited resources for improvement efforts. Furthermore, this study was the first to apply a corrective Cox proportional model within a competing risks analytical framework, demonstrating the need for future research to account for autocorrelation and competing outcomes within child welfare data.