Using Geospatial Analyses of Socioeconomic Factors to Inform Racial and Ethnic Disproportionality in Child Welfare

Background and Purpose

Over the past two decades, meta-analyses have shown the adverse effects of socioeconomic status (SES) on mental health and well-being within child welfare. Information on SES is not always available from data collected by child welfare agencies, which limits investigating the impact of SES on child welfare using agency-collected data to study important topics such as racial disproportionality. Disparities in the child welfare system disproportionately affect families of color, evident from investigation substantiation to outcomes. The evidence linking racial disproportionality, disparities, and SES indicators to negative outcomes underscores a need for comprehensive research considering all these factors. The current study linked publicly available area-level measures of SES to examine the impact of these factors alongside demographic and case factors to examine racial disparities in one state's child welfare system.

Methods

Administrative, retrospective, de-identified data was collected on child welfare cases between 2015 and 2022, including investigation, in-home, and foster care services. Information on census tract and block-level SES factors were collected via secondary sources (e.g., ESRI, Census, ACS) on racial and ethnic demographics, population characteristics, consumer spending, crime, education, health care, housing, poverty, and more. Child welfare records were geocoded and merged with SES factors to provide area-level indicators of SES for each child and family involved with Utah’s child welfare services. Demographic information, case characteristics, and area-level SES factors were examined to evaluate racial disproportionality in child welfare outcomes, such as subsequent reports of maltreatment, removal, and permanency, using descriptive statistics, geospatial analyses, machine learning methods, regression analyses, and propensity score matching methods.

Findings

Significant differences in child welfare case outcomes were identified by race and ethnicity, such as the likelihood of subsequent, future reports of maltreatment, entry into ongoing and out-of-home services, days in out-of-home services, and placements in family-like, permanent settings. Area-level SES factors were also significantly associated with many of these child welfare outcomes. Using propensity score matching by race and ethnicity, there were mixed findings on the degree to which SES factors attributed to the differences found by race and ethnicity. Geospatial mapping tools were also utilized to uncover patterns of child welfare involvement across the state along racial, ethnic, and SES factors.

Conclusion and Implications

While area-level SES data does not provide the same level of detail that individual information would provide, these findings show that even area-level data shows significant impacts of SES on child welfare outcomes and provides additional insight into racial disproportionality. These findings have implications for future research in considering the value of examining area-level SES factors alongside agency-collected administrative data to help provide insights for child welfare practitioners, including identifying small areas of a state that may need additional resources and providing insights into workforce development to help counteract disparities. The novel approach of geocoding and linking child welfare records to area-level SES indicators provides researchers and child welfare administrators with a unique view of population needs and experiences. This approach can better inform service delivery, community engagement, resource allocation, and future evaluation efforts.