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Unveiling Racial Disparities in Child Welfare: Insights from Birth Record Linkages

This study explores racial disparities in child welfare decision-making through birth record linkages, revealing hidden factors beneath summary statistics. It uncovers variations in risk factors and questions service adequacy for Black and White children.

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Unveiling Racial Disparities in Child Welfare: Insights from Birth Record Linkages

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  1. Black/White Racial Disparity in Child Welfare:Findings from Linkages to Birth Record Data Barbara Needell, PhD, MSW Emily Putnam-Hornstein, PhD, MSW Center for Social Services Research University of California at Berkeley We gratefully acknowledge the support of the California Department of Social Services, the Stuart Foundation, and Casey Family Programs

  2. The problem with summary statistics: The average human has one breast and one testicle. * * ~Des McHale www.quotegarden.com/statistics.html

  3. Data • Unique dataset constructed by linking children reported for maltreatment during the first five years of life to their birth record • state CWS/CMS records linked to vital birth records • probabilistic methods (84% of child welfare records matched) • 530,843 children born alive in CA in 2002 • 14% reported for maltreatment (N=74,182) • 6% substantiated as a victims (N=27,805) • 0.8% entered foster care (N=4,388) • Racial disparities? • examined aggregate Black vs. White disparity across decision points • estimated child level risk at each decision point, after adjusting for other risk factors

  4. Racial Disparity…

  5. Across several decision points.

  6. What is hidden beneath the summary statistics? • Significant racial variations in the presence of risk factors that are associated with disparities… • In multivariate models, we adjusted for twelve sociodemographicand biomedical risk factors for contact with child protective services: • child’s sex (n.s.), low birth weight, birth abnormality, prenatal care, maternal birth place, maternal race/ethnicity, birth payment method, maternal age, maternal education, abortion history, paternity information, birth order • Significant interactions between a number of covariates and Medi-Cal coverage led us to stratify models

  7. Summary statistics that indicate Black/White disparity mask large covariate effects Questions these data don’t answer: • Why are people poor?* • Are the service needs of Black and White children and families being addressed? • Are “thresholds” the same for Black and White children and families? • Why is there no disparity (or reverse disparity) in the Medi-Cal group? Why are there relatively large disparities in the non Medi-cal group? • What are the appropriate rates for Black and White children? * http://www.pisab.org/

  8. More to come (e.g., Black vs. Hispanic analysis and full article with detailed tables)

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