Using Data to Identify Changes in Child Welfare Referrals and Screening in the Era of COVID-19

In the context of COVID-19, there's widespread concern about the reduced reporting of child abuse and neglect to child welfare agencies during stay-at-home orders. With schools closed and teachers unable to report suspected cases of abuse and neglect, there is a heightened risk that child abuse will go unnoticed. Little is known, however, about the types of cases that are being reported during the pandemic and how child welfare agencies are responding to them.

On June 16th, Mathematica and its partners, the Centre for Social Data Analytics (CSDA) and the Children’s Data Network, convened a group of experts and staff of county child welfare agencies who are using data from predictive risk models to understand recent changes in the reporting and screening of child abuse and neglect. The webinar featured analysis of current data to deepen our understanding of the COVID-19 pandemic’s impact on child welfare screenings and referrals. In particular, predictive risk scores will give nuanced information about how the child welfare reports that are being received have changed, and how those reports are being screened.

Our expert speakers included:

• Matthew Stagner, Mathematica (host)
• Rhema Vaithianathan, director of CSDA, Auckland University of Technology (NZ) & University of Queensland (Aus)
• Emily Putnam-Hornstein, University of Southern California & Children’s Data Network
• Erin Dalton, Allegheny County, Pennsylvania
• Ruby Richards, Douglas County, Colorado
• Thad Paul, Larimer County, Colorado