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Children in the public benefit system at risk of maltreatment: identification via predictive modeling
Rhema Vaithianathan1, Tim Maloney, Emily Putnam-Hornstein
1Centre for Applied Research in Economics, Department of Economics, University of Auckland, New Zealand. r.vaithianathan@auckland.ac.nz
Insights
Predictive risk models (PRMs) can identify children at high risk for maltreatment using administrative data. This approach helps target early prevention services for child abuse and neglect.
Area of Science:
- Public Health
- Child Welfare
- Data Science
Background:
- Child abuse and neglect are linked to adverse health outcomes.
- Early identification of at-risk children enables targeted prevention.
- Administrative data offers potential for risk assessment.
Purpose of the Study:
- To develop and evaluate a predictive risk model (PRM) for child maltreatment.
- To explore the use of administrative data for early intervention targeting.
- To assess the feasibility of using public benefit and child protection records.
Main Methods:
- Developed a PRM using stepwise probit modeling.
- Utilized integrated administrative data from New Zealand's public benefit and child protection systems.
- Analyzed records for children born between 2003 and 2006.
Main Results:
- The PRM achieved an area under the receiver operating characteristic curve of 76%.
- Children in the highest risk decile had a 47.8% substantiated maltreatment rate by age 5.
- 83% of maltreated children were enrolled in the public benefit system before age 2.
Conclusions:
- PRMs can effectively generate risk scores for substantiated child maltreatment.
- This data-driven approach is a cost-effective method for targeting early prevention.
- PRMs complement clinical assessments for child abuse and neglect risk.
Abstract:
A growing body of research links child abuse and neglect to a range of negative short- and long-term health outcomes. Determining a child's risk of maltreatment at or shortly after birth provides an opportunity for the delivery of targeted prevention services. This study presents findings from a predictive risk model (PRM) developed to estimate the likelihood of substantiated maltreatment among children enrolled in New Zealand's public benefit system. The objective was to explore the potential use of administrative data for targeting prevention and early intervention services to children and families. A data set of integrated public benefit and child protection records for children born in New Zealand between January 1, 2003, and June 1, 2006, was used to develop a risk algorithm using stepwise probit modeling. Data were analyzed in 2012. The final model included 132 variables and produced an area under the receiver operating characteristic curve of 76%. Among children in the top decile of risk, 47.8% had been substantiated for maltreatment by age 5 years. Of all children substantiated for maltreatment by age 5 years, 83% had been enrolled in the public benefit system before age 2 years. This analysis demonstrates that PRMs can be used to generate risk scores for substantiated maltreatment. Although a PRM cannot replace more-comprehensive clinical assessments of abuse and neglect risk, this approach provides a simple and cost-effective method of targeting early prevention services.
