Children in the Public Benefit System at Risk of Maltreatment
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 ser…
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In child welfare, algorithmic risk assessment tools are being adopted to assist caseworkers in screening decisions that determine whether a family is investigated for alleged maltreatment.
“In child welfare, for example, algorithmic risk assessment tools are being adopted to assist caseworkers in screening decisions that determine whether a family is investigated for alleged maltreatment (Chouldechova et al. 2018;Vaithianathan et al. 2013).”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Vaithianathan et al. 2013
Cites
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Algorithmic Fairness: Choices, Assumptions, and Definitions
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Kawakami, A., Coston, A., Zhu, H., Heidari, H., & Holstein, K. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642849 · Cited by 54 · see citing works
Limits of Interventions in AI-Assisted Decision Making
Shrestha, S., Ahn, E., & Vorobeychik, Y. (2026) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v9i4.43309
- Authors
- Vaithianathan, R., Maloney, T., Putnam-Hornstein, E., & Jiang, N.
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2013-09
- Volume
- 45
- Issue
- 3
- Pages
- 354-359
- ISSN
- 0749-3797
- Cited by
- 92
- References
- 6
- Language
- en
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