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Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data

Author

Listed:
  • Michael Rosholm

    (Aarhus University [Aarhus], IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics)

  • Simon Tranberg Bodilsen

    (Aarhus University [Aarhus])

  • Bastien Michel

    (Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université, Aarhus University [Aarhus])

  • Albeck Søren Nielsen

    (Aarhus University [Aarhus])

Abstract

Child maltreatment is a widespread problem with significant costs for both victims and society. In this retrospective cohort study, we develop predictive risk models using Danish administrative data to predict removal decisions among referred children and assess the effectiveness of caseworkers in identifying children at risk of maltreatment. The study analyzes 195,639 referrals involving 102,309 children Danish Child Protection Services received from April 2016 to December 2017. We implement four machine learning models of increasing complexity, incorporating extensive background information on each child and their family. Our best-performing model exhibits robust predictive power, with an AUC-ROC score exceeding 87%, indicating its ability to consistently rank referred children based on their likelihood of being removed. Additionally, we find strong positive correlations between the model's predictions and various adverse child outcomes, such as crime, physical and mental health issues, and school absenteeism. Furthermore, we demonstrate that predictive risk models can enhance caseworkers' decision-making processes by reducing classification errors and identifying at-risk children at an earlier stage, enabling timely interventions and potentially improving outcomes for vulnerable children.

Suggested Citation

  • Michael Rosholm & Simon Tranberg Bodilsen & Bastien Michel & Albeck Søren Nielsen, 2024. "Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data," Post-Print hal-04743190, HAL.
  • Handle: RePEc:hal:journl:hal-04743190
    DOI: 10.1371/journal.pone.0305974
    Note: View the original document on HAL open archive server: https://nantes-universite.hal.science/hal-04743190v1
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    References listed on IDEAS

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    1. Beth Coulthard & John Mallett & Brian Taylor, 2020. "Better Decisions for Children with “Big Data”: Can Algorithms Promote Fairness, Transparency and Parental Engagement?," Societies, MDPI, vol. 10(4), pages 1-16, December.
    2. Križ, Katrin & Skivenes, Marit, 2013. "Systemic differences in views on risk: A comparative case vignette study of risk assessment in England, Norway and the United States (California)," Children and Youth Services Review, Elsevier, vol. 35(11), pages 1862-1870.
    3. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
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