Journal Article

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

Nielsen, Albeck Søren, Bodilsen, Simon Tranberg, Rosholm, Michael, Michel, Bastien

2024

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Absenteeism / Abused children / Adolescent / At risk populations / Biology and Life Sciences / Child / Child abuse / Child abuse & neglect / Child Abuse - statistics & numerical data / Child Protective Services / Child welfare / Child, Preschool / Children / Classification / Cohort analysis / Computer and Information Sciences / Cost analysis / Crime / Criminal investigations / Decision analysis / Decision Making / Denmark - epidemiology / Economics and Finance / Error reduction / Errors / Evidence / Families & family life / Female / Health aspects / Health status / Humanities and Social Sciences / Humans / Infant / Intervention / Juvenile offenders / Machine Learning / Male / Medical referrals / Medicine and Health Sciences / Mental disorders / Mental health / Mental health services / Methods / People and Places / Performance prediction / Prediction models / Predictions / Psychological research / Retrospective Studies / Risk / Risk analysis / Risk assessment / Risk Assessment - methods / Risk factors / School attendance / Sexual abuse / Social aspects / Social Sciences / Victims