Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark.
TrygFonden's Centre for Child Research, Aarhus University, Aarhus, Denmark.
PLoS One. 2024 Jul 10;19(7):e0305974. doi: 10.1371/journal.pone.0305974. eCollection 2024.
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.
儿童虐待是一个普遍存在的问题，对受害者和社会都造成了重大的损失。在这项回顾性队列研究中，我们使用丹麦的行政数据开发了预测风险模型，以预测转介儿童的安置决定，并评估社会工作者识别受虐待风险儿童的能力。该研究分析了 195639 份转介记录，涉及 2016 年 4 月至 2017 年 12 月期间丹麦儿童保护服务机构收到的 102309 名儿童。我们实施了四个机器学习模型，这些模型的复杂度逐渐增加，包含了每个儿童及其家庭的大量背景信息。表现最佳的模型具有稳健的预测能力，AUC-ROC 评分超过 87%，这表明它能够根据转介儿童被安置的可能性对其进行一致的排名。此外，我们发现模型的预测结果与各种不良儿童结局之间存在强烈的正相关关系，例如犯罪、身心健康问题和逃学。此外，我们还证明预测风险模型可以通过减少分类错误和更早地识别高危儿童来增强社会工作者的决策过程，从而实现对弱势儿童的及时干预，并有可能改善他们的结局。
