Abstract：
BACKGROUND: Early identification of children and families who may benefit from support is crucial for implementing strategies that can prevent the onset of child maltreatment. Predictive risk modeling (PRM) may offer valuable and efficient enhancements to existing risk assessment techniques.
OBJECTIVE: To evaluate the PRM\'s effectiveness against the existing assessment tool in identifying children and families needing home visiting services.
METHODS: Children born in hospitals affiliated with the Bridges Maternal Child Health Network in Orange County, California, from 2011 to 2016 (N = 132,216).
METHODS: We developed a PRM tool by integrating a machine learning algorithm with a linked dataset of birth records and child protection system (CPS) records. To align with the existing assessment tool (baseline model), we limited the predicting features to the information used by the existing tool. The need for home visiting services was measured by substantiated maltreatment allegation reported during the first three years of the child\'s life.
RESULTS: Of the children born in Bridges Network hospitals between 2011 and 2016, 2.7 % experienced substantiated maltreatment allegations by the age of three. Within the top 30 % of children with high-risk scores, the PRM tool outperformed the baseline model, accurately identifying 75.3 %-84.1 % of all children who would experience maltreatment substantiation, surpassing the baseline model\'s performance of 46.2 %.
CONCLUSIONS: Our study underscores the potential of PRM in enhancing the risk assessment tool used by a prevention program in a child welfare center in California. The findings provide valuable insights to practitioners interested in utilizing data for PRM development, highlighting the potential of machine learning algorithms to generate accurate predictions and inform targeted preventive services.
OBJECTIVE: To evaluate the PRM\'s effectiveness against the existing assessment tool in identifying children and families needing home visiting services.
METHODS: Children born in hospitals affiliated with the Bridges Maternal Child Health Network in Orange County, California, from 2011 to 2016 (N = 132,216).
METHODS: We developed a PRM tool by integrating a machine learning algorithm with a linked dataset of birth records and child protection system (CPS) records. To align with the existing assessment tool (baseline model), we limited the predicting features to the information used by the existing tool. The need for home visiting services was measured by substantiated maltreatment allegation reported during the first three years of the child\'s life.
RESULTS: Of the children born in Bridges Network hospitals between 2011 and 2016, 2.7 % experienced substantiated maltreatment allegations by the age of three. Within the top 30 % of children with high-risk scores, the PRM tool outperformed the baseline model, accurately identifying 75.3 %-84.1 % of all children who would experience maltreatment substantiation, surpassing the baseline model\'s performance of 46.2 %.
CONCLUSIONS: Our study underscores the potential of PRM in enhancing the risk assessment tool used by a prevention program in a child welfare center in California. The findings provide valuable insights to practitioners interested in utilizing data for PRM development, highlighting the potential of machine learning algorithms to generate accurate predictions and inform targeted preventive services.
摘要：
背景：早期识别可能从支持中受益的儿童和家庭对于实施可以防止儿童虐待发作的策略至关重要。预测风险建模（PRM）可以为现有的风险评估技术提供有价值和有效的增强。
目的：针对现有评估工具，评估PRM在识别需要家访服务的儿童和家庭方面的有效性。
方法：在奥兰治县Bridges母婴健康网络附属医院出生的儿童，加州,从2011年到2016年（N=132,216）。
方法：我们通过将机器学习算法与出生记录和儿童保护系统（CPS）记录的链接数据集集成在一起，开发了一种PRM工具。为了与现有的评估工具（基线模型）保持一致，我们将预测功能限制为现有工具使用的信息。对家访服务的需求是通过在儿童生命的前三年报告的有证据的虐待指控来衡量的。
结果：在2011年至2016年期间在BridgesNetwork医院出生的儿童中，有2.7％的儿童在3岁时经历了经证实的虐待指控。在高风险得分最高的30%的儿童中，PRM工具的性能优于基线模型，准确识别75.3%-84.1%的儿童会出现虐待症状，超过基线模型的46.2%的性能。
结论：我们的研究强调了PRM在增强加利福尼亚州儿童福利中心预防计划使用的风险评估工具方面的潜力。这些发现为有兴趣利用数据进行PRM开发的从业者提供了有价值的见解，强调机器学习算法在生成准确预测和提供有针对性的预防服务方面的潜力。
目的：针对现有评估工具，评估PRM在识别需要家访服务的儿童和家庭方面的有效性。
方法：在奥兰治县Bridges母婴健康网络附属医院出生的儿童，加州,从2011年到2016年（N=132,216）。
方法：我们通过将机器学习算法与出生记录和儿童保护系统（CPS）记录的链接数据集集成在一起，开发了一种PRM工具。为了与现有的评估工具（基线模型）保持一致，我们将预测功能限制为现有工具使用的信息。对家访服务的需求是通过在儿童生命的前三年报告的有证据的虐待指控来衡量的。
结果：在2011年至2016年期间在BridgesNetwork医院出生的儿童中，有2.7％的儿童在3岁时经历了经证实的虐待指控。在高风险得分最高的30%的儿童中，PRM工具的性能优于基线模型，准确识别75.3%-84.1%的儿童会出现虐待症状，超过基线模型的46.2%的性能。
结论：我们的研究强调了PRM在增强加利福尼亚州儿童福利中心预防计划使用的风险评估工具方面的潜力。这些发现为有兴趣利用数据进行PRM开发的从业者提供了有价值的见解，强调机器学习算法在生成准确预测和提供有针对性的预防服务方面的潜力。
