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Published on: June 20, 2020
Leveraging machine learning for effective child maltreatment prevention: A case study of home visiting service
Eunhye Ahn1, Ruopeng An1, Melissa Jonson-Reid1
1Brown School, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, United States of America.
Insights
Predictive risk modeling (PRM) significantly improves early identification of children needing support, outperforming traditional methods. This machine learning approach enhances child welfare services by accurately predicting maltreatment risk.
Area of Science:
- Child welfare research
- Public health informatics
- Machine learning applications
Background:
- Early identification of at-risk children and families is vital for preventing child maltreatment.
- Predictive risk modeling (PRM) offers potential improvements over current risk assessment techniques.
Purpose of the Study:
- To assess the effectiveness of a PRM tool compared to existing methods for identifying families needing home visiting services.
- To evaluate PRM's ability to predict substantiated maltreatment allegations.
Main Methods:
- Developed a PRM tool using machine learning, integrating birth and child protection system records.
- Limited predictive features to those used by the existing baseline assessment tool.
- Measured need for services by substantiated maltreatment allegations within the first three years of a child's life.
Main Results:
- 2.7% of children experienced substantiated maltreatment by age three.
- PRM accurately identified 75.3%-84.1% of children with substantiated maltreatment in the high-risk group.
- PRM significantly outperformed the baseline model's 46.2% identification rate.
Conclusions:
- PRM shows significant potential to enhance risk assessment tools for child welfare prevention programs.
- Machine learning algorithms can generate accurate predictions to inform targeted preventive services.
- Findings offer practical insights for data-driven PRM development in child welfare settings.
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.
Participants And Setting:
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.
