Predicting youth at high risk of aging out of foster care using machine learning methods

Child welfare predictive risk models and legal decision making

BACKGROUND

Child welfare agencies around the world have experimented with algorithmic predictive modeling as a method to assist in decision making regarding foster child risk, removal and placement.

OBJECTIVE

Thus far, all of the predictive risk models have been confined to the employees of the various child welfare agencies at the early removal stages and none have been used by attorneys in legal arguments or by judges in making child welfare legal decisions. This study will show the effects of a predictive model on legal decision making within a child welfare context.

PARTICIPANTS AND SETTING

Lawyers, judges and law students with experience in child welfare or juvenile law were recruited to take an online randomized vignette survey.

METHODS

The survey consisted of two vignettes describing complex foster child removal and placement legal decisions where participants were exposed to one of three randomized predictive risk model scores. They were then asked follow up questions regarding their decisions to see if the risk models changed their answers.

RESULTS

Using structural equation modeling, high predictive model risk scores showed consistent ability to change legal decisions about removal and placement across both vignettes. Medium and low scores, though less consistent, also significantly influenced legal decision making.

CONCLUSIONS

Child welfare legal decision making can be affected by the use of a predictive risk model, which has implications for the development and use of these models as well as legal education for attorneys and judges in the field.

"I Want Everybody to Know as Much of My Life Story as They Can": Life Stories of Former Foster Youth

The power of stories shared by young people in foster care is well-documented. Largely left unexplored is a story of foster care that is told within a fuller context of the life course. Using narrative and life history methods, this study sought to retrospectively identify and connect life experiences in histories of young people and explore how systems are portrayed. Twelve adults formerly in foster care completed three interviews each and nominated six professionals from foster care for an interview. A three-phase analytical process identified and displayed themes across six developmental stages. Results suggest that life stories contained adversities that were: (1) intergenerational, (2) chronic, (3) complex, and (4) structural. The participants intentionally acted to try to mitigate adversities by accessing opportunities for prosocial pathways. These exploratory findings challenge child welfare policy and practice to attend to young people's life stories and their conceptions of systems that advance well-being.

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

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.

Leveraging machine learning for effective child maltreatment prevention: A case study of home visiting service assessments

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.

Use of Population-Level Administrative Data in Developmental Science

Population-level administrative data-data on individuals' interactions with administrative systems (e.g., health, criminal justice, and education)-have substantially advanced our understanding of life-course development. In this review, we focus on five areas where research using these data has made significant contributions to developmental science: (a) understanding small or difficult-to-study populations, (b) evaluating intergenerational and family influences, (c) enabling estimation of causal effects through natural experiments and regional comparisons, (d) identifying individuals at risk for negative developmental outcomes, and (e) assessing neighborhood and environmental influences. Further advances will be made by linking prospective surveys to administrative data to expand the range of developmental questions that can be tested; supporting efforts to establish new linked administrative data resources, including in developing countries; and conducting cross-national comparisons to test findings' generalizability. New administrative data initiatives should involve consultation with population subgroups including vulnerable groups, efforts to obtain social license, and strong ethical oversight and governance arrangements.

Promoting Resiliency and Eliminating Disparities-Best Practices when Working with Child Welfare Involved Youth of Color

The US child welfare system has a long history of racial disproportionality and subsequent disparities. These disparities have been perpetuated by discriminatory laws and policies in our federal and state governments, coupled with a system of care that is fraught with the risk of biases in child welfare practices and procedures. Child psychiatrists should have a knowledge base of these inequities to help ensure culturally and trauma-informed care for child welfare involved youth and families. As physician leaders with knowledge of child development, child psychiatrists can also help inform inclusive legislation, policies, and programs.