The mother’s age. The parents’ history with drug or alcohol abuse. The young person’s experiences with child protective services.
These are among the hundreds of factors that could be digitally analyzed to predict whether a child is at risk of being abused or neglected — and child welfare agencies in places like Pennsylvania, Florida and California have been exploring whether this “big data” can be harnessed to protect the most vulnerable kids.
“We have checklists that help our child welfare workers gather information,” said Emily Putnam-Hornstein, an associate professor at USC’s School of Social Work. “But we often fail to assemble and make good use of historical data.”
Analyzing historical data to forecast an event is called predictive analytics, and it’s already being used in some industries; credit card companies, for instance, use predictive analytic tools to detect fraud. In the case of child and family services, these tools could be used to process historical data culled from multiple sources — such as birth and arrest records — to help flag cases where a parent or caregiver is most likely to harm or neglect a child again.
The interest in predictive analytics comes as local child welfare agencies continue to face scrutiny. An estimated four to eight children die every day from abuse or neglect in the United States, according to a national report last year by a commission appointed by Congress and then-President Obama.
In one high-profile case last year, an 11-year-old boy was found dead in his mother’s closet in Los Angeles. Yonatan Daniel Aguilar, who weighed 34 pounds at the time, had been reported to the county’s Department of Children and Family Services six times, but his case was never thoroughly investigated, according to the Los Angeles Times.
“Anytime a tragedy happens, the community wonders, ‘How did we not connect the dots? How did we miss that this child was in danger despite multiple referrals and other red flags?’” said Putnam-Hornstein, who co-directs the Children’s Data Network, a team of researchers studying how data can be applied to make better decisions about the health and well-being of kids.
The Children’s Data Network received a grant last year from the Laura and John Arnold Foundation and the California Department of Social Services’ Office of Child Abuse Prevention. It is building and testing a predictive analytics program, one that could potentially be deployed to California’s 58 counties. It taps into historical child welfare data, running the information through a computer algorithm to determine the likelihood that a child will be re-reported to the child welfare agencies.
Such a tool could make a difference in how future cases of abuse or neglect are evaluated. Currently, social workers can have varying responses when they look into a child’s situation, depending on their personal biases, level of experience or the information they rely on.
