29 09

Machine learning can help public health officials identify children most at risk of lead poisoning, enabling them to concentrate their limited resources on preventing poisonings rather than remediating homes only after a child suffers elevated blood lead levels, a new study shows.

In a study published today in the journal JAMA Network Open, Ghani and colleagues at the University of Chicago and CDPH report that their machine learning model is about twice as accurate in identifying children at high risk than previous, simpler models, and equitably identifies children regardless of their race or ethnicity.

By contrast, the machine learning model his team devised is more complicated and considers more factors, including 2.5 million surveillance blood tests, 70,000 public health lead investigations, 2 million building permits and violations, as well as age, size and condition of housing, and sociodemographic data from the U.S. Census.

“Remediation can help other children who will live there, but it doesn’t help the child who has already been injured,” said Ghani, who was a leader of the study while on the faculty of the University of Chicago.

“Prevention is the only way to deal with this problem. This more sophisticated approach correctly identified the children at highest risk of lead poisoning 15.5% of the time — about twice the rate of previous predictive models. 

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