Lead exposure still threatens children's health despite policies aiming to identify lead exposure sources. Some US states require de jure universal screening while others target screening, but little research examines the relative benefits of these approaches. We link lead tests for children born in Illinois between 2010 and 2014 to geocoded birth records and potential exposure sources. We train a random forest regression model that predicts children's blood lead levels (BLLs) to estimate the geographic distribution of undetected lead poisoning. We use these estimates to compare de jure universal screening against targeted screening. Because no policy achieves perfect compliance, we analyze different incremental screening expansions. We estimate that 5,819 untested children had a BLL ≥5μg/dL, in addition to the 18,101 detected cases. 80% of these undetected cases should have been screened under the current policy. Model-based targeted screening can improve upon both the status quo and expanded universal screening.
Keywords: Environmental health; Lead poisoning; Machine learning; Screening.
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