ArticleACS environmental Au2026
Modeling Elevated Children's Blood Lead Levels across Five U.S. States: A Statistical Approach to Improve Predictions and Understand Key Drivers.
Article in ACS environmental Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Gaps remain in the interpretability and real-world applicability of children's elevated blood lead level (EBLL) predictive models, which are important for fostering efficient health intervention. We produced predictive statistical models using publicly available population data and children's EBLL rates (≥5 or ≥3.5 μg/dL) at the United States (U.S.) census tract resolution from Michigan (2014-2016), Ohio (2014-2016), California (2018-2022), Minnesota (2017-2021), and Wisconsin (2018-2021). Spatial and nonspatial random forest and logistic regression models were developed for each state to predict whether a census tract was above or below the 75th percentile EBLL rate. Spatial logistic regression models performed the best based on overall predictive performance, evaluated through a 10-fold cross-validation approach. Predictive accuracy ranged from 82% to 90%. The most influential final predictive variables for each optimal model varied, but there were consistencies across states. For example, the percent of homes built before 1940 was significant (
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