Screening for Lead: Predictive Modeling of Indoor Dust Lead Concentrations and Possible Effects of Intervention

Abstract

Lead (Pb) pollution continues to contribute to world-wide morbidity in all countries, particularly low- and middle-income countries. Despite its continued widespread adverse effects on global populations, particularly children, accurate prediction of elevated household dust Pb and the potential implications of simple household interventions at national and global scales have been lacking. A global dataset (~40 countries, n = 1951) of community sourced household dust samples were used to predict whether indoor dust was elevated in Pb, expanding on recent work in the United States (U.S.). Binned housing age category alone was a significant (p<0.01) predictor of elevated dust Pb, but only generated effective predictive accuracy for the U.K. and Australia (sensitivity of ~80%), similar to previous results in the U.S. This likely reflects comparable Pb pollution legacies between these three countries, particularly with residential Pb paint. We also find that the heterogeneity associated with Pb pollution at a global scale can complicate the predictive accuracy of our model, which is lower for countries outside the U.K., U.S., and Australia. This is likely due to differing environmental Pb regulations, sources, and the paucity of dust samples available outside of these three countries. In the U.K., U.S., and Australia, straightforward household intervention could conservatively save $70 billion USD within a four-year period, and as much as $1.68 trillion USD globally with universal household remediation based on our predictive results.