A Multivariate Linear Regression Model for Predicting Children's Blood Lead Levels Based on Soil Lead Levels: A Study at Four Superfund Sites

Abstract

For the purpose of examining the association between blood lead levels and household-specific soil lead levels, we used a multivariate linear regression model to find a slope factor relating soil lead levels to blood lead levels. We used previously collected data from the Agency for Toxic Substances and Disease Registry's (ATSDR's) multisite lead and cadmium study. The data included the blood lead measurements (0.5 to 40.2 μ g/dL) of 1015 children aged 6–71 months, and corresponding household-specific environmental samples. The environmental samples included lead in soil (18.1–9980 mg/kg), house dust (5.2–71,000 mg/kg), interior paint (0–16.5 mg/cm2), and tap water (0.3–103 μ g/L). After adjusting for income, education of the parents, presence of a smoker in the household, sex, and dust lead, and using a double log transformation, we found a slope factor of 0.1388 with a 95% confidence interval of 0.09–0.19 for the dose–response relationship between the natural log of the soil lead level and the natural log of the blood lead level. The predicted blood lead level corresponding to a soil lead level of 500 mg/kg was 5.99 μ g/kg with a 95% prediction interval of 2.08–17.29. Predicted values and their corresponding prediction intervals varied by covariate level. The model shows that increased soil lead level is associated with elevated blood leads in children, but that predictions based on this regression model are subject to high levels of uncertainty and variability.