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Structural equation modeling is a statistical method for partitioning the variance in a set of interrelated multivariate outcomes into that which is due to direct, indirect, and covariate (exogenous) effects. Despite this model's flexibility to handle different experimental designs, postulation of a causal chain among the endogenous variables and the points of influence of the covariates is required. This has motivated the researchers at the University of Cincinnati Department of Environmental Health to be guided by a theoretical model for movement of lead from distal sources (exterior soil or dust and paint lead) to proximal sources (interior dust lead) and then finally to biologic outcomes (handwipe and blood lead). The question of whether a single structural equation model built from proximity arguments can be applied to diverse populations observed in different communities with varying lead amounts, sources, and bioavailabilities is addressed in this article. This reanalysis involved data from 1855 children less than 72 months of age enrolled in 11 studies performed over approximately 15 years. Data from children residing near former ore-processing sites were included in this reanalysis. A single model adequately fit the data from these 11 studies; however, the model needs to be flexible to include pathways that are not frequently observed. As expected, the more proximal sources of interior dust lead and handwipe lead were the most important predictors of blood lead; soil lead often had a number of indirect influences. A limited number of covariates were also isolated as usually affecting the endogenous lead variables. The blood lead levels surveyed at the ore-processing sites were comparable to and actually somewhat lower than those reported in the the Third National Health and Nutrition Examination Survey. Lessened bioavailability of the lead at certain of these sites is a probable reason for this finding.

An empirical comparison of lead exposure pathway models.