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Methods proposed to address confounding variables frequently do not adequately distinguish confounding from covariation. A confounder is a variable that correlates both with the outcome and the major exposure variable. Accurate treatment of confounding is crucial to low dose extrapolation of the effects of chemical exposures based on epidemiology studies. This study explores the limitations of current regression models in extrapolation to the low dose region of the dose-response curve due to the existence of unrecognized and uncontrolled confounding, using epidemiological data for lead. Based on the reported data in analyses by Lanphear and colleagues and Crump and colleagues, and drawing on other studies, Wilson and Wilson considered maternal IQ, HOME score, SES, parental education, birthweight, smoking, and race as characteristic variables which may have interaction effects. This analysis identifies confounding variables based on the seven longitudinal cohorts in analyses conducted by Lanphear and colleagues and by Crump and colleagues and confirms maternal IQ, HOME score, maternal education and maternal marital status at birth are "Highly Likely" confounders, while race is a "Likely" confounder. The cohort data were reanalyzed using the methods presented by Crump and colleagues while also considering the interaction among the identified confounding variables. This analysis determined that confounders influence IQ estimates in a quantifiable way that may exceed or at least obscure previously-reported effects of blood lead on IQ with blood lead levels below 5 µg/dL; however, limitations in the datasets make predictions of the low dose dose-response analysis questionable. [ABSTRACT FROM AUTHOR]