Comparing lead poisoning risk assessment methods: census block group characteristics vs. zip codes as predictors.

Objective: We determined which children should be tested for elevated blood lead levels (BLLs) in the face of financial and practical barriers to universal screening efforts and within 2009 Centers for Disease Control and Prevention recommendations allowing health departments to develop BLL screening strategies.

Methods: We used the Michigan database of BLL tests from 1998 through 2005, which contains address, Medicaid eligibility, and race data. Linking addresses to U.S. Census 2000 data by block group provided neighborhood sociodemographic and housing characteristics. To derive an equation predicting BLL, we treated BLL as a continuous variable and used Hierarchical Linear Modeling to estimate the prediction equation.

Results: Census block groups explained more variance in BLL than tracts and much more than dichotomized zip code risk (which is current pediatric practice). Housing built before 1940, socioeconomic status and racial/ethnic characteristics of the block group, child characteristics, and empirical Bayesian residuals explained more than 41% of the variance in BLL during 1998-2001. By contrast, zip code risk and Medicaid status only explained 15% of the BLL variance. An equation using 1998-2001 BLL data predicted well for BLL tests performed in 2002-2005. While those who received BLL tests had above-average risk, this method produced minimal bias in using the prediction equation for all children.

Conclusions: Our equation offers better specificity and sensitivity than using dichotomized zip codes and Medicaid status, thereby identifying more high-risk children while also offering substantial cost savings. Our prediction equation can be used with a simple Internet-based program that allows health-care providers to enter minimal information and determine whether a BLL test is recommended.