Modeling elevated blood lead level risk across the United States.

Lead exposure adversely affects child health and continues to be a major public health concern in the United States (US). Lead exposure risk has been linked with older housing and households in poverty, but more studies of neighborhood socioeconomic status (SES) and lead exposure risk over large and diverse geographic areas are needed. In this paper, we combined lead test result data over many states for a majority of the US ZIP Codes in order to estimate its association with many SES variables and predict lead exposure risk in all populated ZIP Codes in the US. The methods used for estimation and prediction of lead risk included the Vox lead exposure risk score, random forest, weighted quantile sum (WQS) regression, and a Bayesian SES index model. The results showed that the Bayesian index model had the best overall performance for modeling elevated blood lead level (EBLL) risk and therefore was used to create a lead exposure risk score for US ZIP Codes. There was a statistically significant association between EBLL risk and the SES index and the most important SES variables for explaining EBLL risk were percentage of houses built before 1940 and median home value. When mapping the lead exposure risk scores, there was a clear pattern of elevated risk in the Northeast and Midwest, but areas in the South and Southwest regions of the US also had high risk. In summary, the Bayesian index model was an effective method for modeling EBLL risk associated with neighborhood deprivation while accounting for additional heterogeneity in risk using lead test result data covering a majority of the US. The resulting lead exposure risk score can be used for targeting public health intervention efforts.