Effects of Individual and Neighborhood Characteristics on Childhood Blood Lead Testing and Elevated Blood Lead Levels, A Pennsylvania Birth Cohort Analysis

BACKGROUND: Despite declining lead exposure among U.S. children, childhood blood lead level (BLL) undertesting and elevation remains a public health issue. This study explores the impacts of maternal, infant, and neighborhood characteristics on the receipt of lead testing and having elevated BLLs (EBLLs) among children under age two. METHODS: Pennsylvania infants born in 2015 and 2016 were followed to 24 months. Birth certificate data were linked to 2015 through 2018 blood lead surveillance data and neighborhood data on household income, poverty, and the burden of houses built before 1970. Generalized linear mixed models were used to examine the individual and neighborhood characteristics independently and/or interactively affecting the likelihood of lead testing and of having EBLLs. RESULTS: A total of 48.6% of children were tested for BLLs, and 2.6% of them had confirmed EBLLs. The likelihood of lead testing and of having EBLLs among non-Hispanic black children was respectively 7% and 18% higher than white children. Children born to mothers with the lowest educational attainment (<high school), with self-payment as a payment source for delivery, and without WIC enrollment were at higher risk of undertesting. Children living in neighborhoods of the lowest quartile of household income and the highest quartile of poverty and old housing were more likely to have EBLLs. Different neighborhood characteristics modified the associations between some individual factors (such as race/ethnicity, payment source for delivery, and WIC enrollment) and the odds of undertesting and of having EBLLs. CONCLUSION: This cohort analysis provides more accurate estimates of lead screening rates and the percentages of EBLLs than cross-sectional analysis. Some maternal and infant demographics significantly impact the risk of undertesting and of having EBLLs, and some of the effects vary across different neighborhood characteristics. These findings can help lead prevention programs to target screening and treatment resources to children with specific characteristics.