Increased Risk of Sub-Clinical Blood Lead Levels in the 20-County Metro Atlanta, Georgia Area-A Laboratory Surveillance-Based Study

Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells

Low-level lead exposure in children is a major public health issue. Higher-resolution spatial targeting would significantly improve county and state-wide policies and programs for lead exposure prevention that generally intervene across large geographic areas. We use stack-ensemble machine learning, including an elastic net generalized linear model, gradient-boosted machine, and deep neural network, to predict the number of children with venous blood lead levels (BLLs) ≥2 to <5 µg/dL and ≥5 µg/dL in ~1 km2 raster cells in the metro Atlanta region using a sample of 92,792 children ≤5 years old screened between 2010 and 2018. Permutation-based predictor importance and partial dependence plots were used for interpretation. Maps of predicted vs. observed values were generated to compare model performance. According to the EPA Toxic Release Inventory for air-based toxic release facility density, the percentage of the population below the poverty threshold, crime, and road network density was positively associated with the number of children with low-level lead exposure, whereas the percentage of the white population was inversely associated. While predictions generally matched observed values, cells with high counts of lead exposure were underestimated. High-resolution geographic prediction of lead-exposed children using ensemble machine learning is a promising approach to enhance lead prevention efforts.

Moving Morehouse School of Medicine Translation Tx Research through MDTTs-Multidisciplinary Translational Teams

Morehouse School of Medicine (SOM) works to achieve its vision of advancing health equity through conducting transformational, translation science (Tx). Tx describes our translational research continuum, symbolizing a method and scientific philosophy that intentionally promotes and supports convergence of interdisciplinary approaches and scientists to stimulate exponential advances for the health of diverse communities. Morehouse SOM actualizes Tx through multidisciplinary translational teams (MDTTs). We chronicle the identification of MDTTs by documenting formation, composition, functioning, successes, failures, and sustainability. Data and information were collected through key informant interviews, review of research documents, workshops, and community events. Our scan identified 16 teams that meet our Morehouse SOM definition of an MDTT. These team science workgroups cross basic science, clinical, and public health academic departments, and include community partners and student learners. We present four MDTTs, in various stages of progress, at Morehouse SOM and how they are advancing translational research.