Lead Exposure, Micronutrients, Social Vulnerability, and Preterm Birth (ECHO)

Examining whether prenatal lead exposure increases preterm birth risk, and whether micronutrient adequacy and community-level social vulnerability modify that association, using data from the ECHO Data Warehouse.

Prenatal lead exposure is a well-documented neurodevelopmental hazard, but its role in adverse birth outcomes remains less characterized. This project, part of the NIH ECHO (Environmental influences on Child Health Outcomes) program, examines whether prenatal lead exposure from multiple pathways, including water, air, and housing age, is associated with preterm birth, and whether that association is modified by maternal micronutrient adequacy and community-level social vulnerability.

Using data from the ECHO Data Warehouse, we apply generalized estimating equations to account for clustering across ECHO cohorts, with multiple imputation for missing exposure and covariate data. A secondary aim extends the framework to test whether social vulnerability, as captured by the CDC SVI, conditions the lead-PTB association independently of micronutrient status. This is a collaborative project with Eaves (PI) at UNC Chapel Hill (June 2025 to May 2027).

Related Publications

Re-engineering a Machine Learning Phenotype to Adapt to the Changing COVID-19 Landscape: A Machine Learning Modelling Study from the N3C and RECOVER Consortia

Miles Crosskey, Tomas McIntee, Sandy Preiss, Daniel Brannock, Yun Jae Yoo, Emily Hadley, Frank Blancero, Rob Chew, Johanna Loomba, Abhishek Bhatia, Christopher G. Chute, Melissa Haendel, Richard Moffitt, Emily R. Pfaff, N3C Consortium, the RECOVER EHR Cohort

The Lancet Digital Health PDF for Re-engineering a Machine Learning Phenotype to Adapt to the Changing COVID-19 Landscape: A Machine Learning Modelling Study from the N3C and RECOVER Consortia Project for Re-engineering a Machine Learning Phenotype to Adapt to the Changing COVID-19 Landscape: A Machine Learning Modelling Study from the N3C and RECOVER Consortia

Identifying Who Has Long COVID in the USA: A Machine Learning Approach Using N3C Data

Emily R. Pfaff, Andrew T. Girvin, Tellen D. Bennett, Abhishek Bhatia, Ian M. Brooks, Rachel R. Deer, Jonathan P. Dekermanjian, Sarah Elizabeth Jolley, Michael G. Kahn, Kristin Kostka, Julie A. McMurry, Richard Moffitt, Anita Walden, Christopher G. Chute, Melissa A. Haendel, Carolyn Bramante, David Dorr, Michele Morris, Ann M. Parker, Hythem Sidky, Ken Gersing, Stephanie Hong, Emily Niehaus

The Lancet Digital Health PDF for Identifying Who Has Long COVID in the USA: A Machine Learning Approach Using N3C Data Project for Identifying Who Has Long COVID in the USA: A Machine Learning Approach Using N3C Data

Interested in this work? Get in touch