Superfund Research Program
Analyzing Patterns in Epidemiologic and Toxicologic Data
Project Leader: Veronica M. Vieira (University of California-Irvine)
Grant Number: P42ES007381
Funding Period: 1995-2017
This project focuses on examining and visualizing patterns in epidemiologic data using Generalized Additive Models (GAMs). GAMs are statistical models that allow researchers to describe relations and interactions between risk factors. This is a valuable tool for characterizing how exposures to multiple risk factors or stressors may impact health. The research team has applied GAMs to map geographic differences in health (Girguis et al., 2016; Hoffman et al., in press; Padilla et al., 2016; Vieira et al., in press). Recently, GAMs were used to model exposure to developmental toxicants using measured prenatal exposure to organochlorines and metals in the New Bedford Cohort (NBC), a prospective birth cohort (n~800) started as part of the Harvard Superfund Research Program. These models account for potential complex interactions between space and time (residential location, birth year, and maternal age), in addition to demographic factors, to estimate exposures for all births in the New Bedford area. Using these exposures, the research team can assess the relationship of chemical and non-chemical stressors with disease measures, such as asthma, for the entire community. The research team is currently using GAMs to investigate the relation between chemical and non-chemical stressor mixtures and health in the NBC including ADHD-related and other maladaptive behaviors in children and adolescents. In recent work, it has been found that chemical exposure-associated risk of maladaptive behavior in adolescents varied depending on maternal characteristics. By identifying previously unrecognized combinations of chemical and non-chemical exposures that increase risk of prevalent health disorders, this work promises to provide new insights into disease risk and its mitigation.