Superfund Research Program
Assessing the Relation of Chemical and Non-Chemical Stressors with Risk-Taking Behavior and Related Outcomes among Adolescents Living near the New Bedford Harbor Superfund Site
Project Leader: Jonathan I. Levy
Grant Number: P42ES007381
Funding Period: 2017-2021
- Project Summary
Final Progress Reports
Over the past year, Jonathan I. Levy, Sc.D., and his research team published models of prenatal exposure for all births surrounding the New Bedford Harbor (Khalili et al. 2019), providing a foundation for subsequent investigations using data from the Pregnancy to Early Life Longitudinal (PELL) data system. Manuscripts utilizing these exposure models in PELL are in preparation, as well as parallel analyses, in the New Bedford Cohort focused on risk-taking behaviors and related health outcomes in adolescents. This work was presented at the ISEE and SRA annual meetings (Deville et al. 2019). The research team also published on risk assessment approaches that integrate exposure model and epidemiological findings to quantify the health benefits of exposure reductions (Peters et al. 2019). Roxana Khalili’s, Ph.D., and Junenette Peters', Sc.D., manuscripts were highlighted in SRP Research Brief 299: Modeling Approaches Estimate Exposure and Simulate Impacts on Health, as providing cost-effective strategies to predict chemical exposures and their associated harm in large populations. In addition, the team completed their field study characterizing contemporary exposures to metals in New Bedford, reaching their target of 80 reproductive-aged women. They found metals of interest (Cd, Mn, Pb, Hg, As) to be above the limit of detection in 90-100 percent of samples. More broadly, their findings reinforced the feasibility of biospecimen collection at nail salons, facilitated through strong community partnerships, including mutually beneficial and trusted partnerships with small businesses for recruitment. This work was presented at the ISES annual meeting (Wilson et al. 2019). Finally, the researchers continued to apply the statistical approaches developed and refined within BUSRP (Padilla et al., 2019; Ribeiro et al., 2019; Bai et al., 2019).