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Your Environment. Your Health.

Boston University

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
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2012-2017)

The research team led by Veronica Vieira, Sc.D., evaluates advanced methods for analyzing patterns in epidemiologic and toxicologic data involving combinations of space and time or combinations of chemicals (mixtures). In this research project, two Superfund Research Program (SRP) mandates are addressed: (1) advanced techniques to detect associations between human health effects and environmental hazards; and (2) methods to assess the risks to human health presented by hazardous substances.

In this research project, the investigators will be focusing on four related research goals and one research translation goal:

  1. A rigorous evaluation of the use of tensor product smooths in generalized additive models (GAMs) to analyze space-time interactions in epidemiological data. GAMs predict local disease odds over time while adjusting for known risk factors in the population.
  2. Application of GAM methods to investigate patterns in space and time for two populations: birth defects in parts of Massachusetts and Rhode Island (in collaboration with Ann Aschengrau), and Attention Deficit Hyperactivity Disorder (ADHD)-related behaviors in children born while their mothers were living near the New Bedford Harbor Superfund Site.
  3. Application of the researchers’ spatial methods to the analysis of chemical mixtures (in collaboration with Jennifer Schlezinger and John Stegeman).
  4. Assessment of combinations of exposures in epidemiology, adapting methods from toxicology.
  5. Researchers will make the methods developed in this project freely available via open source software, in collaboration with the Research Translation Core. The researchers expect that the methods developed from this project should be of interest to academia, government and community organizations.
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