Meta-Analytic Approaches for Multistressor Dose-Response Function Development: Strengths, Limitations, and Case Studies

Authors

  • Jonathan I. Levy,

    Corresponding author
    1. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
    • Address correspondence to Jonathan Levy, Department of Environmental Health, Boston University School of Public Health, 715 Albany St. T4W, Boston, MA 02118, USA; tel: 617-638-4663; fax: 617-638-4857; jonlevy@bu.edu.

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  • M. Patricia Fabian,

    1. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
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  • Junenette L. Peters

    1. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
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  • [Corrections added after online publication on April 11, 2014: the ACKNOWLEDGMENTS section was updated.]

Abstract

For many policy analyses, including but not limited to cumulative risk assessments, it is important to characterize the individual and joint health effects of multiple stressors. With an increasing focus on psychosocial and other nonchemical stressors, this often includes epidemiological meta-analysis. Meta-analysis has limitations if epidemiological studies do not include all of the stressors of interest or do not provide multivariable outputs in a format necessary for risk assessment. Given these limitations, novel analytical methods are often needed to synthesize the published literature or to build upon available evidence. In this article, we discuss three recent case studies that highlight the strengths and limitations of meta-analytic approaches and other research synthesis techniques for human health risk assessment applications. First, a literature-based meta-analysis within a risk assessment context informed the design of a new epidemiological investigation of the differential toxicity of fine particulate matter constituents. Second, a literature synthesis for an effects-based cumulative risk assessment of hypertension risk factors led to a decision to develop new epidemiological associations using structural equation modeling. Third, discrete event simulation modeling was used to simulate the impact of changes in the built environment on environmental exposures and associated asthma outcomes, linking literature meta-analyses for key associations with a simulation model to synthesize all of the model components. These case studies emphasize the importance of conducting epidemiology with a risk assessment application in mind, the need for interdisciplinary collaboration, and the value of advanced analytical methods to synthesize epidemiological and other evidence for risk assessment applications.

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