Bayes methods for combining disease and exposure data in assessing environmental justice

Lance A. Waller, Thomas A. Louis, Brad Carlin

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Environmental justice reflects the equitable distribution of the burden of environmental hazards across various sociodemographic groups. The issue is important in environmental regulation, siting of hazardous waste repositories and prioritizing remediation of existing sources of exposure. We propose a statistical framework for assessing environmental justice. The framework includes a quantitative assessment of environmental equity based on the cumulative distribution of exposure within population subgroups linked to disease incidence through a dose-response function. This approach avoids arbitrary binary classifications of individuals solely as 'exposed' or 'unexposed'. We present a Bayesian inferential approach, implemented using Markov chain Monte Carlo methods, that accounts for uncertainty in both exposure and response. We illustrate our method using data on leukaemia deaths and exposure to toxic chemical releases in Allegheny County, Pennsylvania.

Original languageEnglish (US)
Pages (from-to)267-281
Number of pages15
JournalEnvironmental and Ecological Statistics
Volume4
Issue number4
DOIs
StatePublished - Jan 1 1997

Keywords

  • Environmental equity
  • Hierarchical model
  • Markov chain Monte Carlo
  • Regulation

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