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

14 Scopus citations


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
Issue number4
StatePublished - 1997

Bibliographical note

Funding Information:
This research was supported in part by National Institute of Environmental Health Sciences, NIH, grant number 1 R01 ES07750–01A1 (LAW, TAL, BPC), Environmental Protection Agency contract EPA CR 819638 to the National Institute of Statistical Sciences (LAW), University of Minnesota Graduate School Grant-in-Aid of Research, Artistry and Scholarship #15196 (LAW), and National Institute of Allergy and Infectious Diseases (NIAID) FIRST Award 1-R29-AI33466 (BPC). The leukaemia data were supplied by the Allegheny County Health Department, Pittsburgh, Pennsylvania. The Allegheny County Health Department specifically disclaims responsibility for any analyses, interpretations or conclusions. The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIEHS, NIH, EPA, or the Allegheny County Health Department, and no official endorsement should be inferred. The authors gratefully acknowledge the helpful comments of four referees.


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


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