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

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

doi:10.1023/A:1018586715034

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

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

Biostatistics

Research output: Contribution to journal › Article › peer-review

15 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 language	English (US)
Pages (from-to)	267-281
Number of pages	15
Journal	Environmental and Ecological Statistics
Volume	4
Issue number	4
DOIs	https://doi.org/10.1023/A:1018586715034
State	Published - 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.

Keywords

Environmental equity
Hierarchical model
Markov chain Monte Carlo
Regulation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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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.",

keywords = "Environmental equity, Hierarchical model, Markov chain Monte Carlo, Regulation",

author = "Waller, {Lance A.} and Louis, {Thomas A.} and Brad Carlin",

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.",

year = "1997",

doi = "10.1023/A:1018586715034",

language = "English (US)",

volume = "4",

pages = "267--281",

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AU - Waller, Lance A.

AU - Louis, Thomas A.

AU - Carlin, Brad

N1 - 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.

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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.

KW - Environmental equity

KW - Hierarchical model

KW - Markov chain Monte Carlo

KW - Regulation

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

Abstract

Bibliographical note

Keywords

UN SDGs

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