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Cost/variance optimization for human exposure assessment studies.

Roy W Whitmore1, Edo D Pellizzari, Harvey S Zelon

  • 1Research Triangle Institute, PO Box 12194, Research Triangle Park, North Carolina, USA. rww@rti.org

Journal of Exposure Analysis and Environmental Epidemiology
|May 12, 2005
PubMed
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The National Human Exposure Assessment Survey (NHEXAS) collected exposure data on metals and volatile organic compounds (VOCs) in Great Lakes residents. This study developed a cost-minimization model for future exposure assessments.

Area of Science:

  • Environmental Science
  • Public Health
  • Biostatistics

Background:

  • The National Human Exposure Assessment Survey (NHEXAS) collected extensive exposure data in EPA Region V.
  • Data included concentrations of metals and volatile organic compounds (VOCs) in environmental media and human biomarkers.
  • A representative sample of 249 residents from the Great Lakes states participated.

Purpose of the Study:

  • To develop a statistical model for optimizing sample allocation in environmental exposure studies.
  • To minimize study costs while achieving desired precision for monitoring metals and VOCs.
  • To inform the design of future human exposure assessment surveys.

Main Methods:

  • Utilized a variance model for log-transformed concentration measurements to estimate intraclass correlations.

Related Experiment Videos

  • Developed a cost model incorporating fixed and variable costs per sampling unit and participant.
  • Estimated intraclass correlations for media and analytes with adequate sample sizes.

    • Main Results:

    • Intraclass correlations were estimated for various environmental media and chemical analytes.
    • A cost model was developed based on fixed and variable cost components.
    • Demonstrated the application of intraclass correlations and cost components for optimal sample allocation.

    Conclusions:

    • The developed methodology enables cost-effective sample allocation for future exposure studies.
    • Precision constraints can be met by optimizing sample size and distribution.
    • This approach is valuable for designing efficient surveys monitoring environmental contaminants and human exposure.