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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
Summary
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.
- 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.
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