Imputation method for lifetime exposure assessment in air pollution epidemiologic studies
Jan Beyea1, Steven D Stellman, Susan Teitelbaum
1Consulting in the Public Interest (CIPI), Lambertville, NJ, USA. jbeyea@cipi.com
Environmental Health : a Global Access Science Source
|August 8, 2013
Summary
This study introduces a novel multiple imputation method to fill gaps in historical environmental exposure data for life course epidemiology. This approach enhances exposure reconstruction and addresses potential biases in health risk assessments.
Area of Science:
- Environmental Epidemiology
- Biostatistics
- Exposure Science
Background:
- Life course environmental epidemiology requires historical exposure data, often incomplete due to data gaps, particularly in earlier years.
- Accurate reconstruction of lifetime exposure to pollutants is crucial for understanding long-term health risks.
- Existing methods struggle to provide comprehensive historical exposure estimates, limiting epidemiological studies.
Purpose of the Study:
- To present a novel multiple imputation methodology for reconstructing complete lifetime environmental exposure histories.
- To address methodological challenges in historical exposure assessment, including data gaps and potential biases.
- To demonstrate the application of this method in an environmental epidemiology study.
Main Methods:
- Applied multiple imputation techniques to a formal risk equation incorporating lifetime exposure to fill missing historical data.
- Utilized group-level information and interpolation, with a random component to preserve variance, for imputation.
- Introduced an "incompleteness index" (percentage of dose imputed) to assess recall bias and misclassification.
- Calculated "effective doses" using various age-of-exposure sensitivities to compare risk models.
Main Results:
- The developed imputation approach effectively fills gaps in historical exposure data for life course studies.
- Analysis revealed potential recall bias, highlighting the importance of assessing results as a function of the percentage of dose imputed (PDI).
- Imputation methods must be designed to handle non-standard exposure distributions, as some subjects exhibited very high cumulative exposures.
Conclusions:
- The proposed imputation method offers a promising solution for extending historical exposure reconstruction over a lifetime in environmental epidemiology.
- This approach addresses key methodological challenges, improving the accuracy of exposure assessment in long-term health studies.
- Future work will apply this methodology to assess breast cancer risks.
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