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Split and combine simulation extrapolation algorithm to correct geocoding coarsening of built environment exposures.

Jung Y Won1, Emma V Sanchez-Vaznaugh2, Yuqi Zhai1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.

Statistics in Medicine
|January 31, 2022
PubMed
Summary
This summary is machine-generated.

Geocoding errors in built environment studies cause measurement errors, biasing health effect estimates. This study introduces a new method to correct these biases, improving exposure assessment accuracy for environmental health research.

Keywords:
biasgeocode coarseningmeasurement errorsimulation-extrapolation

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Area of Science:

  • Environmental Health
  • Geospatial Analysis
  • Biostatistics

Background:

  • Geocoding errors in built environment data introduce measurement errors in exposure assessments.
  • These errors can lead to biased health effect estimates in epidemiological studies.
  • Accurate exposure assessment is crucial for understanding built environment-health relationships.

Purpose of the Study:

  • To examine measurement error distribution from geocoding errors in point-referenced exposures.
  • To quantify bias in health effect estimates caused by geocode coarsening.
  • To extend the simulation extrapolation (SIMEX) method for bias correction in exposure assessment.

Main Methods:

  • Algebraic analysis and simulation studies to investigate measurement error properties.
  • Application of the simulation extrapolation (SIMEX) method.
  • Development and validation of the SC-SIMEX procedure for bias correction.

Main Results:

  • Geocode coarsening leads to exposure measurement errors with heterogeneous variance and nonzero mean.
  • Bias in health effect estimates can be away from the null due to these errors.
  • The proposed SC-SIMEX procedure effectively corrects bias without external data.

Conclusions:

  • Geocoding errors present a significant challenge in built environment and health research.
  • The SC-SIMEX method offers a robust approach to correct for nonstandard measurement error distributions.
  • Improved bias correction enhances the reliability of health effect estimates in exposure science.