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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering.

Dale L Zimmerman¹, Jie Li, Xiangming Fang

¹Department of Statistics and Actuarial Science and Department of Biostatistics, and Center for Health Policy and Research, University of Iowa, Iowa City, IA 52242, U.S.A. dale-zimmerman@uiowa.edu

Statistics in Medicine

|January 21, 2010

Summary

Automated geocoding errors in health studies are spatially correlated. This spatial autocorrelation helps maintain the power of disease cluster detection tests, unlike independent errors.

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Area of Science:

Spatial epidemiology
Geographic Information Systems (GIS)
Biostatistics

Background:

Automated geocoding is crucial for spatial epidemiology, but introduces positional errors.
These errors can reduce the effectiveness of spatial analysis and disease clustering tests.
Previous research suggested geocoding errors might be spatially correlated, potentially mitigating negative effects.

Purpose of the Study:

To explicitly demonstrate spatial autocorrelation in automated geocoding errors.
To assess the impact of this spatial autocorrelation on disease cluster detection power.
To discuss implications for geographic health data analysis and privacy.

Main Methods:

Analyzed positional errors from geocoding over 6000 addresses in Carroll County, Iowa.
Conducted two simulation studies on disease processes, one using the Carroll County data.
Evaluated the power of two disease clustering tests under correlated versus independent error scenarios.

Main Results:

Positional errors from automated geocoding in the study dataset were confirmed to be spatially autocorrelated.
Spatial autocorrelation of geocoding errors maintained higher power for disease cluster detection tests compared to independent errors.
Simulation results indicated that correlated errors offer a protective effect on statistical power.

Conclusions:

Spatial autocorrelation in geocoding errors is a significant factor in spatial epidemiologic studies.
This correlation can preserve the power of disease clustering detection, contrary to expectations for random errors.
Findings have implications for measurement error modeling, privacy, and enhancing spatial analytic methods in health research.