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Geographic bias related to geocoding in epidemiologic studies
M Norman Oliver1, Kevin A Matthews, Mir Siadaty
1Department of Family Medicine, University of Virginia, Charlottesville, VA, USA. mno3p@virginia.edu
International Journal of Health Geographics
|November 12, 2005
Summary
Geographic bias in spatial analysis can occur due to missing geocodes. This study found that missing data in Virginia prostate cancer cases were concentrated in rural areas, impacting cluster mapping and leading to "cartographic confounding."
Area of Science:
- Geographic Information Systems (GIS)
- Spatial Epidemiology
- Biostatistics
Background:
- Geographic Information Systems (GIS) analyses can be subject to geographic bias when using incomplete or unrepresentative data.
- Missing geocodes in health data, such as prostate cancer incidence, can lead to skewed spatial analyses.
- This study examines geographic bias in a spatial analysis of prostate cancer incidence in Virginia (1990-1999).
Purpose of the Study:
- To describe and quantify geographic bias in GIS analyses stemming from missing geocodes.
- To investigate the spatial patterns of missing geocodes in prostate cancer cases in Virginia.
- To identify factors associated with missing geocodes in health data.
Main Methods:
- Spatial analysis of prostate cancer incidence among white and African American populations in Virginia.
- Statistical tests for spatial clustering and mapping of identified clusters.
- Generalized linear regression models to examine patterns of missing census tract identifiers.
Main Results:
- Of 26,338 prostate cancer cases, 74% were successfully geocoded to census tracts.
- Cluster maps displayed markedly different patterns based on the inclusion of all cases versus only geocoded cases.
- Multivariate regression revealed that higher percentages of elderly populations (over 64) and individuals with less than a high school education in rural counties were independently associated with a higher percentage of missing geocodes.
Conclusions:
- Statistically significant differences in spatial patterns were observed due to non-random variations in geocoding completeness across Virginia.
- The phenomenon of "cartographic confounding" arises from spatially non-random differences in data completeness.
- Accurate interpretation of spatial maps derived from health data necessitates understanding and accounting for potential "cartographic confounding."
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