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

Dale L Zimmerman; Jie Li; Xiangming Fang

doi:10.1002/sim.3836

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

Journal article

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Peer reviewed

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

Dale L Zimmerman, Jie Li and Xiangming Fang

Statistics in medicine, Vol.29(9), pp.1025-1036

01/19/2010

DOI: 10.1002/sim.3836

PMCID: PMC2888969

PMID: 20087879

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https://www.ncbi.nlm.nih.gov/pmc/articles/2888969View

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Abstract

Automated geocoding of patient addresses is an important data assimilation component of many spatial epidemiologic studies. Inevitably, the geocoding process results in positional errors. Positional errors incurred by automated geocoding tend to reduce the power of tests for disease clustering and otherwise affect spatial analytic methods. However, there are reasons to believe that the errors may often be positively spatially correlated and that this may mitigate their deleterious effects on spatial analyses. In this article, we demonstrate explicitly that the positional errors associated with automated geocoding of a dataset of more than 6000 addresses in Carroll County, Iowa are spatially autocorrelated. Furthermore, through two simulation studies of disease processes, including one in which the disease process is overlain upon the Carroll County addresses, we show that spatial autocorrelation among geocoding errors maintains the power of two tests for disease clustering at a level higher than that which would occur if the errors were independent. Implications of these results for cluster detection, privacy protection, and measurement-error modeling of geographic health data are discussed.

Details

Title: Subtitle: Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering
Creators: Dale L Zimmerman - University of Iowa
Jie Li - University of Iowa
Xiangming Fang - East Carolina University
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.29(9), pp.1025-1036
DOI: 10.1002/sim.3836
PMID: 20087879
PMCID: PMC2888969
NLM abbreviation: Stat Med
ISSN: 0277-6715
eISSN: 1097-0258
Grant note: DOI: 10.13039/100000054, name: National Cancer Institute, award: N01-PC-35143; DOI: 10.13039/100000002, name: National Institutes of Health; DOI: 10.13039/100000016, name: U.S. Department of Health and Human Services
Language: English
Date published: 01/19/2010
Academic Unit: Statistics and Actuarial Science; Biostatistics
Record Identifier: 9984257738602771

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