Abstract
The assignment of locational attributes to a study subject in epidemiologic analyses is commonly referred to as georeferencing. When georeferencing study subjects to a point location using their residential street address, most researchers rely on the street centerline data model. This study assessed the potential locational bias introduced using street centerline data. It also evaluated georeferencing effects on a location-dependent, exposure assessment process. For comparison purposes, subjects were georeferenced to the center of their residential parcel of land using digitized parcel maps. A total of 10,026 study subjects residing in Jefferson County, Alabama were georeferenced using both street centerline and residential parcel methods. The mean nondirectional, linear distance between points georeferenced using both methods was 246 ft with a range of 11 to 13,260 ft. Correlation coefficients comparing differences in exposure estimates were generated for all 10,026 subjects. Coefficients increased as the geographic areas of analysis around study subjects increased, indicating the influence of nondifferential exposure misclassification.
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References
Braddock M, Lapidus G, Cromley E, Cromley R, Burke G, and Banco L, Using a geographic information system to understand child pedestrian injury, Am J Public Health (1994) 84: 1158–1161
Casper ML Barnett E Halverson JA Elmes GA Braham VE Majeed ZA Bloom AS and Stanley S, Women and heart disease: an atlas of racial and ethnic disparities in mortality. Office for Social Environmental and Health Research, West Virginia University, Morganstown, WV 1999
Clarke KC McLafferty SL and Tempalski BJ, On epidemiology and geographic information systems: a review and discussion of future directions, Emerg Infect Dis (1996) 2: 85–92
Croner CM Sperling J and Broome FR, Geographic information systems (GIS): new perspectives in understanding human health and environmental relationships, Stat Med (1996) 15: 1961–1977
Devesa SS Grauman DJ Blot WJ Pennello GA Hoover RN and Fraumeni JF, Atlas of cancer mortality in the United States, 1950–94. National Institutes of Health, National Cancer Institute 1999
English P, Neutra R, Scalf R, Sullivan M, Waller L, and Zhu L, Examining associations between childhood asthma and traffic flow using a geographic information system, Environ Health Perspect (1999) 107: 761–767
Glass GE Schwartz BS Morgan JM Johnson DT Noy PM and Israel E, Environmental risk factors for Lyme disease identified with geographic information systems, Am J Public Health (1995) 85: 944–948
Guo HR Chiang HS Hu H Lipsitz SR and Monson RR, Arsenic in drinking water and incidence of urinary cancers, Epidemiology (1997) 8: 545–550
Jacquez GM and Waller LA, The effect of uncertain locations on disease cluster statisticsIn: Mowrer H.T., and Congalton R.G. (Eds.), Quantifying Spatial Uncertainty in Natural Resources: Theory and Applications for GIS and Remote Sensing Arbor Press, Chelsea, MI 2000 pp. 53–64
Lewis-Michl EL Melius JM Kallenbach LR Ju CL Talbot TO Orr MF and Lauridsen PE, Breast cancer risk and residence near industry or traffic in Nassau and Suffolk counties, Long Island, New York, Arch Environ Health (1996) 51: 255–265
Marcus PM Savitz DA Millikan RC and Morgenstern H, Female breast cancer and trihalomethane levels in drinking water in North Carolina, Epidemiology (1998) 9: 156–160
Peek-Asa C Ramirez MR Shoaf K Seligson H and Kraus JF, GIS mapping of earthquake-related deaths and hospital admissions from the 1994 Northridge, California, earthquake, Ann Epidemiol (2000) 10: 5–13
Pickle LW Mungiole M Jones GK and White AA, Atlas of United States Mortality. National Center for Health Statistics, Hyattsville, MD 1996
Ray NF Thamer M Fadillioglu B and Gergen PJ, Race, income, urbanicity, and asthma hospitalization in California, Chest (1998) 113: 1277–1284
Schwartz GG Skinner HG and Duncan R, Solid waste and pancreatic cancer: an ecologic study in Florida, USA, Int J Epidemiol (1998) 27: 781–787
Scott M Cutter SL Menzel C Ji M and Wagner D, Spatial accuracy of the EPA's environmental hazards databases and their use in environmental equity analysis, Appl Geogr Stud (1997) 1: 45–61
Ward MH Nuckols JR Weigel SJ Maxwell SK Cantor KP and Miller RS, Identifying populations potentially exposed to agricultural pesticides using remote sensing and a geographic information system, Environ Health Perspect (2000) 108: 5–12
Acknowledgements
This research was supported in part by grant CA47888 from the National Cancer Institute. The lead author is indebted to Jefferson County Information Services for providing GIS facilities and technical guidance.
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DEARWENT, S., JACOBS, R. & HALBERT, J. Locational uncertainty in georeferencing public health datasets. J Expo Sci Environ Epidemiol 11, 329–334 (2001). https://doi.org/10.1038/sj.jea.7500173
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DOI: https://doi.org/10.1038/sj.jea.7500173
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