Spatial clustering of the failure to geocode and its implications for the detection of disease clustering

Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for s...

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Vydané v:	Statistics in medicine Ročník 27; číslo 21; s. 4254 - 4266
Hlavní autori:	Zimmerman, Dale L., Fang, Xiangming, Mazumdar, Soumya
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Ltd 20.09.2008 Wiley Subscription Services, Inc
Predmet:	Algorithms Cluster Analysis Computer Simulation Disease disease clustering Epidemiologic Methods Epidemiology geocoding Geographic Information Systems Interpolation Simulation spatial cluster detection Carroll County Iowa
ISSN:	0277-6715, 1097-0258
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Abstract	Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground‐truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and geocoding failure are positively associated. Copyright © 2008 John Wiley & Sons, Ltd.
AbstractList	Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground-truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and geocoding failure are positively associated. Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground-truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and geocoding failure are positively associated.Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground-truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and geocoding failure are positively associated. Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground‐truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and geocoding failure are positively associated. Copyright © 2008 John Wiley & Sons, Ltd. Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground-truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and geocoding failure are positively associated. [PUBLICATION ABSTRACT]
Author	Zimmerman, Dale L. Mazumdar, Soumya Fang, Xiangming
Author_xml	– sequence: 1 givenname: Dale L. surname: Zimmerman fullname: Zimmerman, Dale L. email: dale-zimmerman@uiowa.edu organization: Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, U.S.A – sequence: 2 givenname: Xiangming surname: Fang fullname: Fang, Xiangming organization: Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, U.S.A – sequence: 3 givenname: Soumya surname: Mazumdar fullname: Mazumdar, Soumya organization: Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, U.S.A
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References	Duczmal L, Kulldorff M, Huang L. Evaluation of spatial scan statistics for irregularly shaped clusters. Journal of Computational and Graphical Statistics 2006; 15:428-442. Kravets N, Hadden WC. The accuracy of address coding and the effects of coding errors. Health and Place 2007; 13:293-298. Cuzick J, Edwards R. Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society, Series B 1990; 52:73-104 (with Discussion). Zimmerman DL. Estimating the intensity of a spatial point process from locations coarsened by incomplete geocoding. Biometrics 2008; 64:262-270. Zimmerman DL, Fang X, Mazumdar S, Rushton G. Modelling the probability distribution of positional errors incurred by residential address geocoding. International Journal of Health Geographics 2007; 6:1. Waller LA, Gotway CA. Applied Spatial Statistics for Public Health Data. Wiley: Hoboken, NJ, 2004. Waller LA, Hill EG, Rudd RA. The geography of power: statistical performance of tests of clusters and clustering in heterogeneous populations. Statistics in Medicine 2006; 25:853-865. Jacquez GM. Cuzick and Edwards' test when exact locations are unknown. American Journal of Epidemiology 1994; 140:58-64. Klassen AC, Curreiro FC, Hong JH, Williams C, Kulldorff M, Meissner HI, Alberg A, Ensminger M. The role of area-level influences on prostate cancer grade and stage at diagnosis. Preventive Medicine 2004; 39:441-448. Assuncao R, Costa M, Tavares A, Ferreira S. Fast detection of arbitrarily shaped disease clusters. Statistics in Medicine 2006; 25:723-742. Burra T, Jerrett M, Burnett RT, Anderson M. Conceptual and practical issues in the detection of local disease clusters: a study of mortality in Hamilton, Ontario. Canadian Geographer 2002; 46:160-171. Waller LA. Statistical power and design of focused clustering studies. Statistics in Medicine 1996; 15:765-782. 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References_xml	– reference: Jacquez GM. Cuzick and Edwards' test when exact locations are unknown. American Journal of Epidemiology 1994; 140:58-64. – reference: Oliver MN, Matthews KA, Siadaty M, Hauck FR, Pickle LW. Geographic bias related to geocoding in epidemiologic studies. International Journal of Health Geographics 2005; 4:29. – reference: Kravets N, Hadden WC. The accuracy of address coding and the effects of coding errors. Health and Place 2007; 13:293-298. – reference: Gregorio KK, Cromley E, Mrozinski R, Walsh SJ. Subject loss in spatial analysis of breast cancer. Health and Place 1999; 5:173-177. – reference: Duczmal L, Kulldorff M, Huang L. Evaluation of spatial scan statistics for irregularly shaped clusters. Journal of Computational and Graphical Statistics 2006; 15:428-442. – reference: Zimmerman DL, Fang X, Mazumdar S, Rushton G. Modelling the probability distribution of positional errors incurred by residential address geocoding. International Journal of Health Geographics 2007; 6:1. – reference: Zimmerman DL. Estimating the intensity of a spatial point process from locations coarsened by incomplete geocoding. Biometrics 2008; 64:262-270. – reference: Waller LA, Hill EG, Rudd RA. The geography of power: statistical performance of tests of clusters and clustering in heterogeneous populations. Statistics in Medicine 2006; 25:853-865. – reference: Assuncao R, Costa M, Tavares A, Ferreira S. Fast detection of arbitrarily shaped disease clusters. Statistics in Medicine 2006; 25:723-742. – reference: Patil GP, Taillie C. Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environmental and Ecological Statistics 2004; 11:183-197. – reference: Tango T, Takahashi K. A flexibly shaped spatial scan statistic for detecting clusters. International Journal of Health Geographics 2005; 4:11. – reference: Ward MH, Nuckols JR, Giglierano J, Bonner MR, Wolter C, Airola M, Mix W, Colt JS, Hartge P. Positional accuracy of two methods of geocoding. Epidemiology 2005; 16:542-547. – reference: Burra T, Jerrett M, Burnett RT, Anderson M. Conceptual and practical issues in the detection of local disease clusters: a study of mortality in Hamilton, Ontario. Canadian Geographer 2002; 46:160-171. – reference: Waller LA. Statistical power and design of focused clustering studies. Statistics in Medicine 1996; 15:765-782. – reference: Gilboa SM, Mendola P, Olshan AF, Harness C, Loomis D, Langlois PH, Savitz DA, Herring AH. Comparison of residential geocoding methods in population-based study of air quality and birth defects. Environmental Research 2006; 101:256-262. – reference: Waller LA, Gotway CA. Applied Spatial Statistics for Public Health Data. Wiley: Hoboken, NJ, 2004. – reference: Bonner MR, Han D, Nie J, Rogerson P, Vena JE, Freudenheim JL. Positional accuracy of geocoded addresses in epidemiologic research. Epidemiology 2003; 14:408-412. – reference: McElroy JA, Remington PL, Trentham-Dietz A, Robert SA, Newcomb PA. Geocoding addresses from a large population-based study: lessons learned. Epidemiology 2003; 14:399-407. – reference: Cuzick J, Edwards R. Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society, Series B 1990; 52:73-104 (with Discussion). – reference: Klassen AC, Curreiro FC, Hong JH, Williams C, Kulldorff M, Meissner HI, Alberg A, Ensminger M. The role of area-level influences on prostate cancer grade and stage at diagnosis. Preventive Medicine 2004; 39:441-448. – reference: Sweeney SH, Konty KJ. Robust point-pattern inference from spatially censored data. Environment and Planning A 2005; 37:141-159. – reference: Boscoe FP, Kielb CL, Schymura MJ, Bolani TM. Assessing and improving census tract completeness. Journal of Registry Management 2002; 29:117-120. – reference: Kulldorff M, Huang L, Pickle L, Duczmal L. 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SubjectTerms	Algorithms Cluster Analysis Computer Simulation Disease disease clustering Epidemiologic Methods Epidemiology geocoding Geographic Information Systems Interpolation Simulation spatial cluster detection
Title	Spatial clustering of the failure to geocode and its implications for the detection of disease clustering
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