Using imputation to provide location information for nongeocoded addresses.

<h4>Background</h4>The importance of geography as a source of variation in health research continues to receive sustained attention in the literature. The inclusion of geographic information in such research often begins by adding data to a map which is predicated by some knowledge of lo...

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Autores principales:	Frank C Curriero, Martin Kulldorff, Francis P Boscoe, Ann C Klassen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/defe0a68f5f7403b845f0a3a663523d8
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spelling	oai:doaj.org-article:defe0a68f5f7403b845f0a3a663523d82021-11-25T06:25:58ZUsing imputation to provide location information for nongeocoded addresses.1932-620310.1371/journal.pone.0008998https://doaj.org/article/defe0a68f5f7403b845f0a3a663523d82010-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20161766/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>The importance of geography as a source of variation in health research continues to receive sustained attention in the literature. The inclusion of geographic information in such research often begins by adding data to a map which is predicated by some knowledge of location. A precise level of spatial information is conventionally achieved through geocoding, the geographic information system (GIS) process of translating mailing address information to coordinates on a map. The geocoding process is not without its limitations, though, since there is always a percentage of addresses which cannot be converted successfully (nongeocodable). This raises concerns regarding bias since traditionally the practice has been to exclude nongeocoded data records from analysis.<h4>Methodology/principal findings</h4>In this manuscript we develop and evaluate a set of imputation strategies for dealing with missing spatial information from nongeocoded addresses. The strategies are developed assuming a known zip code with increasing use of collateral information, namely the spatial distribution of the population at risk. Strategies are evaluated using prostate cancer data obtained from the Maryland Cancer Registry. We consider total case enumerations at the Census county, tract, and block group level as the outcome of interest when applying and evaluating the methods. Multiple imputation is used to provide estimated total case counts based on complete data (geocodes plus imputed nongeocodes) with a measure of uncertainty. Results indicate that the imputation strategy based on using available population-based age, gender, and race information performed the best overall at the county, tract, and block group levels.<h4>Conclusions/significance</h4>The procedure allows for the potentially biased and likely under reported outcome, case enumerations based on only the geocoded records, to be presented with a statistically adjusted count (imputed count) with a measure of uncertainty that are based on all the case data, the geocodes and imputed nongeocodes. Similar strategies can be applied in other analysis settings.Frank C CurrieroMartin KulldorffFrancis P BoscoeAnn C KlassenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 2, p e8998 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Frank C Curriero Martin Kulldorff Francis P Boscoe Ann C Klassen Using imputation to provide location information for nongeocoded addresses.
description	<h4>Background</h4>The importance of geography as a source of variation in health research continues to receive sustained attention in the literature. The inclusion of geographic information in such research often begins by adding data to a map which is predicated by some knowledge of location. A precise level of spatial information is conventionally achieved through geocoding, the geographic information system (GIS) process of translating mailing address information to coordinates on a map. The geocoding process is not without its limitations, though, since there is always a percentage of addresses which cannot be converted successfully (nongeocodable). This raises concerns regarding bias since traditionally the practice has been to exclude nongeocoded data records from analysis.<h4>Methodology/principal findings</h4>In this manuscript we develop and evaluate a set of imputation strategies for dealing with missing spatial information from nongeocoded addresses. The strategies are developed assuming a known zip code with increasing use of collateral information, namely the spatial distribution of the population at risk. Strategies are evaluated using prostate cancer data obtained from the Maryland Cancer Registry. We consider total case enumerations at the Census county, tract, and block group level as the outcome of interest when applying and evaluating the methods. Multiple imputation is used to provide estimated total case counts based on complete data (geocodes plus imputed nongeocodes) with a measure of uncertainty. Results indicate that the imputation strategy based on using available population-based age, gender, and race information performed the best overall at the county, tract, and block group levels.<h4>Conclusions/significance</h4>The procedure allows for the potentially biased and likely under reported outcome, case enumerations based on only the geocoded records, to be presented with a statistically adjusted count (imputed count) with a measure of uncertainty that are based on all the case data, the geocodes and imputed nongeocodes. Similar strategies can be applied in other analysis settings.
format	article
author	Frank C Curriero Martin Kulldorff Francis P Boscoe Ann C Klassen
author_facet	Frank C Curriero Martin Kulldorff Francis P Boscoe Ann C Klassen
author_sort	Frank C Curriero
title	Using imputation to provide location information for nongeocoded addresses.
title_short	Using imputation to provide location information for nongeocoded addresses.
title_full	Using imputation to provide location information for nongeocoded addresses.
title_fullStr	Using imputation to provide location information for nongeocoded addresses.
title_full_unstemmed	Using imputation to provide location information for nongeocoded addresses.
title_sort	using imputation to provide location information for nongeocoded addresses.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/defe0a68f5f7403b845f0a3a663523d8
work_keys_str_mv	AT frankccurriero usingimputationtoprovidelocationinformationfornongeocodedaddresses AT martinkulldorff usingimputationtoprovidelocationinformationfornongeocodedaddresses AT francispboscoe usingimputationtoprovidelocationinformationfornongeocodedaddresses AT anncklassen usingimputationtoprovidelocationinformationfornongeocodedaddresses
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Using imputation to provide location information for nongeocoded addresses.

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