Using Imputation to Provide Location Information for Nongeocoded Addresses

DSpace/Manakin Repository

Using Imputation to Provide Location Information for Nongeocoded Addresses

Citable link to this page

. . . . . .

Title: Using Imputation to Provide Location Information for Nongeocoded Addresses
Author: Curriero, Frank C.; Boscoe, Francis P.; Klassen, Ann C.; Kulldorff, Martin

Note: Order does not necessarily reflect citation order of authors.

Citation: Curriero, Frank C., Martin Kulldorff, Francis P. Boscoe, and Ann C. Klassen. 2010. Using imputation to provide location information for nongeocoded addresses. PLoS ONE 5(2): e8998.
Full Text & Related Files:
Abstract: Background: 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. Methodology/Principal: Findings 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. Conclusions/Significance: 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.
Published Version: doi:10.1371/journal.pone.0008998
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2818716/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4741829

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters