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Cited by 27Global Cancer Incidence and ScreeningStatistical Methods and Bayesian InferenceStatistical Methods in Epidemiology

Using imputation to provide location information for nongeocoded addresses

Open access
Curriero, Frank C., Kulldorff, Martin, Boscoe, Francis P., Klassen, Ann C.·2010·PLoS ONE
DOI 10.1371/journal.pone.0008998PMC PMC2818716
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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.

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  • Several studies looked at the use of imputation to increase the completeness of datasets and avoid casewise deletion.

    “Several studies looked at the use of imputation, also referred to as indirect estimation, to increase the completeness of datasets and avoid casewise deletion.14,22,30,45,46,50,61,65,67–70”

    From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [67]

    The Introduction (page unavailable) says the paper develops imputation strategies for nongeocoded addresses so analysis can use all case records. The Discussion (page unavailable) describes completing the data with geocoded and imputed records rather than excluding nongeocoded cases. Our copy does not identify imputation as “indirect estimation,” so that part of the attributed sentence is unsupported. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Most of these studies examined methods for imputing race/ethnicity data, although several looked at imputing geocoded patient addresses, and one looked at imputing occupational data.

    “Most of these studies examined methods for imputing race/ethnicity data, although several looked at imputing geocoded patient addresses,67,69,70 and one looked at imputing occupational data.61”

    From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [67]

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References 54 · Cited by 27

Research works printed but not linked: 40

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Authors
Curriero, F. C., Kulldorff, M., Boscoe, F. P., & Klassen, A. C.
Affiliations
  • Johns Hopkins University (US)
  • Harvard University (US)
  • New York State Department of Health (US)
Corresponding author
Curriero, Frank C.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2010-02-10
Volume
5
Issue
2
Pages
e8998
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0008998
PMC
PMC2818716
OpenAlex
W2145058211
Cited by
27
References
54
Citations by year
2026: 1 · 2025: 2 · 2023: 4 · 2021: 3 · 2020: 1 · 2019: 3 · 2018: 3 · 2017: 3 · 2016: 1 · 2015: 1 · 2013: 2 · 2012: 3
FWCI
0.79
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Defense
  • New York City Department of Health and Mental Hygiene
  • Centers for Disease Control and Prevention
  • National Cancer Institute (R21CA124921)
Topics
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
geocoding, multiple imputation, prostate cancer incidence, cancer registry data, vulnerable populations, spatial bias
MeSH terms
Data Collection, Geography, Health Services, Humans, Male, Maryland, Postal Service, Prostatic Neoplasms, Registries, Reproducibility of Results, Geographic Information Systems

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