Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes
Open accessAbstract
Background: There is increasing interest in the study of place effects on health, facilitated in part by geographic information systems. Incomplete or missing address information reduces geocoding success. Several geographic imputation methods have been suggested to overcome this limitation. Accuracy evaluation of these methods can be focused at the level of individuals and at higher group-levels (e.g., spatial distribution). Methods: We evaluated the accuracy of eight geo-imputation methods for address allocation from ZIP codes to census tracts at the individual and group level. The spatial apportioning approaches underlying the imputation methods included four fixed (deterministic) and four random (stochastic) allocation methods using land area, total population, population under age 20, and race/ethnicity as weighting factors. Data included more than 2,000 geocoded cases of diabetes mellitus among youth aged 0-19 in four U.S. regions. The imputed distribution of cases across tracts was compared to the true distribution using a chi-squared statistic. Results: At the individual level, population-weighted (total or under age 20) fixed allocation showed the greatest level of accuracy, with correct census tract assignments averaging 30.01% across all regions, followed by the race/ethnicity-weighted random method (23.83%). The true distribution of cases across census tracts was that 58.2% of tracts exhibited no cases, 26.2% had one case, 9.5% had two cases, and less than 3% had three or more. This distribution was best captured by random allocation methods, with no significant differences (p-value > 0.90). However, significant differences in distributions based on fixed allocation methods were found (p-value < 0.0003). Conclusion: Fixed imputation methods seemed to yield greatest accuracy at the individual level, suggesting use for studies on area-level environmental exposures. Fixed methods result in artificial clusters in single census tracts. For studies focusing on spatial distribution of disease, random methods seemed superior, as they most closely replicated the true spatial distribution. When selecting an imputation approach, researchers should consider carefully the study aims.
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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 [70]
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- Authors
- Hibbert, J. D., Liese, A. D., Lawson, A., Porter, D. E., Puett, R. C., Standiford, D., Liu, L., & Dabelea, D.
- Affiliations
- University of South Carolina (US)
- Medical University of South Carolina (US)
- Cincinnati Children's Hospital Medical Center (US)
- University of Washington (US)
- University of Colorado Denver (US)
- Corresponding author
- Liese, Angela D
- Venue
- International Journal of Health Geographics
- Published
- 2009-10-08
- Volume
- 8
- Issue
- 1
- Pages
- 54-54
- ISSN
- 1476-072X (ISSN-L)
- PMC
- PMC2763852
- OpenAlex
- W2032303483
- Cited by
- 40
- References
- 31
- Citations by year
- 2025: 2 · 2024: 1 · 2022: 3 · 2021: 4 · 2020: 3 · 2019: 4 · 2018: 4 · 2017: 1 · 2016: 1 · 2014: 2 · 2013: 6 · 2012: 4
- FWCI
- 6.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Health Geographics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK077131)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- geocoding, pediatric diabetes, youth-onset diabetes, spatial distribution of disease
- MeSH terms
- Adolescent, Child, Child, Preschool, Data Collection, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Humans, Infant, Infant, Newborn, Postal Service, Stochastic Processes, United States, Reproducibility of Results, Cluster Analysis, Chi-Square Distribution, Geographic Information Systems, Young Adult
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