Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes.
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).


CONCLUSIONS
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.
Hibbert
James D
JD
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA. hibbert@sc.edu
Liese
Angela D
AD
Lawson
Andrew
A
Porter
Dwayne E
DE
Puett
Robin C
RC
Standiford
Debra
D
Liu
Lenna
L
Dabelea
Dana
D
D000293:Adolescent; D016009:Chi-Square Distribution; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003625:Data Collection / Q000592:standards*; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology*; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D040362:Geographic Information Systems / Q000592:standards*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D011177:Postal Service / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D013269:Stochastic Processes; D014481:United States / Q000453:epidemiology; D055815:Young Adult