Abstract
BackgroundThere 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).MethodsWe 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.ResultsAt 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).ConclusionFixed 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.
Highlights
There is increasing interest in the study of place effects on health, facilitated in part by geographic information systems
South Carolina had a markedly higher number of addresses with PO Box or RR designations. Both the Ohio and Colorado sites had the overall highest proportion of successfully geocoded addresses (CO = 86.4%, OH = 89.5%) The geocoding success rate was highly consistent across sites ranging from 92% in Colorado, 97% Ohio, 88% in South Carolina, and 98% in Washington
Each of the eight allocation methods were applied to the site-specific data assuming that the only available piece of address information available was a ZIP code and compared with the known, true location
Summary
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). The advent of geographic information system (GIS) technology and its widespread dissemination has enormously simplified the identification and characterization of place via address match geocoding, i.e. the assignment of geographic coordinates to a street address through interpolation based on a proportional distance between addresses in a record and an address range for a street segment [5]. Successful address match geocoding relies, in part, on the availability of complete and correct address information [2].
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