Terashima M, Kephart G. Misclassification errors from postal code-based geocoding to assign census geography in Nova Scotia, Canada.
Can J Public Health 2016;
107:e424-e430. [PMID:
28026709 PMCID:
PMC6972365 DOI:
10.17269/cjph.107.5459]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 08/28/2016] [Accepted: 07/03/2016] [Indexed: 11/17/2022]
Abstract
OBJECTIVES
Postal codes are often the only available geographic identifiers in many sources of health data in Canada. In order to conduct geographic analyses, postal codes are routinely geocoded to census geography to link to ecological data. Despite common use of this method, the extent of geographic misclassification errors is poorly understood. We estimated misclassification errors in the geocoding of postal codes to assign census geography in Nova Scotia, Canada.
METHODS
We examined differences between counts and match rates for postal-code geocoded and actual locations of buildings in Nova Scotia at two census administrative area levels: dissemination areas (DAs) and census subdivisions (CSDs). Actual locations were based on the data collected by the provincial government containing actual latitude/longitude of buildings. Variation in misclassification by rurality, using Statistics Canada's classification, was also assessed.
RESULTS
Outside two urban areas (Halifax Metro and Sydney) which had <10% differences in counts, many DAs had >30% differences. Match rates showed similar patterns, with the vast majority of non-urban DAs having <40% match rates. Even in major urban areas, 10% of DAs had large misclassification errors. Misclassification errors at the CSD level were still too great to estimate counts or rates without further area aggregation.
CONCLUSION
Routine use of postal code geocoding should be replaced with geocoding of location information using additional identifiers such as civic addresses or latitude and longitude. If data holders did this in-house before providing data to researchers, the accuracy and capacity of geographic analysis would be enhanced while protecting confidentiality.
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