Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Abstract

Using geographic information systems to link administrative databases with demographic, social, and environmental data allows researchers to use spatial approaches to explore relationships between exposures and health. Traditionally, spatial analysis in public health has focused on the county, zip code, or tract level because of limitations to geocoding at highly resolved scales. Using 2005 birth and death data from North Carolina, we examine our ability to geocode population-level datasets at three spatial resolutions – zip code, street, and parcel. We achieve high geocoding rates at all three resolutions, with statewide street geocoding rates of 88.0% for births and 93.2% for deaths. We observe differences in geocoding rates across demographics and health outcomes, with lower geocoding rates in disadvantaged populations and the most dramatic differences occurring across the urban-rural spectrum. Our results suggest highly resolved spatial data architectures for population-level datasets are viable through geocoding individual street addresses. We recommend routinely geocoding administrative datasets to the highest spatial resolution feasible, allowing public health researchers to choose the spatial resolution used in analysis based on an understanding of the spatial dimensions of the health outcomes and exposures being investigated. Such research, however, must acknowledge how disparate geocoding success across subpopulations may affect findings.

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

“…Each birth certificate address was coded with longitude and latitude coordinates by matching home addresses against the county's master address file and then, using ArcGIS software, aggregated to the corresponding census tract. We achieved a 98% match rate, which is consistent with the literature [44,45]. We excluded 2% of the final geocoded addresses based on being outside Mecklenburg County.…”

Potential selection bias associated with using geocoded birth records for epidemiologic research

“…The successful geocoding rate of 88.5% is comparable to other studies that involve geocoding birth certificate data (27, 28). However, a significantly lower proportion of successful geocoded addresses among the rural population was not optimal.…”

Geographic Information Systems to Assess External Validity in Randomized Trials

“…28 There will always be addresses that cannot be geocoded. Edwards and colleagues 50 found geocoding rates were lower in rural areas and among racial and ethnic groups. Misclassification error of assigning diabetes as cause of death may have affected the density mapping.…”

“…Ninety-eight percent of diabetes-related deaths and 93% of EMR patients were geocoded; these rates were consistent with typical rates >90% 26 and reports of geocoding similar databases. 28,50 The 2005 North Carolina death certificate registry was successfully geocoded in 93% of cases of all deaths and 92% of deaths from diabetes-related causes 50 and 88% of 9,700 patients with Type 2 diabetes from the University of California Davis Health System EMR system were successfully geocoded. 28 There will always be addresses that cannot be geocoded.…”