A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes.
To enable spatial analyses within a large, prospective cohort study of nearly 86,000 adults enrolled in a 12-state area in the southeastern United States of America from 2002-2009, a multi-stage geocoding protocol was developed to efficiently maximize the proportion of participants assigned an address level geographic coordinate. Addresses were parsed, cleaned and standardized before applying a combination of automated and interactive geocoding tools. Our full protocol increased the non-Post Office (PO) Box match rate from 74.5% to 97.6%. Overall, we geocoded 99.96% of participant addresses, with only 5.2% at the ZIP code centroid level (2.8% PO Box and 2.3% non-PO Box addresses). One key to reducing the need for interactive geocoding was the use of multiple base maps. Still, addresses in areas with population density <44 persons/km2 were much more likely to require resource-intensive interactive geocoding than those in areas with >920 persons/km2 (odds ratio (OR) = 5.24; 95% confidence interval (CI) = 4.23, 6.49), as were addresses collected from participants during in-person interviews compared with mailed questionnaires (OR = 1.83; 95% CI = 1.59, 2.11). This study demonstrates that population density and address ascertainment method can influence automated geocoding results and that high success in address level geocoding is achievable for large-scale studies covering wide geographical areas.
International Epidemiology Institute, Rockville, MD 20850, USA. Jennifer@iei.us
Jennifer S
JS
Sonderman
Michael T
MT
Mumma
Sarah S
SS
Cohen
Elizabeth L
EL
Cope
William J
WJ
Blot
Lisa B
LB
Signorello
D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D016015:Logistic Models; D008297:Male; D016017:Odds Ratio; D011156:Population Density; D011446:Prospective Studies; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D014481:United States