Researchers are increasingly employing geocoding to identify precise locations of study
participants'' previous residences to identify historical and long term trends in disease,
and assess retrospective environmental exposures, particularly in case-control studies.
However, there is a substantial risk of misclassification of exposure when using older
addresses records, as past redevelopment, and readdressing can alter or remove
addresses, rendering them unable to be located (or located precisely) in current datasets.
Furthermore, evidence suggests that demographic characteristics, notably minority
race/ethnicity and low income, are associated with poor geocoding precision. It is possible
that such socioeconomic disparities in geocoding are magnified or otherwise influenced
by changes in addressing reference data over time. These communities are also at higher
risk for environmental exposures, as minority and low-income communities have
historically been forced into areas adjacent to industrial activity or near nuisance sites,
such as quarries, landfills, or major roadways and related infrastructure. To address the
existing limitations and assess disparities with historical geocoding, we propose a pilot
study that will identify geographic areas where standard geocoding protocols are less
likely to locate individuals precisely, assess demographic characteristics associated with
geocoding precision, and improve geocoding by building and testing a geocoding protocol
to evaluate improved locational precision and environment exposure assessment.