||7R01CA195218-03 Interpret this number
||University Of Colorado Denver
||Innovative Solutions to Spatial Uncertainty in Geocoding
The development of efficient spatial methods and the widespread availability of spatially
referenced data have changed the landscape of exposure assessment in disease
etiology. Most spatial-based exposure-disease assessment approaches require
knowing where study participants are located in space and time, and linking that
information to spatially-referenced data that estimate potential for exposure.
Such approaches not only allow for estimation of both current and past
exposures, but are efficient alternatives to traditional methods of collecting
exposure data longitudinally, enhancing the utility of existing large cohorts by reverse
engineering exposures when improved exposure surfaces become available.
However, while the spatial resolution of exposure surfaces has greatly improved,
our ability to locate people in space (with geocoding) has not, and remains a rate-
limiting factor in accurate exposure assessment. The effort engaged in improving
spatially referenced exposure data is compromised without addressing the problem
of misclassification of the location of people (geocoding uncertainty).
We have developed a geocoding approach that records the exact spatial extent
of the final geocode that fully describes the area in which the study participant is
known to be located, and a novel statistical approach to incorporate variability in
exposure and covariate data based on spatial extent. These combined approaches can
be extended to appropriately incorporate spatial uncertainty from geocoding
misclassification into the overall exposure assessment model.
We aim to test the inclusion of geocode uncertainty into an exposure assessment
model, and then apply that approach in a study of the role of pesticides in childhood
leukemia ? an example which has a high resolution exposure surface, high
variability in geocode accuracy, and is an excellent example of some of the worst
case scenarios in assuming uniform spatial certainty of geocodes. We will ensure
wide dissemination of the approach as a global solution to the problem of misclassified
study participant geolocation.
NFE2L2, PPARGC1?, and pesticides and Parkinson's disease risk and progression.
, Sinsheimer J.S.
, Cockburn M.
, Bronstein J.M.
, Bordelon Y.
, Ritz B.
Mechanisms Of Ageing And Development, 2018 Jul; 173, p. 1-8.