  In the presence of unmeasured spatial confounding, spatial models may
actually increase (rather than decrease) bias, leading to uncertainty as to how
they should be applied in practice. We evaluated spatial modeling approaches
through simulation and application to a big data electronic health record
study. Whereas the risk of bias was high for purely spatial exposures (e.g.,
built environment), we found very limited potential for increased bias for
individual-level exposures that cluster spatially (e.g., smoking status). We
also proposed a novel exposure-penalized spline approach that selects the
degree of spatial smoothing to explain spatial variability in the exposure.
This approach appeared promising for efficiently reducing spatial confounding
bias.
