Accounting for spatial confounding in epidemiological studies with individual-level exposures: An exposure-penalized spline approach

TL;DR

This paper evaluates spatial modeling in epidemiology, highlighting when bias increases and introducing an exposure-penalized spline method to reduce spatial confounding bias effectively.

Contribution

It proposes a novel exposure-penalized spline approach that adaptively selects spatial smoothing to mitigate confounding bias in epidemiological studies.

Findings

Limited bias increase for individual-level exposures with spatial clustering

The proposed spline method effectively reduces spatial confounding bias

Spatial models may not always increase bias for certain exposures

Abstract

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.