Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies.

ABSTRACT: The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been

SUBMITTER: Lee D 

PROVIDER: S-EPMC4975605 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature