Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with...

Air pollution epidemiology studies are trending towards a multi-pollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize this measurement error and develop an analytic bias correction when using penalized regression splines to predict exposure. Our simulations show bias from multi-pollutant measurement error can be severe, and in opposite directions or simultaneously positive or negative. Our analytic bias correction combined with a non-parametric bootstrap yields accurate coverage of 95% confidence intervals. We apply our methodology to analyze the association of systolic blood pressure with PM_2.5 and NO₂ in the NIEHS Sister Study. We find that NO₂ confounds the association of systolic blood pressure with PM_2.5 and vice versa. Elevated systolic blood pressure was significantly associated with increased PM_2.5 and decreased NO₂. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals.