Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines

Air pollution epidemiology studies are trending towards a multipollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted by 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 that bias from multipollutant 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 analyse the association of systolic blood pressure with PM2.5 and NO2 levels in the National Institute of Environmental Health Sciences Sister Study. We find that NO2 confounds the association of systolic blood pressure with PM2.5 levels and vice versa. Elevated systolic blood pressure was significantly associated with increased PM2.5 and decreased NO2 levels. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals.