title: Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines.
abstract: 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 PM2.5and NO2in the NIEHS Sister Study. We find that NO2confounds the association of systolic blood pressure with PM2.5and vice versa. Elevated systolic blood pressure was significantly associated with increased PM2.5and decreased NO2. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals.
authors: Winona State University, Winona, MN.
authors: Silas
authors: S
authors: Bergen
authors: University of Washington, Seattle, WA.
authors: Lianne
authors: L
authors: Sheppard
authors: University of Washington, Seattle, WA.
authors: Joel D
authors: JD
authors: Kaufman
authors: University of Washington, Seattle, WA.
authors: Adam A
authors: AA
authors: Szpiro