Bias correction in estimation of public health risk attributable to short-term air pollution exposure

Abstract

Numerous epidemiologic studies have reported associations between short-term air pollution exposure and mortality. Such short-term risk models include smooth functions of time to control for unmeasured confounding variables. We demonstrate bias in these short-term Generalized Additive Model estimates because of lack of accounting for long timescale variations and propose a family of improved time smoothers to reduce and control the bias. The strengths of the proposed smoother are twofold: a clear separating of short-term and long-term effects and an obvious choice of smoothing parameters from pre-determined timescales of interest. We demonstrate improvements through simulations and analysis of examples of air pollution and mortality data from Chicago, Il. from the National Morbidity, Mortality and Air Pollution Study database, showing reduced bias in the risk estimates.