TAILIEUCHUNG - IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY

In 2002, methodological issues around time series analyses of air pollution and health attracted the attention of the scienti¯c community, policy makers, the press, and the diverse stakeholders con- cerned with air pollution. As the Environmental Protection Agency (EPA) was ¯nalizing its most recent review of epidemiological evidence on particulate matter air pollution (PM), statisticians and epidemiologists found that the S-Plus implementation of Generalized Additive Models (GAM) can overestimate e®ects of air pollution and understate statistical uncertainty in time series studies of air pollution and health. This discovery delayed the completion of the PM Criteria Document prepared as part of the review of the . National Ambient. | IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY Francesca Dominici Aidan McDermott Trevor J. Hastie May 16 2004 Abstract In 2002 methodological issues around time series analyses of air pollution and health attracted the attention of the scientific community policy makers the press and the diverse stakeholders concerned with air pollution. As the Environmental Protection Agency EPA was finalizing its most recent review of epidemiological evidence on particulate matter air pollution PM statisticians and epidemiologists found that the S-Plus implementation of Generalized Additive Models GAM can overestimate effects of air pollution and understate statistical uncertainty in time series studies of air pollution and health. This discovery delayed the completion of the PM Criteria Document prepared as part of the review of the . National Ambient Air Quality Standard NAAQS as the time-series findings were a critical component of the evidence. In addition it raised concerns about the adequacy of current model formulations and their software implementations. In this paper we provide improvements in semi-parametric regression directly relevant to risk estimation in time series studies of air pollution. First we introduce a closed form estimate of the asymptotically exact covariance matrix of the linear component of a GAM. To ease the implementation of these calculations we develop the S package an extended version of gam. Use of allows a more robust assessment of the statistical uncertainty of the estimated pollution coefficients. Second we develop a bandwidth selection method to reduce confounding bias in the pollution-mortality relationship due to unmeasured time-varying factors such as season and influenza epidemics. Third we introduce a conceptual framework to fully explore the sensitivity 1 of the air pollution risk estimates to model choice. We apply our methods to data of the National Mortality Morbidity Air Pollution

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