TAILIEUCHUNG - Model choice in time series studies of air pollution and mortality

Quantifying the magnitude of those impacts in cities worldwide, however, presents considerable challenges due to limited information on both health effects and air pollution exposures in many parts of the world. Man-made outdoor air pollution in the world’s cities, derived largely from combustion processes, is a complex mixture with many toxic components. We indexed this mixture in terms of particulate matter (PM), a component that has been consistently linked with serious health effects and, importantly, that can be estimated worldwide. Exposure to PM has been associated with a wide range of health effects, but its effects on mortality are arguably the most important, and are also most amenable. | J. R. Statist. Soc. A 2006 169 Part 2 pp. 179-203 Model choice in time series studies of air pollution and mortality Roger D. Peng Francesca Dominici and Thomas A. Louis Johns Hopkins Bloomberg School of Public Health Baltimore USA Received September 2004. Final revision July 2005 time series studies of particulate matter and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of theUSnationalambinst air qhalitystandaRsfor particulate issues concerning time series analysis of the relationship between air pollution and health have attracted theattsstion of the scientific community and critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analysed by using log-linear Poisson regression models for overdispersed counts with the daily number of deaths as outcome the possibly lagged daily level of pollution as a linear predictor and smooth functions ofweather variablesandcalendar time used to adjust for timevarying confounders. Investigators around the world have used different approaches to adjust forconfouading makingitdifficultto c n a results across date the statistical properties of these different approaches have not been comprehensively compared. To address fp quantifyandchacaccerize model uncertainty and model choice in adjusting fpr seascaalan ong-tecrntter s ic time eeriesmodels of air pollution and mortality. First we conduct a simulation study to compare and describe the properties of statistical methods that are commonly unee forcontoccdingadjustmennWegenerctehateenSer saharelconfoenSisc scesarios sndhyctematically compWre hhepetfomtanceof the various methodawith sespech th ma mean-equaced ercor orfhecsCimaradmircellutioceoefficient. Wo .

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