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- W2014112838 abstract "ISEE-62 Abstract: In time series studies of particulate matter and mortality, inadequate control for weather might bias estimates of short-term effects of ambient exposure to particulate matter on mortality. Weather conditions are important predictors of mortality, but few would argue that exposure to hot or cold temperatures results in instant death. A far more likely scenario is that a synergy of previous days’ weather with current weather conditions contributes to current day mortality for a population. Time series models estimate associations between day-to-day variations in air pollution and mortality by controlling for the potential confounding effect of weather, but to date they have given less attention to this complex distribution of weather effects over previous days. A natural solution is to employ distributed lag models, which allow for an exposure (in our case, weather) to affect response (mortality) over several time periods. In this talk we present flexible classes of distributed lag models for better adjusting air pollution estimates by the potential confounding for weather. Our distributed lag models allow for weather effects to vary by season, for non-linear weather effects, and for interactions between weather effects over longer periods. We apply these models to the NMMAPS data for the period 1987–2000 to explore the sensitivity of the short-term effects of PM10 on mortality to these richer methods of controlling for weather." @default.
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- W2014112838 date "2004-07-01" @default.
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- W2014112838 title "ARE THE SHORT-TERM EFFECTS OF PM10 ON MORTALITY THE RESULT OF INADEQUATELY MODELED WEATHER?" @default.
- W2014112838 doi "https://doi.org/10.1097/00001648-200407000-00085" @default.
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