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- W2001263782 abstract "ISEE-376 Abstract: Several prospective cohort studies have shown the long term exposure to particulate air pollution is associated with reduced life expectancy. Relatively little has been demonstrated about the shape of the concentration response curve. Further, since each person has been assigned a single long term exposure, these studies provide little evidence as to when, after a change in exposure, we might expect to see changes in life expectancies—-are we looking at lifetime effects, or effects of recent years? The former question may be addressed by splines or smoothing, and the latter by distributed lag models with annual average concentration as a time dependent covariate. Both types of models require choices–how many degrees of freedom and how many lags. While techniques exist to choose “best fitting” models, these approaches do not incorporate model uncertainty. That is, an alternative model that fits nearly as well may look quite different. Bayesian model averaging provides a framework to compute a weighted average of many models, with weights proportional to the posterior probability of the model given the data. We have applied this approach to a re-analysis of the Harvard Six City Cohort Study, with additional mortality follow-up to 1999. Using piecewise linear splines with up to 5 knots at up to 5 different locations, we find the concentration–response relation is essentially linear, with about a 12% increase in risk per 10 □μg/m3 increase in PM2.5. We also find the effect is only in the exposure of the same year and previous year, with longer lags having coefficients indistinguishable from zero." @default.
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- W2001263782 date "2004-07-01" @default.
- W2001263782 modified "2023-09-26" @default.
- W2001263782 title "DOSE, TIME AND DEATH: A BAYESIAN MODEL AVERAGING APPROACH TO ESTIMATING THE ASSOCIATION BETWEEN PARTICLES AND SURVIVAL" @default.
- W2001263782 doi "https://doi.org/10.1097/00001648-200407000-00399" @default.
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