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- W2088067227 abstract "P-008 Abstract: There is growing public health concern about the effect of exposure to airborne particles on cardiovascular disease. Prospective cohort studies have demonstrated associations between cardiovascular mortality and short-term exposure to PM10 and PM2.5 (particles less than or equal to 10 and 2.5 microns in diameter, respectively). The mechanisms underlying these associations are still poorly understood. The present project aimed at exploring the impact of long-term exposure to airborne particles at the residential address level on the development of atherosclerosis. The key issue is, however, how to use existing monitor data to estimate exposure levels at residential addresses for which direct measurements are not available. This paper considers the estimation of PM10 and PM2.5 at residential addresses using the data collected by the Environmental Protection Agency (EPA) from thousands of monitoring stations nationwide. The monthly data are available for up to 20 years at varying number of monitoring stations defined by latitude and longitude. We modelled the PM concentrations using spatiotemporal technique to predict PM10 and PM2.5 for any given latitude, longitude and month. We then apply this model to predict exposure level for study participants at different locations and points in time. Spatial effects are modelled using a nonparametric approach based on thin-plate splines. Time effects are modelled with trend, cyclical, autoregressive effects. These models also include spatial covariates such as population density and other environmental factors such as temperature, visibility and TSP. A Bayesian multivariate formulation and the missing data framework are used to approximately draw from the posterior distribution of monthly exposure values at each residential address. These can be viewed as multiple imputation of missing exposure levels for each address location and month. The multiply imputed values can be used to construct meaningful cumulative total, trend or some other summary measures to study its association with disease. This approach differs from the standard missing data method in that all exposure levels for the subjects in the cohort study are imputed and, hence, the standard multiple imputation methodology cannot be used. This paper also addresses appropriate methods for assessing the relationship between imputed exposure to PM10 and 2.5 and cardiovascular disease and risk factors using multiply imputed exposure data." @default.
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- W2088067227 date "2006-11-01" @default.
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- W2088067227 title "Predicting Cumulative Particulate Matter Exposure Using Space-Time Models and Historical Monitor Data" @default.
- W2088067227 doi "https://doi.org/10.1097/00001648-200611001-00645" @default.
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