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Bayesian Hierarchical Modeling of Cardiac Response to Particulate Matter Exposure
Citation:
McBride, S. J., G. A. NORRIS, R. W. WILLIAMS, AND L. M. NEAS. Bayesian Hierarchical Modeling of Cardiac Response to Particulate Matter Exposure. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 21(1):74-91, (2011).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Studies have linked increased levels of particulate air pollution to decreased autonomic control, as measured by heart rate variability (HRV), particularly in populations such as the elderly. In this study, we use data obtained from the 1998 USEPA epidemiology-exposure longitudinal panel study of elderly adults in a Baltimore retirement home to examine the relationship between HRV and PM2.5 personal exposure. We consider PM2.5 personal exposure in the aggregate and personal exposure to the components of PM2.5 , as estimated in two ways using receptor models. We develop a Bayesian hierarchical model for HRV as a function of personal exposure to PM2.5, which integrates HRV measurements and data obtained from personal, indoor and outdoor PM2.5 monitoring and meteorological data. We found a strong relationship between decreased HRV (HF, LF, r-MSSD and SDNN) and total personal exposure to PM2.5 at a lag of 1 day. Using personal exposure monitoring (PEM) apportionment results, we examined the relative importance of ambient and non-ambient personal PM2.5 exposure to HRV and found the effect of internal non-ambient sources of PM2.5 on HRV to be minimal. Using the PEM apportionment data, a consistent effect of soil at short time scales (lag 0) was found across all five HRV measures, and an effect of sulfate on HRV was seen for HF and r-MSSD at the moving average of lags 0 and 1 days. Modeling of ambient site apportionment data indicated effects of nitrate on HRV at lags of 1 day, and moving averages of days 0 and 1 and day 0-2 for all but the ration LF/HF. Sulfate had an effect on HRV at a lag of 1 days for four HRV measures (HF, LE, r-MSSD, SDNN) and for LF/HF at a moving average of days 0-2.
URLs/Downloads:
Bayesian Hierarchical Modeling of Cardiac Response to Particulate Matter Exposure (PDF, NA pp, 10085 KB, about PDF)Journal of Exposure Science and Environmental Epidemiology
