Bayesian hierarchical modeling of cardiac response to particulate matter exposure

Abstract

Studies have linked increased levels of particulate air pollution to decreased autonomic control, as measured by heart rate variability (HRV), particularly in susceptible 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 PM 2.5 personal exposure. We consider PM 2.5 personal exposure in the aggregate and personal exposure to the components of PM 2.5 as estimated in two ways using receptor models. We develop a Bayesian hierarchical model for HRV as a function of personal exposure to PM 2.5 , which integrates HRV measurements and data obtained from personal, indoor and outdoor PM 2.5 monitoring and meteorological data. We found a strong relationship between decreased HRV (HF, LF, r-MSSD and SDNN) and total personal exposure to PM 2.5 at a lag of 1 day. Using personal exposure monitoring (PEM) apportionment results, we examined the relative importance of ambient and non-ambient personal PM 2.5 exposure to HRV and found the effect of internal non-ambient sources of PM 2.5 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 days 0-2 for all but the ratio LF/HF. Sulfate had an effect on HRV at a lag of 1 day for four HRV measures (HF, LF, r-MSSD, SDNN) and for LF/HF at a moving average of days 0-2.

Structural equation modeling of parasympathetic and sympathetic response to traffic air pollution in a repeated measures study

“…The latent variables correspond to what is “ common ” among the parameters measured and do not preclude in part different biological processes. Several studies have used SEMs that assessed source-specific health effects of air pollution [5,6] and in health effects of methyl mercury [7] and lead on neurodevelopment [8]. Notably, in models with 3 or more surrogates of the latent exposure, SEMs reduce the attenuation due to measurement error which is an ongoing concern in air pollution studies [9].…”

Estimates of community exposure and health risk to sulfur dioxide from power plant emissions using short-term mobile and stationary ambient air monitoring

“…Health effects are believed to lag exposure, and the moving average is a tool used to estimate the cumulative impact of pollutant concentrations and is more consistent with actual exposure than the block averages typically used in compliance monitoring (Amancio et al, 2012;Kloog et al, 2012;Mar et al, 2005;Mcbride et al, 2011). The time span most relevant to human exposure in clinical trials is 5-10 min, as only one study reviewed reported respiratory effects under 5 min (Horstman et al, 1988).…”

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

“…Epidemiological studies show different associations between adverse health effects and particles with aerodynamic diameters of less than 2.5 μm (PM 2.5 ) and less than 10 μm (PM 10 ) (Barmpadimos et al, 2011;Mcbride et al, 2011). The sources of PM 2.5 and PM 10 are different, and include a wide range of natural phenomena and human activities.…”