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On the Use of a PM2.5 Exposure Simulator to Explain Birthweight
Citation:
Berrocal, V. J., A. E. Gelfand, D. M. HOLLAND, J. M. BURKE, AND M. L. Miranda. On the Use of a PM2.5 Exposure Simulator to Explain Birthweight. ENVIRONMETRICS. John Wiley & Sons, Ltd., Indianapolis, IN, 22:553-571, (2011).
Impact/Purpose:
The contribution of this paper is to develop a prototype hierarchical model for assessing the effect of air pollution on a long-term health outcome such as birthweight. We use a stochastic human exposure simulator, captured through empirical distributions, driven by ambient exposure obtained through fusing monitoring data and atmospheric numerical model output. We model at the individual level rather than aggregating to counts at census units in order to incorporate individual level risk factors. However, we introduce spatial random effects at the census unit level. Due to the high spatial and temporal resolution of our pollution data, we could develop novel exposure metrics beyond average concentration that we use in the hierarchical model, propagating uncertainty in exposure to obtain appropriate uncertainty in the effect of exposure as well as in prediction using the model.
Description:
In relating pollution to birth outcomes, maternal exposure has usually been described using monitoring data. Such characterization provides a misrepresentation of exposure as it does not take into account the spatial misalignment between an individual’s residence and moni¬toring sites, and it ignores the fact that individuals spend most of their time indoors. In this paper, we break with previous studies by using a stochastic simulator to describe personal exposure (to particulate matter) and then use this exposure at the individual level to relate it to the health outcome (birthweight) rather than aggregating to a selected spatial unit. We propose a hierarchical model that, at the first stage, specifies a linear relationship be¬tween birthweight and personal exposure, adjusting for individual risk factors and introduces random spatial effects for the census tract of maternal residence. At the second stage, our hierarchical model specifies the distribution of each individual’s personal exposure using the empirical distribution yielded by the stochastic simulator as well as a model for the spatial effects. We have applied our framework to analyze birthweight data from 14 counties in North Carolina in years 2001 and 2002. We investigate whether there are certain aspects and time windows of exposure that are more detrimental to birthweight by building different exposure metrics which we incorporate, one by one, in our hierarchical model. To assess the difference in relating ambient exposure to birthweight versus personal exposure to birthweight, we compare estimates of the effect of air pollution obtained from hierarchical models that linearly relate ambient exposure and birthweight versus our modeling framework. Our analysis does not show a significant effect of PM2.5 on birthweight for reasons which we discuss. However our modeling framework serves as a template for analyzing the relationship between personal exposure and longer term health endpoints.