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Biostatistics Advance Access published online on October 10, 2006
Biostatistics, doi:10.1093/biostatistics/kxl032
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received August 25, 2005
Revised September 13, 2006
Accepted October 3, 2006

Article

An informative bayesian structural equation model to assess source-specific health effects of air pollution

Margaret C. Nikolov 1 *, Brent A. Coull 1, Paul J. Catalano 1, and John J. Godleski 2

1 Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115
2 Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115

* To whom correspondence should be addressed.
Margaret C. Nikolov, E-mail: meg.nikolov{at}gmail.com


  Abstract

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles, or particulate matter (PM). Quantifying source-specific risk is a challenge, because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well-established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model typically involves a large number of parameters, for small sample settings we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of two existing approaches, a tracer approach and a two-stage approach. Simulation results suggest that the proposed informative Bayesian structural equation model is effective in eliminating the bias incurred by the two existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM, and discuss the implications of our findings with respect to the design of future PM concentrator studies.

Keywords: factor analysis; latent variables; measurement error; multivariate receptor model; source apportionment.
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