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

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 (SEM) 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 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 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.

Structural equation models in occupational health: an application to exposure modelling

“…Although the potential advantages of SEMs have been touted for some time in the lead literature18 and have more recently gained traction in studies of mercury2 7 and PM air pollution,5 6 they have not yet gained widespread use in the field of occupational health. Nearly 25 years have passed since Bornschein et al first noted the value of these methods for dealing with complicated epidemiological questions, and a significantly longer period has passed since their widespread use in econometrics.…”

Multiplicative factor analysis with a latent mixed model structure for air pollution exposure assessment

“…First, to identify the model, we constrained parameters in according to the C1-C3 identifiability conditions (Park et al, 2002) as defined in Equation (8). Nikolov et al (2007) found that zeroing out factor loading according to the C1-C3 identifiability conditions does not largely impact inference when the true factor loadings are low (not larger than 0.2). Based on the body of pre-existing work on Boston PM (Oh et al, 1997;Clarke et al, 2000;Batalha et al, 2002), we identified a subset of elements whose contribution by each source is expected to be minimal and constrained these loadings to zero.…”

Structural equation modeling of the inflammatory response to traffic air pollution

“…Notably, the latent variables correspond to what is “common” among the parameters measured and do not preclude, for example, CRP and sVCAM-1 representing in part different biological processes. SEMs have been used in studies that assessed source-specific health effects of air pollution 9,10 and in health effects of methyl mercury 11 and lead to neurodevelopment. 12,13 Notably, SEMs reduce the attenuation due to measurement error in models with 3 or more surrogates of the latent exposure, 14 which is an ongoing concern in air pollution studies.…”