Assessing uncertainty in spatial exposure models for air pollution health effects assessment.

Abstract:

BACKGROUND:Although numerous epidemiologic studies now use models of intraurban exposure, there has been little systematic evaluation of the performance of different models. OBJECTIVES:In this present article we proposed a modeling framework for assessing exposure model performance and the role of spatial autocorrelation in the estimation of health effects. METHODS:We obtained data from an exposure measurement substudy of subjects from the Southern California Children's Health Study. We examined how the addition of spatial correlations to a previously described unified exposure and health outcome modeling framework affects estimates of exposure-response relationships using the substudy data. The methods proposed build upon the previous work, which developed measurement-error techniques to estimate long-term nitrogen dioxide exposure and its effect on lung function in children. In this present article, we further develop these methods by introducing between- and within-community spatial autocorrelation error terms to evaluate effects of air pollution on forced vital capacity. The analytical methods developed are set in a Bayesian framework where multistage models are fitted jointly, properly incorporating parameter estimation uncertainty at all levels of the modeling process. RESULTS:Results suggest that the inclusion of residual spatial error terms improves the prediction of adverse health effects. These findings also demonstrate how residual spatial error may be used as a diagnostic for comparing exposure model performance.

authors

Molitor J,Jerrett M,Chang CC,Molitor NT,Gauderman J,Berhane K,McConnell R,Lurmann F,Wu J,Winer A,Thomas D

doi

10.1289/ehp.9849

subject

Has Abstract

pub_date

2007-08-01 00:00:00

pages

1147-53

issue

8

eissn

0091-6765

issn

1552-9924

journal_volume

115

pub_type

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