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.
journal_name
Environ Health Perspectjournal_title
Environmental health perspectivesauthors
Molitor J,Jerrett M,Chang CC,Molitor NT,Gauderman J,Berhane K,McConnell R,Lurmann F,Wu J,Winer A,Thomas Ddoi
10.1289/ehp.9849subject
Has Abstractpub_date
2007-08-01 00:00:00pages
1147-53issue
8eissn
0091-6765issn
1552-9924journal_volume
115pub_type
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journal_title:Environmental health perspectives
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