| Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment John Molitor,1 Michael Jerrett,2 Chih-Chieh Chang,3 Nuoo-Ting Molitor,1 Jim Gauderman,3 Kiros Berhane,3 Rob McConnell,3 Fred Lurmann,4 Jun Wu,5 Arthur Winer,6 and Duncan Thomas3 1Department of Epidemiology and Public Health, Imperial College London; 2Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, USA; 3Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA; 4Sonoma Technology, Incorporated, Petaluma, California, USA; 5Division of Epidemiology, School of Medicine, University of California, Irvine, California, USA; 6School of Public Health, University of California, Los Angeles, California, USA 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. Key words: air pollution, Bayesian analysis, lung function, measurement error, spatial exposure models. Environ Health Perspect 115:1147–1153 (2007) . doi:10.1289/ehp.9849 available via http://dx.doi.org/ [Online 10 May 2007]
Address correspondence to J. Molitor, Imperial College, St Mary's Campus, Norfolk Place, London W2 1PG, UK. Telephone: 44 20 7594 1000. Fax: 44 20 7402 2150. E-mail: john.molitor@imperial.ac.uk We thank B. Beckerman for his geographic information systems expertise. Funding was provided by Southern California Environmental Health Sciences Center funded by National Institute of Environmental Health Sciences (NIEHS) grant 5P30 ES07048. Additionally we acknowledge funding from U.S. Environmental Protection Agency grant RD83186101 ; NIEHS grants 5P01 ES11627, 5P01 ES09581 ; the Health Effects Institute ; the Hastings Foundation ; Health Canada ; and the Canadian Institutes of Health Research. The authors declare they have no competing financial interests. Received 23 October 2006 ; accepted 10 May 2007.
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