Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment

• Published in: Environmental health perspectives Vol. 115; no. 8; pp. 1147 - 1153
• Main Authors: Molitor, John, Jerrett, Michael, Chih-Chieh Chang, Nuoo-Ting Molitor, Gauderman, Jim, Berhane, Kiros, McConnell, Rob, Lurmann, Fred, Wu, Jun, Arthur Winer, Thomas, Duncan
• Format: Journal Article
• Language: English
• Published: United States National Institute of Environmental Health Sciences. National Institutes of Health. Department of Health, Education and Welfare 01.08.2007; National Institute of Environmental Health Sciences
• Subjects: Adolescent; Air Pollutants - analysis; Air Pollutants - toxicity; Air pollution; Air Pollution - adverse effects; Air Pollution - analysis; Bayes Theorem; California - epidemiology; Child; Children; Communities; Complications and side effects; Environmental Monitoring - statistics & numerical data; Epidemiological Monitoring; Health aspects; Health benefits; Humans; Lung Diseases - epidemiology; Lung Diseases - etiology; Lungs; Modeling; Models, Biological; Musical intervals; Nitrogen Dioxide - analysis; Nitrogen Dioxide - toxicity; Parametric models; Spatial models; Traffic estimation; Uncertainty; Vehicle Emissions - toxicity; Vital Capacity - drug effects; California; United States; USA, California
• ISSN: 0091-6765; 1552-9924
• DOI: 10.1289/ehp.9849

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.