Assessing uncertainty in spatial exposure models for air pollution health effects assessment.
BACKGROUND
Although numerous epidemiologic studies now use models of intraurban exposure, there has been little systematic evaluation of the performance of different models.


OBJECTIVE
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.
Molitor
John
J
Department of Epidemiology and Public Health, Imperial College London, London, UK. john.molitor@imperial.ac.uk
Jerrett
Michael
M
Chang
Chih-Chieh
CC
Molitor
Nuoo-Ting
NT
Gauderman
Jim
J
Berhane
Kiros
K
McConnell
Rob
R
Lurmann
Fred
F
Wu
Jun
J
Winer
Arthur
A
Thomas
Duncan
D
D000293:Adolescent; D000393:Air Pollutants / Q000032:analysis* / Q000633:toxicity; D000397:Air Pollution / Q000009:adverse effects / Q000032:analysis*; D001499:Bayes Theorem; D002140:California / Q000453:epidemiology; D002648:Child; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D006801:Humans; D008171:Lung Diseases / Q000453:epidemiology / Q000209:etiology; D008954:Models, Biological; D009585:Nitrogen Dioxide / Q000032:analysis* / Q000633:toxicity; D035501:Uncertainty; D001335:Vehicle Emissions / Q000633:toxicity; D014797:Vital Capacity / Q000187:drug effects
D000393:Air Pollutants; D001335:Vehicle Emissions; D009585:Nitrogen Dioxide