title: Spatial misalignment in time series studies of air pollution and health data.
abstract: Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter (PM). One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the PM components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. We present a spatial-temporal statistical model for quantifying spatial misalignment error and show how adjusted health risk estimates can be obtained using a regression calibration approach and a 2-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. rpeng@jhsph.edu
authors.forename: Roger D
authors.initials: RD
authors.lastname: Peng
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Bell
mesh_terms: D000397:Air Pollution / Q000009:adverse effects / Q000032:analysis / Q000706:statistics & numerical data*; D000465:Algorithms; D001499:Bayes Theorem; D015982:Bias; D002318:Cardiovascular Diseases / Q000453:epidemiology; D003198:Computer Simulation; D016208:Databases, Factual; D004781:Environmental Exposure / Q000706:statistics & numerical data; D004784:Environmental Monitoring / Q000706:statistics & numerical data; D062665:Epidemiological Monitoring; D005843:Geography / Q000706:statistics & numerical data; D006304:Health Status; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D052638:Particulate Matter / Q000009:adverse effects / Q000032:analysis; D012044:Regression Analysis; D018570:Risk Assessment; D013269:Stochastic Processes; D013997:Time Factors; D014481:United States; D014504:Urban Health / Q000706:statistics & numerical data
chemical_list: D052638:Particulate Matter
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