Spatial misalignment in time series studies of air pollution and health data.
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.
Peng
Roger D
RD
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. rpeng@jhsph.edu
Bell
Michelle L
ML
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
D052638:Particulate Matter
JAMA. 2006 Mar 8;295(10):1127-34
16522832
Environ Health Perspect. 2009 Jun;117(6):957-63
19590690
Res Rep Health Eff Inst. 2000 Jun;(94 Pt 1):5-14; discussion 75-84
11098531
Environ Res. 1998 Apr;77(1):9-19
9593623
J Air Waste Manag Assoc. 2008 May;58(5):711-20
18512448
Environ Health Perspect. 2007 Jul;115(7):989-95
17637911
Environ Int. 2006 Jul;32(5):586-93
16516968
Environ Health Perspect. 2006 Jun;114(6):905-10
16759993
Biostatistics. 2009 Apr;10(2):258-74
18927119
Am J Epidemiol. 2005 Jul 1;162(1):80-8
15961590
J Multivar Anal. 2009 Mar 1;100(3):345-362
20431712
Environmetrics. 2006;17(5):483-506
18163157
Environ Health Perspect. 2000 May;108(5):419-26
10811568
Biostatistics. 2000 Jun;1(2):157-75
12933517
Biometrics. 2006 Sep;62(3):855-63
16984329
Res Rep Health Eff Inst. 2000 Jun;94(Pt 2):5-70; discussion 71-9
11354823
JAMA. 2008 May 14;299(18):2172-9
18477784
Am J Respir Crit Care Med. 2009 Jun 15;179(12):1115-20
19299499
Epidemiology. 2001 Sep;12(5):521-31
11505171