title: A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.
abstract: BACKGROUND
  Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.
  
  
  METHODS
  ZIP-code level estimates of exposure for six pollutants (CO, NOx, EC, PM2.5, SO4, O3) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.
  
  
  RESULTS
  Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NOxor EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate.
  
  
  CONCLUSIONS
  The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.
authors.affiliation: National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA. dionisio.kathie@epa.gov.
authors.forename: Kathie L
authors.identifier: http://orcid.org/0000-0002-3120-0633
authors.initials: KL
authors.lastname: Dionisio
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
authors.forename: Howard H
authors.initials: HH
authors.lastname: Chang
authors.affiliation: National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
authors.forename: Lisa K
authors.initials: LK
authors.lastname: Baxter
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D015982:Bias; D002248:Carbon Monoxide / Q000032:analysis; D002947:Cities / Q000453:epidemiology; D003198:Computer Simulation; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D004781:Environmental Exposure / Q000032:analysis; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D008962:Models, Theoretical; D009589:Nitrogen Oxides / Q000032:analysis; D010126:Ozone / Q000032:analysis; D052638:Particulate Matter / Q000032:analysis; D012140:Respiratory Tract Diseases / Q000453:epidemiology*; D012306:Risk; D013431:Sulfates / Q000032:analysis
chemical_list: D000393:Air Pollutants; D009589:Nitrogen Oxides; D052638:Particulate Matter; D013431:Sulfates; D010126:Ozone; D002248:Carbon Monoxide
keywords: Bias; Copollutant; Exposure assessment; Exposure measurement error; Exposure modeling