title: Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.
abstract: BACKGROUND
  Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.
  
  
  METHODS
  Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.
  
  
  RESULTS
  Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.
  
  
  CONCLUSIONS
  For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.
authors.affiliation: School of Civil and Environmental Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332-0512, USA.
authors.forename: Gretchen T
authors.initials: GT
authors.lastname: Goldman
authors.forename: James A
authors.initials: JA
authors.lastname: Mulholland
authors.forename: Armistead G
authors.initials: AG
authors.lastname: Russell
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Strickland
authors.forename: Mitchel
authors.initials: M
authors.lastname: Klein
authors.forename: Lance A
authors.initials: LA
authors.lastname: Waller
authors.forename: Paige E
authors.initials: PE
authors.lastname: Tolbert
mesh_terms: D000393:Air Pollutants / Q000633:toxicity*; D015982:Bias; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D015986:Confounding Factors, Epidemiologic; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D004781:Environmental Exposure; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D008954:Models, Biological; D016012:Poisson Distribution; D015203:Reproducibility of Results; D018570:Risk Assessment
chemical_list: D000393:Air Pollutants