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.

Figure 1

Map of 20-county metropolitan Atlanta study area. Census tracts, expressways, and ambient air pollutant monitoring sites are shown.

Figure 2

Boxplots of R(InZ, InZ*), with expected correlation coefficients shown in parentheses for 1000 simulated data time-series of error type C (top panel) and type B (bottom panel) simulations.

Figure 3

P-values versus population-weighted semivariance. Half-bars denote standard deviations for 1000 error simulations.

Figure 4

Percent attenuation in risk ratio per ppm (left panel) and per IQR (right panel) due to error versus population-weighted semivariance. Bars denote standard deviations for 1000 error simulations. Pollutant labels are in order of increasing population-weighted semivariance.

Figure 5

Percent attenuation in risk ratio per unit of measurement (ppm) and per IQR for CO error simulations ( = 0.411) with incremental changes in error type ranging from type B (σ_InZ/σ_InZ* = 0.65) to type C (σ_InZ/σ_InZ* = 1.55). Bars denote standard deviations for 1000 simulations.

Figure 6

Attenuation in the risk ratio per unit of measurement (left panel) and per IQR (right panel) due to the introduction of measurement error, modeled both as type B and type C error. Ranges denote standard deviations for 1000 simulations. One-to-one line is also shown.