An Empirical Assessment of Exposure Measurement Error and Effect Attenuation in Bipollutant Epidemiologic Models

Abstract

Background: Using multipollutant models to understand combined health effects of exposure to multiple pollutants is becoming more common. However, complex relationships between pollutants and differing degrees of exposure error across pollutants can make health effect estimates from multipollutant models difficult to interpret.Objectives: We aimed to quantify relationships between multiple pollutants and their associated exposure errors across metrics of exposure and to use empirical values to evaluate potential attenuation of coefficients in epidemiologic models.Methods: We used three daily exposure metrics (central-site measurements, air quality model estimates, and population exposure model estimates) for 193 ZIP codes in the Atlanta, Georgia, metropolitan area from 1999 through 2002 for PM2.5 and its components (EC and SO4), as well as O3, CO, and NOx, to construct three types of exposure error: δspatial (comparing air quality model estimates to central-site measurements), δpopulation (comparing population exposure model estimates to air quality model estimates), and δtotal (comparing population exposure model estimates to central-site measurements). We compared exposure metrics and exposure errors within and across pollutants and derived attenuation factors (ratio of observed to true coefficient for pollutant of interest) for single- and bipollutant model coefficients.Results: Pollutant concentrations and their exposure errors were moderately to highly correlated (typically, > 0.5), especially for CO, NOx, and EC (i.e., “local” pollutants); correlations differed across exposure metrics and types of exposure error. Spatial variability was evident, with variance of exposure error for local pollutants ranging from 0.25 to 0.83 for δspatial and δtotal. The attenuation of model coefficients in single- and bipollutant epidemiologic models relative to the true value differed across types of exposure error, pollutants, and space.Conclusions: Under a classical exposure-error framework, attenuation may be substantial for local pollutants as a result of δspatial and δtotal with true coefficients reduced by a factor typically < 0.6 (results varied for δpopulation and regional pollutants).Citation: Dionisio KL, Baxter LK, Chang HH. 2014. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environ Health Perspect 122:1216–1224; http://dx.doi.org/10.1289/ehp.1307772

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models

“…However while these papers detail work on multipollutant exposure metrics and statistical methods for analyzing multipollutant exposures, previous work to quantify the impact of exposure error on health risk estimates has focused on single-pollutant time-series models [6–8, 11–14]. While there are inherent difficulties in examining multipollutant exposures, we still do not have a clear understanding of the relationship of exposure measurement error in a more simplistic two pollutant model [15–17]. …”

“…We use previously described, daily exposure metrics ranging from CS measurements to more complex modeling approaches [18] to calculate empirical relationships between pollutants (PM 2.5 , SO 4 , O 3 , CO, NO x , EC). Relationships between exposure metrics were previously used to calculate estimates of exposure measurement error due to spatial variability of pollutant concentrations, and human exposure factors at the ZIP code level [17], but did not include the use of empirical health data nor analysis of an epidemiological model. Our previous work showed the potential for bias in model coefficients for copollutant models, motivating the current work to examine the degree of attenuation of relative risks (RRs) empirically.…”

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

“…However, CS measurements do not adequately capture the spatial variability for pollutants with local sources (e.g., NO 2 , CO, EC) . Exposure error may be introduced when CS measurements do not reflect the spatial and/or temporal variability of ambient air pollutant concentrations and their relationships to true personal exposures in the study area (Dionisio et al 2014). Exposure error may introduce bias and could lead to attenuation of the health risk estimate, reduce statistical significance, and lower statistical power (Goldman et al 2010(Goldman et al , 2011(Goldman et al , 2012Zeger et al 2000).…”

“…Exposure error may introduce bias and could lead to attenuation of the health risk estimate, reduce statistical significance, and lower statistical power (Goldman et al 2010(Goldman et al , 2011(Goldman et al , 2012Zeger et al 2000). Further, the magnitude and resultant impact of exposure measurement error on health risk estimates vary by pollutant, and between single and multipollutant models (Dionisio et al 2014;Goldman et al 2010;Tolbert et al 2007;Zeger et al 2000). Many recent epidemiologic studies on the health effects of exposure to ambient air pollution move beyond CS measurements and incorporate alternative exposure estimates intended to reduce exposure error and/or to increase study power.…”

Data Issues and Suggestions in the National Health Insurance Service-National Sample Cohort for Assessing the Long-term Health Effects of Air Pollution Focusing on Mortality

“…Another group of methods have been developed to address errors induced by spatial modeling of exposures or spatial autocorrelation and have shown promise in simulation studies [17,[57][58][59]. Overall, these methods suggest that air pollution studies are subject to complex measurement error structures, and that it is likely a variety of methods are needed to appropriately adjust the large number of study types.…”