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

• Authors: Kathie L. Dionisio, US Environmental Protection Agency; Lisa K. Baxter, US Environmental Protection Agency; Howard Chang, Emory University
• Journal Title: Environmental Health Perspectives
• Volume: Volume 122, Number 11
• Publisher: National Institute of Environmental Health Sciences | 2014-11-01, Pages 1216-1224
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1289/ehp.1307772
• Subjects: Biology, Bioinformatics; Biology, Biostatistics
• Keywords: Science & Technology; Life Sciences & Biomedicine; Environmental Sciences; Public, Environmental & Occupational Health; Toxicology; Environmental Sciences & Ecology; EMERGENCY-DEPARTMENT VISITS; AMBIENT AIR-POLLUTION; TIME-SERIES; PARTICULATE MATTER; NITROGEN-DIOXIDE; ATLANTA; HEALTH; POLLUTANTS; OZONE; QUALITY

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 PM<inf>2.5</inf> and its components (EC and SO<inf>4</inf>), as well as O<inf>3</inf>, CO, and NO<inf>x</inf>, to construct three types of exposure error: δ<inf>spatial</inf> (comparing air quality model estimates to central-site measurements), δ<inf>population</inf> (comparing population exposure model estimates to air quality model estimates), and δ<inf>total</inf> (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, NO<inf>x</inf>, 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 δ<inf>spatial</inf> and δ<inf>total</inf>. 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 δ<inf>spatial</inf> and δ<inf>total</inf> with true coefficients reduced by a factor typically < 0.6 (results varied for δ<inf>population</inf> and regional pollutants).