AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach
GRETCHEN GOLDMAN, James Mulholland, Armistead Russell, Katherine Gass, Matthew Strickland, Paige Tolbert, Georgia Institute of Technology
Abstract Number: 16 Working Group: Health Related Aerosols
Abstract In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants and used to assess the impact of measurement error in a time-series study of emergency department visits for cardiovascular disease. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). The simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends. Simulated monitor data were then generated by adding measurement error representative of instrument imprecision to the simulated concentrations at the locations of actual monitors. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For these metrics, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; larger error amount results in reduced statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e. independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e. independent of the true ambient). Error type affects the bias in the health risk estimate, with bias toward the null and away from the null depending on the exposure metric; population-weighting yielded the least bias.