Effects of ambient air pollution measurement error on health effect estimates in time-series studies: a simulation-based analysis.

In this study, we investigated bias caused by spatial variability and spatial heterogeneity in outdoor air-pollutant concentrations, instrument imprecision, and choice of daily pollutant metric on risk ratio (RR) estimates obtained from a Poisson time-series analysis. Daily concentrations for 12 pollutants were simulated for Atlanta, Georgia, at 5 km resolution during a 6-year period. Viewing these as being representative of the true concentrations, a population-level pollutant health effect (RR) was specified, and daily counts of health events were simulated. Error representative of instrument imprecision was added to the simulated concentrations at the locations of fixed site monitors in Atlanta, and these mismeasured values were combined to create three different city-wide daily metrics (central monitor, unweighted average, and population-weighted average). Given our assumptions, the median bias in the RR per unit increase in concentration was found to be lowest for the population-weighted average metric. Although the Berkson component of error caused bias away from the null in the log-linear models, the net bias due to measurement error tended to be towards the null. The relative differences in bias among the metrics were lessened, although not eliminated, by scaling results to interquartile range increases in concentration.