Estimating the Independent Effects of Multiple Pollutants in the Presence of Measurement Error: An Application of a Measurement-Error–Resistant Technique

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Abstract
Misclassification of exposure usually leads to biased estimates of exposure–response associations. This is particularly an issue in cases with multiple correlated exposures, where the direction of bias is uncertain. It is necessary to address this problem when considering associations with important public health implications such as the one between mortality and air pollution, because biased exposure effects can result in biased risk assessments. The National Morbidity and Mortality Air Pollution Study (NMMAPS) recently reported results from an assessment of multiple pollutants and daily mortality in 90 U.S. cities. That study assessed the independent associations of the selected pollutants with daily mortality in two-pollutant models. Excess mortality was associated with particulate matter of aerodynamic diameter ≤10 μm/m3 (PM10), but not with other pollutants, in these two pollutant models. The extent of bias due to measurement error in these reported results is unclear. Schwartz and Coull recently proposed a method that deals with multiple exposures and, under certain conditions, is resistant to measurement error. We applied this method to reanalyze the data from NMMAPS. For PM10, we found results similar to those reported previously from NMMAPS (0.24% increase in deaths per 10-μg/m3 increase in PM10). In addition, we report an important effect of carbon monoxide that had not been observed previously.