Academic journal article Environmental Health Perspectives

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

Academic journal article Environmental Health Perspectives

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

Article excerpt

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 [less than or equal to] 10 [micro]m/[m.sup.3] ([PM.sub.10]), 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 [PM.sup.10], we found results similar to those reported previously from NMMAPS (0.24% increase in deaths per 10-[micro]g/[m.sup.3] increase in [PM.sub.10]). In addition, we report an important effect of carbon monoxide that had not been observed previously. Key words: air pollution, carbon monoxide, daily mortality, measurement error, particulate matter. Environ Health Perspect 112:1686-1690 (2004). doi: 10.1289/ehp.7286 available via http://dx.doi.org/ [Online 7 September 2004]

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Growing evidence from published studies has shown increased all-cause and specific-cause mortality from short-term exposures to air pollution (Fairley 1999; Katsouyanni et al. 1997; Pope at al. 1995; Schwartz 1993; Schwartz and Dockery 1992). An important piece of that evidence comes from the National Mortality and Morbidity Air Pollution Study (NMMAPS) conducted across 90 U.S. cities (Dominici et al. 2000a; Samet et al. 2000a, 2000b, 2000c). Recent updates of this study reported excess mortality in association with exposures to particulate matter of aerodynamic diameter [less than or equal to] 10 [micro]m ([PM.sub.10]), whereas no independent associations with gaseous pollutants were observed (Dominici et al. 2002, 2003). In these previous studies, effects of pollutants were examined using two- and multiple-pollutant models.

From the public health perspective, when considering the evidence of a positive association between air pollution and mortality, it is important to determine whether such an effect is biased due to exposure misclassification and, if so, to correct for that bias.

The magnitude and direction of uncertainty in the observed effects of air pollution due to exposure measurement error have been argued by several investigators to be limitations in making causal inference for the link between air pollution and health outcomes (Lipfert and Wyzga 1997, 1999). In a single-pollutant model, exposure measurement error, due to the nondifferential misclassification, will underestimate the "true" effects of exposure-response associations (bias toward the null). Because of this, risk assessments based on the findings of observational epidemiologic studies may underestimate the benefits of reducing exposures. This is particularly true for air pollution studies, which, unlike cancer risk assessment, rely on maximum likelihood estimates of risk coefficients and not on upper confidence estimates.

The situation is more complex in the case of multiple correlated pollutants. Here, the measurement error in one pollutant will tend to bias the risk coefficient of that pollutant toward the null. However, measurement error in the second pollutant will contribute some bias to the coefficient of the first pollutant. …

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