We present three types of exposure error ([[delta].
We calculated the between-pollutant Pearson correlations over time for each exposure estimation approach--and for each type of exposure error--to provide information on the collinearity of exposure estimates and exposure error that must be accounted for in a multipollutant model.
The goal of the present analysis was to examine exposure error and between-pollutant relationships and how these differ by pollutant pair and exposure metric.
The magnitude and spatial variability of the three types of normalized exposure error ([[DELTA].
population] for CO, the spatial variability of exposure error was greater for local pollutants than regional pollutants (Figure 2A,C).
The collinearity of exposure error was examined based on Pearson correlations between daily exposure error for local-local and regional-regional pollutant pairs (Figure 2B; see also Supplemental Material, Figure S3B, for local-regional pairs).
Because exposure measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed one systematic conceptual formulation of the problem of exposure error in epidemiologic time-series studies of air pollution and considered the possible consequences for relative risk estimation.
The fundamental concepts of how exposure error can affect an epidemiologic study of pollution and health can be shown by considering the effects of exposure measurement error in a standard linear Gaussian regression model.
The degree of attenuation increases as the variance of the exposure error increases.
It is useful to establish these results on the effects of exposure error on simple linear regression coefficients and helpful to do so in advance of considering a multiple regression case.
To investigate the effects of exposure error in the log-linear regressions widely used to assess the pollutant-mortality association, consider the following model for an individual's risk of mortality: