Methods for detecting and estimating population threshold concentrations for air pollution-related mortality with exposure measurement error

The association between daily fluctuations in ambient particulate matter and daily variations in nonaccidental mortality have been extensively investigated. Although it is now widely recognized that such an association exists, the form of the concentration-response model is still in question. Linear, no threshold and linear threshold models have been most commonly examined. In this paper we considered methods to detect and estimate threshold concentrations using time series data of daily mortality rates and air pollution concentrations. Because exposure is measured with error, we also considered the influence of measurement error in distinguishing between these two completing model specifications. The methods were illustrated on a 15-year daily time series of nonaccidental mortality and particulate air pollution data in Toronto, Canada. Nonparametric smoothed representations of the association between mortality and air pollution were adequate to graphically distinguish between these two forms. Weighted nonlinear regression methods for relative risk models were adequate to give nearly unbiased estimates of threshold concentrations even under conditions of extreme exposure measurement error. The uncertainty in the threshold estimates increased with the degree of exposure error. Regression models incorporating threshold concentrations could be clearly distinguished from linear relative risk models in the presence of exposure measurement error. The assumption of a linear model given that a threshold model was the correct form usually resulted in overestimates in the number of averted premature deaths, except for low threshold concentrations and large measurement error.