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Although particulate air pollution has been associated with increased numbers of daily deaths in dozens of cities around the world, issues still remain about the association. Some have questioned the complex modeling used to control for season in Poisson regression or the role of gaseous air pollutants as potential confounders of the association. I examined the association between deaths and particulate matter with an aerodynamic diameter less than or equal to 10 microm (PM10) using a case-crossover design. In this approach, the pollution on the day of each death is contrasted with the pollution level on control days when the subject did not die. Season and gaseous air pollutants were controlled by matching. Control days were chosen within the same month of the same year to control for season, and matched on either sulfur dioxide (SO2; within 1 ppb), nitrogen dioxide (within 1 ppb), maximum ozone (within 2 ppb), or carbon monoxide (within 0.03 ppm). The analysis was conducted in 14 U.S. cities that have daily PM10 monitoring. After matching, there were about 400,000 deaths in each analysis. Results were combined across cities using a maximum likelihood method. PM10 was a significant predictor of mortality when controlling for gaseous air pollutants, with effect sizes ranging from a 0.45% increase per 10 microg/m3 increment of PM10 [95% confidence interval (CI), 0.12-0.79%] when matched on maximum hourly ozone levels, to a 0.81% increase per 10 microg/m3 increment of PM10 (95% CI, 0.47-1.16%) when matched on 24-hr average SO2.

Is the association of airborne particles with daily deaths confounded by gaseous air pollutants? An approach to control by matching.