Rationale: Air pollution has been associated with changes in daily mortality.
Objectives: Generally, studies use Poisson regression, with complicated modeling strategies, to control for season and weather, raising concerns that the results may be sensitive to these modeling protocols. For studies of ozone, weather control is a particular problem because high ozone days are generally quite hot.
Methods: The case-crossover approach converts this problem into a case-control study, where the control for each person is the same person on a day near in time, when he or she did not die. This method controls for season and individual risk factors by matching. One can also choose the control day to have the same temperature as the event day.
Measurements: I have applied this approach to a study of more than 1 million deaths in 14 U.S. cities.
Main results: I found that, with matching on temperature, a 10-ppb increase in maximum hourly ozone concentrations was associated with a 0.23% (95% confidence interval [CI] 0.01%, 0.44%) increase in the risk of dying. This finding was indistinguishable from the risk when only matching on season and controlling for temperature with regression splines (0.19%; 95% CI 03%, 0.35%). Control for suspended particulate matter with an aerodynamic diameter of 10 mum or less (PM(10)) did not change this risk. However, the association was restricted to the warm months (0.37% increase; 95% CI 0.11%, 0.62%), with no effect in the cold months.
Conclusions: The association between ozone and mortality risk is unlikely to be caused by confounding by temperature.