Background: Understanding the shape of the concentration-response curve for particles is important for public health, and lack of such understanding was recently cited by U.S. Environmental Protection Agency (EPA) as a reason for not tightening the standards. Similarly, the delay between changes in exposure and changes in health is also important in public health decision making. We addressed these issues using an extended follow-up of the Harvard Six Cities Study.
Methods: Cox proportional hazards models were fit controlling for smoking, body mass index, and other covariates. Two approaches were used. First, we used penalized splines, which fit a flexible functional form to the concentration response to examine its shape, and chose the degrees of freedom for the curve based on Akaike's information criterion. Because the uncertainties around the resultant curve do not reflect the uncertainty in model choice, we also used model averaging as an alternative approach, where multiple models are fit explicitly and averaged, weighted by their probability of being correct given the data. We examined the lag relationship by model averaging across a range of unconstrained distributed lag models.
Results: We found that the concentration-response curve is linear, clearly continuing below the current U.S. standard of 15 microg/m3, and that the effects of changes in exposure on mortality are seen within two years.
Conclusions: Reduction in particle concentrations below U.S. EPA standards would increase life expectancy.
Keywords: PM2.5; air pollution; dose response; model averaging; particles; spline; survival; threshold; uncertainty.