Estimating the health effects of environmental mixtures using principal stratification

Michelle Bell and 4 other contributors

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    The control of ambient air quality in the United States has been a major public health success since the passing of the Clean Air Act, with particulate matter (PM) reductions resulting in an estimated 160 000 premature deaths prevented in 2010 alone. Currently, public policy is oriented around lowering the levels of individual pollutants and this focus has driven the nature of much epidemiological research. Recently, attention has been given to viewing air pollution as a complex mixture and to developing a multi-pollutant approach to controlling ambient concentrations. We present a statistical approach for estimating the health impacts of complex environmental mixtures using a mixture-altering contrast, which is any comparison, intervention, policy, or natural experiment that changes a mixture's composition. We combine the notion of mixture-altering contrasts with sliced inverse regression, propensity score matching, and principal stratification to assess the health effects of different air pollution chemical mixtures. We demonstrate the application of this approach in an analysis of the health effects of wildfire PM air pollution in the Western US.