Applying a potential outcomes framework to estimate policy-relevant effects of exposure mixtures

Abstract

BACKGROUND AND AIM: Interest in understanding the joint effects of multiple exposures (i.e., mixtures) is rapidly increasing, with a related proliferation of statistical methods for estimating these effects. However, commonly applied approaches to estimate effects of exposure mixtures often yield results that are difficult to interpret and are not easily mapped to public health actions. METHODS: By applying a potential outcomes framework for causal inference, investigators can ask questions about exposure mixtures that simulate effects of real-world interventions on sources of mixtures components. I discuss how to frame policy-relevant questions on sources of mixture components, and compare the interventions implied by common supervised and unsupervised analytic approaches. I also describe Bayesian g-computation, a promising statistical framework for estimating health effects of interventions on sources of exposure mixture components. RESULTS:Many common statistical approaches to address exposure mixtures yield results in the form of complex exposure-response surfaces or independent effects that do not acknowledge real-world correlation. In contrast, g-computation explicitly specifies a causal question and compares the expected outcome under exposure distributions that would occur with and without a hypothetical intervention on an exposure source. Applying a Bayesian approach to g-computation is useful to address common statistical challenges the occur in mixtures analyses such as high correlation and sparse data. CONCLUSIONS:Framing mixtures questions as potential interventions can improve causal inference and has the advantage of more directly informing potential environmental health practices, programs, and policies. KEYWORDS: Causal inference, Environmental epidemiology, Mixtures analysis