Written by Sharon Murphy

About This Project
The Energy, Policy, and Innovation Center faculty affiliates Dylan Brewer, Daniel Dench, and Interim Director Laura Taylor published an article titled "Advances in Causal Inference at the Intersection of Air Pollution and Health Outcomes." The authors compare the methods used in the epidemiology literature with the causal inference framework used in economics in analyzing the effect of air pollution on health outcomes.

Determining the quality and accuracy of the evidence linking air pollution to human health has been a challenge for research in this area.

Each academic discipline has a unique lens through which they view and solve a problem, which may result in different conclusions being drawn from the same data. While studies that involve randomization across populations can provide evidence and are widely used in medical research, exposures to everyday air pollution cannot be randomized by a researcher.

Many existing studies exploring the health impacts of air pollution rely on establishing correlations between pollutants and health outcomes. However, correlations do not imply causation and can lead to bad policy. In this study, the EPICenter affiliates reviewed methodological contributions made by economists to determine if using statistical methods to the study of the health effects of air pollution can contribute to more robust and reliable findings.

To understand the difficulty researchers face, consider a typical air pollution study that collects health data of residents living near a pollution source, such as a coal-fired power plant. The data would be used to see if there is an increased incidence of adverse health outcomes such as asthma, chronic obstructive pulmonary disease, or cardiopulmonary disease. However, many factors can create a confounding effect on the final results if the researcher doesn’t take them into consideration. For instance, the power plant may have been built in a low-income location, or lower-income households may have moved near the power plant to take advantage of lower rent or property prices. This may conflate the effect of income and air pollution on health.

Simple schematic documenting the path of air pollution from emissions to outcomes. This review discusses the challenges of measuring how emissions of pollutants (step 1) disperse through the air (step 2) to become eventual exposures (step 3) and health outcomes (step 4).

Economists promote the use of natural experiments to overcome confounding factors. Natural experiments mimic familiar laboratory experiments. For instance, in the power plant example, random variation in wind direction would result in some households being randomly more exposed to air pollution, regardless of income. By taking advantage of this randomization, researchers can compare differences in a particular health outcome between those more exposed and less exposed, while overcoming confounding effects such as income, and move one step closer toward improving our understanding of the relationship between air pollution and adverse health outcomes.

The authors conclude by emphasizing the need for creating multidisciplinary teams, including economists, air-quality modelers, and public health and medical researchers. “While one may not think of economists as a natural contributor to this line of research, the analytical framework honed by economists over decades can contribute important expertise to the design of these types of studies,” Taylor concluded, “and result in better evidence for policymakers.”

Read more: https://doi.org/10.1146/annurev-resource-101722-081026