Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures

Most existing epidemiological evidence on the health effects of air pollution has focused on single-pollutant analyses, although recent research has increasingly emphasized estimating the effects of multiple exposures simultaneously. In this report, we used causal inference methodology to highlight four impediments to analyses with multiple exposures: (1) there is little information in the data to estimate effects typically of interest, (2) the effects of air pollution mixtures can be heterogeneous, (3) exposure assessment using an individual's home location can be problematic when daily mobility takes them to areas of different exposure levels, and (4) bias due to unmeasured confounding. The objectives of this report were to address these four concerns through the development of rigorous statistical methodology and to provide a corresponding case study that examines the health effects of air pollution in the Medicare cohort in the United States.

The statistical methodology developed in this report improves the analysis of environmental mixtures in two distinct ways. First, our results highlight inherent difficulties, which require careful consideration in any study of the health effects of multiple exposures. Second, we developed a statistical methodology that broadens the scope of questions that can be answered in analyses of air pollution mixtures and can increase the policy relevance of evidence obtained from epidemiological studies using multiple exposures. Additionally, we illustrated the aforementioned approaches in a nationwide study of the health effects of air pollution in the US Medicare population, extending the existing evidence on the health effects of air pollution within this cohort.

In specific aim 1, we found that quantities typically targeted in studies with multiple exposures are difficult to estimate from the observed data alone, as they frequently rely on model-based extrapolation, which can provide unreliable findings. We presented alternative strategies that provide policy-relevant evidence of health effects, while avoiding issues caused by extrapolation. In specific aim 2, we found that the adverse effects of particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5) components are heterogeneous and that these effects are more pronounced in areas with lower socioeconomic status. Specific aim 3 studied the mobility of individuals and found that ignoring mobility can bias health effects, although typically toward the null of no exposure effect. Incorporating mobility in the Medicare cohort did not lead to substantially different findings; however, accounting for mobility tended to increase the magnitude of estimated health effects. In specific aim 4, we developed a methodology for assessing robustness of health effects to unmeasured confounding bias and found that there is robust evidence overall of a harmful effect of pollution on public health.

Our studies provide strong evidence of air pollution effects on public health, and our methodology gives new insights into key issues about this effect. Specifically, the effects of air pollution are heterogeneous and affect certain subgroups of the population more than others, and these effects are moderately robust to unmeasured confounding bias. Future studies can incorporate the ideas and approaches developed in this report to address important questions in analyses with multiple exposures.