Abstract
Associations between exposure to ambient air pollution and progression of emphysema have been identified in longitudinal observational studies. However, previous work has not used statistical causal inference methods tailored to address bias from time-varying confounding. The objective of this study is to propose an analytical approach for estimating longitudinal health effects of air pollution while accounting for time-varying confounding using marginal structural models and to re-analyze data on air pollution and emphysema progression from the Multi-Ethnic Study of Atherosclerosis using this analytical approach. We estimate weights for continuous exposure levels using two techniques: quantile binning of the exposure and a semiparametric model for the requisite conditional densities. The latter approach incorporates flexible machine learning methods. We find evidence for the harmful effects of ambient ozone pollution during study follow-up on the progression of emphysema, consistent with previously reported results. We find no evidence of effects of NOx during study follow-up. This investigation demonstrates that analyses based on marginal structural models are feasible in studies of the health effects of air pollution and may address possible sources of bias that traditional regression-based methods fail to address. Further investigation is warranted to understand differences between our findings and previously published results.