Marginal structural models for quantifying the causal effects of exposure to ambient air pollution on progression of CT emphysema in the MESA Lung and MESA Air Studies.
Authors
Affiliations (12)
Affiliations (12)
- Dept. of Biostatistics, Columbia University, New York, NY USA.
- Dept. of Epidemiology and Environmental Health, University of Buffalo, Buffalo, NY USA.
- Dept. of Population Health Sciences, Weill Cornell Medicine, New York, NY USA.
- Dept. of Medicine, Columbia University, New York, NY USA.
- Dept. of Medicine, Brigham and Women's Hospital, Boston, MA USA.
- Dept. of Radiology, University of Iowa, Iowa City, IA USA.
- Dept. of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA USA.
- Dept. of Biostatistics, University of Washington, Seattle, WA USA.
- Dept. of Biomedical Engineering, Columbia University, New York, NY USA.
- Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France.
- Department of Medicine, McGill University, Montreal, Quebec, Canada.
- Depts. of Environmental & Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, WA USA.
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 (MESA) 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.