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

Daniel Malinsky; Meng Wang; Rachel Heise; Carrie L Pistenmaa; Eric A Hoffman; Lianne Sheppard; Adam A Szpiro; Andrew Laine; Elsa Angelini; Benjamin M Smith; Joel D Kaufman; R Graham Barr

doi:10.1093/aje/kwaf252

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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

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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

Daniel Malinsky, Meng Wang, Rachel Heise, Carrie L Pistenmaa, Eric A Hoffman, Lianne Sheppard, Adam A Szpiro, Andrew Laine, Elsa Angelini, Benjamin M Smith, …

American journal of epidemiology

11/17/2025

DOI: 10.1093/aje/kwaf252

PMID: 41246939

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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.

Air Pollution

Emphysema

Causal Inference

Details

Title: Subtitle: 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
Creators: Daniel Malinsky - Columbia University
Meng Wang - University at Buffalo, State University of New York
Rachel Heise - Weill Cornell Medicine
Carrie L Pistenmaa - Brigham and Women's Hospital
Eric A Hoffman - Dept. of Radiology, University of Iowa, Iowa City, IA USA
Lianne Sheppard - University of Washington
Adam A Szpiro - University of Washington
Andrew Laine - Columbia University
Elsa Angelini - Institut Polytechnique de Paris
Benjamin M Smith - McGill University
Joel D Kaufman - University of Washington
R Graham Barr - Columbia University
Resource Type: Journal article
Publication Details: American journal of epidemiology
DOI: 10.1093/aje/kwaf252
PMID: 41246939
NLM abbreviation: Am J Epidemiol
ISSN: 0002-9262
eISSN: 1476-6256
Publisher: Oxford University Press
Grant note: National Center for Advancing Translational Sciences (NCATS): RC1-HL100543, R01-HL077612 National Heart, Lung, and Blood Institute: UL1-TR-001420, UL1-TR-0010,79, UL1-TR-000040, N01-HC-95169, N01-,HC-95168, N01-HC-95167, N01-HC-95166, N01-HC-95165, 75N92020D00007, N01-HC-95164, 75N92020D00004, N01-HC-95163, 75N92020D00006, N01-HC-95162, 75N92020D00003, N01-HC-95161, 75N92020D00002, N01-HC-95160, 75N92020D00005, N01-HC-95159, HHSN268201500003I, 75N92020D00001 National Institutes of Health: K25ES034064
D.M. was supported by the National Institutes of Health under award number K25ES034064 from NIEHS. This research was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-,HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute; and grants UL1-TR-000040, UL1-TR-0010,79, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). Funding support for the Lung CT dataset was provided by grants R01-HL077612 and RC1-HL100543.
Language: English
Electronic publication date: 11/17/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9985033875002771

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