Ambient air pollution is a global environmental threat, contributing to millions of deaths and hundreds of millions of disability-adjusted-life-years (DALYs) annually. However, the major limitation of air pollution health studies remains exposure assessment. Although there have been great advances in air pollution assessment and several sophisticated spatio-temporal models have been developed to predict daily air pollution levels at the residential addresses of study participants, the performance of these models varies in space and time. Even the best on average performing prediction model, however, will have limited predictive ability in certain space and time points. Furthermore, the uncertainty associated with use of a single prediction model has been consistently ignored in health studies, which could lead to invalid inferences of the health effect estimates, and inconsistent findings across studies. We propose to address this critical gap by developing a novel ensemble model framework for exposure assessment in air pollution health studies, integrating information across multiple existing prediction models. With this approach, we will for the first time be able to comprehensively quantify any inter- and intra-model uncertainty associated with ambient air pollution exposures. We will develop ensemble methods both for single- and multi-pollutant settings. We propose to apply the developed methods and fully propagate exposure uncertainty in health effect estimation using two nationwide open cohorts, mainly Medicare and Medicaid, as well as an open cohort of hospital admissions in New York State (Statewide Planning and Research Cooperative System, SPARCS). These datasets provide information on approximately all elderly, low-income and disabled Americans across the United States (Medicare and Medicaid, respectively), with residential information the zip-code level, as well as 98% of all hospitalizations in NY State, with information available at the residential address. Specifically we will assess the long- and short-term impact of air pollution exposure on mortality (Medicare and Medicaid), and cardiorespiratory morbidity (all three cohorts).We communicate the air pollution predictions, the spatio-temporal uncertainty of air pollution exposure assessment and related health effect estimates to the public and regulatory agencies. The proposed novel paradigm to assess air pollution exposures in health studies will greatly improve communication of exposure uncertainty in the health effect estimates both to policy makers and the public, exactly responding to one of NIH's priority research areas. Our tools can be easily extended and will benefit integration of information and uncertainty characterization at different locations and at a global scale, as well as for other environmental exposures.

Public Health Relevance

We are proposing to develop a novel ensemble model framework to integrate information across multiple existing air pollution prediction models across the United States and, for the first time, to comprehensively quantify the uncertainty associated with air pollution exposure assessment, in single- and multi-pollutant settings. Subsequently, we propose to propagate the estimated uncertainty in the health effects estimation, to fully characterize the impact of air pollution on adverse health and the shape of the exposure-response curves. The proposed novel paradigm greatly improves communication of exposure uncertainty in the health effect estimates both to policy makers and the public and can easily be extended for use at different locations and at a global scale, as well as for other environmental exposures.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Joubert, Bonnie
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Columbia University (N.Y.)
Public Health & Prev Medicine
Schools of Public Health
New York
United States
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