# Integrating Air Pollution Prediction Models: Uncertainty Quantification and Propagation in Health Studies

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $594,704

## Abstract

Project Summary
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.

## Key facts

- **NIH application ID:** 10127645
- **Project number:** 5R01ES030616-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Francesca Dominici
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $594,704
- **Award type:** 5
- **Project period:** 2020-03-16 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10127645

## Citation

> US National Institutes of Health, RePORTER application 10127645, Integrating Air Pollution Prediction Models: Uncertainty Quantification and Propagation in Health Studies (5R01ES030616-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10127645. Licensed CC0.

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