The Intergovernmental Panel on Climate Change (IPCC) concluded that climate change will likely increase forest fire severity, with fires that bur more intensely and spread more rapidly. NIH identified climate change and forest fires as a critical research need. According to the U.S. Forest Service, fire risk has already increased. Several studies suggest that forest fires affects health;however, the true health burden is unknown as no large- scale studies have been conducted. Current research is limited in scope and methods to estimate exposure to fire smoke. Scientific literature on ambient air pollutants indicates that some populations are more vulnerable than others to health responses. We hypothesize that some populations are more vulnerable to health impacts of forest fires under a changing climate. To estimate this vulnerability, new data and methods are needed. We propose to develop the necessary databases and methods so we can investigate the vulnerability to hospital admissions from forest fires in the western United States.
In Aim 1 (exposure estimation), we will develop a new approach to integrate a chemical transport model, GEOS-Chem, with state-of-the-science forest fire emission inventories to estimate the contribution of forest fires to airborne particulate matter (PM). Our method generates daily, gridded estimates of PM2.5 from forest fire smoke and PM2.5 from other pollutant sources.
In Aim 2 (risk estimation) we will develop a data set that links information on weather conditions, individual-level data on hospital admissions and co-morbidities, and community factors (e.g., unemployment) at the zip-code level with PM2.5 from forest fires. We will develop statistical models for estimating the association between exposure to forest fire smoke and risk of hospitalizations for persons >65 years. In this aim, we identify which populations are most vulnerable with respect to individual factors (age, race, sex, pre- existing conditions), region, community factors (e.g., unemployment) and environmental factors (e.g., weather).
In Aim 3 (exposure and risk prediction through 2050), we will use the approach developed in Aim 1 and climate model projections to predict future area burned and forest fire emissions through 2050. These estimates will be used with chemical transport modeling to estimate future forest fire exposures. We will then use the approach developed in Aim 2 to predict the change in risk of hospitalizations, by vulnerability, under a changing climate accounting for the many sources of uncertainty in exposure and risk estimation. This study would advance understanding of how forest fires affect health in the present day and which populations are most vulnerable, and of how climate change could impact forest fires and thereby health and related vulnerabilities. Our findings would benefit efforts to manage forest fires, allow more targeted public health interventions, and aid effective allocation of resources, which is particularly important given the increased risk under climate change. The databases and methods developed would provide the research framework for studies of other environmental exposures, health outcomes, and climate change scenarios.

Public Health Relevance

The proposed research would investigate our hypothesis that some populations are more vulnerable to health impacts of forest fires under a changing climate. Our findings would benefit efforts to manage forest fires, allow more targeted public health interventions, and aid effective allocation of resources, which is particularly important given the increased risk under climate change. The databases and methods developed would provide the research framework for studies of other environmental exposures, health outcomes, and climate change scenarios.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21ES021427-01
Application #
8266997
Study Section
Special Emphasis Panel (ZRG1-PSE-D (56))
Program Officer
Dilworth, Caroline H
Project Start
2012-06-01
Project End
2014-05-31
Budget Start
2012-06-01
Budget End
2013-05-31
Support Year
1
Fiscal Year
2012
Total Cost
$263,884
Indirect Cost
$48,487
Name
Yale University
Department
None
Type
Schools of Earth Sciences/Natur
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Cefalu, Matthew; Dominici, Francesca (2014) Does exposure prediction bias health-effect estimation?: The relationship between confounding adjustment and exposure prediction. Epidemiology 25:583-90
Kioumourtzoglou, Marianthi-Anna; Coull, Brent A; Dominici, Francesca et al. (2014) The impact of source contribution uncertainty on the effects of source-specific PM2.5 on hospital admissions: a case study in Boston, MA. J Expo Sci Environ Epidemiol 24:365-71
Bell, Michelle L; Zanobetti, Antonella; Dominici, Francesca (2014) Who is more affected by ozone pollution? A systematic review and meta-analysis. Am J Epidemiol 180:15-28
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Bell, Michelle L; Ebisu, Keita; Leaderer, Brian P et al. (2014) Associations of PMýýý.ýýý constituents and sources with hospital admissions: analysis of four counties in Connecticut and Massachusetts (USA) for persons ýýý 65 years of age. Environ Health Perspect 122:138-44
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Kioumourtzoglou, Marianthi-Anna; Zanobetti, Antonella; Schwartz, Joel D et al. (2013) The effect of primary organic particles on emergency hospital admissions among the elderly in 3 US cities. Environ Health 12:68
Anderson, G Brooke; Krall, Jenna R; Peng, Roger D et al. (2013) The authors reply. Am J Epidemiol 177:1461-2
Bell, Michelle L; Zanobetti, Antonella; Dominici, Francesca (2013) Evidence on vulnerability and susceptibility to health risks associated with short-term exposure to particulate matter: a systematic review and meta-analysis. Am J Epidemiol 178:865-76
Yue, Xu; Mickley, Loretta J; Logan, Jennifer A et al. (2013) Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century. Atmos Environ (1994) 77:767-780

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