Ozone, Fine Particulate Matter, and Chronic Lower Respiratory Disease Mortality in the United States
Published Date:Aug 1 2015
Source:Am J Respir Crit Care Med. 192(3):337-341.
Bayesian Hierarchical Spatial Models
Chronic Lower Respiratory Disease Mortality
Respiratory Tract Diseases
Pubmed Central ID:PMC4937454
Funding:CC999999/Intramural CDC HHS/United States
Short-term effects of air pollution exposure on respiratory disease mortality are well established. However, few studies have examined the effects of long-term exposure, and among those that have, results are inconsistent.
To evaluate long-term association between ambient ozone, fine particulate matter (PM2.5, particles with an aerodynamic diameter of 2.5 µm or less), and chronic lower respiratory disease (CLRD) mortality in the contiguous United States.
We fit Bayesian hierarchical spatial Poisson models, adjusting for five county-level covariates (percentage of adults aged ≥65 years, poverty, lifetime smoking, obesity, and temperature), with random effects at state and county levels to account for spatial heterogeneity and spatial dependence.
Measurements and Main Results
We derived county-level average daily concentration levels for ambient ozone and PM2.5 for 2001–2008 from the U.S. Environmental Protection Agency’s down-scaled estimates and obtained 2007–2008 CLRD deaths from the National Center for Health Statistics. Exposure to ambient ozone was associated with an increased rate of CLRD deaths, with a rate ratio of 1.05 (95% credible interval, 1.01–1.09) per 5-ppb increase in ozone; the association between ambient PM2.5 and CLRD mortality was positive but statistically insignificant (rate ratio, 1.07; 95% credible interval, 0.99–1.14).
This study links air pollution exposure data with CLRD mortality for all 3,109 contiguous U.S. counties. Ambient ozone may be associated with an increased rate of death from CLRD in the contiguous United States. Although we adjusted for selected county-level covariates and unobserved influences through Bayesian hierarchical spatial modeling, the possibility of ecologic bias remains.
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