Acute effect of fine particulate matter on mortality in three Southeastern states from 2007-2011

Lee, M; Koutrakis, P; Coull, B; Kloog, I; Schwartz, J

HERO ID

3002381

Reference Type

Journal Article

Year

2015

Language

English

PMID

26306925

HERO ID 3002381
In Press No
Year 2015
Title Acute effect of fine particulate matter on mortality in three Southeastern states from 2007-2011
Authors Lee, M; Koutrakis, P; Coull, B; Kloog, I; Schwartz, J
Journal Journal of Exposure Science & Environmental Epidemiology
Volume 26
Issue 2
Page Numbers 173-179
Abstract Epidemiologic studies on acute effects of air pollution have generally been limited to larger cities, leaving questions about rural populations behind. Recently, we had developed a spatiotemporal model to predict daily PM2.5 level at a 1 km(2) using satellite aerosol optical depth (AOD) data. Based on the results from the model, we applied a case-crossover study to evaluate the acute effect of PM2.5 on mortality in North Carolina, South Carolina, and Georgia between 2007 and 2011. Mortality data were acquired from the Departments of Public Health in the States and modeled PM2.5 exposures were assigned to the zip code of residence of each decedent. We performed various stratified analyses by age, sex, race, education, cause of death, residence, and environmental protection agency (EPA) standards. We also compared results of analyses using our modeled PM2.5 levels and those imputed daily from the nearest monitoring station. 848,270 non-accidental death records were analyzed and we found each 10 μg/m(3) increase in PM2.5 (mean lag 0 and lag 1) was associated with a 1.56% (1.19 and 1.94) increase in daily deaths. Cardiovascular disease (2.32%, 1.57-3.07) showed the highest effect estimate. Blacks (2.19%, 1.43-2.96) and persons with education ≤8 year (3.13%, 2.08-4.19) were the most vulnerable populations. The effect of PM2.5 on mortality still exists in zip code areas that meet the PM2.5 EPA annual standard (2.06%, 1.97-2.15). The effect of PM2.5 below both EPA daily and annual standards was 2.08% (95% confidence interval=1.99-2.17). Our results showed more power and suggested that the PM2.5 effects on rural populations have been underestimated due to selection bias and information bias. We have demonstrated that our AOD-based exposure models can be successfully applied to epidemiologic studies. This will add new study populations in rural areas, and will confer more generalizability to conclusions from such studies.Journal of Exposure Science and Environmental Epidemiology advance online publication, 26 August 2015; doi:10.1038/jes.2015.47.
Doi 10.1038/jes.2015.47
Pmid 26306925
Wosid WOS:000371448900007
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English
Keyword case-crossover study; criteria pollutants; epidemiology; exposure modeling; PM2.5