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Long- and Short-Term Exposure to PM2.5 and Mortality: Using Novel Exposure Models
被引:228
|作者:
Kloog, Itai
[1
]
Ridgway, Bill
[2
]
Koutrakis, Petros
[1
]
Coull, Brent A.
[3
]
Schwartz, Joel D.
[1
]
机构:
[1] Harvard Univ, Sch Publ Hlth, Epidemiol & Risk Program, Dept Environm Hlth Exposure, Boston, MA 02215 USA
[2] Sci Syst & Applicat Inc, Lanham, MD USA
[3] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA USA
关键词:
PARTICULATE AIR-POLLUTION;
EXTENDED FOLLOW-UP;
HOSPITAL ADMISSIONS;
AIRBORNE PARTICLES;
TIME-SERIES;
CARDIOVASCULAR-DISEASE;
DAILY DEATHS;
ASSOCIATION;
HEART;
RISK;
D O I:
10.1097/EDE.0b013e318294beaa
中图分类号:
R1 [预防医学、卫生学];
学科分类号:
1004 ;
120402 ;
摘要:
Background: Many studies have reported associations between ambient particulate matter (PM) and adverse health effects, focused on either short-term (acute) or long-term (chronic) PM exposures. For chronic effects, the studied cohorts have rarely been representative of the population. We present a novel exposure model combining satellite aerosol optical depth and land-use data to investigate both the long- and short-term effects of PM2.5 exposures on population mortality in Massachusetts, United States, for the years 2000-2008. Methods: All deaths were geocoded. We performed two separate analyses: a time-series analysis (for short-term exposure) where counts in each geographic grid cell were regressed against cell-specific short-term PM2.5 exposure, temperature, socioeconomic data, lung cancer rates (as a surrogate for smoking), and a spline of time (to control for season and trends). In addition, for long-term exposure, we performed a relative incidence analysis using two long-term exposure metrics: regional 10 x 10 km PM2.5 predictions and local deviations from the cell average based on land use within 50 m of the residence. We tested whether these predicted the proportion of deaths from PM-related causes (cardiovascular and respiratory diseases). Results: For short-term exposure, we found that for every 10-mu g/m(3) increase in PM (2.5) exposure there was a 2.8% increase in PM-related mortality (95% confidence interval [CI] = 2.0-3.5). For the long-term exposure at the grid cell level, we found an odds ratio (OR) for every 10-mu g/m(3) increase in long-term PM2.5 exposure of 1.6 (CI = 1.5-1.8) for particle-related diseases. Local PM2.5 had an OR of 1.4 (CI = 1.3-1.5), which was independent of and additive to the grid cell effect. Conclusions: We have developed a novel PM2.5 exposure model based on remote sensing data to assess both short- and long-term human exposures. Our approach allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a selective sample from urban locations.
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页码:555 / 561
页数:7
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