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 × 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-µg/m 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-µg/m 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.
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 × 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-µg/m 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-µg/m 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.
Authors: A Peters; M Fröhlich; A Döring; T Immervoll; H E Wichmann; W L Hutchinson; M B Pepys; W Koenig Journal: Eur Heart J Date: 2001-07 Impact factor: 29.983
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Authors: D W Dockery; C A Pope; X Xu; J D Spengler; J H Ware; M E Fay; B G Ferris; F E Speizer Journal: N Engl J Med Date: 1993-12-09 Impact factor: 91.245
Authors: C Arden Pope; Richard T Burnett; Michael J Thun; Eugenia E Calle; Daniel Krewski; Kazuhiko Ito; George D Thurston Journal: JAMA Date: 2002-03-06 Impact factor: 56.272
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Authors: Johanna Lepeule; Augusto A Litonjua; Brent Coull; Petros Koutrakis; David Sparrow; Pantel S Vokonas; Joel Schwartz Journal: Am J Respir Crit Care Med Date: 2014-09-01 Impact factor: 21.405
Authors: Maayan Yitshak-Sade; Jennifer F Bobb; Joel D Schwartz; Itai Kloog; Antonella Zanobetti Journal: Sci Total Environ Date: 2018-05-26 Impact factor: 7.963
Authors: M Pandolfi; A Tobias; A Alastuey; J Sunyer; J Schwartz; J Lorente; J Pey; X Querol Journal: Sci Total Environ Date: 2014-07-20 Impact factor: 7.963