Publication related to RSI or an RSI staff member
Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates.
BACKGROUND: Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM(2.5)) is associated with mortality. OBJECTIVES: We compared relative risk estimates of mortality from diseases of the circulatory system for PM(2.5) modeled from RS with that for PM(2.5) modeled using ground-level information. METHODS: We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM(2.5) levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM(2.5) with circulatory mortality and ischemic heart disease. RESULTS: Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 mug/m(3) increment in PM(2.5)]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 mug/m(3) increment in PM(2.5)). CONCLUSIONS: We found significant associations between PM(2.5) and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.
Authors
- Jerrett, Michael, Jerrett M, Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, USA.
- Turner, Michelle C, Turner MC,
- Beckerman, Bernardo S, Beckerman BS,
- Pope, C Arden, Pope CA,
- van Donkelaar, Aaron, van Donkelaar A,
- Martin, Randall V, Martin RV,
- Serre, Marc, Serre M,
- Crouse, Dan, Crouse D,
- Gapstur, Susan M, Gapstur SM,
- Krewski, Daniel, Krewski D,
- Diver, W Ryan, Diver WR,
- Coogan, Patricia F, Coogan PF,
- Thurston, George D, Thurston GD,
- Burnett, Richard T, Burnett RT,
BACKGROUND: Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM(2.5)) is associated with mortality. OBJECTIVES: We compared relative risk estimates of mortality from diseases of the circulatory system for PM(2.5) modeled from RS with that for PM(2.5) modeled using ground-level information. METHODS: We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM(2.5) levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM(2.5) with circulatory mortality and ischemic heart disease. RESULTS: Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 mug/m(3) increment in PM(2.5)]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 mug/m(3) increment in PM(2.5)). CONCLUSIONS: We found significant associations between PM(2.5) and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.
