TY - JOUR
T1 - Estimation of excess mortality due to long-term exposure to PM2.5 in continental United States using a high-spatiotemporal resolution model
AU - Vodonos, Alina
AU - Schwartz, Joel
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Background: Exposure to fine particulate matter (<2.5 mm in aerodynamic diameter, PM2.5) pollution, even at low concentrations is associated with increased mortality. Estimates of the magnitude of the effect of particulate air pollution on mortality are generally done on a coarse spatial scale, such as 0.5°, and may fail to capture small spatial differences in exposure and baseline rates, which can bias results and impede the ability to consider environmental justice. We estimated the burden of mortality attributable to long-term exposure to ambient PM2.5 among adults in the Continental United States on a 1 km scale, in order to provide information for decision makers setting health priorities. Methods: We conducted a health impact assessment for 2015 using a model predicting U.S. PM2.5 concentrations at a spatial resolution of 1 km cells. We applied a concentration-response curve from a recently published meta-analysis of long-term PM2.5 mortality association which incorporates new findings at high and low PM2.5 concentrations. We computed the change in deaths in each grid cell, based on its grid cell population, Zip code level baseline mortality rates, and exposure under two scenarios; a decrease of PM2.5 exposure levels by 40% and a decrease of PM2.5 exposure levels to the county minimum PM2.5 concentrations. Results: We estimated the deaths would fall by 104,786 (95% CI 57,016–135,726) and 112,040 (95% CI 63,261-159,116) attributable to 40% reduction and reduction to the county minimum PM2.5 concentrations, respectively. The greatest mortality impact due to 40% reduction in PM2.5 was observed in California with; 11,621 (95% CI; 7156-15,989) and Texas with; 9616 (95% CI; 5798–13,352) excess deaths attributable to annual mean PM2.5 concentrations of 9.54 and 9.12 μg m−3, respectively. Within city analyses showed substantial heterogeneity in risk. The estimated Attributable fraction (AF %) in locations with high PM2.5 levels was 8.6% (95% CI 5.4–11.7) compared to the overall AF% of 4.9% (95% CI; 2.9–6.8). In comparison, results using county average PM2.5 were smaller than the estimates from the 1 km PM2.5 datasets. Similarly, estimates using county-level mortality rates were smaller than estimates based on Zip-code level mortality rates. Conclusions: Our study provides evidence of major health benefits expected from reducing PM2.5 exposure, even in regions with relatively low PM2.5 concentrations. Spatial characteristics of exposure and baseline mortality (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results.
AB - Background: Exposure to fine particulate matter (<2.5 mm in aerodynamic diameter, PM2.5) pollution, even at low concentrations is associated with increased mortality. Estimates of the magnitude of the effect of particulate air pollution on mortality are generally done on a coarse spatial scale, such as 0.5°, and may fail to capture small spatial differences in exposure and baseline rates, which can bias results and impede the ability to consider environmental justice. We estimated the burden of mortality attributable to long-term exposure to ambient PM2.5 among adults in the Continental United States on a 1 km scale, in order to provide information for decision makers setting health priorities. Methods: We conducted a health impact assessment for 2015 using a model predicting U.S. PM2.5 concentrations at a spatial resolution of 1 km cells. We applied a concentration-response curve from a recently published meta-analysis of long-term PM2.5 mortality association which incorporates new findings at high and low PM2.5 concentrations. We computed the change in deaths in each grid cell, based on its grid cell population, Zip code level baseline mortality rates, and exposure under two scenarios; a decrease of PM2.5 exposure levels by 40% and a decrease of PM2.5 exposure levels to the county minimum PM2.5 concentrations. Results: We estimated the deaths would fall by 104,786 (95% CI 57,016–135,726) and 112,040 (95% CI 63,261-159,116) attributable to 40% reduction and reduction to the county minimum PM2.5 concentrations, respectively. The greatest mortality impact due to 40% reduction in PM2.5 was observed in California with; 11,621 (95% CI; 7156-15,989) and Texas with; 9616 (95% CI; 5798–13,352) excess deaths attributable to annual mean PM2.5 concentrations of 9.54 and 9.12 μg m−3, respectively. Within city analyses showed substantial heterogeneity in risk. The estimated Attributable fraction (AF %) in locations with high PM2.5 levels was 8.6% (95% CI 5.4–11.7) compared to the overall AF% of 4.9% (95% CI; 2.9–6.8). In comparison, results using county average PM2.5 were smaller than the estimates from the 1 km PM2.5 datasets. Similarly, estimates using county-level mortality rates were smaller than estimates based on Zip-code level mortality rates. Conclusions: Our study provides evidence of major health benefits expected from reducing PM2.5 exposure, even in regions with relatively low PM2.5 concentrations. Spatial characteristics of exposure and baseline mortality (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results.
UR - http://www.scopus.com/inward/record.url?scp=85101841879&partnerID=8YFLogxK
U2 - 10.1016/j.envres.2021.110904
DO - 10.1016/j.envres.2021.110904
M3 - Article
C2 - 33636186
AN - SCOPUS:85101841879
SN - 0013-9351
VL - 196
JO - Environmental Research
JF - Environmental Research
M1 - 110904
ER -
