Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter

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Significance

Exposure to outdoor concentrations of fine particulate matter is considered a leading global health concern, largely based on estimates of excess deaths using information integrating exposure and risk from several particle sources (outdoor and indoor air pollution and passive/active smoking). Such integration requires strong assumptions about equal toxicity per total inhaled dose. We relax these assumptions to build risk models examining exposure and risk information restricted to cohort studies of outdoor air pollution, now covering much of the global concentration range. Our estimates are severalfold larger than previous calculations, suggesting that outdoor particulate air pollution is an even more important population health risk factor than previously thought.

Abstract

Exposure to ambient fine particulate matter (PM _2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM _2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM _2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM _2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries—the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5–10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9–8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3–4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM _2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

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Most cited references 29

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Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project

Rob Beelen, Ole Raaschou-Nielsen, Massimo Stafoggia … (2014)

Few studies on long-term exposure to air pollution and mortality have been reported from Europe. Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants. We used data from 22 European cohort studies, which created a total study population of 367,251 participants. All cohorts were general population samples, although some were restricted to one sex only. With a strictly standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of particulate matter (PM) with diameters of less than 2.5 μm (PM2.5), less than 10 μm (PM10), and between 10 μm and 2.5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land use regression models. We also investigated two traffic intensity variables-traffic intensity on the nearest road (vehicles per day) and total traffic load on all major roads within a 100 m buffer. We did cohort-specific statistical analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional hazards models with a common protocol. We obtained pooled effect estimates through a random-effects meta-analysis. The total study population consisted of 367,251 participants who contributed 5,118,039 person-years at risk (average follow-up 13.9 years), of whom 29,076 died from a natural cause during follow-up. A significantly increased hazard ratio (HR) for PM2.5 of 1.07 (95% CI 1.02-1.13) per 5 μg/m(3) was recorded. No heterogeneity was noted between individual cohort effect estimates (I(2) p value=0.95). HRs for PM2.5 remained significantly raised even when we included only participants exposed to pollutant concentrations lower than the European annual mean limit value of 25 μg/m(3) (HR 1.06, 95% CI 1.00-1.12) or below 20 μg/m(3) (1.07, 1.01-1.13). Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even within concentration ranges well below the present European annual mean limit value. European Community's Seventh Framework Program (FP7/2007-2011). Copyright © 2014 Elsevier Ltd. All rights reserved.

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Long-Term Ozone Exposure and Mortality in a Large Prospective Study.

Michelle Turner, Michael Jerrett, C. Pope … (2016)

Tropospheric ozone (O3) is potentially associated with cardiovascular disease risk and premature death. Results from long-term epidemiological studies on O3 are scarce and inconclusive.

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Long-Term Effects of Traffic-Related Air Pollution on Mortality in a Dutch Cohort (NLCS-AIR Study)

Rob Beelen, Gerard Hoek, Piet van den Brandt … (2007)

Background Several studies have found an effect on mortality of between-city contrasts in long-term exposure to air pollution. The effect of within-city contrasts is still poorly understood. Objectives We studied the association between long-term exposure to traffic-related air pollution and mortality in a Dutch cohort. Methods We used data from an ongoing cohort study on diet and cancer with 120,852 subjects who were followed from 1987 to 1996. Exposure to black smoke (BS), nitrogen dioxide, sulfur dioxide, and particulate matter ≤mu;M2.5), as well as various exposure variables related to traffic, were estimated at the home address. We conducted Cox analyses in the full cohort adjusting for age, sex, smoking, and area-level socioeconomic status. Results Traffic intensity on the nearest road was independently associated with mortality. Relative risks (95% confidence intervals) for a 10-μg/m3 increase in BS concentrations (difference between 5th and 95th percentile) were 1.05 (1.00–1.11) for natural cause, 1.04 (0.95–1.13) for cardiovascular, 1.22 (0.99–1.50) for respiratory, 1.03 (0.88–1.20) for lung cancer, and 1.04 (0.97–1.12) for mortality other than cardiovascular, respiratory, or lung cancer. Results were similar for NO2 and PM2.5, but no associations were found for SO2. Conclusions Traffic-related air pollution and several traffic exposure variables were associated with mortality in the full cohort. Relative risks were generally small. Associations between natural-cause and respiratory mortality were statistically significant for NO2 and BS. These results add to the evidence that long-term exposure to ambient air pollution is associated with increased mortality.

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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc. Natl. Acad. Sci. U.S.A

Journal ID (hwp): pnas

Journal ID (pmc): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Print): 18 September 2018

Publication date (Electronic): 4 September 2018

Publication date PMC-release: 4 September 2018

Volume: 115

Issue: 38

Pages: 9592-9597

Affiliations

[1] ^aPopulation Studies Division, Health Canada , Ottawa, ON K1A 0K9, Canada;

[2] ^bDepartment of Environmental and Occupational Health, Public Health Ontario , Toronto, ON M5G 1V2, Canada;

[3] ^cRisk and Benefits Group, Office of Air Quality Planning and Standards, US Environmental Protection Agency , Washington, DC 20460;

[4] ^dOffice of Research and Development, US Environmental Protection Agency , Washington, DC 20460;

[5] ^eDepartment of Economics, Brigham Young University , Provo, UT 84602;

[6] ^fDepartment of Civil, Architectural and Environmental Engineering, University of Texas at Austin , Austin, TX 78712;

[7] ^gSchool of Population and Public Health, University of British Columbia , Vancouver, BC V6T 1Z3, Canada;

[8] ^hHealth Effects Institute , Boston, MA 02110-1817;

[9] ⁱDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University , Montreal, QC H3A 0G4, Canada;

[10] ^jGerald Bronfman Department of Oncology, McGill University , Montreal, QC H3A 0G4, Canada;

[11] ^kDepartment of Applied Economics, University of Minnesota , Minneapolis, MN 55455;

[12] ^lDepartment of Biostatistics, Harvard T. H. Chan School of Public Health , Boston, MA 02115;

[13] ^mInstitute for Risk Assessment Sciences, Universiteit Utrecht , 3512 JE Utrecht, The Netherlands;

[14] ⁿInstitute for Health Metrics and Evaluation, University of Washington , Seattle, WA 98195;

[15] ^oSchool of Public Health, Fudan University , Shanghai 200433, China;

[16] ^pEnvironmental Medicine and Population Health, Program in Human Exposures and Health Effects, New York University School of Medicine , New York, NY 10016;

[17] ^qDepartment of Population Health, NYU Langone Medical Center , New York, NY 10016;

[18] ^rDepartment of Environmental Medicine, New York University School of Medicine , New York, NY 10016;

[19] ^sISGlobal, Barcelona Institute for Global Health , 08036 Barcelona, Spain;

[20] ^tDepartment of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles , CA 90095;

[21] ^uMcLaughlin Centre for Population Health Risk Assessment, University of Ottawa , Ottawa, ON K1N 6N5, Canada;

[22] ^vEpidemiology Research Program, American Cancer Society, Inc. , Atlanta, GA 30303;

[23] ^wDepartment of Civil and Environmental Engineering, University of California, Davis , CA 95616;

[24] ^xCancer Prevention Institute of California , Fremont, CA 94538;

[25] ^yDepartment of Sociology, University of New Brunswick , Fredericton, NB E3B 5A3, Canada;

[26] ^zDepartment of Physics and Atmospheric Science, Dalhousie University , Halifax, NS B3H 4R2, Canada;

[27] ^aaDepartment of Health Sciences, Carleton University , Ottawa, ON K1S 5B6, Canada;

[28] ^bbDepartment of Geography and Environment, Carleton University , Ottawa, ON K1S 5B6, Canada;

[29] ^ccNew Brunswick Institute for Research, Data and Training, University of New Brunswick , Fredericton, NB E3B 5A3, Canada;

[30] ^ddHealth Analysis Division, Statistics Canada , Ottawa, ON K1A 0T6, Canada;

[31] ^eeDalla Lana School of Public Health, University of Toronto , Toronto, ON M5T 3M7, Canada;

[32] ^ffNational Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention , Beijing 100050, China;

[33] ^ggNational Institute for Public Health and the Environment , 3720 BA Bilthoven, The Netherlands;

[34] ^hhPopulation Health Research Institute, St. George’s, University of London , London SW17 0RE, United Kingdom;

[35] ⁱⁱMRC-PHE Centre for Environment and Health, St. George’s, University of London , London SW17 0RE, United Kingdom;

[36] ^jjSchool of Public Health, University of Hong Kong , Hong Kong, China;

[37] ^kkDepartment of Environmental Health, Harvard C.T. Channing School of Public Health, Harvard University , Boston, MA 02115;

[38] ^llDepartment of Epidemiology, Regional Health Service , ASL Roma 1, 00147 Rome, Italy;

[39] ^mmInstitute of Epidemiology and Medical Biometry, Ulm University , 89081 Ulm, Germany;

[40] ⁿⁿAgency for Preventive and Social Medicine , 6900 Bregenz, Austria;

[41] ^ooSpadaro Environmental Research Consultants (SERC) , Philadelphia, PA 19142

Author notes

¹To whom correspondence should be addressed. Email: mietek.szyszkowicz@ 123456canada.ca .

Edited by Maureen L. Cropper, University of Maryland, College Park, MD, and approved July 23, 2018 (received for review February 22, 2018)

Author contributions: R.B., M.S., and A.C. designed research; R.B., H. Chen, M.S., N.F., B.H., C.A.P., M.B., A.C., Q.D., B.B., J.F., S.S.L., H.K., K.D.W., G.D.T., R.B.H., C.C.L., M.C.T., M.J., D.K., S.M.G., W.R.D., B.O., D.G., D.L.C., R.V.M., P.P., L.P., M.T., A.v.D., P.J.V., A.B.M., P.Y., M.Z., L.W., N.A.H.J., M.M., R.W.A., H.T., T.Q.T., J.B.C., R.T.A., J.E.H., F.L., G.C., F.F., G.W., A.J., G.N., H. Concin, and J.V.S. performed research; M.S., B.B., M.J., D.K., D.L.C., A.v.D., P.Y., and M.Z. contributed new reagents/analytic tools; H. Chen, M.S., N.F., B.H., C.A.P., J.S.A., M.B., A.C., S.W., J.B.C., Q.D., B.B., J.F., S.S.L., H.K., K.D.W., G.D.T., R.B.H., C.C.L., M.C.T., M.J., D.K., S.M.G., W.R.D., B.O., D.G., D.L.C., R.V.M., P.P., L.P., M.T., A.v.D., P.J.V., A.B.M., P.Y., M.Z., L.W., N.A.H.J., M.M., R.W.A., H.T., T.Q.T., J.B.C., R.T.A., J.E.H., F.L., G.C., F.F., G.W., A.J., G.N., H. Concin, and J.V.S. analyzed data; and R.B., M.S., M.B., A.C., and S.W. wrote the paper.