Hao Yin1, Michael Brauer2, Junfeng Jim Zhang3, Wenjia Cai4, Ståle Navrud5, Richard Burnett6, Courtney Howard7, Zhu Deng4, Daniel M Kammen8, Hans Joachim Schellnhuber9, Kai Chen10, Haidong Kan11, Zhan-Ming Chen12, Bin Chen13, Ning Zhang14, Zhifu Mi15, D'Maris Coffman15, Aaron J Cohen16, Dabo Guan17, Qiang Zhang4, Peng Gong4, Zhu Liu18. 1. Ministry of Education Key Laboratory for Earth System modeling, Department of Earth System Science, Tsinghua University, Beijing, China; School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada; Energy and Resources Group, University of California, Berkeley, CA, USA. 2. School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USAxs. 3. Nicholas School of the Environment and Duke Global Health Institute, Duke University, Durham, NC, USA; Duke Kunshan University, Kunshan, Jiangsu, China. 4. Ministry of Education Key Laboratory for Earth System modeling, Department of Earth System Science, Tsinghua University, Beijing, China. 5. School of Economics and Business, Norwegian University of Life Sciences, Ås, Norway. 6. Population Studies Division, Health Canada, Ottawa, ON, Canada. 7. Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Planetary Health, Dahdaleh Institute for Global Health Research, York University, Toronto, ON, Canada. 8. Energy and Resources Group, University of California, Berkeley, CA, USA; Goldman School of Public Policy, University of California, Berkeley, CA, USA; Renewable and Appropriate Energy Laboratory, University of California, Berkeley, CA, USA. 9. Potsdam Institute for Climate Impact Research, Potsdam, Germany. 10. Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA. 11. School of Public Health, Fudan University, Shanghai, China. 12. Department of Energy Economics, School of Economics, Renmin University of China, Beijing, China. 13. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, China. 14. Institute of Blue and Green Development, Shandong University, Weihai, China. 15. The Bartlett School of Sustainable Construction, University College London, London, UK. 16. Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USAxs; Health Effects Institute, Boston, MA, USA. 17. Ministry of Education Key Laboratory for Earth System modeling, Department of Earth System Science, Tsinghua University, Beijing, China; The Bartlett School of Sustainable Construction, University College London, London, UK. 18. Ministry of Education Key Laboratory for Earth System modeling, Department of Earth System Science, Tsinghua University, Beijing, China. Electronic address: zhuliu@tsinghua.edu.cn.
Abstract
BACKGROUND: The health impacts of ambient air pollution impose large costs on society. Although all people are exposed to air pollution, the older population (ie, those aged ≥60 years) tends to be disproportionally affected. As a result, there is growing concern about the health impacts of air pollution as many countries undergo rapid population ageing. We investigated the spatial and temporal variation in the economic cost of deaths attributable to ambient air pollution and its interaction with population ageing from 2000 to 2016 at global and regional levels. METHODS: In this global analysis, we developed an age-adjusted measure of the value of a statistical life-year (VSLY) to estimate the economic cost of deaths attributable to ambient PM2·5 pollution using Global Burden of Diseases, Injuries, and Risk Factors Study 2017 data and country-level socioeconomic information. First, we estimated the global age-specific and cause-specific mortality and years of life lost (YLLs) attributable to PM2·5 pollution using the global exposure mortality model and global estimates of exposure at 0·1° × 0·1° (about 11 km × 11 km at the equator) resolution. Second, for each year between 2000 and 2016, we translated the YLLs within each age group into a health-related cost using a country-specific, age-adjusted measure of VSLY. Third, we decomposed the major driving factors that contributed to the temporal change in health costs related to PM2·5. Finally, we did a sensitivity test to analyse the variability of the estimated health costs to four alternative valuation measures. We identified the uncertainty intervals (UIs) from 1000 draws of the parameters and concentration-response functions by age, cause, country, and year. All economic values are reported in 2011 purchasing power parity-adjusted US dollars. All simulations were done with R, version 3.6.0. FINDINGS: Globally, in 2016, PM2·5 was estimated to have caused 8·42 million (95% UI 6·50-10·52) attributable deaths, which was associated with 163·68 million (116·03-219·44) YLLs. In 2016, the global economic cost of deaths attributable to ambient PM2·5 pollution for the older population was US$2·40 trillion (1·89-2·93) accounting for 59% (59-60) of the cost for the total population ($4·09 trillion [3·19-5·05]). The economic cost per capita for the older population was $2739 (2160-3345) in 2016, which was 10 times that of the younger population (ie, those aged <60 years). By assessing the factors that contributed to economic costs, we found that increases in these factors changed the total economic cost by 77% for gross domestic product (GDP) per capita, 21% for population ageing, 16% for population growth, -41% for age-specific mortality, and -0·4% for PM2·5 exposure. INTERPRETATION: The economic cost of ambient PM2·5 borne by the older population almost doubled between 2000 and 2016, driven primarily by GDP growth, population ageing, and population growth. Compared with younger people, air pollution leads to disproportionately higher health costs among older people, even after accounting for their relatively shorter life expectancy and increased disability. As the world's population is ageing, the disproportionate health cost attributable to ambient PM2·5 pollution potentially widens the health inequities for older people. Countries with severe air pollution and rapid ageing rates need to take immediate actions to improve air quality. In addition, strategies aimed at enhancing health-care services, especially targeting the older population, could be beneficial for reducing the health costs of ambient air pollution. FUNDING: National Natural Science Foundation of China, China Postdoctoral Science Foundation, and Qiushi Foundation.
BACKGROUND: The health impacts of ambient air pollution impose large costs on society. Although all people are exposed to air pollution, the older population (ie, those aged ≥60 years) tends to be disproportionally affected. As a result, there is growing concern about the health impacts of air pollution as many countries undergo rapid population ageing. We investigated the spatial and temporal variation in the economic cost of deaths attributable to ambient air pollution and its interaction with population ageing from 2000 to 2016 at global and regional levels. METHODS: In this global analysis, we developed an age-adjusted measure of the value of a statistical life-year (VSLY) to estimate the economic cost of deaths attributable to ambient PM2·5 pollution using Global Burden of Diseases, Injuries, and Risk Factors Study 2017 data and country-level socioeconomic information. First, we estimated the global age-specific and cause-specific mortality and years of life lost (YLLs) attributable to PM2·5 pollution using the global exposure mortality model and global estimates of exposure at 0·1° × 0·1° (about 11 km × 11 km at the equator) resolution. Second, for each year between 2000 and 2016, we translated the YLLs within each age group into a health-related cost using a country-specific, age-adjusted measure of VSLY. Third, we decomposed the major driving factors that contributed to the temporal change in health costs related to PM2·5. Finally, we did a sensitivity test to analyse the variability of the estimated health costs to four alternative valuation measures. We identified the uncertainty intervals (UIs) from 1000 draws of the parameters and concentration-response functions by age, cause, country, and year. All economic values are reported in 2011 purchasing power parity-adjusted US dollars. All simulations were done with R, version 3.6.0. FINDINGS: Globally, in 2016, PM2·5 was estimated to have caused 8·42 million (95% UI 6·50-10·52) attributable deaths, which was associated with 163·68 million (116·03-219·44) YLLs. In 2016, the global economic cost of deaths attributable to ambient PM2·5 pollution for the older population was US$2·40 trillion (1·89-2·93) accounting for 59% (59-60) of the cost for the total population ($4·09 trillion [3·19-5·05]). The economic cost per capita for the older population was $2739 (2160-3345) in 2016, which was 10 times that of the younger population (ie, those aged <60 years). By assessing the factors that contributed to economic costs, we found that increases in these factors changed the total economic cost by 77% for gross domestic product (GDP) per capita, 21% for population ageing, 16% for population growth, -41% for age-specific mortality, and -0·4% for PM2·5 exposure. INTERPRETATION: The economic cost of ambient PM2·5 borne by the older population almost doubled between 2000 and 2016, driven primarily by GDP growth, population ageing, and population growth. Compared with younger people, air pollution leads to disproportionately higher health costs among older people, even after accounting for their relatively shorter life expectancy and increased disability. As the world's population is ageing, the disproportionate health cost attributable to ambient PM2·5 pollution potentially widens the health inequities for older people. Countries with severe air pollution and rapid ageing rates need to take immediate actions to improve air quality. In addition, strategies aimed at enhancing health-care services, especially targeting the older population, could be beneficial for reducing the health costs of ambient air pollution. FUNDING: National Natural Science Foundation of China, China Postdoctoral Science Foundation, and Qiushi Foundation.
Authors: Arindam Nandi; Nathaniel Counts; Simiao Chen; Benjamin Seligman; Daniel Tortorice; Daniel Vigo; David E Bloom Journal: EClinicalMedicine Date: 2022-07-22
Authors: Longhui Fu; Qibang Wang; Jianhui Li; Huiran Jin; Zhen Zhen; Qingbin Wei Journal: Int J Environ Res Public Health Date: 2022-09-15 Impact factor: 4.614