Tao Xue1, Tong Zhu2, Wei Peng3, Tianjia Guan4, Shiqiu Zhang5, Yixuan Zheng6, Guannan Geng7, Qiang Zhang8. 1. Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China. 2. Beijing Innovation Center for Engineering Science and Advanced Technology/State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Science and Engineering, Peking University, Beijing, China. 3. School of International Affairs and Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, United States of America. 4. School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 5. College of Environmental Science and Engineering, Peking University, Beijing, China. 6. Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, China. 7. School of Environment, Tsinghua University, Beijing, China. 8. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing China.
Abstract
BACKGROUND: Exposure to air pollution, a leading contributor to the global burden of disease, can cause economic losses. Driven by clean air policies, the air quality in China, one of the most polluted countries, has improved rapidly since 2013. This has enabled a unique, quasi-experiment to assess the economic impact of air pollution empirically. METHODS AND FINDINGS: Using a series of nation-scale longitudinal surveys in 2011, 2013, and 2015, we first examined the questionnaire-based medical expenditure changes before and after the policy intervention for air pollution. Using a state-of-the-art estimator of the historical concentration of particulate matters with diameter less than 2.5 μm (particulate matter (PM)2.5), we further quantified the association between household medical expenditure and PM2.5 using mixed-effect models of the repeated measurements from 26,511 households in 126 cities. Regression models suggest a robust linear association between reduced PM2.5 and saved medical expenditures, since the association did not vary significantly across models with different covariate adjustments, subregions, or subpopulations. Each 10 μg/m3 reduction in PM2.5 was associated with a saving of 251.6 (95% CI: 30.8, 472.3; p-value = 0.026) Yuan in per capita annual medical expenditure. However, due to limitations in data quality (e.g., self-reported expenditures), and imperfect control for unmeasured confounders or impact from concurrent healthcare reform in China, the causality underlying our findings should be further confirmed or refuted. CONCLUSION: In this study, we observed that compared with the PM2.5 reduction in 2013, the PM2.5 reduction in 2017 was associated with a saving of 552 (95% CI: 68, 1036) Yuan / (person × year), or approximately 736 billion Yuan (equivalent to 111 billion US dollar) per year nationally, which is equivalent to approximately 1% of the national gross domestic product of China.
BACKGROUND: Exposure to air pollution, a leading contributor to the global burden of disease, can cause economic losses. Driven by clean air policies, the air quality in China, one of the most polluted countries, has improved rapidly since 2013. This has enabled a unique, quasi-experiment to assess the economic impact of air pollution empirically. METHODS AND FINDINGS: Using a series of nation-scale longitudinal surveys in 2011, 2013, and 2015, we first examined the questionnaire-based medical expenditure changes before and after the policy intervention for air pollution. Using a state-of-the-art estimator of the historical concentration of particulate matters with diameter less than 2.5 μm (particulate matter (PM)2.5), we further quantified the association between household medical expenditure and PM2.5 using mixed-effect models of the repeated measurements from 26,511 households in 126 cities. Regression models suggest a robust linear association between reduced PM2.5 and saved medical expenditures, since the association did not vary significantly across models with different covariate adjustments, subregions, or subpopulations. Each 10 μg/m3 reduction in PM2.5 was associated with a saving of 251.6 (95% CI: 30.8, 472.3; p-value = 0.026) Yuan in per capita annual medical expenditure. However, due to limitations in data quality (e.g., self-reported expenditures), and imperfect control for unmeasured confounders or impact from concurrent healthcare reform in China, the causality underlying our findings should be further confirmed or refuted. CONCLUSION: In this study, we observed that compared with the PM2.5 reduction in 2013, the PM2.5 reduction in 2017 was associated with a saving of 552 (95% CI: 68, 1036) Yuan / (person × year), or approximately 736 billion Yuan (equivalent to 111 billion US dollar) per year nationally, which is equivalent to approximately 1% of the national gross domestic product of China.
Authors: Anil Markandya; Jon Sampedro; Steven J Smith; Rita Van Dingenen; Cristina Pizarro-Irizar; Iñaki Arto; Mikel González-Eguino Journal: Lancet Planet Health Date: 2018-03-02
Authors: Aaron J Cohen; Michael Brauer; Richard Burnett; H Ross Anderson; Joseph Frostad; Kara Estep; Kalpana Balakrishnan; Bert Brunekreef; Lalit Dandona; Rakhi Dandona; Valery Feigin; Greg Freedman; Bryan Hubbell; Amelia Jobling; Haidong Kan; Luke Knibbs; Yang Liu; Randall Martin; Lidia Morawska; C Arden Pope; Hwashin Shin; Kurt Straif; Gavin Shaddick; Matthew Thomas; Rita van Dingenen; Aaron van Donkelaar; Theo Vos; Christopher J L Murray; Mohammad H Forouzanfar Journal: Lancet Date: 2017-04-10 Impact factor: 79.321
Authors: Laura Pimpin; Lise Retat; Daniela Fecht; Laure de Preux; Franco Sassi; John Gulliver; Annalisa Belloni; Brian Ferguson; Emily Corbould; Abbygail Jaccard; Laura Webber Journal: PLoS Med Date: 2018-07-10 Impact factor: 11.069