Zihui Wang1, Yumin Zhou1, Yongbo Zhang2, Xiaoliang Huang3, Xianzhong Duan4, Duohong Chen5, Yubo Ou5, Longhui Tang1, Shiliang Liu6, Wei Hu3, Chenghao Liao2, Yijia Zheng2, Long Wang2, Min Xie5, Jinzhen Zheng1, Sha Liu1, Ming Luo7, Fan Wu1, Zhishan Deng1, Heshen Tian1, Jieqi Peng1, Huajing Yang1, Shan Xiao1, Xinwang Wang1, Nanshan Zhong1, Pixin Ran8. 1. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China. 2. Department of Environmental Protection of Guangdong Province, Guangdong Provincial Academy of Environmental Science, Guangzhou, China. 3. Department of Health of Guangdong Province, Government Affairs Service Center of Health Commission of Guangdong Province, Guangzhou, China. 4. Department of Environmental Protection of Guangdong Province, Department of Ecology and Environment of Guangdong Province, Guangzhou, China. 5. Department of Environmental Protection of Guangdong Province, Guangdong Environmental Monitoring Center, Key Laboratory of Regional Air Quality Monitoring, Ministry of Environmental Protection, Guangzhou, China. 6. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China; Center for Surveillance and Applied Research, Public Health Agency of Canada, Ottawa, Canada. 7. School of Geography and Planning, Sun Yat Sen University, Guangzhou, China. 8. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China. Electronic address: pxran@gzhmu.edu.cn.
Abstract
AIMS: To assess possible effect of air quality improvements, we investigated the temporal change in hospital admissions for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) associated with pollutant concentrations. METHODS: We collected daily concentrations of particulate matter (i.e., PM2.5, PM10 and PMcoarse), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and admissions for AECOPD for 21 cities in Guangdong from 2013 to 2017. We examined the association of air pollution with AECOPD admissions using two-stage time-series analysis, and estimated the annual attributable fractions, numbers, and direct hospitalization costs of AECOPD admissions with principal component analysis. RESULTS: From 2013-2017, mean daily concentrations of SO2, PM10 and PM2.5 declined by nearly 40%, 30%, and 26% respectively. As the average daily 8 h O3 concentration increased considerably, the number of days exceeding WHO target (i.e.,100 μg/m³) increased from 103 in 2015-152 in 2017. For each interquartile range increase in pollutant concentration, the relative risks of AECOPD admission at lag 0-3 were 1.093 (95% CI 1.06-1.13) for PM2.5, 1.092 (95% CI 1.08-1.11) for O3, and 1.092 (95% CI 1.05-1.14) for SO2. Attributable fractions of AECOPD admission advanced by air pollution declined from 9.5% in 2013 to 4.9% in 2016, then increased to 6.0% in 2017. A similar declining trend was observed for direct AECOPD hospitalization costs. CONCLUSION: Declined attributable hospital admissions for AECOPD may be associated with the reduction in concentrations of PM2.5, PM10 and SO2 in Guangdong, while O3 has emerged as an important risk factor. Summarizes the main finding of the work： Reduction in PM may result in declined attributable hospitalizations for AECOPD, while O3 has emerged as an important risk factor following an intervention.
AIMS: To assess possible effect of air quality improvements, we investigated the temporal change in hospital admissions for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) associated with pollutant concentrations. METHODS: We collected daily concentrations of particulate matter (i.e., PM2.5, PM10 and PMcoarse), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and admissions for AECOPD for 21 cities in Guangdong from 2013 to 2017. We examined the association of air pollution with AECOPD admissions using two-stage time-series analysis, and estimated the annual attributable fractions, numbers, and direct hospitalization costs of AECOPD admissions with principal component analysis. RESULTS: From 2013-2017, mean daily concentrations of SO2, PM10 and PM2.5 declined by nearly 40%, 30%, and 26% respectively. As the average daily 8 h O3 concentration increased considerably, the number of days exceeding WHO target (i.e.,100 μg/m³) increased from 103 in 2015-152 in 2017. For each interquartile range increase in pollutant concentration, the relative risks of AECOPD admission at lag 0-3 were 1.093 (95% CI 1.06-1.13) for PM2.5, 1.092 (95% CI 1.08-1.11) for O3, and 1.092 (95% CI 1.05-1.14) for SO2. Attributable fractions of AECOPD admission advanced by air pollution declined from 9.5% in 2013 to 4.9% in 2016, then increased to 6.0% in 2017. A similar declining trend was observed for direct AECOPD hospitalization costs. CONCLUSION: Declined attributable hospital admissions for AECOPD may be associated with the reduction in concentrations of PM2.5, PM10 and SO2 in Guangdong, while O3 has emerged as an important risk factor. Summarizes the main finding of the work： Reduction in PM may result in declined attributable hospitalizations for AECOPD, while O3 has emerged as an important risk factor following an intervention.
Authors: Sai Li; Gang Wang; Beibei Wang; Suzhen Cao; Kai Zhang; Xiaoli Duan; Wei Wu Journal: Int J Environ Res Public Health Date: 2022-10-01 Impact factor: 4.614
Authors: Yun Jung Jung; Eun Jin Kim; Jung Yeon Heo; Young Hwa Choi; Dae Jung Kim; Kyoung Hwa Ha Journal: Int J Environ Res Public Health Date: 2022-02-28 Impact factor: 3.390