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Pan R, Wang W, Wei N, Liu L, Yi W, Song J, Cheng J, Su H, Fan Y. Does the morphology of residential greenspaces contribute to the development of a cardiovascular-healthy city? ENVIRONMENTAL RESEARCH 2024; 257:119280. [PMID: 38821460 DOI: 10.1016/j.envres.2024.119280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/04/2024] [Accepted: 05/29/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUNDS Greenspaces are indispensable for the construction of a healthy city. Research has shown that greenspaces contribute to the reduction of cardiovascular risks. However, the role of greenspace morphology in the development of a healthy city is not well understood. METHODS Our study utilized data from a cardiovascular disease screening cohort comprising 106,238 residents in Anhui Province, China, aged between 35 and 75 years. We calculated landscape indices of each participant using high-resolution land cover data to measure the greenness, fragmentation, connectivity, aggregation, and shape of greenspaces. We used a multivariate linear regression model to assess the associations between these landscape indices and triglyceride risk, and employed a structural equation model to explore the potential contributions of heatwaves and fine particulate matter (PM2.5) to this association. RESULTS Overall, triglyceride was expected to increase by 0.046% (95% CI: 0.040%, 0.052%) with a 1% increase in the percentage of built-up area. Conversely, an increase in the percentage of greenspace was associated with a 0.270% (95% CI: 0.337%, -0.202%) decrease in triglyceride levels. Furthermore, when the total greenspace was held constant, the shape, connectedness, and aggregation of greenspace were inversely correlated with triglyceride levels, with effects of -0.605% (95% CI: 1.012%, -0.198%), -0.031% (95% CI: 0.039%, -0.022%), and -0.049% (95% CI: 0.058%, -0.039%), respectively. Likewise, the protective effect of the area-weighed mean shape index was higher than that of the total amount of greenspace. The stratification results showed that urban residents benefited more from greenspace exposure. Greenspace morphology can minimize triglyceride risk by reducing pollutant and heatwaves, with aggregation having the greatest effect on reducing pollutants whereas fragmentation is more efficient at reducing heatwaves. CONCLUSION Exposure to the greenspaces morphology is associated with a reduction in triglyceride risk. The study has important practical and policy implications for early health monitoring and the spatial layout of greenspace and will provide scientific information for healthy urban planning by reducing unfavorable health consequences.
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Affiliation(s)
- Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Weiqiang Wang
- Suzhou Hospital of Anhui Medical University, Suzhou, Anhui, China
| | - Ning Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Li Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China.
| | - Yinguang Fan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China.
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Lei Y, Yin Z, Lu X, Zhang Q, Gong J, Cai B, Cai C, Chai Q, Chen H, Chen R, Chen S, Chen W, Cheng J, Chi X, Dai H, Feng X, Geng G, Hu J, Hu S, Huang C, Li T, Li W, Li X, Liu J, Liu X, Liu Z, Ma J, Qin Y, Tong D, Wang X, Wang X, Wu R, Xiao Q, Xie Y, Xu X, Xue T, Yu H, Zhang D, Zhang N, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Z, Zheng B, Zheng Y, Zhou J, Zhu T, Wang J, He K. The 2022 report of synergetic roadmap on carbon neutrality and clean air for China: Accelerating transition in key sectors. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100335. [PMID: 37965046 PMCID: PMC10641488 DOI: 10.1016/j.ese.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/16/2023]
Abstract
China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.
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Affiliation(s)
- Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, 200032, China
| | - Shi Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Wenhui Chen
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jing Cheng
- Department of Earth System Science, University of California, Irvine, CA, 92697, USA
| | - Xiyuan Chi
- National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Xiangzhao Feng
- Policy Research Center for Environment and Economy, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100029, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shan Hu
- Building Energy Research Center, School of Architecture, Tsinghua University, Beijing, 100084, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xiaomei Li
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Yue Qin
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Rui Wu
- Transport Planning and Research Institute (TPRI) of the Ministry of Transport, Beijing, 100028, China
| | - Qingyang Xiao
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Xiaolong Xu
- China Association of Building Energy Efficiency, Beijing, 100029, China
| | - Tao Xue
- 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, Beijing, 100080, China
| | - Haipeng Yu
- Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Ning Zhang
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Xin Zhang
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Zengkai Zhang
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Jian Zhou
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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Du H, Yan M, Liu X, Zhong Y, Ban J, Lu K, Li T. Exposure to Concurrent Heatwaves and Ozone Pollution and Associations with Mortality Risk: A Nationwide Study in China. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:47012. [PMID: 38662525 PMCID: PMC11045006 DOI: 10.1289/ehp13790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 02/01/2024] [Accepted: 03/18/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Concurrent extreme events are projected to occur more frequently under a changing climate. Understanding the mortality risk and burden of the concurrent heatwaves and ozone (O 3 ) pollution may support the formulation of adaptation strategies and early warning systems for concurrent events in the context of climate change. OBJECTIVES We aimed to estimate the mortality risk and excess deaths of concurrent heatwaves and O 3 pollution across 250 counties in China. METHODS We collected daily mortality, meteorological, and air pollution data for the summer (1 June to 30 September) during 2013-2018. We defined heatwaves and high O 3 pollution days, then we divided the identified days into three categories: a) days with only heatwaves (heatwave-only event), b) days with only high O 3 pollution (high O 3 pollution-only event), and c) days with concurrent heatwaves and high O 3 pollution (concurrent event). A generalized linear model with a quasi-Poisson regression was used to estimate the risk of mortality associated with extreme events for each county. Then we conducted a random-effects meta-analysis to pool the county-specific estimates to derive the overall effect estimates. We used mixed-effects meta-regression to identify the drivers of the heterogeneity. Finally, we estimated the excess death attributable to extreme events (heatwave-only, high O 3 pollution-only, and concurrent events) from 2013 to 2020. RESULTS A higher all-cause mortality risk was associated with exposure to the concurrent heatwaves and high O 3 pollution than exposure to a heatwave-only or a high O 3 pollution-only event. The effects of a concurrent event on circulatory and respiratory mortality were higher than all-cause and nonaccidental mortality. Sex and age significantly impacted the association of concurrent events and heatwave-only events with all-cause mortality. We estimated that annual average excess deaths attributed to the concurrent events were 6,249 in China from 2017 to 2020, 5.7 times higher than the annual average excess deaths attributed to the concurrent events from 2013 to 2016. The annual average proportion of excess deaths attributed to the concurrent events in the total excess deaths caused by three types of events (heatwave-only events, high O 3 pollution-only events, and concurrent events) increased significantly in 2017-2020 (31.50%; 95% CI: 26.73%, 35.53%) compared with 2013-2016 (9.65%; 95% CI: 5.67%, 10.81%). Relative excess risk due to interaction revealed positive additive interaction considering the concurrent effect of heatwaves and high O 3 pollution. DISCUSSION Our findings may provide scientific basis for establishing a concurrent event early warning system to reduce the adverse health impact of the concurrent heatwaves and high O 3 pollution. https://doi.org/10.1289/EHP13790.
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Affiliation(s)
- Hang Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Meilin Yan
- Department of Environmental Science and Engineering, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
| | - Xin Liu
- Energy Foundation China, Beijing, China
| | - Yu Zhong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jie Ban
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kailai Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
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Xie Y, Zhou Z, Sun Q, Zhao M, Pu J, Li Q, Sun Y, Dai H, Li T. Social-economic transitions and vulnerability to extreme temperature events from 1960 to 2020 in Chinese cities. iScience 2024; 27:109066. [PMID: 38361620 PMCID: PMC10867637 DOI: 10.1016/j.isci.2024.109066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/13/2023] [Accepted: 01/25/2024] [Indexed: 02/17/2024] Open
Abstract
Climate change leads to more frequent and intense extreme temperature events, causing a significant number of excess deaths. Using an epidemiological approach, we analyze all-cause deaths related to heatwaves and cold spells in 2,852 Chinese counties from 1960 to 2020. Economic losses associated with these events are determined through the value of statistical life. Findings reveal that cold-related cumulative excess deaths (1,133 thousand) are approximately 2.5 times higher than heat-related deaths, despite an increase in heat-related fatalities in recent decades. Monetized mortality due to heat-related events is estimated at 1,284 billion CNY, while cold-related economic loss is 1,510 billion CNY. Notably, cities located in colder regions experience more heat-related excess deaths, and vice versa. Economic development does not significantly reduce mortality risks to heatwaves across China. This study provides insights into the spatial-temporal heterogeneity of heatwaves and cold spells mortality, essential for policymakers ensuring long-term climate adaptation and sustainability.
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Affiliation(s)
- Yang Xie
- School of Economics and Management, Beihang University, Beijing, China
| | - Ziqiao Zhou
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Qinghua Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mengdan Zhao
- School of Economics and Management, Beihang University, Beijing, China
| | - Jinlu Pu
- School of Economics and Management, Beihang University, Beijing, China
| | - Qiutong Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yue Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
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Jiang Z, Lin Z, Li Z, Yu M, He G, Hu J, Meng R, Hou Z, Zhu S, Zhou C, Xiao Y, Huang B, Xu X, Jin D, Qin M, Xu Y, Liu T, Ma W. Joint effects of heat-humidity compound events on drowning mortality in Southern China. Inj Prev 2024:ip-2023-045036. [PMID: 38443161 DOI: 10.1136/ip-2023-045036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 02/17/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Several previous studies have examined the association of ambient temperature with drowning. However, no study has investigated the effects of heat-humidity compound events on drowning mortality. METHODS The drowning mortality data and meteorological data during the five hottest months (May to September) were collected from 46 cities in Southern China (2013-2018 in Guangdong, Hunan and Zhejiang provinces). Distributed lag non-linear model was first conducted to examine the association between heat-humidity compound events and drowning mortality at city level. Then, meta-analysis was employed to pool the city-specific exposure-response associations. Finally, we analysed the additive interaction of heat and humidity on drowning mortality. RESULTS Compared with wet-non-hot days, dry-hot days had greater effects (excess rate (ER)=32.34%, 95% CI: 24.64 to 40.50) on drowning mortality than wet-hot days (ER=14.38%, 95%CI: 6.80 to 22.50). During dry-hot days, males (ER=42.40%, 95% CI: 31.92 to 53.72), adolescents aged 0-14 years (ER=45.00%, 95% CI: 21.98 to 72.35) and urban city (ER=36.91%, 95% CI: 23.87 to 51.32) showed higher drowning mortality risk than their counterparts. For wet-hot days, males, adolescents and urban city had higher ERs than their counterparts. Attributable fraction (AF) of drowning attributed to dry-hot days was 23.83% (95% CI: 21.67 to 26.99) which was significantly higher than that for wet-hot days (11.32%, 95% CI: 9.64 to 13.48%). We also observed that high temperature and low humidity had an additive interaction on drowning mortality. CONCLUSION We found that dry-hot days had greater drowning mortality risk and burden than wet-hot days, and high temperature and low humidity might have synergy on drowning mortality.
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Affiliation(s)
- Zhiying Jiang
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
| | - Ziqiang Lin
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
| | - Zhixing Li
- Department of Public Health, Jinan University, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Min Yu
- Division of NCD Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
| | - Jianxiong Hu
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
| | - Ruilin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Zhulin Hou
- Jilin Provincial Center for Disease Control and Prevention, Changchun, China
| | - Sui Zhu
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
| | - Chunliang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Yize Xiao
- Yunnan Provincial Center for Disease Control and Prevention, Kunming, China
| | - Biao Huang
- Jilin Provincial Center for Disease Control and Prevention, Changchun, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Donghui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Mingfang Qin
- Yunnan Provincial Center for Disease Control and Prevention, Kunming, China
| | - Yiqing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, Jinan University, Guangzhou, China
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Ye Z, Li X, Lang H, Xin J, Xu H, Fang Y. Long-term effect of extreme temperature on cognitive function of middle-aged and older adults in China. Int J Geriatr Psychiatry 2024; 39:e6063. [PMID: 38400786 DOI: 10.1002/gps.6063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/27/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Several studies have explored the association between temperature and cognitive function. However, few studies have examined the effect of extreme temperature on cognitive function. In this study, we aimed to quantify the long-term effect of extreme temperature (e.g., heat waves, cold spells, and hot night excess (HNE)) on cognitive performance in middle-aged and older people in China. METHOD We investigated 7915 aged >45 years people from the China Health and Retirement Longitudinal Study (CHARLS), surveyed in 2011 and 2015. A structured questionnaire was utilized to assess cognitive function, including four dimensions: episodic memory, attention, orientation, and visuo-construction. Hourly ambient temperature from the ERA5-Land datasets were used to calculate variables indicating extreme temperature. We performed difference-in-difference (DID) models to assess the potential causal relationship between extreme temperature and cognitive function. RESULTS Non-linear analyses suggested that both sustained increases in temperature and excessive variability in temperature increased the risk of cognitive decline. Meanwhile, we observed the extra risk of global cognitive function decline was 2.3% (95% Confidence interval (95% CI): 0.2%, 4.4%) for heat waves (one unit increase) and 5.9% (95% CI: 0.6%, 11.6%) for HNE (one unit increase), while the association for cold spells was insignificant. Two cognitive dimensions, episodic memory and visuo-construction, were sensitive to these two heat-related factors. CONCLUSION Extreme temperature was inversely related to cognitive performance in middle-aged and older adults, which was substantial for heat waves and HNE particularly. The effect size varied by cognitive dimensions.
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Affiliation(s)
- Zirong Ye
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Xueru Li
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Haoxiang Lang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Jiawei Xin
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
- Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
| | - Haibin Xu
- Fujian Medical University, Fuzhou, China
| | - Ya Fang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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Cheng BJ, Li H, Meng K, Li TL, Meng XC, Wang J, Wang C, Jiang N, Sun MJ, Yang LS, Zhu XY, Liu R. Short-term effects of heatwaves on clinical and subclinical cardiovascular indicators in Chinese adults: A distributed lag analysis. ENVIRONMENT INTERNATIONAL 2024; 183:108358. [PMID: 38056095 DOI: 10.1016/j.envint.2023.108358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/11/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
AIMS Previous studies have related heat waves to morbidity and mortality of cardiovascular diseases; however, potential mechanisms remained limited. Our aims were to investigate the short-term effects of heat waves on a series of clinical/subclinical indicators associated with cardiovascular health. METHODS Our study used 80,574 health examination records from the Health Management Center of Nanjing Zhongda Hospital during the warm seasons of 2019-2021, including 62,128 participants. A total of 11 recognized indicators of cardiovascular risk or injury were assessed. Air pollution and meteorological data were obtained from the Nanjing Ecological Environment Bureau and the China Meteorological Data Network, respectively. Heat waves were defined as a daily average temperature over the 95th percentile for three or more consecutive days from May to September. We used a combination of linear mixed effects models and distributed lag nonlinear models to assess the lagged effects of heat waves on clinical and subclinical cardiovascular indicators. Stratified analyses based on individuals' characteristics, including gender, age, body mass index (BMI), diabetes, and hypertension, were also performed. RESULTS Heat waves were related to significant changes in most indicators, with the magnitude of effects generally peaking at a lag of 0 to 3 days. Moreover, the cumulative percentage changes over lag 0-7 days were -0.82 % to -2.55 % in blood pressure, 1.32 % in heart rate, 0.20 % to 2.66 % in systemic inflammation markers, 0.36 % in a blood viscosity parameter, 9.36 % in homocysteine, and 1.35 % to 3.25 % in injuring myocardial enzymes. Interestingly, females and males showed distinct susceptibilities in different indicators. Stronger effects were also found in participants aged 50 years or over, individuals with abnormal BMI status, and patients with diabetes. CONCLUSION Short-term exposure to heat waves could significantly alter clinical/subclinical cardiovascular indicator profiles, including blood pressure changes, increased heart rate, acute systemic inflammation, elevated blood viscosity, and myocardial injury.
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Affiliation(s)
- Bei-Jing Cheng
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Hui Li
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Ke Meng
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Tian-Lin Li
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Xing-Chen Meng
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Jia Wang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Chun Wang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Nan Jiang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Ming-Jun Sun
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China
| | - Lin-Sheng Yang
- School of Public Health, Anhui Medical University, Hefei 230032, Anhui, China
| | - Xin-Yi Zhu
- The Affiliated Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, Jiangsu, China
| | - Ran Liu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China.
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Huang Z, Li Z, Hu J, Zhu S, Gong W, Zhou C, Meng R, Dong X, Yu M, Xu X, Lin L, Xiao J, Zhong J, Jin D, Xu Y, Liu T, Lin Z, He G, Ma W. The association of heatwave with drowning mortality in five provinces of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166321. [PMID: 37586513 DOI: 10.1016/j.scitotenv.2023.166321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/29/2023] [Accepted: 08/13/2023] [Indexed: 08/18/2023]
Abstract
Drowning is a serious public health problem in the world. Several studies have found that ambient temperature is associated with drowning, but few have investigated the effect of heatwave on drowning. This study aimed to explore the associations between heatwave and drowning mortality, and further estimate the mortality burden of drowning attributed to heatwave in China. Drowning mortality data were collected in 71 prefectures in China during 2013-2018 from provincial vital register system. Meteorological data at the same period were collected from European Centre for Medium-Range Weather Forecasts (ECMWF). A distributed lag non-linear model (DLNM) was first to explore the association between heatwave and drowning mortality in each prefecture. Secondly, the prefecture-specific associations were pooled using meta-analysis. Finally, attributable fractions (AFs) of drowning deaths caused by heatwave were estimated. Compared to normal day, the mortality risk of drowning significantly increased during heatwave (RR = 1.20, 95%CI: 1.18-1.23). Higher risks were observed in males (RR = 1.23, 95%CI: 1.20-1.27) than females (RR = 1.18, 95%CI: 1.13-1.23), in children aged 5-14 years old (RR = 1.24, 95%CI: 1.15-1.33) than other age groups, in urban city (RR = 1.32, 95%CI: 1.28-1.36) than rural area (RR = 1.09, 95%CI: 1.07-1.12) and in Jilin province (RR = 2.85, 95%CI: 1.61-5.06) than other provinces. The AF of drowning deaths due to heatwave was 11.4 % (95%CI: 10.0 %-12.9 %) during heatwave and 1.0 % (95%CI: 0.9 %-1.1 %) during study period, respectively. Moreover, the AFs during study period were higher for male (1.2 %, 95%CI: 1.0 %-1.3 %), children 5-14 years (1.1 %, 95%CI: 0.7 %-1.6 %), urban city (1.6 %, 95%CI: 1.4 %-1.8 %) than their correspondents. These differences were also observed in AFs during heatwave. We found that heatwave may significantly increase the mortality risk of drowning mortality, and its mortality burden attributable to heatwave was noteworthy. Targeted intervention should be carried out to decrease drowning mortality during heatwave.
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Affiliation(s)
- Zhongguo Huang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Zhixing Li
- Department of Nosocomial Infection Management, Nanfang Hospital, Southern Medical University, China
| | - Jianxiong Hu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China; Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Sui Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Weiwei Gong
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310009, China
| | - Chunliang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Ruilin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Xiaomei Dong
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Min Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310009, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jieming Zhong
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310009, China
| | - Donghui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Yiqing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Ziqiang Lin
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China; Disease Control and Prevention Institute, Jinan University, Guangzhou 511443, China.
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China; Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
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9
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Zhang Q, Yin Z, Lu X, Gong J, Lei Y, Cai B, Cai C, Chai Q, Chen H, Dai H, Dong Z, Geng G, Guan D, Hu J, Huang C, Kang J, Li T, Li W, Lin Y, Liu J, Liu X, Liu Z, Ma J, Shen G, Tong D, Wang X, Wang X, Wang Z, Xie Y, Xu H, Xue T, Zhang B, Zhang D, Zhang S, Zhang S, Zhang X, Zheng B, Zheng Y, Zhu T, Wang J, He K. Synergetic roadmap of carbon neutrality and clean air for China. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2023; 16:100280. [PMID: 37273886 PMCID: PMC10236195 DOI: 10.1016/j.ese.2023.100280] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 06/06/2023]
Abstract
It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.
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Affiliation(s)
- Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Zhanfeng Dong
- Institute of Environmental Policy Management, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Jianing Kang
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yongsheng Lin
- School of Economics and Resource Management, Beijing Normal University, Beijing, 100875, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Zhili Wang
- State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Honglei Xu
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People's Republic of China, Beijing, 100028, China
| | - Tao Xue
- 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, Beijing, 100080, China
| | - Bing Zhang
- State Key Laboratory of Pollution Control & Resource Reuse School of Environment, Nanjing University, Nanjing, 210008, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Institute of Environmental Policy Management, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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10
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Li Z, Fan Y, Su H, Xu Z, Ho HC, Zheng H, Tao J, Zhang Y, Hu K, Hossain MZ, Zhao Q, Huang C, Cheng J. The 2022 Summer record-breaking heatwave and health information-seeking behaviours: an infodemiology study in Mainland China. BMJ Glob Health 2023; 8:e013231. [PMID: 37730248 PMCID: PMC10510944 DOI: 10.1136/bmjgh-2023-013231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/20/2023] [Indexed: 09/22/2023] Open
Abstract
INTRODUCTION Heatwave is a major global health concern. Many countries including China suffered a record-breaking heatwave during the summer of 2022, which may have a significant effect on population health or health information-seeking behaviours but is yet to be examined. METHODS We derived health information-seeking data from the Baidu search engine (similar to Google search engine). The data included city-specific daily search queries (also referred to Baidu Search Index) for heat-sensitive diseases from 2021 to 2022, including heatstroke, hospital visits, cardiovascular diseases and diabetes, respiratory diseases, mental health and urological diseases. For each city, the record-breaking heatwave days in 2022 were matched to days in the same calendar month in 2021. RESULTS The 2022 record-breaking heatwave hit most cities (83.64%) in Mainland China. The average heatwave duration was 13 days and the maximum temperature was 3.60°C higher than that in 2021 (p<0.05). We observed increased population behaviours of seeking information on respiratory diseases (RR=1.014, 95% CI: 1.008 to 1.020), urological diseases (RR=1.011, 95% CI: 1.006 to 1.016) and heatstroke (RR=1.026, 95% CI: 1.016 to 1.036) associated with the heatwave intensity in 2022 (per 1°C increase). The heatwave duration in 2022 (per 1 day increase) was also associated with an increase in seeking information on cardiovascular diseases and diabetes (RR=1.003, 95% CI: 1.002 to 1.004), urological diseases (RR=1.005, 95% CI: 1.002 to 1.008), mental health (RR=1.009, 95% CI: 1.006 to 1.012) and heatstroke (RR=1.038, 95% CI: 1.032 to 1.043). However, there were substantial geographical variations in the effect of the 2022 heatwave intensity and duration on health information-seeking behaviours. CONCLUSION This infodemiology study suggests that the 2022 summer unprecedented heatwave in Mainland China has significantly increased population demand for health-related information, especially for heatstroke, urological diseases and mental health. Population-based research of real-time disease data is urgently needed to estimate the negative health impact of the exceptional heatwave in Mainland China and elsewhere.
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Affiliation(s)
- Zhiwei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Yinguang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia
| | - Hung Chak Ho
- Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | | | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
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11
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Liu J, Dong H, Li M, Wu Y, Zhang C, Chen J, Yang Z, Lin G, Liu DL, Yang J. Projecting the excess mortality due to heatwave and its characteristics under climate change, population and adaptation scenarios. Int J Hyg Environ Health 2023; 250:114157. [PMID: 36989996 DOI: 10.1016/j.ijheh.2023.114157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Heatwaves have significant adverse effects on human health. The frequency, duration, and intensity of heatwaves are projected to increase dramatically, in the context of global warming. However, there are few comprehensive assessments of the health impact of heatwaves considering different definitions, and their characteristics under climate change scenarios. OBJECTIVE We aimed to compare future excess mortality related to heatwaves among different definitions under climate change, population, and adaptation scenarios in China and further explore the mortality burden associated with heatwave characteristics. METHODS Daily data during 2010-2019 were collected in Guangzhou, China. We adopted nine common heatwave definitions and applied quasi-Poisson models to estimate the effects of heatwaves and their characteristics' impact on mortality. We then projected the excess mortality associated with heatwaves and their characteristics concerning climate change, population, and adaptation scenarios. RESULTS The relative risks of the nine common heatwave definitions ranged from 1.05 (95% CI: 1.01, 1.10) to 1.24 (95% CI: 1.13, 1.35). Heatwave-related excess mortality will consistently increase in the future decades considering multiple heatwave definitions, with more rapidly increasing rates under the Shared Socioeconomic Path5-8.5 and non-adaptability scenarios. Regarding heatwave characteristics, the intensity is the main factor involved in the threat of heatwaves. The increasing trend of characteristic-related mortality burden is similar to that of heatwaves, and the mortality burden caused by the duration of the heatwaves was the largest among all characteristics. CONCLUSIONS This study provides a comprehensive picture of the impact of heatwaves and their characteristics on public health under various climate change scenarios, population changes, and adaptive assumptions. The results may provide important public health implications for policymakers in planning climate change adaptation and mitigation policies, and implementing specific plans.
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12
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Pan R, Xie M, Chen M, Zhang Y, Ma J, Zhou J. The impact of heat waves on the mortality of Chinese population: A systematic review and meta-analysis. Medicine (Baltimore) 2023; 102:e33345. [PMID: 37000079 PMCID: PMC10063284 DOI: 10.1097/md.0000000000033345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Many studies had shown that with global warming, heat waves may increase the mortality risk of Chinese populations. However, these findings are not consistent. Therefore, we elucidated the associations by meta-analysis and quantified the magnitude of these risks, as well as the underlying factors. METHODS We searched the China National Knowledge Infrastructure (CNKI), Wanfang database, PubMed, EMBASE, and Web of Science for literature screening up to Nov 10, 2022, to analyze the effect of heat waves on mortality in the Chinese population. Literature screening and data extraction were performed independently by two researchers and the data were merged by meta-analysis. In addition, we conducted subgroup analysis by sex, age, years of education, region, and number of events to explore the source of heterogeneity. RESULTS Fifteen related studies on the impact on heat waves of the death of Chinese people were included in this study. The results of the meta-analysis showed that heat waves were significantly associated with increased mortality from non-accidental deaths, cardiovascular diseases, stroke, respiratory diseases, and circulatory diseases in the Chinese population: non-accidental mortality (RR = 1.19, 95% CI: 1.13-1.27, P < .01), cardiovascular diseases (RR = 1.25, 95% CI: 1.14-1.38), stroke (RR = 1.11, 95% CI: 1.03-1.20), respiratory diseases (RR = 1.18, 95% CI: 1.09-1.28), and circulatory diseases (RR = 1.11, 95% CI: 1.06-1.17). Subgroup analyses showed that heat waves had a higher risk of non-accidental death for those with <6 years of education than for those with ≥6 years of education. Meta-regression analysis showed that the contribution of the study year to the inter studied heterogeneity was 50.57%. The sensitivity analysis showed that the exclusion of any single study did not materially alter the overall combined effect. The meta-analysis method indicated no obvious evidence of publication bias. CONCLUSIONS The results of the review indicated that heat waves were associated with increased mortality in the Chinese population, that attention should be paid to high-risk groups, and that public health policies and strategies should be implemented to more effectively respond to and adapt to climate change.
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Affiliation(s)
- Ranran Pan
- Department of Science and Education, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
| | - Ming Xie
- Department of Science and Education, The Third Hospital of Changsha, Changsha, China
| | - Mengxiang Chen
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, China
| | - Yannan Zhang
- Jiangyin Center for Disease Control and Prevention, Jiangyin, China
| | - Jian Ma
- Huaian Center for Disease Control and Prevention, Huaian, China
| | - Junhua Zhou
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, China
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13
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Yan M, Xie Y, Zhu H, Ban J, Gong J, Li T. Cardiovascular mortality risks during the 2017 exceptional heatwaves in China. ENVIRONMENT INTERNATIONAL 2023; 172:107767. [PMID: 36716635 DOI: 10.1016/j.envint.2023.107767] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/11/2022] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Climate change has made disastrous heatwaves more frequent. Heatwave-related health impacts are much more devastating for more intense heatwaves. In the summer of 2017, exceptional heatwaves occurred in many regions, including China. This study aims to evaluate the cardiovascular mortality risk associated with the 2017 exceptional heatwaves and compare the mortality risk of the severe heatwaves with those in other years. Using daily data for a spectrum of cardiovascular mortality and temperature for 102 Chinese counties (2014-2017), we estimated the association between heatwave and mortality by generalized linear mixed-effects models. Compared with matched non-heatwave days, mortality risks on heatwaves days in 2017 increased 27.8% (95% CI, 14.8-42.3%), 26.7% (8.0-48.5%), 30.1% (10.2-53.7%), 27.3% (1.4-59.9%), 32.2% (3.4-68.4%), and 25.2% (1.0-57.7%) for total circulatory diseases, cerebrovascular disease, ischemic heart disease (IHD), acute IHD, chronic IHD, and myocardial infarction. The 2017 exceptional heatwaves impacted ischemic heart disease mortality and myocardial infarction mortality more than heatwaves in 2014-2016. Here we show that the severe heatwaves in 2017 posed catastrophic death threats for those under-studied cardiovascular diseases.
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Affiliation(s)
- Meilin Yan
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, China; Future Cities Lab, Beihang University, China
| | - Huanhuan Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jie Ban
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jicheng Gong
- Beijing Innovation Center for Engineering Science and Advanced Technology and State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
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14
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Alegría N, Hernández-Ceballos MÁ, Cinelli G, Peñalva I, Muñoz JM. Analysis of 222Rn Surface Concentrations in the Basque Country (Spain): A Case Study of Heat Waves. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2105. [PMID: 36767472 PMCID: PMC9915027 DOI: 10.3390/ijerph20032105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The objective of this study was to characterize radon concentrations registered in the Radiological Surveillance Network of the Basque country in relation to local meteorological parameters, and to determine its behaviour under heatwave events. For this purpose, radon measurements and meteorological parameters from June 2012 to June 2015 were analysed at two sites, Bilbao and Vitoria (northern Spain), in a region characterized by complex orography, causing large temporal and spatial variability in meteorological conditions. Yearly, seasonal, and diurnal cycle differences and similarities were investigated at both sites. The temporal evolution of radon concentration was analysed at both sites during the two heatwave periods officially identified by the State Meteorological Agency (8-11 August 2012 and 17-23 August 2012). The analysis revealed two different patterns of radon concentrations, in terms of both time and intensity, under this synoptic pattern, making it also possible to identify regional transport channels of radon concentrations between the two sites. This set of results evidences the adequate position of both stations to represent the spatial and temporal evolution of radiological variables continuously in this region.
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Affiliation(s)
- Natalia Alegría
- Department of Energy Engineering, University of the Basque Country, 48013 Bilbao, Spain
| | | | - Giorgia Cinelli
- Laboratory of Observations and Measurements for the Climate and the Environment, National Agency for New Technologies, Energy, and Sustainable Economic Development (ENEA), 21027, Ispra, Italy
| | - Igor Peñalva
- Department of Energy Engineering, University of the Basque Country, 48013 Bilbao, Spain
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15
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Fang W, Li Z, Gao J, Meng R, He G, Hou Z, Zhu S, Zhou M, Zhou C, Xiao Y, Yu M, Huang B, Xu X, Lin L, Xiao J, Jin D, Qin M, Yin P, Xu Y, Hu J, Liu T, Huang C, Ma W. The joint and interaction effect of high temperature and humidity on mortality in China. ENVIRONMENT INTERNATIONAL 2023; 171:107669. [PMID: 36508749 DOI: 10.1016/j.envint.2022.107669] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/20/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Although many studies have reported the mortality effect of temperature, there were few studies on the mortality risk of humidity, let alone the joint effect of temperature and humidity. This study aimed to investigate the joint and interaction effect of high temperature and relative humidity on mortality in China, which will deepen understanding the health risk of mixture climate exposure. METHODS The mortality and meteorological data were collected from 353 locations in China (2013-2017 in Jilin, Hunan, Guangdong and Yunnan provinces, 2009-2017 in Zhejiang province, and 2006-2011 in other Provinces). We defined location-specific daily mean temperature ≥ 75th percentile of distribution as high temperature, while minimum mortality relative humidity as the threshold of high relative humidity. A time-series model with a distributed lag non-linear model was first employed to estimate the location-specific associations between humid-hot events and mortality, then we conducted meta-analysis to pool the mortality effect of humid-hot events. Finally, an additive interaction model was used to examine the interactive effect between high temperature and relative humidity. RESULTS The excess rate (ER) of non-accidental mortality attributed to dry-hot events was 10.18% (95% confidence interval (CI): 8.93%, 11.45%), which was higher than that of wet-hot events (ER = 3.21%, 95% CI: 0.59%, 5.89%). The attributable fraction (AF) of mortality attributed to dry-hot events was 10.00% (95% CI: 9.50%, 10.72%) with higher burden for females, older people, central China, cardiovascular diseases and urban city. While for wet-hot events, AF was much lower (3.31%, 95% CI: 2.60%, 4.30%). We also found that high temperature and low relative humidity had synergistic additive interaction on mortality risk. CONCLUSION Dry-hot events may have a higher risk of mortality than wet-hot events, and the joint effect of high temperature and low relative humidity may be greater than the sum of their individual effects.
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Affiliation(s)
- Wen Fang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Zhixing Li
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Jinghua Gao
- School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Ruilin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Zhulin Hou
- Jilin Provincial Center for Disease Control and Prevention, Changchun 130062, China
| | - Sui Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Maigeng Zhou
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing 100050, China
| | - Chunliang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Yize Xiao
- Yunnan Provincial Center for Disease Control and Prevention, Kunming 650034, China
| | - Min Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310009, China
| | - Biao Huang
- Jilin Provincial Center for Disease Control and Prevention, Changchun 130062, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Donghui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Mingfang Qin
- Yunnan Provincial Center for Disease Control and Prevention, Kunming 650034, China
| | - Peng Yin
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing 100050, China
| | - Yiqing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China.
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16
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He F, Wei J, Dong Y, Liu C, Zhao K, Peng W, Lu Z, Zhang B, Xue F, Guo X, Jia X. Associations of ambient temperature with mortality for ischemic and hemorrhagic stroke and the modification effects of greenness in Shandong Province, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158046. [PMID: 35987239 DOI: 10.1016/j.scitotenv.2022.158046] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/28/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Evidence is scant on the relative and attributable contributions of ambient temperature on stroke subtypes mortality. Few studies have examined modification effects of multiple greenness indicators on such contributions, especially in China. We quantified the associations between ambient temperature and overall, ischemic, and hemorrhagic stroke mortality; further examined whether the associations were modified by greenness. METHODS We conducted a multicenter time-series analysis from January 1, 2013 to December 31, 2019. we adopted a distributed lag non-linear model to evaluate county-specific temperature-stroke mortality associations. We then applied a random-effects meta-analysis to pool county-specific effects. Attributable mortality was calculated for cold and heat, defined as temperatures below and above the minimum mortality temperature (MMT). Finally, We conducted a multivariate meta-regression to determine associations between greenness and stroke mortality risks for cold and heat, using normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) as quantitative indicators of greenness exposure. RESULTS In the study period, 138,749 deaths from total stroke were reported: 86,873 ischemic and 51,876 hemorrhagic stroke. We observed significant W-shaped relationships between temperature and stroke mortality, with substantial differences among counties and regions. With MMT as the temperature threshold, 17.16 % (95 % empirical CI, 13.38 %-19.75 %) of overall, 20.05 % (95 % eCI, 16.46 %-22.70 %) of ischemic, and 12.55 % (95 % eCI, 5.59 %-16.24 %) of hemorrhagic stroke mortality were attributable to non-optimum temperature (combining cold and heat), more mortality was caused by cold (14.94 %; 95 % eCI, 11.57 %-17.34 %) than by heat (2.22 %; 95 % eCI, 1.54 %-2.72 %). Higher levels of NDVI, SAVI and EVI were related to mitigated effects of non-optimum temperatures-especially heat. CONCLUSIONS Exposure to non-optimum temperatures aggravated stroke mortality risks; increasing greenness could alleviate that risks. This evidence has important implications for local communities in developing adaptive strategies to minimize the health consequences of adverse temperatures.
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Affiliation(s)
- Fenfen He
- Department of Epidemiology and Statistics, Bengbu Medical College, Bengbu, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | - Yilin Dong
- Department of Epidemiology and Statistics, Bengbu Medical College, Bengbu, China
| | - Chao Liu
- Department of Epidemiology and Statistics, Bengbu Medical College, Bengbu, China
| | - Ke Zhao
- Department of Epidemiology and Statistics, Bengbu Medical College, Bengbu, China
| | - Wenjia Peng
- School of Public Health, Fudan University, Shanghai, China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, Jinan, China
| | - Bingyin Zhang
- Shandong Center for Disease Control and Prevention, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, Jinan, China.
| | - Xianjie Jia
- Department of Epidemiology and Statistics, Bengbu Medical College, Bengbu, China.
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17
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Liu J, Yu W, Pan R, He Y, Wu Y, Yan S, Yi W, Li X, Song R, Yuan J, Liu L, Wei N, Jin X, Li Y, Liang Y, Sun X, Mei L, Song J, Cheng J, Su H. Association between sequential extreme precipitation-heatwaves events and hospitalizations for schizophrenia: The damage amplification effects of sequential extremes. ENVIRONMENTAL RESEARCH 2022; 214:114143. [PMID: 35998693 DOI: 10.1016/j.envres.2022.114143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES In the context of frequent global extreme weather events, there are few studies on the effects of sequential extreme precipitation (EP) and heatwaves (HW) events on schizophrenia. We aimed to quantify the effects of the events on hospitalizations for schizophrenia and compare them with EP and HW alone to explore the amplification effect of successive extremes on health loss. METHODS A time-series Poisson regression model combined with a distributed lag non-linear model was applied to estimate the association between sequential EP and HW events (EP-HW) and schizophrenia hospitalizations. The effects of EP-HW with different intervals and intensities on the admission of schizophrenia were compared. In addition, we calculated the mean attributable fraction (AF) and attributable numbers (AN) per exposure of extreme events to reflect the amplification effect of sequential extreme events on health hazards compared with individual extreme events. RESULTS EP-HW increased the risk of hospitalization for schizophrenia, with significant effects lasting from lag0 (RR and 95% CI: 1.150 (1.041-1.271)) to lag11 (1.046 (1.000-1.094)). Significant associations were found in the subgroups of male, female, married people, and those aged≥ 40 years old. Shorter-interval (0-3days) or higher-intensity EP-HW (both precipitation ≥ P97.5 and mean temperature ≥ P97.5) had a longer lag effect compared to EP-HW with longer intervals or lower intensity. We found that the mean AF and AN caused by each exposure to EP-HW (AF: 0.074% (0.015%-0.123%); AN: 4.284 (0.862-7.118)) were higher than those induced by each exposure to HW occurring alone (AF:0.032% (0.004%-0.058%); AN:1.845 (0.220-3.329)). CONCLUSIONS Sequential extreme precipitation-heatwaves events significantly increase the risk of hospitalizations for schizophrenia, with greater impact and disease burden than independently occurring extremes. The impact of consecutive extremes is supposed to be considered in local sector early warning systems for comprehensive public health decision-making.
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Affiliation(s)
- Jintao Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Wenping Yu
- Department of Geriatrics, Shandong Daizhuang Hospital, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yudong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Shuangshuang Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xuanxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Rong Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Jiajun Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Li Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Ning Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xiaoyu Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yuxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yunfeng Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xiaoni Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Lu Mei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China.
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Yan M, Xie Y, Zhu H, Ban J, Gong J, Li T. The exceptional heatwaves of 2017 and all-cause mortality: An assessment of nationwide health and economic impacts in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 812:152371. [PMID: 34919930 DOI: 10.1016/j.scitotenv.2021.152371] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 06/14/2023]
Abstract
Heatwaves with unprecedented conditions have devastating health impacts. The summer of 2017 saw unusual heat in China and other regions on earth. Although epidemiologic evidence is clear for elevated mortality risks of heatwaves, the economic impacts due to heatwave-associated mortality remain poorly characterized. Hence, this study systematically assessed the mortality and economic impacts of the 2017 exceptional heatwaves in China. We first used the generalized linear mixed-effect model with Poisson distribution to examine the mortality risks of the 2017 heatwaves in 91 Chinese counties. Further, we calculated the excess deaths attributable to heatwaves in 2852 counties. Finally, we evaluated the city- and province-level death-related economic burden of the 2017 heatwaves based on the value of statistical life (VSL). We found that the 2017 exceptional heatwaves had a statistically significant association (relative risk was 1.23, 95% confidence interval 1.14-1.32) with all-cause mortality across 91 Chinese counties. Nationwide, a total of 16,299 all-cause deaths that occurred in 2017 were attributable to the exceptional heatwaves, resulting in an overall death-related economic loss of 61,304 million RMB as valued by VSL. Given that extraordinary heatwaves are projected to be more frequent under global climate change, our findings could enhance the current understanding of heatwaves' health and economic impacts and add valuable insights in projection studies of estimating the future health burden of heatwaves.
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Affiliation(s)
- Meilin Yan
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, China; Future Cities Lab, Beihang University, China
| | - Huanhuan Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jie Ban
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jicheng Gong
- Beijing Innovation Center for Engineering Science and Advanced Technology and State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
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19
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Tang C, Ji Y, Li Q, Yao Z, Cheng J, He Y, Liu X, Pan R, Wei Q, Yi W, Su H. Effects of different heat exposure patterns (accumulated and transient) and schizophrenia hospitalizations: a time-series analysis on hourly temperature basis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:69160-69170. [PMID: 34286435 DOI: 10.1007/s11356-021-15371-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Growing studies have shown that high temperature is a potential risk factor of schizophrenia occurrence. Therefore, elaborate analysis of different temperature exposure patterns, such as cumulative heat exposure within a time period and transient exposure at a particular time point, is of important public health significance. This study aims to utilize hourly temperature data to better capture the effects of cumulative and transient heat exposures on schizophrenia during the warm season in Hefei, China. We included the daily mean temperature and daily schizophrenia hospitalizations into the distributed lag non-linear model (DLNM) to simulate the exposure-response curve and determine the heat threshold (19.4 °C). We calculated and applied a novel indicator-daily excess hourly heat (DEHH)-to examine the effects of cumulative heat exposure over a day on schizophrenia hospitalizations. Temperature measurements at each time point were also incorporated in the DLNM as independent exposure indicators to analyze the impact of transient heat exposure on schizophrenia. Each increment of interquartile range (IQR) in DEHH was associated with elevated risk of schizophrenia hospitalizations from lag 1 (RR = 1.036, 95% confidence interval (CI): 1.016, 1.057) to lag 4 (RR = 1.025, 95% CI: 1.005, 1.046). Men and people over 40 years old were more susceptible to DEHH. Besides, we found a greater risk of heat-related schizophrenia hospitalizations between 0 a.m. and 6 a.m. This study revealed the adverse effects of accumulated and transient heat exposures on schizophrenia hospitalizations. Our findings need to be further tested in other regions with distinct regional features.
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Affiliation(s)
- Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Yifu Ji
- Anhui Mental Health Center, Hefei, 230032, Anhui, China
| | - Qingru Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Zhenhai Yao
- Anhui Public Meteorological Service Center, Hefei, 230011, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China.
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20
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Grammatikopoulou MG, Gkiouras K, Pepa A, Persynaki A, Taousani E, Milapidou M, Smyrnakis E, Goulis DG. Health status of women affected by homelessness: A cluster of in concreto human rights violations and a time for action. Maturitas 2021; 154:31-45. [PMID: 34736578 DOI: 10.1016/j.maturitas.2021.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/10/2021] [Accepted: 09/17/2021] [Indexed: 10/20/2022]
Abstract
Health problems of women experiencing homelessness are driven either from the usual background characteristics of this population, or from the homeless lifestyle. Apart from poverty and unemployment, transition to homelessness is often associated with substance abuse, history of victimization, stress, poor mental health and human immunodeficiency virus (HIV). Water insecurity can undermine bodily hygiene and dental health, posing a greater risk of dehydration and opportunistic infections. Exposure to extreme environmental conditions like heat waves and natural disasters increases morbidity, accelerates aging, and reduces life expectancy. Nutrition-wise, a high prevalence of food insecurity, obesity, and micronutrient deficiencies are apparent due to low diet quality and food waste. Poor hygiene, violence, and overcrowding increase the susceptibility of these women to communicable diseases, including sexually transmitted ones and COVID-19. Furthermore, established cardiovascular disease and diabetes mellitus are often either undertreated or neglected, and their complications are more widespread than in the general population. In addition, lack of medical screening and contraception non-use induce a variety of reproductive health issues. All these health conditions are tightly related to violations of human rights in this population, including the rights to housing, water, food, reproduction, health, work, and no discrimination. Thus, the care provided to women experiencing homelessness should be optimized at a multidimensional level, spanning beyond the provision of a warm bed, to include access to clean water and sanitation, psychological support and stress-coping strategies, disease management and acute health care, food of adequate quality, opportunities for employment and support for any minor dependants.
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Affiliation(s)
- Maria G Grammatikopoulou
- Unit of Reproductive Endocrinology, 1st Department of Obstetrics and Gynecology, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece; Department of Nutritional Sciences and Dietetics, Faculty of Health Sciences, Alexander Campus, International Hellenic University, Thessaloniki, Greece
| | - Konstantinos Gkiouras
- Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aleks Pepa
- Department of Food Science and Human Nutrition, Agricultural University of Athens, Greece
| | | | - Eleftheria Taousani
- Department of Midwifery, Faculty of Health Sciences, International Hellenic University, Alexander Campus, Thessaloniki, Greece
| | - Maria Milapidou
- Dr. Juris, Post Doc Researcher, Faculty of Law, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Emmanouil Smyrnakis
- Laboratory of Primary Health Care, General Practice and Health Services Research, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios G Goulis
- Unit of Reproductive Endocrinology, 1st Department of Obstetrics and Gynecology, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
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21
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Yang Z, Yang J, Zhou M, Yin P, Chen Z, Zhao Q, Hu K, Liu Q, Ou CQ. Hourly temperature variability and mortality in 31 major Chinese cities: Effect modification by individual characteristics, season and temperature zone. ENVIRONMENT INTERNATIONAL 2021; 156:106746. [PMID: 34247007 DOI: 10.1016/j.envint.2021.106746] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective. METHODS Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007-2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region. RESULTS HTV calculated in a relatively long-time window like 18 d (HTV0-17) could capture the impact of HTV adequately. Per 1 °C raise of HTV0-17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone. CONCLUSION HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.
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Affiliation(s)
- Zhou Yang
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Jun Yang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China; JNU-QUT Joint Laboratory for Air Quality Science and Management, Jinan University, Guangzhou 511443, China.
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing 100050, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing 100050, China
| | - Zhaoyue Chen
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Qi Zhao
- Department of Epidemiology, Shandong University, Jinan, China
| | - Kejia Hu
- Institute of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, 310058, China
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.
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Wang Y, Chen Y, Chen J, Wu R, Guo P, Zha S, Zhang Q. Mortality risk attributable to diurnal temperature range: a multicity study in Yunnan of southwest China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:60597-60608. [PMID: 34160766 DOI: 10.1007/s11356-021-14981-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/11/2021] [Indexed: 02/05/2023]
Abstract
We aimed to estimate the non-accidental and cause-specific mortality burden attributed to diurnal temperature range (DTR) and the relative contributions of low, high, and extremely low and extremely high DTR in Yunnan, southwest China. Furthermore, we explored the possible effect modification of the DTR-mortality association by season, sex, age, ethnicity, marital status, and occupation. A standard time-series quasi-Poisson regression model combined with a distributed lag nonlinear model was used to derive estimates of city-specific DTR-mortality associations, then random effects meta-analysis was used to pool the estimated city-specific overall cumulative DTR-mortality association, estimating empirical confidence intervals (eCIs). The overall fraction of non-accidental mortality caused by DTR was 11.00% (95% eCI 3.40-17.28): high DTR accounted for most of burden (total estimate 10.03%, 95% eCI 2.59-16.32). The estimated mortality risk attributable to DTR was significantly associated with cardiovascular and respiratory mortality, with attributable fractions of 13.61% (95% eCI 3.91-21.13) and 14.32% (95% eCI 0.47-21.44), respectively. The estimated risk attributable to DTR was slightly greater for males, people ≥75 years old, married people, and non-farmers than their corresponding categories. Most of the DTR-related mortality burden was attributable to high DTR, and the mortality risk attributable to DTR might be modified by specific causes, sex, age, marital status, and occupation.
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Affiliation(s)
- Yujin Wang
- Department of Preventive Medicine, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Yang Chen
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, Yunnan, China
| | - Jiaqi Chen
- Department of Preventive Medicine, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Rong Wu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Pi Guo
- Department of Preventive Medicine, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Shun Zha
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, Yunnan, China.
| | - Qingying Zhang
- Department of Preventive Medicine, Shantou University Medical College, Shantou, 515041, Guangdong, China.
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, 515041, China.
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Osyaeva MK, Tikhaze AK, Konovalova GG, Heimets GI, Martynyuk TV, Lankin VZ. Impact of summer heat waves on key parameters of oxidative stress in patients with coronary artery disease. TERAPEVT ARKH 2021; 93:421-426. [DOI: 10.26442/00403660.2021.04.200684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/02/2021] [Indexed: 11/22/2022]
Abstract
Aim. To assess the impact of summer heat waves on key parameters of oxidative stress in patients with coronary heart disease.
Materials and methods. We included 30 male patients aged 5213 years with stable angina pectoris of IIIII functional class with at least one coronary artery stenosis proved by angiography (ischemic group) in comparison with 10 male patients aged 487 years with no angiographic sings of significant coronary stenosis and without angina manifestation (non-ischemic group). The following parameters were studied: activity of superoxide dismutase (Cu,Zn-SOD), catalase (CAT), glutathione peroxidase (GSH-Px), the level of malondialdehyde (MDA) and MDA-modified low-density lipoproteins (MDA-LDL). The analysis of indicators was performed at normal average daily temperature (daytime temperature not higher than 20С) and after a heat wave (daytime temperature above 27C for more than 2 consecutive days).
Results. Our study revealed the decrease of CAT and GSH-Px activities with increased activity of Cu,Zn-SOD in both groups after the heat wave. At the same time we observed accumulation of MDA and increased MDA-LDL level in both groups. Initially ischemic patients showed significantly increased level of CAT and GSH-Px activity compared to the non-ischemic group, while it was no difference in activity of Cu,Zn-SOD and MDA and MDA-LDL level. We observed significant reduce of Cu,Zn-SOD activity in ischemic patients compared to non-ischemic group with no significant differences in all other studied parameters of oxidative stress after heat wave.
Conclusion. Changes in the key parameters of oxidative stress in patients with ischemic heart disease during summer heat waves are comparable to those in patients without ischemia, however significantly greater inhibition of GSH-Px activity and significantly lower increase in Cu,Zn-SOD activity was noted. These results may indicate misregulation of free radical processes in patients with ischemic heart decease
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