1
|
Moreira RP, da Silva CBC, de Sousa TC, Leitão FLBF, Morais HCC, de Oliveira ASS, Duarte-Clíments G, Gómez MBS, Cavalcante TF, Costa AC. The Influence of Climate, Atmospheric Pollution, and Natural Disasters on Cardiovascular Diseases and Diabetes Mellitus in Drylands: A Scoping Review. Public Health Rev 2024; 45:1607300. [PMID: 39176255 PMCID: PMC11338784 DOI: 10.3389/phrs.2024.1607300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 07/26/2024] [Indexed: 08/24/2024] Open
Abstract
Objectives In the face of escalating global aridification, this study examines the complex relationship between climate variability, air pollution, natural disasters, and the prevalence of cardiovascular disease (CVD) and diabetes mellitus (DM) in arid regions. Methods The study conducted a scoping review of multiple databases using JBI guidelines and included 74 studies. Results The results show that acute myocardial infarction (n = 20) and stroke (n = 13) are the primary CVDs affected by these factors, particularly affecting older adults (n = 34) and persons with hypertension (n = 3). Elevated air temperature and heat waves emerge as critical risk factors for CVD, exacerbating various cardiovascular mechanisms. Atmospheric pollutants and natural disasters increase this risk. Indirect effects of disasters amplify risk factors such as socioeconomic vulnerability (n = 4), inadequate medical care (n = 3), stress (n = 3), and poor diet (n = 2), increasing CVD and DM risk. Conclusion The study underscores the need for nations to adhere to the Paris Agreement, advocating for reduced air pollutants, resilient environments, and collaborative, multidisciplinary research to develop targeted health interventions to mitigate the adverse effects of climate, pollution, and natural disasters.
Collapse
Affiliation(s)
- Rafaella Pessoa Moreira
- Institute of Health Sciences, University of International Integration of Afro-Brazilian Lusophony, Redenção, Brazil
| | - Clara Beatriz Costa da Silva
- Institute of Health Sciences, University of International Integration of Afro-Brazilian Lusophony, Redenção, Brazil
| | - Tainara Chagas de Sousa
- Institute of Health Sciences, University of International Integration of Afro-Brazilian Lusophony, Redenção, Brazil
| | | | | | | | - Gonzalo Duarte-Clíments
- School of Nursing, University of La Laguna, San Cristóbal de La Laguna, Spain
- School of Nursing, Valencian International University, Castelló de la Plana, Spain
| | - María Begoña Sánchez Gómez
- School of Nursing, University of La Laguna, San Cristóbal de La Laguna, Spain
- Department of Nursing, UCAM Catholic University of Murcia, Guadalupe, Spain
| | - Tahissa Frota Cavalcante
- Institute of Health Sciences, University of International Integration of Afro-Brazilian Lusophony, Redenção, Brazil
| | - Alexandre Cunha Costa
- Institute of Engineering and Sustainable Development, University of International Integration of Afro-Brazilian Lusophony, Redenção, Brazil
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Ji Y, Huang Z, Yuan Z, Xiong J, Li L. Exposure to low humidex increases the risk of hip fracture admissions in a subtropical coastal Chinese city. Bone 2024; 181:117032. [PMID: 38307177 DOI: 10.1016/j.bone.2024.117032] [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: 12/06/2023] [Revised: 01/15/2024] [Accepted: 01/30/2024] [Indexed: 02/04/2024]
Abstract
OBJECTIVE The adverse impacts of meteorological factors on human health have attracted great attention. However, no studies have investigated the nonlinear effects of humidex on hip fractures (HF), particularly in middle-aged and older adults. This study aimed to quantify the impacts of humidex, a comprehensive index of temperature and relative humidity, on HF admissions. METHODS Daily HF admissions, meteorological variables and air pollutants in the subtropical coastal city of Shantou, China, from 2015 to 2020 were collected. A generalized linear regression model combined with a distributed lag nonlinear model was applied to explore the exposure-lag-response relationship between humidex and HF admissions. Subgroup analyses were also conducted by gender, age and season. Attributable fractions (AF) and attributable numbers (AN) were used to represent the burden of disease. RESULTS A total of 6200 HF admissions were identified during the study period. Taking the median humidex (31.9) as a reference, the single-day lag effects of low humidex (13, 2.5th percentile) were significant at lag 0 [relative risk (RR) = 1.145, 95 % confidence interval (CI): 1.041-1.259] to lag 2 (RR = 1.049, 95 % CI: 1.010-1.089). The cumulative lag effects of low humidex were significant at lag 0-0 (RR = 1.145, 95 % CI: 1.041-1.259) to lag 0-6 (RR = 1.258, 95 % CI: 1.010-1.567) and reached a maximum at lag 0-3 (RR = 1.330, 95 % CI: 1.113-1.590). High humidex (44, 97.5th percentile) was not associated with the risk of HF. Females and people over the age of 75 appeared to be more susceptible to low humidex. In addition, the adverse effects of low humidex were more pronounced in the cold season. The AF and AN of low humidex on HF admissions were 24.8 % (95 % CI: 10.2-37.1 %) and 1538, respectively. CONCLUSION Low humidex was associated with an increased risk of HF admissions. The government should take timely measures to prevent people from being exposed to low humidex to effectively reduce HF admissions.
Collapse
Affiliation(s)
- Yanhu Ji
- School of Public Health, Shantou University, 515063 Shantou, China
| | - Zepeng Huang
- The Second Affiliated Hospital of Shantou University Medical College, 515041 Shantou, China
| | | | - Jianping Xiong
- The First Affiliated Hospital of Shantou University Medical College, 515041 Shantou, China
| | - Liping Li
- School of Public Health, Shantou University, 515063 Shantou, China.
| |
Collapse
|
4
|
Ma Y, Zhang X, Zhang Y, Du J, Chu N, Wei J, Cui L, Zhou C. The threaten of typhoons to the health of residents in inland areas: a study on the vulnerability of residents to death risk during typhoon "Lekima" : In Jinan, China. BMC Public Health 2024; 24:606. [PMID: 38409004 PMCID: PMC10895747 DOI: 10.1186/s12889-024-17667-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 01/04/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Studies had suggested increased risk of death of residents was associated with typhoons, particularly coastal regions. However, these findings ignored the impact of inland typhoons on the health of residents, especially the indirect death risk caused by typhoons. This study aimed to investigate the acute death risk of residents during inland typhoon Lekima in Jinan, further identify vulnerable populations and areas. METHODS We selected the daily death from 11 to 27th August 2019 in Jinan as case period, and conducted a time-stratified case-crossover design to match the contemporaneous data from 2016 to 2018 as control period. We used the generalized linear Poisson models to estimate the related effects of death risk during typhoon Lekima and lag days. RESULTS During the Lekima typhoon month, there were 3,366 deaths occurred in Jinan. Compared to unexposed periods, the acute death risk of non-accidental diseases (especially circulatory diseases), female and the older adults increased significantly in the second week after the typhoon. The maximum significant effect of circulatory disease deaths, female and older adult deaths were appeared on lag9, lag9, and lag13 respectively. And the typhoon-associated RR were 1.19 (95%CI:1.05,1.34), 1.28 (95%CI:1.08,1.52), and 1.22 (95%CI:1.06,1.42) respectively. The acute death risk of residents living in TQ and CQ increased significantly on Lag2 and Lag6 after the typhoon, respectively, while those living in LX, LC, HY, JY, and SH occurred from Lag 8 to Lag 13 after the typhoon. LC lasted the longest days. CONCLUSIONS Typhoons would increase the vulnerability of residents living in Jinan which mainly occurred from the seventh day after the typhoon. Residents suffering from non-accidental diseases (circulatory diseases), female and the older adults were more vulnerable. The vulnerability of TQ and CQ occurred on Lag2 and Lag6 after typhoon Lekima, respectively, and the other areas except ZQ and PY occurred from Lag 8 to Lag 13. LC lasted the longest duration. Our findings emphasized the importance of the emergency response, which would help policymakers to identify vulnerable regions and populations accurately during typhoons and formulate the emergency response plan.
Collapse
Affiliation(s)
- Yiwen Ma
- School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China
| | - Xianhui Zhang
- Jinan Municipal Center for Disease Control and Prevention, affiliated to Shandong University, 2 Weiliu Road, Huaiyin District, Jinan, 250021, China
| | - Yingjian Zhang
- Jinan Municipal Center for Disease Control and Prevention, affiliated to Shandong University, 2 Weiliu Road, Huaiyin District, Jinan, 250021, China
| | - Jipei Du
- School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China
| | - Nan Chu
- School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China
| | - Jinli Wei
- School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China
| | - Liangliang Cui
- Jinan Municipal Center for Disease Control and Prevention, affiliated to Shandong University, 2 Weiliu Road, Huaiyin District, Jinan, 250021, China.
| | - Chengchao Zhou
- School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China.
- NHC Key Lab of Health Economics and Policy Research, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China.
- Institute of Health and Elderly Care, Shandong University, 44 Wen-hua-xi Road, Jinan, Shandong, 250012, China.
| |
Collapse
|
5
|
Ji Y, Xiong J, Yuan Z, Huang Z, Li L. Risk assessment and disease burden of extreme precipitation on hospitalizations for acute aortic dissection in a subtropical coastal Chinese city. Front Public Health 2023; 11:1216847. [PMID: 37457244 PMCID: PMC10343949 DOI: 10.3389/fpubh.2023.1216847] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023] Open
Abstract
Background Extreme precipitation events are becoming more frequent due to climate change. The present study aimed to explore the impacts of extreme precipitation on hospitalizations for acute aortic dissection (AAD) and to identify susceptible populations and quantify the corresponding disease burden. Methods The present study used a distributed lag nonlinear model (DLNM) with a quasi-Poisson function to investigate the association between extreme precipitation (≥95th percentile) and the risk of hospitalizations for AAD from 2015 to 2020 in Shantou, Guangdong Province, China. Results The significant adverse effects of extreme precipitation (relative to no precipitation) on daily AAD hospitalizations lasted from lag 5 [relative risk (RR): 1.0318, 95% confidence interval (CI): 1.0067-1.0575] to lag 9 (RR: 1.0297, 95% CI: 1.0045-1.0555) and reached its maximum at lag 7 (RR: 1.0382, 95% CI: 1.0105-1.0665). Males and older adult individuals (≥60 years) were more susceptible to extreme precipitation. A total of 3.68% (118 cases) of AAD hospitalizations were due to extreme precipitation. Conclusion Extreme precipitation was significantly correlated with AAD hospitalizations. Government departments should actively implement extreme precipitation intervention measures to strengthen the protection of males and the older adult (≥60 years) and effectively reduce AAD hospitalizations.
Collapse
Affiliation(s)
- Yanhu Ji
- School of Public Health, Shantou University, Shantou, China
| | - Jianping Xiong
- The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | | | - Zepeng Huang
- The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Liping Li
- School of Public Health, Shantou University, Shantou, China
- Injury Prevention Research Center, Shantou University Medical College, Shantou, China
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Chen Y, Chang Z, Zhao Y, Liu Y, Fu J, Liu Y, Liu X, Kong D, Han Y, Tang S, Fan Z. Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study. Front Public Health 2022; 10:1024816. [PMID: 36238253 PMCID: PMC9551252 DOI: 10.3389/fpubh.2022.1024816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/13/2022] [Indexed: 01/28/2023] Open
Abstract
Background In the context of global climate changes, increasing extreme weather events have aroused great public concern. Limited evidence has focused on the association between extreme precipitation and hospitalizations for acute myocardial infarction (AMI). Our study aimed to examine the effect of extreme precipitation on AMI hospitalizations. Methods Daily AMI hospitalizations, weather variables and air pollution data in Beijing from 2013 to 2018 were obtained. We used a time-series analysis with a distributed lag model to evaluate the association of extreme precipitation (≥95th percentile of daily precipitation) with AMI hospitalizations. Subgroup analysis was conducted to identify the vulnerable subpopulations and further assessed the attributable burden. Results Extreme precipitation increased the risk of AMI hospitalizations with significant single-day effects from Lag 4 to Lag 11, and the maximum cumulative effects at Lag 0-14 (CRR = 1.177, 95% CI: 1.045, 1.326). Older people (≥65 years) and females were more vulnerable to extreme precipitation. The attributable fraction and numbers of extreme precipitation on AMI hospitalizations were 0.68% (95% CI: 0.20%, 1.12%) and 854 (95% CI: 244, 1,395), respectively. Conclusion Extreme precipitation is correlated with a higher risk of AMI hospitalizations. The elderly (≥65 years) and females are more susceptible to AMI triggered by extreme precipitation.
Collapse
|
8
|
Jiang G, Ji Y, Chen C, Wang X, Ye T, Ling Y, Wang H. Effects of extreme precipitation on hospital visit risk and disease burden of depression in Suzhou, China. BMC Public Health 2022; 22:1710. [PMID: 36085022 PMCID: PMC9463798 DOI: 10.1186/s12889-022-14085-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/25/2022] [Indexed: 11/14/2022] Open
Abstract
Background The purpose of this study was to explore the impact of extreme precipitation on the risk of outpatient visits for depression and to further explore its associated disease burden and vulnerable population. Methods A quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was used to investigate the exposure-lag-response relationship between extreme precipitation (≥95th percentile) and depression outpatient visits from 2017 to 2019 in Suzhou city, Anhui Province, China. Results Extreme precipitation was positively associated with the outpatient visits for depression. The effects of extreme precipitation on depression firstly appeared at lag4 [relative risk (RR): 1.047, 95% confidence interval (CI): 1.005–1.091] and lasted until lag7 (RR = 1.047, 95% CI: 1.009–1.087). Females, patients aged ≥65 years and patients with multiple outpatient visits appeared to be more sensitive to extreme precipitation. The attributable fraction (AF) and numbers (AN) of extreme precipitation on outpatient visits for depression were 5.00% (95% CI: 1.02–8.82%) and 1318.25, respectively. Conclusions Our findings suggested that extreme precipitation may increase the risk of outpatient visits for depression. Further studies on the burden of depression found that females, aged ≥65 years, and patients with multiple visits were priority targets for future warnings. Active intervention measures against extreme precipitation events should be taken to reduce the risk of depression outpatient visits. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-14085-w.
Collapse
|
9
|
Wu Y, Yao Z, Ma G, Cheng J, Xu H, Qin W, Yi W, Pan R, Wei Q, Tang C, Liu X, He Y, Yan S, Li Y, Jin X, Liang Y, Sun X, Mei L, Song J, Song S, Su H. Effects of extreme precipitation on hospitalization risk and disease burden of schizophrenia in urban and rural Lu'an, China, from 2010 to 2019. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:19176-19184. [PMID: 34713403 DOI: 10.1007/s11356-021-16913-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 10/03/2021] [Indexed: 06/13/2023]
Abstract
With the increasing frequency of extreme events caused by global climate change, the association between extreme precipitation (EP) and disease has aroused concern currently. However, no study has examined the relationship between EP and schizophrenia. Our study aimed to explore the relationship between EP and schizophrenia, and to further examine the difference between urban and rural areas. This study used quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) to estimate the association between EP (≥ 95th percentile) and hospitalization for schizophrenia from 2010 to 2019 in the city of Lu'an, China. EP could significantly increase the risk of hospitalization for schizophrenia. The effect firstly appeared at lag1 [relative risk (RR): 1.056, 95% confidence interval (95%CI): 1.003-1.110] and continued until lag17 (RR: 1.039, 95%CI: 1.004-1.075). Our research showed that EP had a significant effect on the hospitalization for schizophrenia in both urban and rural areas, and no significant difference was found (p>0.05). EP exerted more acute effects on schizophrenia living in rural areas than those in urban areas in the cold season. Further studies on the burden of schizophrenia found that patients who are male, aged ≤ 39 years or less, and living in urban areas are a priority for future warnings. We should pay more attention to the impact of EP on burden of schizophrenia, especially during the cold season, targeting those vulnerable groups, thereby implementing more accurate and timely preventive measures.
Collapse
Affiliation(s)
- Yudong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Zhenghai Yao
- Anhui Public Meteorological Service Center, Hefei, Anhui, China
| | - Gongyan Ma
- Lu'an Municipal Center for Disease Control and Prevention, Lu'an, 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 Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Huabin Xu
- Affiliated Hospital of West Anhui Health Vocational College, Lu'an, China
| | - Wei Qin
- Lu'an Municipal Center for Disease Control and Prevention, Lu'an, 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 Province Key Laboratory of Major Autoimmune 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 Province Key Laboratory of Major Autoimmune 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 Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune 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 Province Key Laboratory of Major Autoimmune 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 Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Shuangshuang Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Yuxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Xiaoyu Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Yunfeng Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Xiaoni Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Lu Mei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China
| | - Shasha Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune 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 Province Key Laboratory of Major Autoimmune Diseases, Hefei, 230032, Anhui, China.
| |
Collapse
|
10
|
Li K, Wang L, Feng M. Relationship between built environments and risks of ischemic stroke based on meteorological factors: A case study of Wuhan's main urban area. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 769:144331. [PMID: 33736230 DOI: 10.1016/j.scitotenv.2020.144331] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 11/14/2020] [Accepted: 12/04/2020] [Indexed: 06/12/2023]
Abstract
Ischemic stroke is one of the most common causes of death worldwide, and uncomfortable meteorological and built environments may increase its risk. Residents in different built environments are exposed to different risks of ischemic stroke in cold and hot weather. By using the data from 3547 patients hospitalized, a distributed lag non-linear model was established to compare the differences in the risk of ischemic stroke in urban areas with respect to different Building Height, Building Density, Normalized Differential Vegetation Index, and Distance to Water under the meteorological condition. The results showed that lower Building Height is related to the negative cold effects in winter, and higher Building Height is related to increased risks at high temperatures. Built environments with Building Heights of 10-15 m in hot weather and above 15 m in cold weather have low risks. Higher Building Density was found to be associated with reduced negative cold effects; however, the negative hot effects increased in summer. Built environments with a Building Density of more than 0.3 showed low risks, regardless of the weather conditions. Increasing NDVI seemed to mitigate negative effects in uncomfortable weather, and built environments with higher NDVI were found to be associated with lower risks of ischemic stroke. Built environments with shorter Distance to Water seemed to pose higher risks in summer, and longer Distance to Water was correlated with higher risks in winter. Built environments with Distance to Water in the range of 0.65-2.30 km showed low risks. The research results could have some implications for urban planners to form reasonable built environments under certain meteorological factors which can be beneficial for the mitigation of incidence of ischemic stroke.
Collapse
Affiliation(s)
- Kun Li
- School of Urban Design, Wuhan University, Wuhan, China.
| | - Lantao Wang
- School of Urban Design, Wuhan University, Wuhan, China.
| | - Maohui Feng
- Zhongnan Hospital of Wuhan University, Wuhan, China.
| |
Collapse
|
11
|
Towards Understanding Interactions between Sustainable Development Goals: The Role of Climate-Well-Being Linkages. Experiences of EU Countries. ENERGIES 2021. [DOI: 10.3390/en14072025] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The 2030 Agenda with 17 Sustainable Development Goals (SDGs) is a challenge for all countries in the world. Their implementation may turn out to be a compromise or the creation of effective interactions that dynamize sustainable development. To achieve the SDGs, it is essential to understand how they interact with each other. It seems that in the times of the climate and health crisis caused by the COVID-19 pandemic, caring for the environment and ensuring a healthy life and promoting well-being at all ages is the basis for environmental, economic and social sustainable development. The aim of the study is to compare the degree of implementation of the goals of sustainable development in the scope of goal 13 “Climate action” and goal 3 “Good health and well-being” in the EU countries. In addition, we analyze how trade-offs and synergies between these goals have developed. Data from the Eurostat database were used to achieve the goal. The study used the method of multivariate comparative analysis—linear ordering of objects. The technique for order preference by similarity to an ideal solution (TOPSIS) method was used to measure the studied phenomenon. The results indicate a different degree of implementation of the sustainable development goals related to climate change and the improvement of health and social well-being. Only a few countries have synergy in achieving these goals, most of them compromise, manifesting themselves in improving one goal over another. In the group of analyzed EU countries, a simultaneous deterioration in the effectiveness of achieving both objectives were also noted. Our research also shows that energy policy is an important attribute in improving the achievement of these goals. The conducted analysis fills the gap in the research on the implementation of selected sustainable development goals and their interactions. It contributes to the discussion on increasing the links between them, in particular with regard to emerging compromises. This research can provide a basis for re-prioritizing and intensifying the actions where individual EU countries are lagging most behind.
Collapse
|