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Dewi SP, Kasim R, Sutarsa IN, Dykgraaf SH. A scoping review of the impact of extreme weather events on health outcomes and healthcare utilization in rural and remote areas. BMC Health Serv Res 2024; 24:1333. [PMID: 39487458 PMCID: PMC11529210 DOI: 10.1186/s12913-024-11695-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 10/03/2024] [Indexed: 11/04/2024] Open
Abstract
BACKGROUND Extreme weather events affect health by directly and indirectly increasing illness burdens and changing healthcare usage patterns. These effects can be especially severe in rural and remote areas, exacerbating existing health disparities, and necessitating urgent mitigation or adaptation strategies. Despite increased research on health and climate change, studies focusing on rural and remote populations remain limited. This study aimed to review the relationships among extreme weather events, healthcare utilization, and health outcomes in rural and remote populations, identify research gaps, and inform policy development for adaptation and disaster management in these settings. METHODS A systematic scoping review was registered and conducted following the PRISMA-ScR guidelines. The search databases included PubMed, Web of Science, Scopus, the Cochrane Library, ProQuest, and the WHO IRIS. The included studies were primary research, focused on rural or remote areas, and investigated the effects of extreme weather events on either health outcomes or healthcare utilization. There were no methodological, date or language restrictions. We excluded protocols, reviews, letters, editorials, and commentaries. Two reviewers screened and extracted all data, other reviewers were invited to resolve conflicts. Findings are presented numerically or narratively as appropriate. RESULTS The review included 135 studies from 31 countries, with most from high-income countries. Extreme weather events exacerbate communicable and noncommunicable diseases, including cardiorespiratory, mental health, and malnutrition, and lead to secondary impacts such as mass migration and increased poverty. Healthcare utilization patterns changed during these events, with increased demand for emergency services but reduced access to routine care due to disrupted services and financial constraints. CONCLUSIONS The results highlighted the essential role of community and social support in rural and remote areas during extreme weather events and the importance of primary healthcare services in disaster management. Future research should focus on developing and implementing effective mitigation and adaptation programs tailored to the unique challenges faced by these populations.
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Affiliation(s)
- Sari Puspa Dewi
- Rural Clinical School, School of Medicine and Psychology, The Australian National University, Florey Building 54 Mills Road, Canberra, ACT, 2601, Australia.
- Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Jalan Raya Bandung Sumedang KM 21 Jatinangor, Jatinangor, West Java, 45363, Indonesia.
| | - Rosny Kasim
- Rural Clinical School, School of Medicine and Psychology, The Australian National University, Florey Building 54 Mills Road, Canberra, ACT, 2601, Australia
| | - I Nyoman Sutarsa
- Rural Clinical School, School of Medicine and Psychology, The Australian National University, Florey Building 54 Mills Road, Canberra, ACT, 2601, Australia
| | - Sally Hall Dykgraaf
- Rural Clinical School, School of Medicine and Psychology, The Australian National University, Florey Building 54 Mills Road, Canberra, ACT, 2601, Australia
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Wu H, Zhang X, Zhang T, Li G, Xu L, Li Z, Ren Y, Zhao Y, Pan F. The relationship of short-term exposure to meteorological factors on diabetes mellitus mortality risk in Hefei, China: a time series analysis. Int Arch Occup Environ Health 2024; 97:991-1005. [PMID: 39369358 DOI: 10.1007/s00420-024-02102-x] [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: 05/26/2024] [Accepted: 09/23/2024] [Indexed: 10/07/2024]
Abstract
OBJECTIVES The study aims to explore whether short-term exposure to meteorological factors has a potential association with the risk of diabetes mellitus (DM) mortality. METHODS During the period 2015-2018, we collected daily data on meteorological factors and deaths of diabetic patients in Hefei. A total of 1101 diabetic deaths were recorded. We used structural equation modeling to initially explore the relationships among air pollutants, meteorological variables, and mortality, and generalized additive modeling (GAM) and distributional lag nonlinear modeling (DLNM) to explore the relationship between meteorological factors and the mortality risk of DM patients. We also stratified by age and gender. The mortality risk in diabetic patients was expressed by relative risks (RR) and 95% confidence intervals (CI) for both single and cumulative days. RESULTS Single-day lagged results showed a high relative humidity (RH) (75th percentile, 83.71%), a fairly high average temperature (T mean) (95th percentile, 30.32 °C), and an extremely low diurnal temperature range (DTR) (5th percentile, 3.13 °C) were positively related to the mortality risk of DM. Stratified results showed that high and very high levels of T mean were significantly positively linked to the mortality risk of DM among females and the elderly, while very high levels of DTR were linked to the mortality risk in men and younger populations. CONCLUSION In conclusion, this study found that short-duration exposure to quite high T mean, high RH, and very low DTR were significantly positively related to the mortality risk of DM patients. For women and older individuals, exposure to high and very high T mean environments should be minimized. Men and young adults should be aware of daily temperature changes.
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Affiliation(s)
- Hanqing Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Xu Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Tao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Guoqing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Longbao Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Ziqi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Yuxin Ren
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Yanyu Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Faming Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
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Zhou L, Wei Y, Ge Y, Li Y, Liu K, Gao Y, Song B, Li Y, Zhang D, Bo Y, Zhang J, Xu Y, Duan X. Global, regional, and national burden of stroke attributable to extreme low temperatures, 1990-2019: A global analysis. Int J Stroke 2024; 19:676-685. [PMID: 38425241 DOI: 10.1177/17474930241238636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
BACKGROUND Extreme ambient temperatures have been linked to increased risks of stroke morbidity and mortality. However, global estimates of the burden of stroke due to extreme low temperatures are not well-defined. AIMS This study aimed to determine the global burden of stroke due to extreme low temperatures and its spatiotemporal trend from 1990 to 2019. METHODS Based on the Global Burden of Disease Study 2019, we obtained global, regional, and national data on deaths, disability-adjusted life years (DALYs), age-standardized mortality rate (ASMR), and age-standardized rate of DALYs (ASDR) of stroke attributed to extreme low temperatures, further stratified by age, sex, and sociodemographic index (SDI). RESULTS Globally, in 2019, an estimated 474,000 stroke deaths with the corresponding ASMR (6.2 (95% uncertainty interval (UI): 4.6-7.9)) and ASDR (103.9 (95% UI: 77.0-134.5)) per 100,000 population, were attributable to extreme low temperatures. The most significant burden was observed in Central Asia, followed by Eastern Europe and East Asia. From 1990 to 2019, the global burden of stroke and its subtypes (ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage) attributable to extreme low temperatures exhibited a decrease in both ASMR and ASDR. Significant decreases in stroke burden occurred in the high-SDI regions, high-income Asia Pacific, and subarachnoid hemorrhage cases. Moreover, the ASMR and ASDR increased with age and were higher in males than females. CONCLUSION The global stroke burden due to extreme low temperatures remains high despite a decreasing trend over the past three decades. The stroke burden due to extreme low temperatures was more notable for Central Asia, older people, and the male sex.
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Affiliation(s)
- Lue Zhou
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yujie Wei
- National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yahao Ge
- National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yapeng Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kai Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuan Gao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bo Song
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yusheng Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Daping Zhang
- Department of Cardiology, Huaihe Hospital of Henan University, Kaifeng, China
| | - Yacong Bo
- Department of Nutrition, College of Public Health, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, China
| | - Junxi Zhang
- NHC Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, China
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoran Duan
- National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Li XC, Qian HR, Zhang YY, Zhang QY, Liu JS, Lai HY, Zheng WG, Sun J, Fu B, Zhou XN, Zhang XX. Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation. Infect Dis Model 2024; 9:618-633. [PMID: 38645696 PMCID: PMC11026972 DOI: 10.1016/j.idm.2024.03.001] [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: 09/29/2023] [Revised: 02/27/2024] [Accepted: 03/09/2024] [Indexed: 04/23/2024] Open
Abstract
The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.
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Affiliation(s)
- Xin-Chen Li
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Hao-Ran Qian
- School of Data Science, Fudan University, Shanghai, People's Republic of China
| | - Yan-Yan Zhang
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Qi-Yu Zhang
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Jing-Shu Liu
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Hong-Yu Lai
- School of Data Science, Fudan University, Shanghai, People's Republic of China
| | - Wei-Guo Zheng
- School of Data Science, Fudan University, Shanghai, People's Republic of China
| | - Jian Sun
- School of Data Science, Fudan University, Shanghai, People's Republic of China
| | - Bo Fu
- School of Data Science, Fudan University, Shanghai, People's Republic of China
| | - Xiao-Nong Zhou
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Xiao-Xi Zhang
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
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Ning Z, He S, Liao X, Ma C, Wu J. Health impacts of a cold wave and its economic loss assessment in China's high-altitude city, Xining. Arch Public Health 2024; 82:52. [PMID: 38632636 PMCID: PMC11025205 DOI: 10.1186/s13690-024-01284-7] [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: 02/08/2024] [Accepted: 04/06/2024] [Indexed: 04/19/2024] Open
Abstract
OBJECTIVE Amidst climate change, extensive research has centered on the health impacts of heatwaves, yet the consequences of cold spells, particularly in cooler, higher-altitude regions, remain under-explored. METHODS Analyzing climatic data and non-accidental mortality in Xining, China's second-highest provincial capital, from 2016 to 2020, this study defines cold spells as daily mean temperatures below the 10th, 7.5th, or 5th percentiles for 2-4 consecutive days. A time-stratified case-crossover approach and distributional lag nonlinear modeling were used to assess the link between cold spells and mortality, calculating attributable fractions (AFs) and numbers (ANs) of deaths. The study also examined the impact of cold spells over different periods and analyzed the value of a statistical life (VSL) loss in 2018, a year with frequent cold spells. Stratified analyses by sex, age, and education level were conducted. RESULTS A significant association was found between cold spells and non-accidental mortality, with a relative risk of 1.548 (95% CI: 1.300, 1.845). The AF was 33.48%, with an AN of 9,196 deaths during the study's cold period. A declining trend in mortality risk was observed from 2019-2020. The 2018 VSL was approximately 2.875 billion CNY, about 1.75% of Xining's GDP. Higher risks were noted among males, individuals aged ≥ 65, and those with lower education levels. CONCLUSION The findings underscore the vulnerability and economic losses of high-altitude cities to cold spells. Implementing interventions such as improved heating, educational programs, and community support is vital for mitigating these adverse health effects.
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Affiliation(s)
- Zhenxu Ning
- Department of Public Health, Faculty of Medicine, Qinghai University, Xining, China
| | - Shuzhen He
- Department of Public Health, Xining Centre for Disease Control and Prevention, Xining, China.
| | - Xinghao Liao
- Department of Public Health, Faculty of Medicine, Qinghai University, Xining, China
| | - Chunguang Ma
- Xining Centre for Disease Control and Prevention, Xining, China
| | - Jing Wu
- Xining Centre for Disease Control and Prevention, Xining, China
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Zhang T, Ni M, Jia J, Deng Y, Sun X, Wang X, Chen Y, Fang L, Zhao H, Xu S, Ma Y, Zhu J, Pan F. Research on the relationship between common metabolic syndrome and meteorological factors in Wuhu, a subtropical humid city of China. BMC Public Health 2023; 23:2363. [PMID: 38031031 PMCID: PMC10685562 DOI: 10.1186/s12889-023-17299-8] [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: 06/25/2023] [Accepted: 11/22/2023] [Indexed: 12/01/2023] Open
Abstract
As climate conditions deteriorate, human health faces a broader range of threats. This study aimed to determine the risk of death from metabolic syndrome (MetS) due to meteorological factors. We collected daily data from 2014 to 2020 in Wuhu City, including meteorological factors, environmental pollutants and death data of common MetS (hypertension, hyperlipidemia and diabetes), as well as a total number of 15,272 MetS deaths. To examine the relationship between meteorological factors, air pollutants, and MetS mortality, we used a generalized additive model (GAM) combined with a distributed delay nonlinear model (DLNM) for time series analysis. The relationship between the above factors and death outcomes was preliminarily evaluated using Spearman analysis and structural equation modeling (SEM). As per out discovery, diurnal temperature range (DTR) and daily mean temperature (T mean) increased the MetS mortality risk notably. The ultra low DTR raised the MetS mortality risk upon the general people, with the highest RR value of 1.033 (95% CI: 1.002, 1.065) at lag day 14. In addition, T mean was also significantly associated with MetS death. The highest risk of ultra low and ultra high T mean occured on the same day (lag 14), RR values were 1.043 (95% CI: 1.010, 1.077) and 1.032 (95% CI: 1.003, 1.061) respectively. Stratified analysis's result showed lower DTR had a more pronounced effect on women and the elderly, and ultra low and high T mean was a risk factor for MetS mortality in women and men. The elderly need to take extra note of temperature changes, and different levels of T mean will increase the risk of death. In warm seasons, ultra high RH and T mean can increase the mortality rate of MetS patients.
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Affiliation(s)
- Tao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Man Ni
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Juan Jia
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Yujie Deng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Xiaoya Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Xinqi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Yuting Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Lanlan Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Hui Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Shanshan Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Yubo Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Jiansheng Zhu
- Wuhu center for disease control and prevention, Wuhu, Anhui, China
| | - Faming Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
- Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
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Wang G, Lin G, Yang FF, Wang Z. Effect of abnormal values of three temperature indicators on ischemic stroke hospital admissions in Guangzhou, China. J Therm Biol 2023; 116:103649. [PMID: 37478582 DOI: 10.1016/j.jtherbio.2023.103649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/21/2023] [Accepted: 06/29/2023] [Indexed: 07/23/2023]
Abstract
Abnormal temperature has important effects on the occurrence of ischemic stroke (IS). However, relatively less efforts have been taken to systematically unravel the association between various abnormal temperature and IS hospital admission. Focusing on three temperature indicators (i.e., mean temperature, maximum temperature, and minimum temperature), this study attempts to analyse how their abnormal values affect IS hospital admission. The dataset covers the period between September 17, 2012 and August 28, 2018, and includes a total of 1464 cases who were admitted to the hospital for the first onset of IS and lived in the main urban area of Guangzhou. The study adopts the time-stratified case-crossover analysis. Abnormal values of temperature were measured using the 2.5th and 97.5th quantile values of each temperature indicator, with the former refers to a low value whereas the latter a high one. The effects of abnormal temperature on IS hospital admission were assessed through calculating the relative risks induced by the low and high values (the median values of each temperature indicators were taken as the references). The results show that the risk window periods for IS hospital admission associated with the low values of the temperature indicators are the lags of 3-7 days and 18-19 days. The risks of high temperature values on IS admission, however, are insignificant with either one-day lag or cumulative lag. As to different population groups, females show higher risks of IS hospital admission at low temperature values than males; and elderly people, compared with young people, are more vulnerable to low temperature values. To cities with similar climate of Guangzhou, particular attention should be paid to the impact of low temperature values, especially the low value of minimum temperature, on IS admission, and to females and elderly people who are more sensitive to abnormal temperatures.
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Affiliation(s)
- Guobin Wang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China
| | - Geng Lin
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
| | - Fiona Fan Yang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Zhuoqing Wang
- Department of Scientific Research & Discipline Development, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
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Wang G, Yang FF, Lin G, Wang Z, Zhang X. Modification of low temperature-related hospital admissions for cardiovascular diseases by multiple green space indicators at multiple spatial scales: Evidence from Guangzhou, China. Int J Hyg Environ Health 2023; 251:114193. [PMID: 37247607 DOI: 10.1016/j.ijheh.2023.114193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 05/21/2023] [Accepted: 05/24/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Extreme temperatures have an adverse effect on the occurrence of cardiovascular diseases (CVDs). Previous literatures tend to discuss the modification of CVDs occurrence by green space under high temperature. Relatively less attention is paid to the modification under low temperature. The variation of different attributes and spatial scales of green space in affecting CVDs occurrence are also overlooked. METHODS This study collected a total of 4364 first-time admission cases due to CVDs in a tertiary hospital in Guangzhou from 2012 to 2018, measured the scale of green space by greening rate (GR) and percentage of landscape (PLAND), the distribution of green space by patch density (PD), mean nearest neighbor distance (ENN_MN) and largest patch index (LPI), and the accessibility of green space by green patch accessibility index (GPAI). Using the time stratified case crossover design method, the modification of low temperature-related CVDs occurrence by the above green space indicators is evaluated in an area with a radius of 100-1000 m which is further divided at an interval of 100 m. RESULTS We found high GR, high PLAND, high PD, low ENN_MN, high LPI, and low GPAI corresponds to low risk of CVDs occurrence, the optimal modification scale of each green space indicator, which is radius corresponding to the maximum risk difference between high and low indicator subgroups, is around 800 m (GR), 600 m (PLAND and PD), 500 m (GPAI), and 300 m (LPI and ENN_MN), respectively. As the temperature decreases further, the health benefit from low GPAI at the optimal scale is weakened, whereas the benefits from the others are strengthened. CONCLUSIONS Low temperature related CVDs occurrence risk can be modified by multiple green space indicators, and these modifications have spatial scale effect. Our findings have important theoretical and practical significance for the formulation and implementation of local green space policies.
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Affiliation(s)
- Guobin Wang
- School of Geography and Planning, Sun Yat-Sen University, GuangZhou, 510006, China
| | - Fiona Fan Yang
- School of Geography and Planning, Sun Yat-Sen University, GuangZhou, 510006, China
| | - Geng Lin
- School of Geography and Planning, Sun Yat-Sen University, GuangZhou, 510006, China.
| | - Zhuoqing Wang
- Department of Scientific Research & Discipline Development, The First Affiliated Hospital Sun Yat-sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China.
| | - Xiangxue Zhang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
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9
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Bai J, Cui J, Yu C. Burden of chronic obstructive pulmonary disease attributable to non-optimal temperature from 1990 to 2019: a systematic analysis from the Global Burden of Disease Study 2019. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:68836-68847. [PMID: 37129808 DOI: 10.1007/s11356-023-27325-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 04/26/2023] [Indexed: 05/03/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) has been the third leading cause of death worldwide. As the traditional risk factors (like smoking and ambient air pollution) on the burden of COPD being well characterized, the burden of COPD due to non-optimal temperature has been widely concerned. In this study, we extracted the relevant burden data of COPD attributable to non-optimal temperature from GBD 2019 and adopted estimated annual percent changes, Gaussian process regression (GPR), and age-period-cohort model to evaluate the spatiotemporal patterns, relationships with socio-demographic level, and the independent effects of age, period and cohort from 1990 to 2019. In brief, the global COPD burden attributable to non-optimal temperatures showed declining trends but was still more severe in the elderly, males, Asia, and regions with low socio-demographic index (SDI). And cold had a greater burden than heat. The inverted U-shape is expected for the relationship between SDI and the burden of COPD caused by non-optimal temperatures according to the GPR model, with the inflection point around SDI 0.45. Besides, the improvements were observed in period and cohort effects but were relatively limited in low and low-middle SDI regions. Public health managers should execute more targeted programs to lessen this burden predominantly among lower SDI countries.
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Affiliation(s)
- Jianjun Bai
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No.185 Donghu Road, Wuhan, 430071, China
| | - Jiaxin Cui
- School of Nursing, Wuhan University, No.115 Donghu Road, Wuhan, 430071, China
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No.185 Donghu Road, Wuhan, 430071, China.
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10
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Navas-Martín MÁ, López-Bueno JA, Ascaso-Sánchez MS, Follos F, Vellón JM, Mirón IJ, Luna MY, Sánchez-Martínez G, Díaz J, Linares C. Territory Differences in Adaptation to Heat among Persons Aged 65 Years and Over in Spain (1983-2018). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4168. [PMID: 36901177 PMCID: PMC10002076 DOI: 10.3390/ijerph20054168] [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: 12/29/2022] [Revised: 02/23/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Climate change is currently regarded as the greatest global threat to human health, and its health-related consequences take different forms according to age, sex, socioeconomic level, and type of territory. The aim of this study is to ascertain the differences in vulnerability and the heat-adaptation process through the minimum mortality temperature (MMT) among the Spanish population aged ≥65 years by territorial classification. A retrospective, longitudinal, ecological time-series study, using provincial data on daily mortality and maximum daily temperature across the period 1983-2018, was performed, differentiating between urban and nonurban populations. The MMTs in the study period were higher for the ≥65-year age group in urban provinces, with a mean value of 29.6 °C (95%CI 29.2-30.0) versus 28.1 °C (95%CI 27.7-28.5) in nonurban provinces. This difference was statistically significant (p < 0.05). In terms of adaptation levels, higher average values were obtained for nonurban areas, with values of 0.12 (95%CI -0.13-0.37), than for urban areas, with values of 0.09 (95%CI -0.27-0.45), though this difference was not statistically significant (p < 0.05). These findings may contribute to better planning by making it possible to implement more specific public health prevention plans. Lastly, they highlight the need to conduct studies on heat-adaptation processes, taking into account various differential factors, such as age and territory.
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Affiliation(s)
- Miguel Ángel Navas-Martín
- National School of Public Health, Carlos III Institute of Health, 28029 Madrid, Spain
- Doctorate Program in Biomedical Sciences and Public Health, National University of Distance Education, 28015 Madrid, Spain
| | | | | | - Fernando Follos
- Tdot Soluciones Sostenibles, SL. Ferrol, 15401 A Coruña, Spain
| | | | - Isidro Juan Mirón
- Regional Health Authority of Castile La Mancha, 45500 Torrijos, Spain
| | | | | | - Julio Díaz
- National School of Public Health, Carlos III Institute of Health, 28029 Madrid, Spain
| | - Cristina Linares
- National School of Public Health, Carlos III Institute of Health, 28029 Madrid, Spain
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11
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Pacheco P, Mera E, Fuentes V. Intensive Urbanization, Urban Meteorology and Air Pollutants: Effects on the Temperature of a City in a Basin Geography. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3941. [PMID: 36900952 PMCID: PMC10001953 DOI: 10.3390/ijerph20053941] [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: 01/13/2023] [Revised: 02/11/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
A qualitative study of thermal transfers is carried out from a record of measurements (time series) of meteorological variables (temperature, relative humidity and magnitude of wind speeds) and pollutants (PM10, PM2.5 and CO) in six localities located at different heights in the geographic basin of Santiago de Chile. The measurements were made in two periods, 2010-2013 and 2017-2020 (a total of 2,049,336 data), the last period coinciding with a process of intense urbanization, especially high-rise construction. The measurements, in the form of hourly time series, are analyzed on the one hand according to the theory of thermal conduction discretizing the differential equation of the temporal variation in the temperature and, on the other hand, through the theory of chaos that provides the entropies (S). Both procedures demonstrate, comparatively, that the last period of intense urbanization presents an increase in thermal transfers and temperature, which affects urban meteorology and makes it more complex. As shown by the chaotic analysis, there is a faster loss of information for the period 2017-2020. The consequences of the increase in temperature on human health and learning processes are studied.
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12
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Liu B, Fang XY, Yan YL, Wu J, Lv XJ, Zhang J, Qi LW, Qian TT, Cai YY, Fan YG, Ye DQ. Short-term effect of ambient temperature and ambient temperature changes on the risk of warts outpatient visits in Hefei, China: a retrospective time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:19342-19355. [PMID: 36239885 DOI: 10.1007/s11356-022-23522-7] [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: 06/02/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Concerns are growing about the adverse health effects of ambient temperature and ambient temperature changes. However, the association between ambient temperature and ambient temperature changes on the risk of warts outpatient visits is poorly understood. Our study used the distributed lag non-linear model (DLNM) aimed to evaluate the association between ambient temperature, ambient temperature changes (including temperature change between neighboring days (TCN) and diurnal temperature range (DTR)), and warts outpatient visits. We also performed subgroup analyses in order to find susceptible populations by gender and age groups. The maximum relative risk (RR) of low ambient temperature (0 °C) for warts outpatient visits was 1.117 (95% CI: 1.041-1.198, lag 04 days), and the maximum RR of high ambient temperature (32 °C) for warts outpatient visits was 1.318 (95% CI: 1.083-1.605, lag 07 days). The large temperature drop (TCN = - 3 °C) decreased the risk of warts visits, with the lowest RR value at the cumulative exposure of lag 7 days (RR = 0.888, 95% CI: 0.822-0.959), and the large temperature rise (TCN = 2 °C) increased the risk of warts visits, with the highest RR value at the cumulative exposure of lag 7 days (RR = 1.080, 95% CI: 1.022-1.142). Overall, both low and high ambient temperatures and large temperature rise can increase the risk of warts visits, while large temperature drop is a protective factor for warts visits. However, we did not find any association between DTR and warts visits. Furthermore, subgroup analyses showed that males and the young (0-17 years old) were more sensitive to low and high ambient temperatures, and the elderly (≥ 65 years old) were more susceptible to TCN. The results may provide valuable evidence for reducing the disease burden of warts in the future.
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Affiliation(s)
- Bo Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Xin-Yu Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Yu-Lu Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jun Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Xiao-Jie Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Liang-Wei Qi
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Ting-Ting Qian
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Yu-Yu Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Yin-Guang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Dong-Qing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Mei-Shan Road, Hefei, Anhui, China.
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China.
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13
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Navas-Martín MÁ, López-Bueno JA, Ascaso-Sánchez MS, Follos F, Vellón JM, Mirón IJ, Luna MY, Sánchez-Martínez G, Linares C, Díaz J. Heat Adaptation among the Elderly in Spain (1983-2018). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1314. [PMID: 36674069 PMCID: PMC9858820 DOI: 10.3390/ijerph20021314] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/02/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
The capacity for adaptation to climate change is limited, and the elderly rank high among the most exposed population groups. To date, few studies have addressed the issue of heat adaptation, and little is known about the long-term effects of exposure to heat. One indicator that allows the ascertainment of a population's level of adaptation to heat is the minimum mortality temperature (MMT), which links temperature and daily mortality. The aim of this study was to ascertain, firstly, adaptation to heat among persons aged ≥ 65 years across the period 1983 to 2018 through analysis of the MMT; and secondly, the trend in such adaptation to heat over time with respect to the total population. A retrospective longitudinal ecological time series study was conducted, using data on daily mortality and maximum daily temperature across the study period. Over time, the MMT was highest among elderly people, with a value of 28.6 °C (95%CI 28.3-28.9) versus 28.2 °C (95%CI 27.83-28.51) for the total population, though this difference was not statistically significant. A total of 62% of Spanish provinces included populations of elderly people that had adapted to heat during the study period. In general, elderly persons' level of adaptation registered an average value of 0.11 (°C/decade).
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Affiliation(s)
- Miguel Ángel Navas-Martín
- National School of Public Health, Carlos III Institute of Health, 28029 Madrid, Spain
- Doctorate Program in Biomedical Sciences and Public Health, National University of Distance Education, 28015 Madrid, Spain
| | | | | | - Fernando Follos
- Tdot Soluciones Sostenibles, SL., Ferrol, 15401 A Coruña, Spain
| | | | - Isidro Juan Mirón
- Regional Health Authority of Castile La Mancha, 45500 Torrijos, Spain
| | | | | | - Cristina Linares
- National School of Public Health, Carlos III Institute of Health, 28029 Madrid, Spain
| | - Julio Díaz
- National School of Public Health, Carlos III Institute of Health, 28029 Madrid, Spain
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14
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Liu Y, Wen H, Bai J, Shi F, Bi R, Yu C. Burden of diabetes and kidney disease attributable to non-optimal temperature from 1990 to 2019: A systematic analysis from the Global Burden of Disease Study 2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156495. [PMID: 35671854 DOI: 10.1016/j.scitotenv.2022.156495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/26/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
INTRODUCTION This study quantitatively described the disease burden of diabetes and kidney disease attributable to non-optimal temperatures and explored the influencing factors. METHODS We quantitatively described the mortality burden of diabetes and kidney disease attributable to non-optimal temperatures in six countries (China, USA, South Africa, Australia, Iraq, Portugal), and compare trends in mortality in six countries from 1990 to 2019. We used the APC model to analyse age, period, and cohort effects on mortality in six countries. We used restricted cubic splines and quantile regression to analyse the association of SDI with mortality and YLL using data from 21 regions in the world. RESULTS The mortality rates of diabetes and kidney disease in the six countries in 2019 were 1.72% (Australia), 1.83% (China), 2.99% (USA), 3% (Portugal), 7.45% (South Africa) and 8.71% (Iraq) attributable to non-optimal temperatures. Cold was more harmful than heat. The mortality, YLLs of diabetes and kidney disease of male were higher than females. The mortality rate showed an upwards trend with age. The period effect had little changes or showed a slight upwards trend. The cohort effect showed a downwards trend. The regions with higher mortality or YLLs rates were mainly had SDI values of 0.45-0.80. CONCLUSIONS Among the death burdens of diabetes and kidney disease attributed to non-optimal temperatures, cold had a greater burden than heat. The burden of death was affected by sex, age, period, cohort, and SDI.
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Affiliation(s)
- Yan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Haoyu Wen
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Jianjun Bai
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Fang Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Ran Bi
- College of Letter and Science, University of California, Davis, CA 95618, the United States of America
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China.
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