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Hundessa S, Huang W, Zhao Q, Wu Y, Wen B, Alahmad B, Armstrong B, Gasparrini A, Sera F, Tong S, Madureira J, Kyselý J, Schwartz J, Vicedo-Cabrera AM, Hales S, Johnson A, Li S, Guo Y. Global and Regional Cardiovascular Mortality Attributable to Nonoptimal Temperatures Over Time. J Am Coll Cardiol 2024; 83:2276-2287. [PMID: 38839202 DOI: 10.1016/j.jacc.2024.03.425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND The association between nonoptimal temperatures and cardiovascular mortality risk is recognized. However, a comprehensive global assessment of this burden is lacking. OBJECTIVES The goal of this study was to assess global cardiovascular mortality burden attributable to nonoptimal temperatures and investigate spatiotemporal trends. METHODS Using daily cardiovascular deaths and temperature data from 32 countries, a 3-stage analytical approach was applied. First, location-specific temperature-mortality associations were estimated, considering nonlinearity and delayed effects. Second, a multivariate meta-regression model was developed between location-specific effect estimates and 5 meta-predictors. Third, cardiovascular deaths associated with nonoptimal, cold, and hot temperatures for each global grid (55 km × 55 km resolution) were estimated, and temporal trends from 2000 to 2019 were explored. RESULTS Globally, 1,801,513 (95% empirical CI: 1,526,632-2,202,831) annual cardiovascular deaths were associated with nonoptimal temperatures, constituting 8.86% (95% empirical CI: 7.51%-12.32%) of total cardiovascular mortality corresponding to 26 deaths per 100,000 population. Cold-related deaths accounted for 8.20% (95% empirical CI: 6.74%-11.57%), whereas heat-related deaths accounted for 0.66% (95% empirical CI: 0.49%-0.98%). The mortality burden varied significantly across regions, with the highest excess mortality rates observed in Central Asia and Eastern Europe. From 2000 to 2019, cold-related excess death ratios decreased, while heat-related ratios increased, resulting in an overall decline in temperature-related deaths. Southeastern Asia, Sub-Saharan Africa, and Oceania observed the greatest reduction, while Southern Asia experienced an increase. The Americas and several regions in Asia and Europe displayed fluctuating temporal patterns. CONCLUSIONS Nonoptimal temperatures substantially contribute to cardiovascular mortality, with heterogeneous spatiotemporal patterns. Effective mitigation and adaptation strategies are crucial, especially given the increasing heat-related cardiovascular deaths amid climate change.
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Affiliation(s)
- Samuel Hundessa
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Wenzhong Huang
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yao Wu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Bo Wen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Barrak Alahmad
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Ben Armstrong
- Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Antonio Gasparrini
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence, Florence, Italy
| | - Shilu Tong
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia; National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Joana Madureira
- Environmental Health Department, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
| | - Jan Kyselý
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Ana Maria Vicedo-Cabrera
- Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Simon Hales
- Department of Public Health, University of Otago, Wellington, New Zealand
| | - Amanda Johnson
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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Gao Y, Liu Y, He J, Zhang Y, Wang T, Wu L, Sun N, Fang T, Mao H, Tang NJ, Chen X. Effects of heat waves and cold spells on blood parameters: a cohort study of blood donors in Tianjin, China. Environ Health Prev Med 2024; 29:25. [PMID: 38658361 PMCID: PMC11058483 DOI: 10.1265/ehpm.24-00023] [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: 01/29/2024] [Accepted: 04/03/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND With the increasing occurrence of extreme temperature events due to climate change, the attention has been predominantly focused on the effects of heat waves and cold spells on morbidity and mortality. However, the influence of these temperature extremes on blood parameters has been overlooked. METHODS We conducted a cohort study involving 2,752 adult blood donors in Tianjin, China, between January 18, 2013, and June 25, 2021. The generalized additive mixed model was used to investigate the effects and lagged effects of heat waves and cold spells on six blood parameters of blood donors, including alanine aminotransferase (ALT), white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), and platelet count (PLT). Subgroup analyses were stratified by sex, age, and BMI. RESULTS Heat waves and cold spells are associated with changes in blood parameters, particularly HB and PLT. Heat waves increased HB and PLT, while cold spells increased HB and decreased PLT. The effect of heat waves is greater than that of cold spells. The largest effect of heat waves on HB and PLT occurred at lag1 with 2.6 g/L (95% CI: 1.76 to 3.45) and lag7 with 9.71 × 10^9/L (95% CI: 6.26 to 13.17), respectively, while the largest effect of cold spells on HB and PLT occurred at lag0 with 1.02 g/L (95% CI: 0.71 to 1.33) and lag2 with -3.85 × 10^9/L (95% CI: -5.00 to -2.70), respectively. In subgroup analysis, the effect of cold spells on ALT was greater in the 40-49 age group. CONCLUSION We indicated that heat waves and cold spells can impact hemoglobin and platelet counts in the human body. These findings provide evidence linking heat waves or cold spells to diseases and may reduce health risks caused by extreme temperature events.
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Affiliation(s)
- Yutong Gao
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Sciences and Engineering, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Tianjin Medical University, Tianjin, 300070, China
| | - Yifan Liu
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, 300070, China
- Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Tianjin Medical University, Tianjin, 300070, China
| | - Jiayu He
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, 300070, China
- Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Tianjin Medical University, Tianjin, 300070, China
| | - Yin Zhang
- Tianjin Blood Center, 424 Huanghe Road, Tianjin 300110, China
| | - Ting Wang
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Sciences and Engineering, Nankai University, Tianjin 300071, China
| | - Lin Wu
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Sciences and Engineering, Nankai University, Tianjin 300071, China
| | - Naixiu Sun
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Sciences and Engineering, Nankai University, Tianjin 300071, China
| | - Tiange Fang
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Sciences and Engineering, Nankai University, Tianjin 300071, China
| | - Hongjun Mao
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Sciences and Engineering, Nankai University, Tianjin 300071, China
| | - Nai-jun Tang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, 300070, China
- Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Tianjin Medical University, Tianjin, 300070, China
| | - Xi Chen
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, 300070, China
- Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Tianjin Medical University, Tianjin, 300070, China
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Lee GW, Vine K, Atkinson AR, Tong M, Longman J, Barratt A, Bailie R, Vardoulakis S, Matthews V, Rahman KM. Impacts of Climate Change on Health and Health Services in Northern New South Wales, Australia: A Rapid Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6285. [PMID: 37444133 PMCID: PMC10341403 DOI: 10.3390/ijerph20136285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/09/2023] [Accepted: 06/16/2023] [Indexed: 07/15/2023]
Abstract
Climate change is exposing populations to increasing temperatures and extreme weather events in many parts of Australia. To prepare for climate challenges, there is a growing need for Local Health Districts (LHDs) to identify potential health impacts in their region and strengthen the capacity of the health system to respond accordingly. This rapid review summarised existing evidence and research gaps on the impact of climate change on health and health services in Northern New South Wales (NSW)-a 'hotspot' for climate disaster declarations. We systematically searched online databases and selected 11 peer-reviewed studies published between 2012-2022 for the Northern NSW region. The most explored health outcome was mental health in the aftermath of floods and droughts, followed by increased healthcare utilisation due to respiratory, cardiovascular and mortality outcomes associated with bushfire smoke or heat waves. Future research directions were recommended to understand: the compounding impacts of extreme events on health and the health system, local data needs that can better inform models that predict future health risks and healthcare utilisation for the region, and the needs of vulnerable populations that require a whole-of-system response during the different phases of disasters. In conclusion, the review provided climate change and health research directions the LHD may undertake to inform future adaptation and mitigation policies and strategies relevant to their region.
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Affiliation(s)
- Grace W. Lee
- University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia; (G.W.L.); (K.V.); (A.-R.A.); (J.L.); (V.M.)
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia;
| | - Kristina Vine
- University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia; (G.W.L.); (K.V.); (A.-R.A.); (J.L.); (V.M.)
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
| | - Amba-Rose Atkinson
- University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia; (G.W.L.); (K.V.); (A.-R.A.); (J.L.); (V.M.)
- School of Public Health, Faculty of Medicine, the University of Queensland, St. Lucia, QLD 4072, Australia
| | - Michael Tong
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
- College of Health and Medicine, The Australian National University, Canberra, ACT 2601, Australia
| | - Jo Longman
- University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia; (G.W.L.); (K.V.); (A.-R.A.); (J.L.); (V.M.)
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
- Sydney Environment Institute, University of Sydney, Camperdown, NSW 2006, Australia
| | - Alexandra Barratt
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia;
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
| | - Ross Bailie
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
- School of Medicine, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia
| | - Sotiris Vardoulakis
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
- College of Health and Medicine, The Australian National University, Canberra, ACT 2601, Australia
| | - Veronica Matthews
- University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia; (G.W.L.); (K.V.); (A.-R.A.); (J.L.); (V.M.)
- Healthy Environments And Lives (HEAL) National Research Network, Canberra, ACT 2601, Australia; (M.T.); (R.B.); (S.V.)
| | - Kazi Mizanur Rahman
- University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia; (G.W.L.); (K.V.); (A.-R.A.); (J.L.); (V.M.)
- Sydney Environment Institute, University of Sydney, Camperdown, NSW 2006, Australia
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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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Zhao Z, Chu J, Xu X, Cao Y, Schikowski T, Geng M, Chen G, Bai G, Hu K, Xia J, Ma W, Liu Q, Lu Z, Guo X, Zhao Q. Association between ambient cold exposure and mortality risk in Shandong Province, China: Modification effect of particulate matter size. Front Public Health 2023; 10:1093588. [PMID: 36684922 PMCID: PMC9850236 DOI: 10.3389/fpubh.2022.1093588] [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: 11/09/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction Numerous studies have reported the modification of particulate matters (PMs) on the association between cold temperature and health. However, it remains uncertain whether the modification effect may vary by size of PMs, especially in Shandong Province, China where the disease burdens associated with cold temperature and PMs are both substantial. This study aimed to examine various interactive effects of cold exposure and ambient PMs with diameters ≤1/2.5 μm (PM1 and PM2.5) on premature deaths in Shandong Province, China. Methods In the 2013-2018 cold seasons, data on daily mortality, PM1 and PM2.5, and weather conditions were collected from the 1822 sub-districts of Shandong Province. A time-stratified case-crossover study design was performed to quantify the cumulative association between ambient cold and mortality over lag 0-12 days, with a linear interactive term between temperature and PM1 and PM2.5 additionally added into the model. Results The mortality risk increased with temperature decline, with the cumulative OR of extreme cold (-16.9°C, the 1st percentile of temperature range) being 1.83 (95% CI: 1.66, 2.02), compared with the minimum mortality temperature. The cold-related mortality risk was 2.20 (95%CI: 1.83, 2.64) and 2.24 (95%CI: 1.78, 2.81) on high PM1 and PM2.5 days, which dropped to 1.60 (95%CI: 1.39, 1.84) and 1.60 (95%CI: 1.37, 1.88) on low PM1 and PM2.5 days. PM1 showed greater modification effect for per unit concentration increase than PM2.5. For example, for each 10?g/m3 increase in PM1 and PM2.5, the mortality risk associated with extreme cold temperature increased by 7.6% (95% CI: 1.3%, 14.2%) and 2.6% (95% CI: -0.7%, 5.9%), respectively. Discussion The increment of smaller PMs' modification effect varied by population subgroups, which was particularly strong in the elderly aged over 75 years and individuals with middle school education and below. Specific health promotion strategies should be developed towards the greater modification effect of smaller PMs on cold effect.
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Affiliation(s)
- Zhonghui Zhao
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China
| | - Jie Chu
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Xiaohui Xu
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Yanwen Cao
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China
| | - Tamara Schikowski
- Department of Epidemiology, Leibniz Institute for Environmental Medicine (IUF)-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Mengjie Geng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Gongbo Chen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Guannan Bai
- Department of Child Health Care, The Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Jingjing Xia
- School of Life Sciences, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China
| | - Qiyong Liu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, Jinan, China,Academy of Preventive Medicine, Shandong University, Jinan, China,Xiaolei Guo ✉
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Shandong University, Jinan, China,Shandong University Climate Change and Health Center, Jinan, China,Department of Epidemiology, Leibniz Institute for Environmental Medicine (IUF)-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany,*Correspondence: Qi Zhao ✉
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6
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Zheng H, Wang Q, Fu J, Ding Z, Cheng J, Xu Z, Xu Y, Xia Y. Geographical variation in the effect of ambient temperature on infectious diarrhea among children under 5 years. ENVIRONMENTAL RESEARCH 2023; 216:114491. [PMID: 36208789 DOI: 10.1016/j.envres.2022.114491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 09/22/2022] [Accepted: 10/01/2022] [Indexed: 06/16/2023]
Abstract
Understanding the geographical distribution in the association of temperature with childhood diarrhea can assist in formulating effective localized diarrhea prevention practices. This study aimed to identify the geographical variation in terms of temperature thresholds, lag effects, and attributable fraction (AF) in the effects of ambient temperature on Class C Other Infectious Diarrhea (OID) among children <5 years in Jiangsu Province, China. Daily data of OID cases and meteorological variables from 2015 to 2019 were collected. City-specific minimum morbidity temperature (MMT), increasing risk temperature (IRT), maximum risk temperature (MRT), maximum risk lag day (MRD), and lag day duration (LDD) were identified as risk indicators for the temperature-OID relationship using distributed lag non-linear models. The AF of OID incidence due to temperature was evaluated. Multivariable regression was also applied to explore the underlying modifiers of the AF. The geographical distributions of MMT, IRT, and MRT generally decreased with the latitude increment varying between 22.3-34.7 °C, -2.9-18.1 °C, and -6.8-23.2 °C. Considerable variation was shown in the AF ranging from 0.2 to 8.5%, and the AF significantly increased with latitude (95% confidence interval (CI): -3.458, -0.987) and economic status decrement (95% CI: -0.161, -0.019). Our study demonstrated between-city variations in the association of temperature with OID, which should be considered in the localized clinical and public health practices to decrease the incidence of childhood diarrhea.
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Affiliation(s)
- Hao Zheng
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - QingQing Wang
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jianguang Fu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China; Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Nanjing, China
| | - Zhen Ding
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhiwei Xu
- School of Public Health, University of Queensland, Queensland, Australia
| | - Yan Xu
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China; Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Nanjing, China.
| | - Yankai Xia
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
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Mei Y, Li A, Zhao M, Xu J, Li R, Zhao J, Zhou Q, Ge X, Xu Q. Associations and burdens of relative humidity with cause-specific mortality in three Chinese cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:3512-3526. [PMID: 35947256 DOI: 10.1007/s11356-022-22350-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
This study aimed to investigate the association between relative humidity (RH) and various cause of mortality, and then quantify the RH-related mortality fraction of low and high RH under the assumption that causal effects exist. Daily cause-specific mortality counts from 2008 to 2011, and contemporaneous meteorological data in three Chinese cities were collected. Distributed lag nonlinear models were adopted to quantify the nonlinear and delayed effects of RH on mortality risk. Low and high RH were defined as RH lower or higher than the minimum mortality risk RH (MMRH), respectively. Corresponding RH-related mortality fractions were calculated in the explanatory analysis. From the three cities, 736,301 deaths were collected. RH (mean ± standard deviation) were 50.9 ± 20.0 for Beijing, 75.5 ± 8.6 for Chengdu, and 70.8 ± 14.6 for Nanjing. We found that low RH in Beijing and high RH (about 80-90%) in Chengdu was associated with increased all-cause mortality risk. Both low and high RH may increase the CVD mortality risk in Beijing. Both low and high (about 80-85%) RH may increase the COPD mortality risk in Chengdu. Low RH (about < 45%) was associated with increased diabetes mortality risk in Nanjing. Effects of extreme low and extreme high RH were delayed in these cities, except that extreme low effects on COPD mortality appeared immediately in Chengdu. The effects of extreme low RH are higher than that of the extreme high RH in Beijing and Nanjing, while contrary in Chengdu. Finally, under the causal effect assumption, 6.80% (95% eCI: 2.90, 10.73) all-cause mortality and 12.48% (95% eCI: 7.17, 16.80) CVD deaths in Beijing, 9.59% (95% eCI: 1.38, 16.88) COPD deaths in Chengdu, and 23.79% (95% eCI: 0.92, 387.93) diabetes mortality in Nanjing were attributable to RH. Our study provided insights into RH-mortality risk, helped draw relative intervention policies, and is also significant for future predictions of climate change effects under different scenarios.
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Affiliation(s)
- Yayuan Mei
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Ang Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Meiduo Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Jing Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Runkui Li
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jiaxin Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Quan Zhou
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Xiaoyu Ge
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Qun Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China.
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China.
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8
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Chaseling GK, Morris NB, Ravanelli N. Extreme Heat and Adverse Cardiovascular Outcomes in Australia and New Zealand: What Do We Know? Heart Lung Circ 2023; 32:43-51. [PMID: 36424263 DOI: 10.1016/j.hlc.2022.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/29/2022] [Accepted: 10/05/2022] [Indexed: 11/23/2022]
Abstract
Extreme heat events are a leading natural hazard risk to human health. Under all future climate change models, extreme heat events will continue to increase in frequency, duration, and intensity. Evidence from previous extreme heat events across the globe demonstrates that adverse cardiovascular events are the leading cause of morbidity and mortality, particularly amongst the elderly and those with pre-existing cardiovascular disease. However, less is understood about the adverse effects of extreme heat amongst specific cardiovascular diseases (i.e., heart failure, dysrhythmias) and demographics (sex, ethnicity, age) within Australia and New Zealand. Furthermore, although Australia has implemented regional and state heat warning systems, most personal heat-health protective advice available in public health policy documents is either insufficient, not grounded in scientific evidence, and/or does not consider clinical factors such as age or co-morbidities. Dissemination of evidence-based recommendations and enhancing community resilience to extreme heat disasters within Australia and New Zealand should be an area of critical focus to reduce the burden and negative health effects associated with extreme heat. This narrative review will focus on five key areas in relation to extreme heat events within Australia and New Zealand: 1) the potential physiological mechanisms that cause adverse cardiovascular outcomes during extreme heat events; 2) how big is the problem within Australia and New Zealand?; 3) what the heat-health response plans are; 4) research knowledge and translation; and, 5) knowledge gaps and areas for future research.
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Affiliation(s)
- Georgia K Chaseling
- Engagement and Co-design Research Hub, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; SOLVE-CHD NHMRC Synergy Grant, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
| | - Nathan B Morris
- Department of Human Physiology & Nutrition, University of Colorado, Colorado Springs, CO, USA
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9
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Psistaki K, Dokas IM, Paschalidou AK. The Impact of Ambient Temperature on Cardiorespiratory Mortality in Northern Greece. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:555. [PMID: 36612877 PMCID: PMC9819162 DOI: 10.3390/ijerph20010555] [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: 12/09/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
It is well-established that exposure to non-optimum temperatures adversely affects public health, with the negative impact varying with latitude, as well as various climatic and population characteristics. This work aims to assess the relationship between ambient temperature and mortality from cardiorespiratory diseases in Eastern Macedonia and Thrace, in Northern Greece. For this, a standard time-series over-dispersed Poisson regression was fit, along with a distributed lag nonlinear model (DLNM), using a maximum lag of 21 days, to capture the non-linear and delayed temperature-related effects. A U-shaped relationship was found between temperature and cardiorespiratory mortality for the overall population and various subgroups and the minimum mortality temperature was observed around the 65th percentile of the temperature distribution. Exposure to extremely high temperatures was found to put the highest risk of cardiorespiratory mortality in all cases, except for females which were found to be more sensitive to extreme cold. It is remarkable that the highest burden of temperature-related mortality was attributed to moderate temperatures and primarily to moderate cold. The elderly were found to be particularly susceptible to both cold and hot thermal stress. These results provide new evidence on the health response of the population to low and high temperatures and could be useful to local authorities and policy-makers for developing interventions and prevention strategies for reducing the adverse impact of ambient temperature.
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Affiliation(s)
- Kyriaki Psistaki
- Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 68200 Orestiada, Greece
| | - Ioannis M. Dokas
- Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
| | - Anastasia K. Paschalidou
- Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 68200 Orestiada, Greece
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10
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A systematic review and meta-analysis of intraday effects of ambient air pollution and temperature on cardiorespiratory morbidities: First few hours of exposure matters to life. EBioMedicine 2022; 86:104327. [PMID: 36323182 PMCID: PMC9626385 DOI: 10.1016/j.ebiom.2022.104327] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/25/2022] [Accepted: 10/13/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND A growing number of studies have reported an increased risk of cardiovascular disease (CVD) and respiratory disease (RD) within hours after exposure to ambient air pollution or temperature. We assemble published evidence on the sub-daily associations of CVD and RD with ambient air pollution and temperature. METHODS Databases of PubMed and Web of Science were searched for original case-crossover and time-series designs of English articles examining the intra-day effects of ambient air pollution [particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10), 2.5-10μm (PM10-2.5), and < 7 μm (SPM), O3, SO2, NO2, CO, and NO] and temperatures (heat and cold) on cardiorespiratory diseases within 24 h after exposure in the general population by comparing with exposure at different exposure levels or periods. Meta-analyses were conducted to pool excess risks (ERs, absolute percentage increase in risk) of CVD and RD morbidities associated with an increase of 10 μg/m3 in particulate matters, 0.1 ppm in CO, and 10 ppb in other gaseous pollutants. FINDINGS Final analysis included thirty-three papers from North America, Europe, Oceania, and Asia. Meta-analysis found an increased risk of total CVD morbidity within 3 h after exposure to PM2.5 [ER%: 2.65% (95% CI: 1.00% to 4.34%)], PM10-2.5 [0.31% (0.02% to 0.59%)], O3 [1.42% (0.14% to 2.73%)], and CO [0.41% (0.01% to 0.81%)]. The risk of total RD morbidity elevated at lag 7-12 h after exposure to PM2.5 [0.69% (0.14% to 1.24%)] and PM10 [0.38% (0.02% to 0.73%)] and at lag 12-24 h after exposure to SO2 [2.68% (0.94% to 4.44%)]. Cause-specific CVD analysis observed an increased risk of myocardial infarction morbidity within 6 h after exposure to PM2.5, PM10, and NO2, and an increased risk of out-of-hospital cardiac arrest morbidity within 12 h after exposure to CO. Risk of total CVD also increased within 24 h after exposure to heat. INTERPRETATION This study supports a sudden risk increase of cardiorespiratory diseases within a few hours after exposure to air pollution or heat, and some acute and highly lethal diseases such as myocardial infarction and cardiac arrest could be affected within a shorter time. FUNDING The National Natural Science Foundation of China (Grant No. 42105165; 81773518), the High-level Scientific Research Foundation of Anhui Medical University (Grant No. 0305044201), and the Discipline Construction of Anhui Medical University (Grant No. 0301001836).
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11
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Adnan MSG, Dewan A, Botje D, Shahid S, Hassan QK. Vulnerability of Australia to heatwaves: A systematic review on influencing factors, impacts, and mitigation options. ENVIRONMENTAL RESEARCH 2022; 213:113703. [PMID: 35716815 DOI: 10.1016/j.envres.2022.113703] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 06/04/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Heatwaves have received major attention globally due to their detrimental effects on human health and the environment. The frequency, duration, and severity of heatwaves have increased recently due to changes in climatic conditions, anthropogenic forcing, and rapid urbanization. Australia is highly vulnerable to this hazard. Although there have been an increasing number of studies conducted in Australia related to the heatwave phenomena, a systematic review of heatwave vulnerability has rarely been reported in the literature. OBJECTIVES This study aims to provide a systematic and overarching review of the different components of heatwave vulnerability (e.g., exposure, sensitivity, and adaptive capacity) in Australia. METHODS A systematic review was conducted using the PRISMA protocol. Peer-reviewed English language articles published between January 2000 and December 2021 were selected using a combination of search keywords in Web of Science, Scopus, and PubMed. Articles were critically analyzed based on three specific heatwave vulnerability components: exposure, sensitivity, and adaptive capacity. RESULTS AND DISCUSSION A total of 107 articles meeting all search criteria were chosen. Although there has been an increasing trend of heat-related studies in Australia, most of these studies have concentrated on exposure and adaptive capacity components. Evidence suggests that the frequency, severity, and duration of heatwaves in Australian cities has been increasing, and that this is likely to continue under current climate change scenarios. This study noted that heatwave vulnerability is associated with geographical and climatic factors, space, time, socioeconomic and demographic factors, as well as the physiological condition of people. Various heat mitigation and adaptation measures implemented around the globe have proven to be efficient in reducing the impacts of heatwaves. CONCLUSION This study provides increased clarity regarding the various drivers of heatwave vulnerability in Australia. Such knowledge is crucial in informing extreme heat adaptation and mitigation planning.
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Affiliation(s)
- Mohammed Sarfaraz Gani Adnan
- Department of Urban and Regional Planning, Chittagong University of Engineering and Technology (CUET), Chittagong, 4319, Bangladesh; Environmental Change Institute, School of Geography and the Environment, University of Oxford, OX1 3QY, United Kingdom.
| | - Ashraf Dewan
- School of Earth and Planetary Sciences, Curtin University, Perth, WA, 6102, Australia
| | - Dirk Botje
- School of Earth and Planetary Sciences, Curtin University, Perth, WA, 6102, Australia
| | - Shamsuddin Shahid
- Department of Hydraulics & Hydrology, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
| | - Quazi K Hassan
- Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary Alberta, T2N 1N4, Canada
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12
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Huang Y, Yang J, Chen J, Shi H, Lu X. Association between ambient temperature and age-specific mortality from the elderly: Epidemiological evidence from the Chinese prefecture with most serious aging. ENVIRONMENTAL RESEARCH 2022; 211:113103. [PMID: 35278469 DOI: 10.1016/j.envres.2022.113103] [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: 10/02/2021] [Revised: 02/28/2022] [Accepted: 03/07/2022] [Indexed: 05/16/2023]
Abstract
Older people are main susceptible group affected by non-optimal temperature. The aim of the study was to determine how mortality of older people with different ages are affected by temperatures. For this study, we collected data of all-cause death of 256,037 people aged between 65 and 104 years of age from a prefecture located in the north subtropical area with most serious aging rate in 2000, 2010 and 2020 in China. A distributed lag nonlinear model under different age groups was used to estimate non-optimal temperature associations to mortality. The results revealed: (1) With increasing age, older people were more likely to die during moderate low temperature, the proportion of attributable fraction of moderate low temperature in all temperature gradually increased with age. (2) Moderate low temperature could be divided into two parts, the lower part caused most death at age 65-79 and the higher part was not so dangerous, while for age 80+, preventive actions should be taken for both parts. (3) A leveling-off and deceleration phenomenon was observed at age 95-99 for low temperature, but not 100-104, it may be virtually a consequence of "harvesting effect" in that susceptible and common people have died before age 95, it was coincidence with mortality deceleration at extreme old ages found by demographic scholars over the past 200 years. (4) Heat wave had much higher relative risk than cold spell compared with moderate high and low temperature because of steeper slope of relative risk at the period of moderate-extreme conversion of high temperature, the older people should pay more attention to weather with moderate-extreme conversion of high temperature. Furthermore, our findings could help improve the understanding of non-optimal temperature on health of older people and support the development of response strategies for different seasons at different ages.
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Affiliation(s)
- Yi Huang
- School of Geographic Sciences, Nantong University, Nantong, 226000, China.
| | - Jun Yang
- School of Public Health, Guangzhou Medical University, Guangzhou, 511436, China
| | - Jianwei Chen
- School of Geographic Sciences, Nantong University, Nantong, 226000, China
| | - Hujing Shi
- School of Geographic Sciences, Nantong University, Nantong, 226000, China
| | - Xianjing Lu
- School of Geographic Sciences, Nantong University, Nantong, 226000, China
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13
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Zhou CL, Lv LS, Jin DH, Xie YJ, Ma WJ, Hu JX, Wang CE, Xu YQ, Zhang XE, Lu C. Temperature Change between Neighboring Days Contributes to Years of Life Lost per Death from Respiratory Disease: A Multicounty Analysis in Central China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105871. [PMID: 35627408 PMCID: PMC9141323 DOI: 10.3390/ijerph19105871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Many epidemiological studies have recently assessed respiratory mortality attributable to ambient temperatures. However, the associations between temperature change between neighboring days and years of life lost are insufficiently studied. Therefore, we assessed the attributable risk of temperature change between neighboring days on life loss due to respiratory disease. METHODS We obtained daily mortality and weather data and calculated crude rates of years of life lost for 70 counties in Hunan Province, Central China, from 2013 to 2017. A time-series design with distributed lag nonlinear model and multivariate meta-regression was used to pool the relationships between temperature change between neighboring days and rates of years of life lost. Then, we calculated the temperature change between neighboring days related to average life loss per death from respiratory disease. RESULTS The total respiratory disease death was 173,252 during the study period. The association between temperature change and years of life lost rates showed a w-shape. The life loss per death attributable to temperature change between neighboring days was 2.29 (95% CI: 0.46-4.11) years, out of which 1.16 (95% CI: 0.31-2.01) years were attributable to moderately high-temperature change between neighboring days, and 0.99 (95% CI: 0.19-1.79) years were attributable to moderately low-temperature change between neighboring days. The temperature change between neighboring days related to life loss per respiratory disease death for females (2.58 years, 95% CI: 0.22-4.93) and the younger group (2.97 years, 95% CI: -1.51-7.44) was higher than that for males (2.21 years, 95% CI: 0.26-4.16) and the elderly group (1.96 years, 95% CI: 0.85-3.08). An average of 1.79 (95% CI: 0.18-3.41) life loss per respiratory disease death was related to non-optimal ambient temperature. CONCLUSIONS The results indicated that more attention should be given to temperature change, and more public health policies should be implemented to protect public health.
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Affiliation(s)
- Chun-Liang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Ling-Shuang Lv
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
- Correspondence: (L.-S.L.); (C.L.)
| | - Dong-Hui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Yi-Jun Xie
- Hunan Provincial Climate Center, Changsha 410007, China;
| | - Wen-Jun Ma
- School of Medicine, Jinan University, Guangzhou 510632, China;
| | - Jian-Xiong Hu
- Guangdong Provincial Institute of Public Health, Guangzhou 511430, China;
| | - Chun-E Wang
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Yi-Qing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Xing-E Zhang
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Chan Lu
- XiangYa School of Public Health, Central South University, Changsha 410078, China
- Correspondence: (L.-S.L.); (C.L.)
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14
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Wu Y, Li S, Zhao Q, Wen B, Gasparrini A, Tong S, Overcenco A, Urban A, Schneider A, Entezari A, Vicedo-Cabrera AM, Zanobetti A, Analitis A, Zeka A, Tobias A, Nunes B, Alahmad B, Armstrong B, Forsberg B, Pan SC, Íñiguez C, Ameling C, De la Cruz Valencia C, Åström C, Houthuijs D, Van Dung D, Royé D, Indermitte E, Lavigne E, Mayvaneh F, Acquaotta F, de'Donato F, Rao S, Sera F, Carrasco-Escobar G, Kan H, Orru H, Kim H, Holobaca IH, Kyselý J, Madureira J, Schwartz J, Jaakkola JJK, Katsouyanni K, Hurtado Diaz M, Ragettli MS, Hashizume M, Pascal M, de Sousa Zanotti Stagliorio Coélho M, Ortega NV, Ryti N, Scovronick N, Michelozzi P, Correa PM, Goodman P, Nascimento Saldiva PH, Abrutzky R, Osorio S, Dang TN, Colistro V, Huber V, Lee W, Seposo X, Honda Y, Guo YL, Bell ML, Guo Y. Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study. Lancet Planet Health 2022; 6:e410-e421. [PMID: 35550080 PMCID: PMC9177161 DOI: 10.1016/s2542-5196(22)00073-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 05/08/2023]
Abstract
BACKGROUND Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6). INTERPRETATION Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING Australian Research Council, Australian National Health & Medical Research Council.
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Affiliation(s)
- Yao Wu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Qi Zhao
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bo Wen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Antonio Gasparrini
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Shilu Tong
- Shanghai Children's Medical Centre, Shanghai Jiao Tong University, Shanghai, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China; Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ala Overcenco
- National Agency for Public Health of the Ministry of Health, Labour and Social Protection of the Republic of Moldova, Chișinău, Moldova
| | - Aleš Urban
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Alireza Entezari
- Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
| | - Ana Maria Vicedo-Cabrera
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Antonis Analitis
- Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece
| | - Ariana Zeka
- Institute for Environment, Health and Societies, Brunel University London, London, UK
| | - Aurelio Tobias
- Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Baltazar Nunes
- Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Barrak Alahmad
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Ben Armstrong
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Bertil Forsberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Shih-Chun Pan
- NationalInstitute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan
| | - Carmen Íñiguez
- Department of Statistics and Computational Research, Universitat de València, València, Spain; CIBER of Epidemiology and Public Health, Madrid, Spain
| | - Caroline Ameling
- National Institute for Public Health and the Environment (RIVM), Centre for Sustainability and Environmental Health, Bilthoven, Netherlands
| | | | - Christofer Åström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Danny Houthuijs
- National Institute for Public Health and the Environment (RIVM), Centre for Sustainability and Environmental Health, Bilthoven, Netherlands
| | - Do Van Dung
- Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Dominic Royé
- CIBER of Epidemiology and Public Health, Madrid, Spain; Department of Geography, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Ene Indermitte
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Eric Lavigne
- School of Epidemiology & Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada; Air Health Science Division, Health Canada, Ottawa, ON, Canada
| | - Fatemeh Mayvaneh
- Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
| | | | | | - Shilpa Rao
- Norwegian Institute of Public Health, Oslo, Norway
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications "G Parenti", University of Florence, Florence, Italy
| | - Gabriel Carrasco-Escobar
- Health Innovation Lab, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Haidong Kan
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China
| | - Hans Orru
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Ho Kim
- Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | | | - Jan Kyselý
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Joana Madureira
- Environmental Health Department, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
| | - Joel Schwartz
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jouni J K Jaakkola
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Klea Katsouyanni
- Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece; School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Magali Hurtado Diaz
- Department of Environmental Health, National Institute of Public Health, Cuernavaca Morelos, Mexico
| | - Martina S Ragettli
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Masahiro Hashizume
- Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mathilde Pascal
- Santé Publique France, Department of Environmental and Occupational Health, French National Public Health Agency, Saint Maurice, France
| | | | | | - Niilo Ryti
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Noah Scovronick
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | | | - Patrick Goodman
- School of Physics, Technological University Dublin, Dublin, Ireland
| | | | - Rosana Abrutzky
- Universidad de Buenos Aires, Facultad de Ciencias Sociales, Instituto de Investigaciones Gino Germani, Buenos Aires, Argentina
| | - Samuel Osorio
- Department of Environmental Health, University of São Paulo, São Paulo, Brazil
| | - Tran Ngoc Dang
- Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Valentina Colistro
- Department of Quantitative Methods, School of Medicine, University of the Republic, Montevideo, Uruguay
| | - Veronika Huber
- IBE-Chair of Epidemiology, LMU Munich, Munich, Germany; Department of Physical, Chemical and Natural Systems, Universidad Pablo de Olavide, Sevilla, Spain
| | - Whanhee Lee
- School of the Environment, Yale University, New Haven, CT, USA; Department of Occupational and Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Xerxes Seposo
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Yasushi Honda
- Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan
| | - Yue Leon Guo
- NationalInstitute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan; Environmental and Occupational Medicine, National Taiwan University College of Medicine and NTU Hospital, National Taiwan University, Taipei, Taiwan; Graduate Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
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15
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He YS, Wang GH, Wu ZD, Sam NB, Chen Y, Tao JH, Fang XY, Xu Z, Pan HF. Association between non-optimal temperature and hospitalizations for gout in Anqing, China: a time-series analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:13797-13804. [PMID: 34599442 DOI: 10.1007/s11356-021-16580-w] [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: 07/15/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
Previous studies have found that non-optimal temperature influences the development of gout, but the results have been inconsistent. The present study aimed to explore the effects of high temperature and high temperature variation on hospitalizations for gout in Anqing, China. We collected daily data on air pollutants, meteorological factors, and hospitalizations for gout between 1January 2016 and 31 December 2020 in Anqing City, China. We used Poisson generalized linear regression model and a distributed lag non-linear model (DLNM) to explore the relationship of high temperature, diurnal temperature range (DTR), and temperature change between neighboring days (TCN) with hospitalizations for gout. Stratified analysis by gender (male, female) and age (<65 years, ≥65 years) was conducted. Hospitalizations for gout attributed to high temperature, high DTR, and high TCN were also quantified. A total of 8675 hospitalized patients with gout were reported during the study period. We observed that exposure to high temperature was linked with an increased risk of hospitalizations for gout (lag 0, RR: 1.081, 95% confidence interval (CI): 1.011, 1.155). Exposure to high DTR was also associated with increased risk of hospitalizations for gout (lag9, RR: 1.017, 95% CI: 1.001,1.035). A large drop in temperature between neighboring days was associated an increased risk of hospitalizations for gout (lag 0-2 days, RR: 1.234, 95% CI: 1.017, 1.493). Stratified analysis results revealed that older adults and men were more sensitive to high-level DTR exposure than their counterparts. Nearly 15% of hospitalizations for gout could be attributable to high temperature (attributable fraction: 14.93%, 95% CI: 5.99%, 22.11%). This study suggests that high temperature and high temperature variation may trigger hospitalizations for gout, indicating that patients with gout need to take proactive actions in the face of days with non-optimal temperature.
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Affiliation(s)
- Yi-Sheng He
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China
| | - Gui-Hong Wang
- Department of Rheumatology, Anqing Hospital Affiliated to Anhui Medical University, Anqing, Anhui, China
| | - Zheng-Dong Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China
| | - Napoleon Bellua Sam
- Department of Medical Research and Innovation, School of Medicine, University for Development Studies, Tamale, Ghana
| | - Yue Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China
| | - Jin-Hui Tao
- Department of Rheumatology and Immunology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xin-Yu Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia.
| | - Hai-Feng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China.
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China.
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16
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Yu Y, Luo S, Zhang Y, Liu L, Wang K, Hong L, Wang Q. Comparative analysis of daily and hourly temperature variability in association with all-cause and cardiorespiratory mortality in 45 US cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:11625-11633. [PMID: 34537946 DOI: 10.1007/s11356-021-16476-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/05/2021] [Indexed: 06/13/2023]
Abstract
Temperature variability (TV) has been widely associated with increased mortality risk and burden. Extensive researches have used the standard deviations of several days' daily maximum and minimum temperatures or hourly mean temperatures as daily and hourly TV measures (TVdaily and TVhourly). However, comparative analysis of daily and hourly TV related to cardiorespiratory mortality is still limited. We collected daily mortality and meteorological data in 45 US metropolises, 1987-2000. A three-stage analysis was adopted to investigate TV-mortality associations using TVdaily and TVhourly as exposure metrics. We first applied a time-series quasi-Poisson regression to estimate location-specific TV-mortality relationships, which were then pooled using random-effects meta-analysis with maximum likelihood estimation. We additionally calculated attributable fraction (AF) as a reflection of mortality burden associated with TV. Stratified analyses by age were also performed to identify the susceptible group to TV-related risks. There were a total of 15.4 million all-cause deaths, of which 6.1 million were from cardiovascular causes and 1.2 million were from respiratory causes. Per 1 °C increase in TVdaily and TVhourly was associated with an increase of 0.53% (95% confidence interval: 0.31-0.76%) and 0.52% (0.26-0.79%) in cardiovascular mortality risks, 0.62% (0.26-0.98%) and 0.53% (0.13-0.94%) in respiratory mortality risks. Estimates of cardiovascular AF for TVdaily and TVhourly were 2.43% (1.42-3.43%) vs. 1.63% (0.82-2.43%), whereas estimates of respiratory AF were 3.07% (1.11-4.99%) vs. 1.89% (0.43-3.34%). Both daily and hourly TV indexes showed approximately linear relationships with different mortality categories and similar lag patterns, but greater fractions were estimated using TVdaily than those using TVhourly. People over 75 years old were relatively more vulnerable to TV-induced risks of mortality. In conclusion, both TVdaily and TVhourly significantly increased all-cause and cardiorespiratory mortality risks and burden. Daily and hourly TV metrics exhibited comparable effects of mortality risk, while greater mortality burden was estimated using TVdaily than TVhourly. Our findings may add significance to TV-mortality research and help to promote optimal health management strategies to better mitigate TV-related health effects.
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Affiliation(s)
- Yong Yu
- School of Public Health, Hubei University of Medicine, Shiyan, 442000, China
| | - Siqi Luo
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Linjiong Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Ke Wang
- Department of Nursing, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Le Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Qun Wang
- School of Public Health, Hubei University of Medicine, Shiyan, 442000, China.
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17
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Pan R, Yao Z, Yi W, Wei Q, He Y, Tang C, Liu X, Son S, Ji Y, Song J, Cheng J, Ji Y, Su H. Temporal trends of the association between temperature variation and hospitalizations for schizophrenia in Hefei, China from 2005 to 2019: a time-varying distribution lag nonlinear model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:5184-5193. [PMID: 34417696 DOI: 10.1007/s11356-021-15797-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
Along with climate change, unstable weather patterns are becoming more frequent. However, the temporal trend associated with the effect of temperature variation on schizophrenia (SCZ) is not clear. Daily time-series data on SCZ and meteorological factors for 15-year between January 1, 2005 and December 31, 2019 were collected. And we used the Poisson regression model combined with the time-varying distribution lag nonlinear model (DLNM) to explore the temporal trend of the association between three temperature variation indicators (diurnal temperature range, DTR; temperature variability, TV; temperature change between neighboring days, TCN) and SCZ hospitalizations, respectively. Meanwhile, we also explore the temporal trend of the interaction between temperature and temperature variation. Stratified analyses were performed in different gender, age, and season. Across the whole population, we found a decreasing trend in the risk of SCZ hospitalizations associated with high DTR (from 1.721 to 1.029), TCN (from 1.642 to 1.066), and TV (TV0-1, from 1.034 to 0.994; TV0-2, from 1.041 to 0.994, TV0-3, from 1.044 to 0.992, TV0-4, from 1.049 to 0.992, TV0-5, from 1.055 to 0.993, TV0-6, from 1.059 to 0.991, TV0-7, from 1.059 to 0.990), but an increasing trend in low DTR (from 0.589 to 0.752). Subgroup analysis results further revealed different susceptible groups. Besides, the interactive effect suggests that temperature variation may cause greater harm under low-temperature conditions. There was a synergy between TCN and temperature on the addition and multiplication scales, which were 1.068 (1.007, 1.133) and 0.067 (0.009, 0.122), respectively. Our findings highlight public health interventions to mitigate temperature variation effects needed to focus not only on high temperature variations but also moderately low temperature variations. Future hospitalizations for SCZ associated with temperature variation may be more severely affected by temperature variability from low temperature environments. The temporal trend is associated with the effect of temperature variation on schizophrenia (SCZ).
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Affiliation(s)
- Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Zhenhai Yao
- Anhui Public Meteorological Service Center, Hefei, 230011, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Shasha Son
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Yanhu Ji
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Yifu Ji
- The Fourth People's Hospital, Hefei, China.
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China.
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China.
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18
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Anyachor CP, Sikoki FD. Assessing the nutritional and biochemical composition of the African catfish (Clarias Gariepinus) exposed to the antifoam polydimethylsiloxane. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:5923-5930. [PMID: 34435288 DOI: 10.1007/s11356-021-15871-6] [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: 11/04/2019] [Accepted: 08/04/2021] [Indexed: 06/13/2023]
Abstract
There exists a visible evidence of linkage between pollutant exposure and nutritional deficiency in many organisms. The aim of the present study was to analyze the proximate composition of juvenile African catfish (Clarias gariepinus) exposed to polydimethylsiloxane (antifoam) for 56 days using a renewal bioassay. The moisture, crude protein, ash, and fat content of the fish samples were analyzed using standard methods. Antifoam concentrations at 0.0mg/L, 63.96mg/L, 127.9mg/L, 255.82mg/L, and 511.64mg/L were used for the sublethal exposure after which the proximate composition was analyzed. The water quality variables including dissolved oxygen, conductivity, and total dissolved solids varied as the concentration increased. The moisture and lipid contents were highest at 511.64mg/L concentration while the control(0.0mg/L) had the highest percentage composition of ash, protein, carbohydrate, and fiber contents which were normal values. The differences in proximate values were slightly significant at P>0.05 among the different concentrations. The findings in this study may be an indication that antifoam can significantly affect some water quality variables and proximate composition, while also portraying the risk associated with the consumption of such exposed fish.
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Affiliation(s)
- Chidinma Promise Anyachor
- African Centre of Excellence, Centre for Public Health and Toxicological Research, University of Port-Harcourt, Port Harcourt, Nigeria.
| | - Francis David Sikoki
- Department of Animal and Environmental Biology, University of Port-Harcourt, Port Harcourt, Nigeria
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Reactive Oxygen Species Are Essential for Vasoconstriction upon Cold Exposure. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2021; 2021:8578452. [PMID: 34868457 PMCID: PMC8635890 DOI: 10.1155/2021/8578452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 10/20/2021] [Indexed: 12/02/2022]
Abstract
Purpose We explored the role of ROS in cold-induced vasoconstriction and corresponding mechanism. Methods Three experiments were performed. First, we measured blood flow in human hands before and after cold exposure. Second, 24 mice were randomly divided into 3 groups: 8 mice received saline injection, 8 received subcutaneous Tempol injection, and 8 received intrathecal Tempol injection. After 30 min, we determined blood flow in the skin before and after cold exposure. Finally, we used Tempol, CCG-1423, and Go 6983 to pretreat HAVSMCs and HUVECs for 24 h. Then, cells in the corresponding groups were exposed to cold (6 h, 4°C). After cold exposure, the cytoskeleton was stained. Intracellular Ca2+ and ROS levels were measured by flow cytometry and fluorescence microscopy. We measured protein expression via Western blotting. Results In the first experiment, after cold exposure, maximum skin blood flow decreased to 118.4 ± 50.97 flux units. Then, Tempol or normal saline pretreatment did not change skin blood flow. Unlike intrathecal Tempol injection, subcutaneous Tempol injection increased skin blood flow after cold exposure. Finally, cold exposure for 6 h shrank the cells, making them narrower, and increased intracellular Ca2+ and ROS levels in HUVECs and HAVSMCs. Tempol reduced cell shrinkage and decreased intracellular Ca2+ levels. In addition, Tempol decreased intracellular ROS levels. Cold exposure increased RhoA, Rock1, p-MLC-2, ET-1, iNOS, and p-PKC expression and decreased eNOS expression. Tempol or CCG-1423 pretreatment decreased RhoA, Rock1, and p-MLC-2 levels in HAVSMCs. Furthermore, Tempol or Go 6983 pretreatment decreased ET-1, iNOS, and p-PKC expression and increased eNOS expression in HUVECs. Conclusion ROS mediate the vasoconstrictor response within the cold-induced vascular response, and ROS in blood vessel tissues rather than nerve fibers are involved in vasoconstriction via the ROS/RhoA/ROCK1 and ROS/PKC/ET-1 pathways in VSMCs and endothelial cells.
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Tang C, Ji Y, Li Q, Yao Z, Cheng J, He Y, Liu X, Pan R, Wei Q, Yi W, Su H. Effects of different heat exposure patterns (accumulated and transient) and schizophrenia hospitalizations: a time-series analysis on hourly temperature basis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:69160-69170. [PMID: 34286435 DOI: 10.1007/s11356-021-15371-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Growing studies have shown that high temperature is a potential risk factor of schizophrenia occurrence. Therefore, elaborate analysis of different temperature exposure patterns, such as cumulative heat exposure within a time period and transient exposure at a particular time point, is of important public health significance. This study aims to utilize hourly temperature data to better capture the effects of cumulative and transient heat exposures on schizophrenia during the warm season in Hefei, China. We included the daily mean temperature and daily schizophrenia hospitalizations into the distributed lag non-linear model (DLNM) to simulate the exposure-response curve and determine the heat threshold (19.4 °C). We calculated and applied a novel indicator-daily excess hourly heat (DEHH)-to examine the effects of cumulative heat exposure over a day on schizophrenia hospitalizations. Temperature measurements at each time point were also incorporated in the DLNM as independent exposure indicators to analyze the impact of transient heat exposure on schizophrenia. Each increment of interquartile range (IQR) in DEHH was associated with elevated risk of schizophrenia hospitalizations from lag 1 (RR = 1.036, 95% confidence interval (CI): 1.016, 1.057) to lag 4 (RR = 1.025, 95% CI: 1.005, 1.046). Men and people over 40 years old were more susceptible to DEHH. Besides, we found a greater risk of heat-related schizophrenia hospitalizations between 0 a.m. and 6 a.m. This study revealed the adverse effects of accumulated and transient heat exposures on schizophrenia hospitalizations. Our findings need to be further tested in other regions with distinct regional features.
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Affiliation(s)
- Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Yifu Ji
- Anhui Mental Health Center, Hefei, 230032, Anhui, China
| | - Qingru Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Zhenhai Yao
- Anhui Public Meteorological Service Center, Hefei, 230011, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China.
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21
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Li Z, Liu Q, Zhan M, Tao B, Wang J, Lu W. Meteorological factors contribute to the risk of pulmonary tuberculosis: A multicenter study in eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148621. [PMID: 34328976 DOI: 10.1016/j.scitotenv.2021.148621] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/16/2021] [Accepted: 06/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Most studies on associations between meteorological factors and tuberculosis (TB) were conducted in a single city, used different lag times, or merely explored the qualitative associations between meteorological factors and TB. Thus, we performed a multicenter study to quantitatively evaluate the effects of meteorological factors on the risk of pulmonary tuberculosis (PTB). METHODS We collected data on newly diagnosed PTB cases in 13 study sites in Jiangsu Province between January 1, 2014, and December 31, 2019. Data on meteorological factors, air pollutants, and socioeconomic factors at these sites during the same period were also collected. We applied the generalized additive mixed model to estimate the associations between meteorological factors and PTB. RESULTS There were 20,472 newly diagnosed PTB cases reported in the 13 study sites between 2014 and 2019. The median (interquartile range) weekly average temperature, weekly average wind speed, and weekly average relative humidity of these sites were 17.3 °C (8.0-24.1), 2.2 m/s (1.8-2.7), and 75.1% (67.1-82.0), respectively. In the single-meteorological-factor models, for a unit increase in weekly average temperature, weekly average wind speed, and weekly average relative humidity, the risk of PTB decreased by 0.9% [lag 0-13 weeks, 95% confidence interval (CI): -1.5, -0.4], increased by 56.2% (lag 0-16 weeks, 95% CI: 32.6, 84.0) when average wind speed was <3 m/s, and decreased by 28.1% (lag 0-14 weeks, 95% CI: -39.2, -14.9) when average relative humidity was ≥72%, respectively. Moreover, the associations remained significant in the multi-meteorological-factor models. CONCLUSIONS Average temperature and average relative humidity (≥72%) are negatively associated with the risk of PTB. In contrast, average wind speed (<3 m/s) is positively related to the risk of PTB, suggesting that an environment with low temperature, relatively high wind speed, and low relative humidity is conducive to the transmission of PTB.
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Affiliation(s)
- Zhongqi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing 210009, China
| | - Mengyao Zhan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Bilin Tao
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Jianming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
| | - Wei Lu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing 210009, China.
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Cheng J, Su H, Xu Z, Tong S. Extreme temperature exposure and acute myocardial infarction: Elevated risk within hours? ENVIRONMENTAL RESEARCH 2021; 202:111691. [PMID: 34331920 DOI: 10.1016/j.envres.2021.111691] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/08/2021] [Accepted: 07/11/2021] [Indexed: 06/13/2023]
Abstract
Day-to-day change in ambient temperature is associated with acute myocardial infarction (AMI) attacks, but evidence is scarce about the effects of extreme temperatures on the risk of AMI within hours of exposure. This study investigated the hour-level associations between extreme temperatures and AMI occurrence. State-wide data on AMI patients and temperature during winter and summer of 2013-2015 were obtained for Queensland state of Australia. We employed a fixed time-stratified case-crossover analysis to quantify the risk of AMI associated with temperature within 24 h after exposure. Subgroups analyses by age, gender and disease history were also conducted. We observed a very acute effect of cold on men (occurred 9-10 h after exposure), women (19-22 h after exposure), and the elderly (4-20 h after exposure). Cold was associated with elevated AMI risk for men within 9 h (OR = 2.1, 95 % CI: 1.2-3.6), women within 19 h (OR = 2.5, 95 % CI: 1.0-6.0), and the elderly within 4 h (OR: 2.0, 95 % CI: 1.0-4.0). However, elevated risk of AMI associated with heat occurred 15 h later for men (OR: 3.9; 95 % CI: 1.1-13.9) and 23 h later for adults (OR: 4.1, 95 % CI: 1.1-15.4). People never suffered AMI and the elderly with diabetes or hyperlipidaemia were particularly vulnerable to cold. Those that were particularly vulnerable to heat were men never experienced AMI or having hypertension or having hyperlipidaemia as well as women ever suffered AMI. Effects of temperature on AMI risk at sub-daily timescales should be considered to prevent cardiac events.
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Affiliation(s)
- Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Shilu Tong
- Department of Clinical Epidemiology and Biostatistics, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China.
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Cheng J, Ho HC, Su H, Huang C, Pan R, Hossain MZ, Zheng H, Xu Z. Low ambient temperature shortened life expectancy in Hong Kong: A time-series analysis of 1.4 million years of life lost from cardiorespiratory diseases. ENVIRONMENTAL RESEARCH 2021; 201:111652. [PMID: 34246637 DOI: 10.1016/j.envres.2021.111652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/26/2021] [Accepted: 07/02/2021] [Indexed: 06/13/2023]
Abstract
Ambient temperature is an important contributor to mortality burden worldwide, most of which is from cold exposure. However, little is known about the cold impact on life expectancy loss. This paper aimed to estimate cold-related life expectancy loss from cause-, age-, and gender-specific cardiovascular and respiratory diseases. Daily deaths from cardiovascular and respiratory diseases and weather records were acquired for Hong Kong, China during 2000-2016. Years of life lost (YLL) that considers life expectancy at the time of death was calculated by matching each death by age and sex to annual life tables. Using a generalized additive model that fits temperature-YLL association, we estimated loss of years in life expectancy from cold. Cold was estimated to cause life expectancy loss of 0.9 years in total cardiovascular disease, with more years of loss in males than in females and in people aged 65 years and older than in people aged up to 64 years. Cold-related life expectancy loss in total respiratory diseases was 1.2 years, with more years of loss in females than in males and comparable years of loss in people aged up to 64 years and in people aged 65 years and older. Among cause-specific diseases, we observed the greatest life expectancy loss in pneumonia (1.5 years), followed by ischaemic heart disease (1.2 years), COPD (1.1 years), and stroke (0.3 years). Between two periods of 2000-2007 and 2008-2016, cold-related life expectancy loss due to cardiovascular disease did not decrease and cold-related life expectancy loss due to respiratory disease even increased by five times. Our findings suggest an urgent need to develop prevention measures against adverse cold effects on cardiorespiratory disease in Hong Kong.
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Affiliation(s)
- Jian Cheng
- School of Public Health, Department of Epidemiology and Biostatistics, Anhui Medical University, 81 Meishan Road, 230022, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, 81 Meishan Road, 230022, Hefei, China
| | - Hung Chak Ho
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China
| | - Hong Su
- School of Public Health, Department of Epidemiology and Biostatistics, Anhui Medical University, 81 Meishan Road, 230022, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, 81 Meishan Road, 230022, Hefei, China
| | - Cunrui Huang
- School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Guangzhou, 510080, China
| | - Rubing Pan
- School of Public Health, Department of Epidemiology and Biostatistics, Anhui Medical University, 81 Meishan Road, 230022, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, 81 Meishan Road, 230022, Hefei, China
| | - Mohammad Zahid Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, 210009, Nanjing, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, 288 Herston Road, Herston, Brisbane, Queensland, 4006, Australia.
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Tian H, Zhou Y, Wang Z, Huang X, Ge E, Wu S, Wang P, Tong X, Ran P, Luo M. Effects of high-frequency temperature variabilities on the morbidity of chronic obstructive pulmonary disease: Evidence in 21 cities of Guangdong, South China. ENVIRONMENTAL RESEARCH 2021; 201:111544. [PMID: 34157271 DOI: 10.1016/j.envres.2021.111544] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND While temperature changes have been confirmed as one of the contributory factors affecting human health, the association between high-frequency temperature variability (HFTV, i.e., temperature variation at short time scales such as 1, 2, and 5 days) and the hospitalization of chronic obstructive pulmonary disease (COPD) was rarely reported. OBJECTIVES To evaluate the associations between high-frequency temperature variabilities (i.e., at 1, 2, and 5-day scales) and daily COPD hospitalization. METHODS We collected daily records of COPD hospitalization and meteorological variables from 2013 to 2017 in 21 cities of Guangdong Province, South China. A quasi-Poisson regression with a distributed lag nonlinear model was first employed to quantify the effects of two HFTV measures, i.e., the day-to-day (DTD) temperature change and the intraday-interday temperature variability (IITV), on COPD morbidity for each city. Second, we used multivariate meta-analysis to pool the city-specific estimates, and stratified analyses were performed by age and sex to identify vulnerable groups. Then, the meta-regression with city-level characteristics was employed to detect the potential sources of the differences among 21 cities. RESULTS A monotonic increasing curve of the overall exposure-response association was observed, suggesting that positive HFTV (i.e., increased DTD and IITV) will significantly increase the risk of COPD admission. Negative DTD was associated with reduced COPD morbidity while positive DTD elevated the COPD risk. An interquartile range (IQR) increase in DTD was associated with a 24% (95% CI: 12-38%) increase in COPD admissions. An IQR increase in IITV0-1 was associated with 18% (95% CI: 7-27%) increase in COPD admissions. Males and people aged 0-64 years appeared to be more vulnerable to the DTD effect than others. Potential sources of the disparity among different cities include urbanization level, sex structure, industry structure, gross domestic product (GDP), health care services, and air quality. CONCLUSIONS The increases of DTD and IITV have significant adverse impacts on COPD hospitalization. As climate change intensifies, precautions need to be taken to mitigate the impacts of high-frequency temperature changes.
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Affiliation(s)
- Hao Tian
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Yumin Zhou
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zihui Wang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoliang Huang
- Department of Health of Guangdong Province, Government Affairs Service Center of Health Commission of Guangdong Province, Guangzhou, China
| | - Erjia Ge
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada
| | - Sijia Wu
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Peng Wang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Xuelin Tong
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Pixin Ran
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Ming Luo
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, China.
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Xiao Y, Meng C, Huang S, Duan Y, Liu G, Yu S, Peng J, Cheng J, Yin P. Short-Term Effect of Temperature Change on Non-Accidental Mortality in Shenzhen, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18168760. [PMID: 34444520 PMCID: PMC8392083 DOI: 10.3390/ijerph18168760] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 11/16/2022]
Abstract
Temperature change is an important meteorological indicator reflecting weather stability. This study aimed to examine the effects of ambient temperature change on non-accidental mortality using diurnal temperature change (DTR) and temperature change between neighboring days (TCN) from two perspectives, intra-day and inter-day temperature change, and further, to explore seasonal variations of mortality, identify the susceptible population and investigate the interaction between temperature change and apparent temperature (AT). We collected daily data on cause-specific mortality, air pollutants and meteorological indicators in Shenzhen, China, from 1 January 2013 to 29 December 2017. A Quasi-Poisson generalized linear regression combined with distributed lag non-linear models (DLNMs) were conducted to estimate the effects of season on temperature change-related mortality. In addition, a non-parametric bivariate response surface model was used to explore the interaction between temperature change and AT. The cumulative effect of DTR was a U-shaped curve for non-accidental mortality, whereas the curve for TCN was nearly monotonic. The overall relative risks (RRs) of non-accidental, cardiovascular and respiratory mortality were 1.407 (95% CI: 1.233-1.606), 1.470 (95% CI: 1.220-1.771) and 1.741 (95% CI: 1.157-2.620) from exposure to extreme large DTR (99th) in cold seasons. However, no statistically significant effects were observed in warm seasons. As for TCN, the effects were higher in cold seasons than warm seasons, with the largest RR of 1.611 (95% CI: 1.384-1.876). The elderly and females were more sensitive, and low apparent temperature had a higher effect on temperature change-related non-accidental mortality. Temperature change was positively correlated with an increased risk of non-accidental mortality in Shenzhen. Both female and elderly people are more vulnerable to the potential adverse effects, especially in cold seasons. Low AT may enhance the effects of temperature change.
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Affiliation(s)
- Yao Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (Y.X.); (C.M.); (Y.D.)
| | - Chengzhen Meng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (Y.X.); (C.M.); (Y.D.)
| | - Suli Huang
- Shenzhen Center for Disease Control and Prevention, 8 Longyuan Rd, Shenzhen 518055, China; (S.H.); (G.L.); (S.Y.)
| | - Yanran Duan
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (Y.X.); (C.M.); (Y.D.)
| | - Gang Liu
- Shenzhen Center for Disease Control and Prevention, 8 Longyuan Rd, Shenzhen 518055, China; (S.H.); (G.L.); (S.Y.)
| | - Shuyuan Yu
- Shenzhen Center for Disease Control and Prevention, 8 Longyuan Rd, Shenzhen 518055, China; (S.H.); (G.L.); (S.Y.)
| | - Ji Peng
- Shenzhen Center for Chronic Disease Control, 2021 Buxin Rd, Shenzhen 518020, China
- Correspondence: (J.P.); (J.C.); (P.Y.)
| | - Jinquan Cheng
- Shenzhen Center for Disease Control and Prevention, 8 Longyuan Rd, Shenzhen 518055, China; (S.H.); (G.L.); (S.Y.)
- Correspondence: (J.P.); (J.C.); (P.Y.)
| | - Ping Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (Y.X.); (C.M.); (Y.D.)
- Correspondence: (J.P.); (J.C.); (P.Y.)
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Ma P, Zhang Y, Wang X, Fan X, Chen L, Hu Q, Wang S, Li T. Effect of diurnal temperature change on cardiovascular risks differed under opposite temperature trends. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:39882-39891. [PMID: 33768454 DOI: 10.1007/s11356-021-13583-5] [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: 12/08/2020] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
Temperature change between neighboring days (TCN) is an important trigger for cardiovascular diseases, but the modulated effects by seasonal temperature trends have been barely taken into account. A quantified comparison between impacts of positive TCNs (temperature rise) and negative situations (temperature drop) is also needed. We evaluated the associations of TCNs with emergency room (ER) visits for coronary heart disease (CHD) and cerebral infarction (CI) in Beijing, China, from 2008 to 2012. A year was divided into two segments dominated by opposite temperature trends, quasi-Poisson regression with distributed lag nonlinear models estimating TCN-morbidity relations were employed, separately for each period. High morbidities of CHD and CI both occurred in transitional seasons accompanied by large TCNs. Under warming backgrounds, positive TCNs increased CHD risk in patients younger than 65 years, and old people showed limited sensitivity. In the cooling periods, negative TCNs induced CHD risk in females and the elderly; the highest RR showed on lag 6 d. In particular, a same diurnal temperature decrease (e.g., - 2°C) induced greater RR (RR = 1.113, 95% CIs: 1.033-1.198) on old people during warming periods than cooling counterparts (RR = 1.055, 95% CIs: 1.011-1.100). Moreover, positive TCNs elevated CI risk regardless of background temperatures, and males were particularly vulnerable. Seasonal temperature trends modify TCN-cardiovascular morbidity associations significantly, which may provide new insights into the health impact of unstable weathers.
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Affiliation(s)
- Pan Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Ying Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Xinzi Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Xingang Fan
- Department of Geography and Geology, Western Kentucky University, Bowling Green, KY, 42101, USA
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Lei Chen
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Qin Hu
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Shigong Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Tanshi Li
- Chinese PLA General Hospital, Beijing, 100000, China
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Wu W, Chen B, Wu G, Wan Y, Zhou Q, Zhang H, Zhang J. Increased susceptibility to temperature variation for non-accidental emergency ambulance dispatches in Shenzhen, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:32046-32056. [PMID: 33624238 DOI: 10.1007/s11356-021-12942-6] [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: 10/30/2020] [Accepted: 02/09/2021] [Indexed: 06/12/2023]
Abstract
Most studies focused on the temporal trend of mortality risk associated with temperature exposure. The relative role of heat, cold, and temperature variation (TV) on morbidity and its temporal trends are explored insufficiently. This study aims to investigate the temporal trends of emergency ambulance dispatch (EAD) risk and the attributable burden of heat, cold, and hourly temperature variation (HTV). We collected time-series data of daily EAD and ambient temperature in Shenzhen from 2010 to 2017. HTV was calculated as the standard deviation of the hourly temperatures between 2 consecutive days. Quasi-Poisson generalized additive models (GAM) with a time-varying distributed lag nonlinear model (DLNM) were applied to examine temporal trends of the HTV-, heat-, and cold-EAD association. The temporal variation of the attributable fraction (AF%) and attributable number (AN) for different temperature exposures was also calculated. The largest RR was observed in extreme cold [1.30 (95% CI: 1.18, 1.43)] and moderate cold [1.25 (95% CI: 1.17, 1.34)]. Significant increasing trends in HTV-related effects and burden were observed, especially for the extreme HTV effects (P for interaction < 0.05). Decreasing trends were observed in the heat-related effect and burden, though it showed no significance (P for interaction = 0.46). There was no clear change pattern of cold-related effects and burdens. Overall, the three temperature exposure caused 13.7% of EAD, of which 4.1%, 4.3%, and 5.3% were attributed to HTV, heat, and cold, respectively. All the temperature indexes in this study, especially the cold effect, are responsible for the increased risk of EAD. People have become more susceptible to HTV over the recent decade. However, there is no clear evidence to support the temporal change of the population's susceptibility to heat and cold. Thus, in addition to heat and cold, the emergency ambulance service department should pay more attention to HTV under climate change.
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Affiliation(s)
- Wenjing Wu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Section 3, Renmin South Road, Chengdu, 610044, China
| | - Bo Chen
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Section 3, Renmin South Road, Chengdu, 610044, China
| | - Gonghua Wu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yunying Wan
- Shenzhen Emergency Medical Center, Shenzhen, 518035, China
| | - Qiang Zhou
- Shenzhen Emergency Medical Center, Shenzhen, 518035, China
| | - Hua Zhang
- Shenzhen Emergency Medical Center, Shenzhen, 518035, China
| | - Juying Zhang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Section 3, Renmin South Road, Chengdu, 610044, China.
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28
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Yi W, Cheng J, Wei Q, Pan R, Song S, He Y, Tang C, Liu X, Zhou Y, Su H. Disparities of weather type and geographical location in the impacts of temperature variability on cancer mortality: A multicity case-crossover study in Jiangsu Province, China. ENVIRONMENTAL RESEARCH 2021; 197:110985. [PMID: 33744269 DOI: 10.1016/j.envres.2021.110985] [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: 09/04/2020] [Revised: 03/02/2021] [Accepted: 03/04/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Considering the serious health burden caused by adverse weather events, increasing researches focused on the relationship between temperature variability (TV) and cause-specific mortality, but its association with cancer was not well explored. We aimed to investigate the impacts of TV on cancer mortality and examine the modifying effects of weather type and geographical location as well as other characteristics. MATERIALS AND METHODS Daily city-specific data of cancer deaths, mean temperature (Tmean), maximum and minimum temperatures (Tmax and Tmin), relative humidity (RH), rainfall, and air pollutants were collected during 2016-2017 in 13 cities in Jiangsu Province, China. TV0-t was defined as the standard deviation of the daily Tmax and Tmin on the exposure 0-t days. A two-stage analysis was applied. First, a time-stratified case-crossover design was used to examine the odds ratio (OR) and attributable fraction of cancer mortality per 1 °C increase in TV by adjusting for potential confounders. Random effect meta-analysis was used to summarize the pooled ORs. Second, stratified analysis was performed for weather type, geographical location, demographics, and other city-level characteristics. The weather was defined as four types according to days during warm or cold season combined with high or low RH. RESULTS A total of 303670 cases were included in our study. Meta-analysis showed that the ORs of cancer mortality per 1 °C increase in TV0-t significantly increased and peaked in TV0-2 (OR=1.0098, 95% CI: 1.0039-1.0157). The attributable fraction of TV0-2 on cancer mortality was 4.74%, accounting for 14395 deaths in the study period. Significant ORs of TV-related cancer mortality were found during the warm season combined with high RH and in the northern region of Jiangsu. Susceptible groups of TV-related cancer mortality were identified as female patients, patients aged 45-65 years, and those living in cities with lower per capita green area. CONCLUSIONS TV can significantly increase the risk of cancer mortality, especially during warm and humid days and in the northern region of Jiangsu. Findings are of great significance to formulate urban planning, resource allocation, and health intervention to prolong the life of cancer patients.
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Affiliation(s)
- Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Shasha Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Yu Zhou
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China.
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Liu X, He Y, Tang C, Wei Q, Xu Z, Yi W, Pan R, Gao J, Duan J, Su H. Association between cold spells and childhood asthma in Hefei, an analysis based on different definitions and characteristics. ENVIRONMENTAL RESEARCH 2021; 195:110738. [PMID: 33485910 DOI: 10.1016/j.envres.2021.110738] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/04/2021] [Accepted: 01/07/2021] [Indexed: 06/12/2023]
Abstract
As the global climate continues to warm, there is an increased focus on heat, but the role of low temperatures on health has been overlooked, especially for developing countries. Methods We collected the admission data of childhood asthma in 2013-2016 from Anhui Provincial Children's Hospital, as well as meteorological data from the Meteorological Bureau for the study period and collected data of pollutants from 10 monitoring stations around Hefei city. Poisson's generalized additive model (GAM) combined with a distributed lag non-linear model (DLNM) was used to estimate the short-term effects of cold spell on childhood asthma in cold seasons (November to March). 16 definitions of cold spells were clearly compared, which combining 4 temperature indexes (daily minimum and mean temperature; daily minimum and mean apparent temperature), 2 temperature thresholds (2.5th and 5th) and 3 durations of at least 2-4 days. We then have an analysis of the modifying effect of characteristics of cold spells and individuals(gender and age), with a view to discovering the susceptible population to cold spell. Results There was significant association between cold spells and admission risk for childhood asthma. And the definition, in which daily minimum apparent temperature falls below 5th percentile for at least 3 consecutive days, produced the optimum model fit performance. Based on this optimal fit we found that, for the total population, the effect of cold spell lasted approximately five days (lag1-lag5), with the largest effect occurring in lag 3 (RR = 1.110; 95% CI: 1.052-1.170). In subgroup analysis, the cumulative effect of lag0-7 was higher in males and school-age children than in females and other age groups, respectively. In addition, we found that the effect of is higher as the duration increases. Conclusion This study suggests an association between cold spell and childhood asthma, and minimum AT may be a better indicator to define the cold spells. Boys and school-age children are more vulnerable to cold spell. And one of our very interesting findings is that if a cold spell lasts for several days, the impact of the cold spell on those later days is likely to be greater than that of the previous days. In conclusion, we should pay more attention to the protection of boys and school-aged children in our future public health protection and give more attention to those cold spells that last longer. Therefore, we recommend that schools and health authorities need to take targeted measures to reduce the risk of asthma in children during the cold spell.
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Affiliation(s)
- Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Zihan Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Jiaojiao Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Jun Duan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, China.
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Pan R, Wang Q, Yi W, Wei Q, Cheng J, Su H. Temporal trends of the association between extreme temperatures and hospitalisations for schizophrenia in Hefei, China from 2005 to 2014. Occup Environ Med 2021; 78:oemed-2020-107181. [PMID: 33737328 DOI: 10.1136/oemed-2020-107181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 01/06/2021] [Accepted: 02/03/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVE We aimed to examine the temporal trends of the association between extreme temperature and schizophrenia (SCZ) hospitalisations in Hefei, China. METHODS We collected time-series data on SCZ hospitalisations for 10 years (2005-2014), with a total of 36 607 cases registered. We used quasi-Poisson regression and distributed lag non-linear model (DLNM) to assess the association between extreme temperature (cold and heat) and SCZ hospitalisations. A time-varying DLNM was then used to explore the temporal trends of the association between extreme temperature and SCZ hospitalisations in different periods. Subgroup analyses were conducted by age (0-39 and 40+ years) and gender, respectively. RESULTS We found that extreme cold and heat significantly increased the risk of SCZ hospitalisations (cold: 1st percentile of temperature 1.19 (95% CI 1.04 to 1.37) and 2.5th percentile of temperature 1.16 (95% CI 1.03 to 1.31); heat: 97.5th percentile of temperature 1.37 (95% CI 1.13 to 1.66) and 99th percentile of temperature 1.38 (95% CI 1.13 to 1.69)). We found a slightly decreasing trend in heat-related SCZ hospitalisations and a sharp increasing trend in cold effects from 2005 to 2014. However, the risk of heat-related hospitalisation has been rising since 2008. Stratified analyses showed that age and gender had different modification effects on temporal trends. CONCLUSIONS The findings highlight that as temperatures rise the body's adaptability to high temperatures may be accompanied by more threats from extreme cold. The burden of cold-related SCZ hospitalisations may increase in the future.
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Affiliation(s)
- Rubing Pan
- Epidemiology and Health Statistics, Anhui Medical University, Hefei, Anhui, China
| | - Qizhi Wang
- Chinese Academy of Agricultural Sciences, Haidian District, Beijing, China
| | - Weizhuo Yi
- Epidemiology and Health Statistics, Anhui Medical University, Hefei, Anhui, China
| | - Qiannan Wei
- Epidemiology and Health Statistics, Anhui Medical University, Hefei, Anhui, China
| | - Jian Cheng
- Epidemiology and Health Statistics, Anhui Medical University, Hefei, Anhui, China
| | - Hong Su
- Epidemiology and Health Statistics, Anhui Medical University, Hefei, Anhui, China
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Liu T, Zhou C, Zhang H, Huang B, Xu Y, Lin L, Wang L, Hu R, Hou Z, Xiao Y, Li J, Xu X, Jin D, Qin M, Zhao Q, Gong W, Yin P, Xu Y, Hu J, Xiao J, Zeng W, Li X, Chen S, Guo L, Rong Z, Zhang Y, Huang C, Du Y, Guo Y, Rutherford S, Yu M, Zhou M, Ma W. Ambient Temperature and Years of Life Lost: A National Study in China. Innovation (N Y) 2021; 2:100072. [PMID: 34557729 PMCID: PMC8454660 DOI: 10.1016/j.xinn.2020.100072] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/12/2020] [Indexed: 12/27/2022] Open
Abstract
Although numerous studies have investigated premature deaths attributable to temperature, effects of temperature on years of life lost (YLL) remain unclear. We estimated the relationship between temperatures and YLL, and quantified the YLL per death caused by temperature in China. We collected daily meteorological and mortality data, and calculated the daily YLL values for 364 locations (2013–2017 in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces, and 2006–2011 in other locations) in China. A time-series design with a distributed lag nonlinear model was first employed to estimate the location-specific associations between temperature and YLL rates (YLL/100,000 population), and a multivariate meta-analysis model was used to pool location-specific associations. Then, YLL per death caused by temperatures was calculated. The temperature and YLL rates consistently showed U-shaped associations. A mean of 1.02 (95% confidence interval: 0.67, 1.37) YLL per death was attributable to temperature. Cold temperature caused 0.98 YLL per death with most from moderate cold (0.84). The mean YLL per death was higher in those with cardiovascular diseases (1.14), males (1.15), younger age categories (1.31 in people aged 65–74 years), and in central China (1.34) than in those with respiratory diseases (0.47), females (0.87), older people (0.85 in people ≥75 years old), and northern China (0.64) or southern China (1.19). The mortality burden was modified by annual temperature and temperature variability, relative humidity, latitude, longitude, altitude, education attainment, and central heating use. Temperatures caused substantial YLL per death in China, which was modified by demographic and regional characteristics. Years of life lost (YLL) is used to estimate the effects of temperature Both low and high temperatures can increase the YLLs Average 1.02 YLL per death is attributed to temperature exposure Temperature causes larger YLLs per death in males, younger people, and central China
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Affiliation(s)
- Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Chunliang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Haoming Zhang
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Biao Huang
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Yanjun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lijun Wang
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, 100050, China
| | - Ruying Hu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Zhulin Hou
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Yize Xiao
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Junhua Li
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Donghui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Mingfang Qin
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Qinglong Zhao
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Weiwei Gong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Peng Yin
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, 100050, China
| | - Yiqing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Siqi Chen
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lingchuan Guo
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Zuhua Rong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Cunrui Huang
- School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yaodong Du
- Guangdong Provincial Climate Center, Guangzhou, 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3800, Australia
| | | | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Maigeng Zhou
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, 100050, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
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Kang Y, Tang H, Jiang L, Wang S, Wang X, Chen Z, Zhang L, Zheng C, Wang Z, Huang G, Gao R. Air temperature variability and high-sensitivity C reactive protein in a general population of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 749:141588. [PMID: 32846352 DOI: 10.1016/j.scitotenv.2020.141588] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/10/2020] [Accepted: 08/07/2020] [Indexed: 05/09/2023]
Abstract
BACKGROUND Along with global climate change, the relationship between temperature variability (TV) and cardiovascular hospitalization and deaths have been well established. However, limited studies were conducted to reveal the underlying mechanism for TV-related cardiovascular diseases. OBJECTIVES In the current study, a novel TV calculation, taking account for both interday and intraday TV as well as lag effects, was used to investigate the effect of short-term TV on the level of high-sensitivity C reactive protein (hs-CRP), which is a crucial preclinical predictor for cardiovascular disease (CVD). RESULTS Among the 11,623 Chinese population (46.0% male; mean age 49.8 years), the average hs-CRP was 1.4 mg/ L (standard deviation 1.6 mg/L). Statistical significance between TV and hs-CRP was observed for different TV exposure days (TV01-TV07) in adjusted model, with highest effect for TV06. Specifically, per 1 °C increase in TV06 led to 2.241% (95%CI: 1.552%-2.935%) increase in hs-CRP. Female, obesity and elderly population were more susceptible to TV. The largest mediator for the association of TV and hs-CRP was lipoprotein(a), accounting for 8.68%, followed by smoking status (4.78%), alcohol use (3.95%) and systolic BP (3.20%). CONCLUSION Short-term TV will significantly increase the level of hs-CRP, suggesting hs-CRP to be the potential biologic mechanisms underlying the cardiovascular effects of TV. And more attention should be paid to unstable weather in the global climate change context. Further developing efficient public health policies on climate change may benefit for global heath.
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Affiliation(s)
- Yuting Kang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Haosu Tang
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Linlin Jiang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Su Wang
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Zuo Chen
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Linfeng Zhang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Congyi Zheng
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Zengwu Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102308, China.
| | - Gang Huang
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Runlin Gao
- Department of Cardiology, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100037, China
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Huang K, Yang XJ, Hu CY, Ding K, Jiang W, Hua XG, Liu J, Cao JY, Sun CY, Zhang T, Kan XH, Zhang XJ. Short-term effect of ambient temperature change on the risk of tuberculosis admissions: Assessments of two exposure metrics. ENVIRONMENTAL RESEARCH 2020; 189:109900. [PMID: 32980000 DOI: 10.1016/j.envres.2020.109900] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 06/20/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Although the effects of seasonal variations and ambient temperature on the incidence of tuberculosis (TB) have been well documented, it is still unknown whether ambient temperature change is an independent risk factor for TB. The aim of this study was to assess the association between ambient temperature change and the risk of TB admissions. METHOD A distributed lag non-linear model (DLNM) combined with Poisson generalized linear regression model was performed to assess the association between ambient temperature change and the risk of TB admissions from 2014 to 2018 in Hefei, China. Two temperature change metrics including temperature change between neighboring days (TCN) and diurnal temperature range (DTR) were used to assess the effects of temperature change exposure. Subgroup analyses were performed by gender, age and season. Besides, the attributable risk was calculated to evaluated the public health significance. RESULTS The overall exposure-response curves suggested that there were statistically significant associations between two temperature change metrics and the risk of TB admissions. The maximum lag-specific relative risk (RR) of TB admissions was 1.088 (95%CI: 1.012-1.171, lag 4 day) for exposing to large temperature drop (TCN= -4 °C) in winter. Besides, the overall cumulative risk of TB admissions increased continuously and peaked at a lag of 7 days (RR=1.350, 95%CI: 1.120-1.628). Subgroup analysis suggested that exposure to large temperature drop had an adverse effect on TB admissions among males, females and adults. Similarly, large level of DTR exposure (DTR=15 °C) in spring also increased the risk of TB admissions on lag 0 day (RR=1.039, 95%CI: 1.016-1.063), and the cumulative RRs peaked at a lag of 1 days (RR=1.029, 95%CI: 1.012-1.047). We also found that females and elderly people were more vulnerable to the large level of DTR exposure. Additionally, the assessment of attributable risk suggested that taking target measures for the upcoming large temperature drop (b-AF = 4.17%, 95% eCI: 1.24%, 7.22%, b-AN = 1195) may achieve great public health benefits for TB prevention. CONCLUSION This study suggests that ambient temperature change is associated with the risk of TB admissions. Besides, TCN may be a better predictor for the TB prevention and public health.
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Affiliation(s)
- Kai Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Xiao-Jing Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Cheng-Yang Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Kun Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Wen Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Xiao-Guo Hua
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Jie Liu
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Ji-Yu Cao
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Chen-Yu Sun
- AMITA Health Saint Joseph Hospital Chicago, 2900 N. Lake Shore Drive, Chicago, 60657, Illinois, USA
| | - Tao Zhang
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China
| | - Xiao-Hong Kan
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China; Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei, 230022, China.
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China.
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Zhan ZY, Tian Q, Chen TT, Ye Y, Lin Q, Han D, Ou CQ. Temperature Variability and Hospital Admissions for Chronic Obstructive Pulmonary Disease: Analysis of Attributable Disease Burden and Vulnerable Subpopulation. Int J Chron Obstruct Pulmon Dis 2020; 15:2225-2235. [PMID: 33061340 PMCID: PMC7519840 DOI: 10.2147/copd.s260988] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose Chronic obstructive pulmonary disease (COPD) is a major cause of chronic diseases causing considerable social and economic burden globally. Despite substantial evidence on temperature-COPD association, few studies have investigated the acute effect of temperature variability (TV), a potential trigger of exacerbation of COPD disease, and it remains unknown what fraction of the disease burden of COPD is attributable to TV. Patients and Methods Based on 71,070 COPD hospitalizations during 2013–2015 in Guangzhou, China, we conducted a time-series analysis using quasi-Poisson regression to assess the association between TV and hospital admission for COPD after adjusting for daily mean temperature. Short-term TV was captured by the standard deviation of hourly or daily temperatures across various exposure days. We also provided the fraction (total number) of COPD attributable to TV. Stratified analyses by admission route, sex, age, occupation, marital status and season were performed to identify vulnerable subpopulations. Results We found a linear relationship between TV and COPD hospitalization, with a 1°C increase in hourly TV and daily TV associated with 4.3% (95%CI: 2.2–6.4) and 4.0% (2.3–5.8) increases in COPD, respectively. The greater relative risks of TV identified males, people aged 0–64 years, blue collar, and divorced/widowed people as vulnerable population. There were 12.0% (8500 cases) of COPD hospitalization attributable to hourly TV during the study period. Daily TV produced similar estimates of relative effects (relative risk) but grater estimates of absolute effects (attributable fraction) than hourly TV. Conclusion We concluded that TV was an independent risk factor of COPD morbidity, especially among the susceptible subgroups. These findings would be helpful to guide the development of targeted public intervention.
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Affiliation(s)
- Zhi-Ying Zhan
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People's Republic of China.,Department of Health Care Management and Social Medicine, School of Public Health, Fujian Medical University, Fuzhou, People's Republic of China
| | - Qi Tian
- Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People's Republic of China
| | - Ting-Ting Chen
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People's Republic of China
| | - Yunshao Ye
- Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People's Republic of China
| | - Qiaoxuan Lin
- Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People's Republic of China
| | - Dong Han
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People's Republic of China
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People's Republic of China
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Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 724:138201. [PMID: 32408450 DOI: 10.1016/j.scitotenv.2020.138201(2020)] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 03/23/2020] [Indexed: 05/21/2023]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus. METHODS Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail. RESULTS The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses. CONCLUSIONS Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.
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Affiliation(s)
- Jingui Xie
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Brunel Business School, Brunel University London, Uxbridge, United Kingdom.
| | - Yongjian Zhu
- School of Management, University of Science and Technology of China, Hefei, China.
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Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 724:138201. [PMID: 32408450 PMCID: PMC7142675 DOI: 10.1016/j.scitotenv.2020.138201] [Citation(s) in RCA: 479] [Impact Index Per Article: 119.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 03/23/2020] [Indexed: 04/13/2023]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus. METHODS Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail. RESULTS The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses. CONCLUSIONS Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.
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Affiliation(s)
- Jingui Xie
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Brunel Business School, Brunel University London, Uxbridge, United Kingdom.
| | - Yongjian Zhu
- School of Management, University of Science and Technology of China, Hefei, China.
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Cheng J, Bambrick H, Tong S, Su H, Xu Z, Hu W. Winter temperature and myocardial infarction in Brisbane, Australia: Spatial and temporal analyses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 715:136860. [PMID: 32040995 DOI: 10.1016/j.scitotenv.2020.136860] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 01/09/2020] [Accepted: 01/20/2020] [Indexed: 06/10/2023]
Abstract
Myocardial infarction (MI) incidence often peaks in winter, but it remains unclear how winter temperature affects MI temporally and spatially. We examined the short-term effects of winter temperature on the risk of MI and explored spatial associations of winter MI hospitalizations with temperature and socioeconomic status (area-based index) in Brisbane, Australia. We used a distributed lag non-linear model to fit the association at the city level between population-weighted daily mean temperature and daily MI hospitalizations during 11 winters of 2005-2015. For each winter, a Bayesian spatial conditional autoregressive model was fitted to examine the associations at postal code level of MI hospitalisations with temperature and socioeconomic status measured as the Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD). Area-specific winter temperature was categorised into three levels: cold (<25th percentile of average winter temperature across postal areas), mild (25th-75th percentile) and warm (>75th percentile). This study included 4978 MI hospitalizations. At the city level, each 1 °C drop in temperature below a threshold of 15.6 °C was associated with a relative risk (RR) of 1.016 (95% confidence interval (CI): 1.008-1.024) for MI hospitalizations on the same day. Low temperature had a much delayed and transient effect on women but an immediate and longer-lasting effect on men. Winter MI incidence rate varied spatially in Brisbane, with a higher incidence rate in warmer areas (RR for mild areas: 1.214, 95%CI: 1.116-1.320; RR for warm areas: 1.251, 95%CI: 1.127-1.389; cold areas as the reference) and in areas with lower socioeconomic levels (RR: 0.900, 95%CI: 0.886-0.914 for each decile increase in IRSAD). This study provides compelling evidence that short-term winter temperature drops were associated with an elevated risk of MI in the subtropical region with a mild winter. Particular attention also needs to be paid to people living in relatively warm and socioeconomically disadvantaged communities in winter.
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Affiliation(s)
- Jian Cheng
- School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia
| | - Hilary Bambrick
- School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia
| | - Shilu Tong
- School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia; Shanghai Children's Medical Center, Shanghai Jiao Tong University, Shanghai, China; School of Public Health, Anhui Medical University, Hefei, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Zhiwei Xu
- School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
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Itani M, Ghaddar N, Ghali K, Laouadi A. Bioheat modeling of elderly and young for prediction of physiological and thermal responses in heat-stressful conditions. J Therm Biol 2020; 88:102533. [PMID: 32125972 DOI: 10.1016/j.jtherbio.2020.102533] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/06/2020] [Accepted: 02/07/2020] [Indexed: 11/17/2022]
Abstract
Exposure to hot and humid conditions results in physiological changes in metabolism, cardiac output and thermoregulation of the young adult and these changes deviate with elderly due to aging. The elderly population is more vulnerable than the healthy and young population due to age-weakened physiology and thermoregulatory functions. There are, however, limited bioheat models addressing such changes due to hot exposure in the young and the elderly. This paper develops robust bioheat models for young and elderly while incorporating the physiological changes under exposure to heat-stressful conditions for both age groups the age-related changes in physiology and thermoregulation to an elderly human. However, due to a large variability of thermoregulation among the elderly population, a sensitivity analysis revealed that the average elderly is characterized by metabolic rate and cardiac output, which are lower than those of the young by 21% and 14.4%, respectively. Moreover, the thresholds of the onset of vasodilation and sweating are delayed from those of young adults by 0.5 °C and 0.21 °C, respectively. The elderly and young bioheat models were validated with number of independent published experimental studies under hot exposures in steady and transient conditions. Model predictions of core and mean skin temperatures showed good agreement with published experimental data with a discrepancy of 0.1 °C and 0.5 °C, respectively.
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Affiliation(s)
- Mariam Itani
- Mechanical Engineering Department, American University of Beirut, P.O. Box 11-0236, Beirut, 1107-2020, Lebanon; Mechanical Engineering Department, Phoenicia University, District of Zahrani, Lebanon, P.O. Box 11-7790, Beirut, Lebanon
| | - Nesreen Ghaddar
- Mechanical Engineering Department, American University of Beirut, P.O. Box 11-0236, Beirut, 1107-2020, Lebanon.
| | - Kamel Ghali
- Mechanical Engineering Department, American University of Beirut, P.O. Box 11-0236, Beirut, 1107-2020, Lebanon
| | - Abdelaziz Laouadi
- Construction Research Centre, National Research Council Canada, 1200 Montréal Road, Ottawa, ON, K1A 0R6, Canada
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Linares C, Díaz J, Negev M, Martínez GS, Debono R, Paz S. Impacts of climate change on the public health of the Mediterranean Basin population - Current situation, projections, preparedness and adaptation. ENVIRONMENTAL RESEARCH 2020; 182:109107. [PMID: 32069750 DOI: 10.1016/j.envres.2019.109107] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 12/24/2019] [Accepted: 12/31/2019] [Indexed: 05/04/2023]
Abstract
The Mediterranean Basin is undergoing a warming trend with longer and warmer summers, an increase in the frequency and the severity of heat waves, changes in precipitation patterns and a reduction in rainfall amounts. In this unique populated region, which is characterized by significant gaps in the socio-economic levels particularly between the North (Europe) and South (Africa), parallel with population growth and migration, increased water demand and forest fires risk - the vulnerability of the Mediterranean population to human health risks increases significantly. Indeed, climatic changes impact the health of the Mediterranean population directly through extreme heat, drought or storms, or indirectly by changes in water availability, food provision and quality, air pollution and other stressors. The main health effects are related to extreme weather events (including extreme temperatures and floods), changes in the distribution of climate-sensitive diseases and changes in environmental and social conditions. The poorer countries, particularly in North Africa and the Levant, are at highest risk. Climate change affects the vulnerable sectors of the region, including an increasingly older population, with a larger percentage of those with chronic diseases, as well as poor people, which are therefore more susceptible to the effects of extreme temperatures. For those populations, a better surveillance and control systems are especially needed. In view of the climatic projections and the vulnerability of Mediterranean countries, climate change mitigation and adaptation become ever more imperative. It is important that prevention Health Action Plans will be implemented, particularly in those countries that currently have no prevention plans. Most adaptation measures are "win-win situation" from a health perspective, including reducing air pollution or providing shading solutions. Additionally, Mediterranean countries need to enhance cross-border collaboration, as adaptation to many of the health risks requires collaboration across borders and also across the different parts of the basin.
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Affiliation(s)
- Cristina Linares
- National School of Public Health. Carlos III Institute of Health, Madrid, Spain
| | - Julio Díaz
- National School of Public Health. Carlos III Institute of Health, Madrid, Spain
| | - Maya Negev
- School of Public Health, University of Haifa, Israel
| | | | | | - Shlomit Paz
- Department of Geography and Environmental Studies, University of Haifa, Israel.
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Lin S, Han L, Li D, Wang T, Wu Z, Zhang H, Xiao Z, Wu Y, Huang J, Wang M, Zhu Y. The Association between Meteorological Factors and the Prevalence of Acute-on-chronic Liver Failure: A Population-based Study, 2007-2016. J Clin Transl Hepatol 2019; 7:341-345. [PMID: 31915603 PMCID: PMC6943211 DOI: 10.14218/jcth.2019.00044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/13/2019] [Accepted: 12/06/2019] [Indexed: 12/04/2022] Open
Abstract
Background and Aims: The aim of this study was to investigate the effect(s) of meteorological factors on the prevalence of acute-on-chronic liver failure (ACLF) based on 10-years' worth of population data. Methods: We retrospectively collected ACLF case data from January 2007 to December 2016 from three major hospitals in Fuzhou City, China. Climatic data, including rainfall, mean temperature, differences in temperature (delta temperature) and mean humidity for each month were downloaded from the China Climatic Data Service Center. Following data collection, Poisson regression analysis was used to estimate the effect(s) of climatic factors on the risk of the prevalence of ACLF. Results: The population consisted of a total of 3510 cases, with a mean age of 44.7 ± 14.8 years-old and with 79.8% being male. Upon analyzing the population data, we found a growing trend and seasonal pattern of monthly counts of ACLF-related hospitalization throughout the past decade. Specifically, the primary peak of ACLF prevalence was in January and the secondary peak was in July. Poisson regression showed mean temperature (risk ratio = 0.991, 95%CI = 0.986-0.996) and mean humidity (risk ratio = 1.011, 95%CI = 1.006-1.017) to be independently correlated with the monthly cases of ACLF. The results suggest that every unit increase of mean temperature (1°C) and mean humidity (1%) are associated with 0.991- and 1.011-fold changes of ACLF cases, respectively. Rainfall and delta temperature did not appear to affect the prevalence of this disease. Conclusions: The hospitalization for ACLF peaks in January and July. Low temperature and high humidity appear to function as factors contributing to this seasonal pattern.
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Affiliation(s)
- Su Lin
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Lifen Han
- Department of Infectious Disease, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Dongliang Li
- Department of Hepatobiliary Disease, 900 Hospital of PLA, Fuzhou, Fujian, China
| | - Ting Wang
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Zimu Wu
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Haoyang Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | | | - Yinlian Wu
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jiaofeng Huang
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Mingfang Wang
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yueyong Zhu
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Correspondence to: Yueyong Zhu, Department of Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350001, China. Tel: +86-591-87981656, Fax: +86-591-87982526, E-mail:
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Evidence of Adaptation to Increasing Temperatures. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010097. [PMID: 31877767 PMCID: PMC6981699 DOI: 10.3390/ijerph17010097] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 11/25/2022]
Abstract
In times of rising temperatures, the question arises on how the human body adapts. When assumed that changing climate leads to adaptation, time series analysis should reveal a shift in optimal temperatures. The city of Vienna is especially affected by climate change due to its location in the Alpine Range in Middle Europe. Based on mortality data, we calculated shifts in optimal temperature for a time period of 49 years in Vienna with Poisson regression models. Results show a shift in optimal temperature, with optimal temperature increasing more than average temperature. Hence, results clearly show an adaptation process, with more adaptation to warmer than colder temperatures. Nevertheless, some age groups remain more vulnerable than others and less able to adapt. Further research focusing on vulnerable groups should be encouraged.
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Zhang Y, Xiang Q, Yu C, Bao J, Ho HC, Sun S, Ding Z, Hu K, Zhang L. Mortality risk and burden associated with temperature variability in China, United Kingdom and United States: Comparative analysis of daily and hourly exposure metrics. ENVIRONMENTAL RESEARCH 2019; 179:108771. [PMID: 31574448 DOI: 10.1016/j.envres.2019.108771] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/12/2019] [Accepted: 09/22/2019] [Indexed: 05/09/2023]
Abstract
BACKGROUND Temperature variability (TV) is closely associated with climate change, but there is no unified TV definition worldwide. Two novel composite TV indexes were developed recently by calculating the standard deviations of several days' daily maximum and minimum temperatures (TVdaily), or hourly mean temperatures (TVhourly). OBJECTIVES This study aimed to compare the mortality risks and burden associated with TVdaily and TVhourly using large time-series datasets collected from multiple locations in China, United Kingdom and United States. METHODS We collected daily mortality and hourly temperature data through 1987 to 2012 from 63 locations in China (8 communities, 2006-2012), United Kingdom (10 regions, 1990-2012), and USA (45 cities, 1987-2000). TV-mortality associations were investigated using a three-stage analytic approach separately for China, UK, and USA. First, we applied a time-series regression for each location to derive location-specific TV-mortality curves. A second-stage meta-analysis was then performed to pool these estimated associations for each country. Finally, we calculated mortality fraction attributable to TV based on above-described location-specific and pooled estimates. RESULTS Our dataset totally consisted of 23, 089, 328 all-cause death cases, including 93, 750 from China, 7,573,716 from UK and 15, 421, 862 from USA, respectively. In despite of a relatively wide uncertainty in China, approximately linear relationships were consistently identified for TVdaily and TVhourly. In the three countries, generally similar lag patterns of TV effects were consistently observed for TVdaily and TVhourly. A 1 °C rise in TVdaily and TVhourly at lag 0-7 days was associated with mortality increases of 0.93% (95% confidence interval [CI]: 0.12, 1.74) and 0.97% (0.18, 1.77) in China, 0.33% (0.15, 0.51) and 0.41% (0.21, 0.60) in UK, and 0.55% (0.41, 0.70) and 0.51% (0.35, 0.66) in USA, respectively. Larger attributable fractions were estimated using TVdaily than those using TVhourly, with estimates at 0-10 days of 3.69% (0.51, 6.75) vs. 2.59% (0.10, 5.01) in China, 1.14% (0.54, 1.74) vs. 0.98% (0.55, 1.42) in UK, and 2.57% (1.97, 3.16) vs. 1.67% (1.15, 2.18) in USA, respectively. Our meta-regression analyses indicated higher vulnerability to TV-induced mortality risks in warmer locations. CONCLUSIONS Our study added multi-country evidence for increased mortality risk associated with short-term exposure to large temperature variability. Daily and hourly TV exposure metrics produced generally comparable risk effects, but the attributable mortality burden tended to be higher using TVdaily instead of TVhourly.
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Affiliation(s)
- Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China; Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Qianqian Xiang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, 430079, China
| | - Chuanhua Yu
- Department of Preventive Medicine, School of Health Sciences, Wuhan University, 185 Donghu Road, Wuhan, 430071, China
| | - Junzhe Bao
- Department of Health Policy and Management, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Hung Chak Ho
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
| | - Shengzhi Sun
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, 02912, USA
| | - Zan Ding
- The Institute of Metabolic Diseases, Baoan Central Hospital of Shenzhen, The Fifth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, 518102, China
| | - Kejia Hu
- Department of Precision Health and Data Science, School of Public Health, Zhejiang University, Hangzhou, 310003, China
| | - Ling Zhang
- Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, 430065, China; Department of Environmental Hygiene and Occupational Medicine, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China
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Pan R, Zhang X, Gao J, Yi W, Wei Q, Xu Z, Duan J, Bai L, Cheng Q, Zhang Y, Su H. Impacts of heat and cold on hospitalizations for schizophrenia in Hefei, China: An assessment of disease burden. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 694:133582. [PMID: 31394323 DOI: 10.1016/j.scitotenv.2019.133582] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 07/23/2019] [Accepted: 07/23/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Compared with risk data (e.g., RR or OR), attributable fraction (AF) provides more information on the formulation of policies and measures in the field of public health. However, to date, existing AF evidence is scarce for the relationship between temperature and the hospitalizations for SCZ. OBJECTIVES Our primary goal is to estimate the attributable burden of hospitalizations for SCZ related to cold and heat, respectively. Furthermore, to identify vulnerable populations due to heat and cold. METHODS Poisson generalized linear models combined with DLNMs were used to estimate the association between hospitalizations for SCZ and temperature from 2005 to 2014. The minimum risk temperature (MRT) was used as a reference, to calculate the burden of disease caused by cold and heat. RESULTS We found that the majority hospitalizations attributed to heat (70.9%). In different individual levels, men are more sensitive to heat exposure while women are more vulnerable to cold. Among different age groups, the results showed that the attributable risk was slightly higher in the over-40s than in the under-40s. Besides, under different marital conditions, it showed that the unmarried had a little higher attributional risk than the married. CONCLUSIONS We should pay attention to the impact of heat on hospitalizations for SCZ, especially in those over 40 years old, men and non-married. Our research will provide a basis for policymakers to develop intervention strategies to minimize the impact of adverse temperatures on hospitalizations for SCZ, thereby reducing the burden of disease.
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Affiliation(s)
- 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
| | - Xulai Zhang
- Fourth People's Hospital of Hefei, Anhui, China
| | - Jiaojiao Gao
- 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
| | - Qiannan 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
| | - Zihan Xu
- 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
| | - Jun Duan
- 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
| | - Lijun Bai
- 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
| | - Qiang 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
| | - Yanwu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China.
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Oh HJ, Yang DM, Kim CH, Jeon JG, Jung NH, Kim CY, Symanzik J, Oh HW, Edwin A, Il S, Ahn JY. Exploring Mortality Rates for Major Causes of Death in Korea. ACTA ACUST UNITED AC 2019. [DOI: 10.2174/1874944501912010016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background:The trends and patterns of the mortality rates for causes of death are meaningful information. They can provide a basis for national demographic and health care policies by identifying the number, causes, and geographical distribution of deaths.Objective:To explore and analyze the characteristics of the mortality rates for major causes of death in Korea.Methods:Some common data analysis methods were used to describe the data. We also used some visualization techniques such as heat maps and line plots to present mortality rates by gender, age, and year.Results:Our analysis shows the crude mortality rates have continually decreased over the last 25 years from 1983, though they have increased slightly since 2006. In addition, the top eight causes of death accounted for 80% of all Korean deaths in 2015. During the period 2005-2015, the leading cause of death was cancer in male and circulatory diseases in female. The trend for respiratory diseases shows a steep upward trend in males, while a similar trend can be observed for respiratory and nervous system diseases in females.Conclusion:The deaths for circulatory, respiratory, nervous system, digestive, and infectious diseases are the highest in the age 80 to 84, while cancer is the leading cause of death for ages 75 to 79. In addition, the mortality rates for circulatory, nervous, and respiratory diseases increase rapidly after the age of 80. Therefore, policies on health and welfare for the elderly are getting more and more important.
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