Yunquan Zhang1, Qianqian Xiang2, Chuanhua Yu3, Junzhe Bao4, Hung Chak Ho5, Shengzhi Sun6, Zan Ding7, Kejia Hu8, Ling Zhang9. 1. 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. Electronic address: YunquanZhang@wust.edu.cn. 2. Hubei Provincial Center for Disease Control and Prevention, Wuhan, 430079, China. 3. Department of Preventive Medicine, School of Health Sciences, Wuhan University, 185 Donghu Road, Wuhan, 430071, China. 4. Department of Health Policy and Management, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China. 5. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China. 6. Department of Epidemiology, Brown University School of Public Health, Providence, RI, 02912, USA. 7. The Institute of Metabolic Diseases, Baoan Central Hospital of Shenzhen, The Fifth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, 518102, China. 8. Department of Precision Health and Data Science, School of Public Health, Zhejiang University, Hangzhou, 310003, China. 9. 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.
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.
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.
Authors: Yao Wu; Shanshan Li; Qi Zhao; Bo Wen; Antonio Gasparrini; Shilu Tong; Ala Overcenco; Aleš Urban; Alexandra Schneider; Alireza Entezari; Ana Maria Vicedo-Cabrera; Antonella Zanobetti; Antonis Analitis; Ariana Zeka; Aurelio Tobias; Baltazar Nunes; Barrak Alahmad; Ben Armstrong; Bertil Forsberg; Shih-Chun Pan; Carmen Íñiguez; Caroline Ameling; César De la Cruz Valencia; Christofer Åström; Danny Houthuijs; Do Van Dung; Dominic Royé; Ene Indermitte; Eric Lavigne; Fatemeh Mayvaneh; Fiorella Acquaotta; Francesca de'Donato; Shilpa Rao; Francesco Sera; Gabriel Carrasco-Escobar; Haidong Kan; Hans Orru; Ho Kim; Iulian-Horia Holobaca; Jan Kyselý; Joana Madureira; Joel Schwartz; Jouni J K Jaakkola; Klea Katsouyanni; Magali Hurtado Diaz; Martina S Ragettli; Masahiro Hashizume; Mathilde Pascal; Micheline de Sousa Zanotti Stagliorio Coélho; Nicolás Valdés Ortega; Niilo Ryti; Noah Scovronick; Paola Michelozzi; Patricia Matus Correa; Patrick Goodman; Paulo Hilario Nascimento Saldiva; Rosana Abrutzky; Samuel Osorio; Tran Ngoc Dang; Valentina Colistro; Veronika Huber; Whanhee Lee; Xerxes Seposo; Yasushi Honda; Yue Leon Guo; Michelle L Bell; Yuming Guo Journal: Lancet Planet Health Date: 2022-05
Authors: Sebastian T Rowland; Robbie M Parks; Amelia K Boehme; Jeff Goldsmith; Johnathan Rush; Allan C Just; Marianthi-Anna Kioumourtzoglou Journal: Environ Res Date: 2021-04-28 Impact factor: 8.431