Jing Li1, Xin Xu2, Jun Yang3, Zhidong Liu4, Lei Xu3, Jinghong Gao3, Xiaobo Liu3, Haixia Wu3, Jun Wang3, Jieqiong Yu4, Baofa Jiang5, Qiyong Liu6. 1. Department of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, PR China; State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, PR China; Center for Climate Change and Health, School of Public Health, Shandong University, Jinan City, Shandong Province, PR China. 2. Department of Dentistry, Affiliated Hospital, Weifang Medical University, Weifang 261031, Shandong Province, PR China. 3. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, PR China. 4. Department of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, PR China. 5. Department of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, PR China; Center for Climate Change and Health, School of Public Health, Shandong University, Jinan City, Shandong Province, PR China. Electronic address: bjiang@sdu.edu.cn. 6. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, PR China; Center for Climate Change and Health, School of Public Health, Shandong University, Jinan City, Shandong Province, PR China. Electronic address: liuqiyong@icdc.cn.
Abstract
BACKGROUND: Understanding the health consequences of continuously rising temperatures-as is projected for China-is important in terms of developing heat-health adaptation and intervention programs. This study aimed to examine the association between mortality and daily maximum (Tmax), mean (Tmean), and minimum (Tmin) temperatures in warmer months; to explore threshold temperatures; and to identify optimal heat indicators and vulnerable populations. METHODS: Daily data on temperature and mortality were obtained for the period 2007-2013. Heat thresholds for condition-specific mortality were estimated using an observed/expected analysis. We used a generalised additive model with a quasi-Poisson distribution to examine the association between mortality and Tmax/Tmin/Tmean values higher than the threshold values, after adjustment for covariates. RESULTS: Tmax/Tmean/Tmin thresholds were 32/28/24°C for non-accidental deaths; 32/28/24°C for cardiovascular deaths; 35/31/26°C for respiratory deaths; and 34/31/28°C for diabetes-related deaths. For each 1°C increase in Tmax/Tmean/Tmin above the threshold, the mortality risk of non-accidental-, cardiovascular-, respiratory, and diabetes-related death increased by 2.8/5.3/4.8%, 4.1/7.2/6.6%, 6.6/25.3/14.7%, and 13.3/30.5/47.6%, respectively. Thresholds for mortality differed according to health condition when stratified by sex, age, and education level. For non-accidental deaths, effects were significant in individuals aged ≥65 years (relative risk=1.038, 95% confidence interval: 1.026-1.050), but not for those ≤64 years. For most outcomes, women and people ≥65 years were more vulnerable. CONCLUSION: High temperature significantly increases the risk of mortality in the population of Jinan, China. Climate change with rising temperatures may bring about the situation worse. Public health programs should be improved and implemented to prevent and reduce health risks during hot days, especially for the identified vulnerable groups.
BACKGROUND: Understanding the health consequences of continuously rising temperatures-as is projected for China-is important in terms of developing heat-health adaptation and intervention programs. This study aimed to examine the association between mortality and daily maximum (Tmax), mean (Tmean), and minimum (Tmin) temperatures in warmer months; to explore threshold temperatures; and to identify optimal heat indicators and vulnerable populations. METHODS: Daily data on temperature and mortality were obtained for the period 2007-2013. Heat thresholds for condition-specific mortality were estimated using an observed/expected analysis. We used a generalised additive model with a quasi-Poisson distribution to examine the association between mortality and Tmax/Tmin/Tmean values higher than the threshold values, after adjustment for covariates. RESULTS: Tmax/Tmean/Tmin thresholds were 32/28/24°C for non-accidental deaths; 32/28/24°C for cardiovascular deaths; 35/31/26°C for respiratory deaths; and 34/31/28°C for diabetes-related deaths. For each 1°C increase in Tmax/Tmean/Tmin above the threshold, the mortality risk of non-accidental-, cardiovascular-, respiratory, and diabetes-related death increased by 2.8/5.3/4.8%, 4.1/7.2/6.6%, 6.6/25.3/14.7%, and 13.3/30.5/47.6%, respectively. Thresholds for mortality differed according to health condition when stratified by sex, age, and education level. For non-accidental deaths, effects were significant in individuals aged ≥65 years (relative risk=1.038, 95% confidence interval: 1.026-1.050), but not for those ≤64 years. For most outcomes, women and people ≥65 years were more vulnerable. CONCLUSION: High temperature significantly increases the risk of mortality in the population of Jinan, China. Climate change with rising temperatures may bring about the situation worse. Public health programs should be improved and implemented to prevent and reduce health risks during hot days, especially for the identified vulnerable groups.
Authors: Holly Elser; Sebastian T Rowland; Sara Y Tartof; Robbie M Parks; Katia Bruxvoort; Rachel Morello-Frosch; Sarah C Robinson; Alice R Pressman; Rong X Wei; Joan A Casey Journal: Environ Int Date: 2022-05-21 Impact factor: 13.352
Authors: Marc Marí-Dell'Olmo; Aurelio Tobías; Anna Gómez-Gutiérrez; Maica Rodríguez-Sanz; Patricia García de Olalla; Esteve Camprubí; Antonio Gasparrini; Carme Borrell Journal: Int J Public Health Date: 2018-03-26 Impact factor: 3.380
Authors: Günay Can; Ümit Şahin; Uğurcan Sayılı; Marjolaine Dubé; Beril Kara; Hazal Cansu Acar; Barış İnan; Özden Aksu Sayman; Germain Lebel; Ray Bustinza; Hüseyin Küçükali; Umur Güven; Pierre Gosselin Journal: Int J Environ Res Public Health Date: 2019-11-07 Impact factor: 3.390
Authors: Sida Liu; Emily Yang Ying Chan; William Bernard Goggins; Zhe Huang Journal: Int J Environ Res Public Health Date: 2020-10-07 Impact factor: 3.390