TY - JOUR
T1 - Ambient Temperature and Years of Life Lost
T2 - A National Study in China
AU - Liu, Tao
AU - Zhou, Chunliang
AU - Zhang, Haoming
AU - Huang, Biao
AU - Xu, Yanjun
AU - Lin, Lifeng
AU - Wang, Lijun
AU - Hu, Ruying
AU - Hou, Zhulin
AU - Xiao, Yize
AU - Li, Junhua
AU - Xu, Xiaojun
AU - Jin, Donghui
AU - Qin, Mingfang
AU - Zhao, Qinglong
AU - Gong, Weiwei
AU - Yin, Peng
AU - Xu, Yiqing
AU - Hu, Jianxiong
AU - Xiao, Jianpeng
AU - Zeng, Weilin
AU - Li, Xing
AU - Chen, Siqi
AU - Guo, Lingchuan
AU - Rong, Zuhua
AU - Zhang, Yonghui
AU - Huang, Cunrui
AU - Du, Yaodong
AU - Guo, Yuming
AU - Rutherford, Shannon
AU - Yu, Min
AU - Zhou, Maigeng
AU - Ma, Wenjun
PY - 2021/2/28
Y1 - 2021/2/28
N2 - 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.
AB - 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.
KW - China
KW - distributed lag nonlinear model
KW - mortality burden
KW - multivariate meta-analysis
KW - temperature
KW - years of life lost
UR - https://www.scopus.com/pages/publications/85102024741
U2 - 10.1016/j.xinn.2020.100072
DO - 10.1016/j.xinn.2020.100072
M3 - Article
AN - SCOPUS:85102024741
SN - 2666-6758
VL - 2
JO - The Innovation
JF - The Innovation
IS - 1
M1 - 100072
ER -