Liu Jiangmei, Liu Tao, Burkart Katrin G, Wang Haidong, He Guanhao, Hu Jianxiong, Xiao Jianpeng, Yin Peng, Wang Lijun, Liang Xiaofeng, Zeng Fangfang, Stanaway Jeffrey D, Brauer Michael, Ma Wenjun, Zhou Maigeng
The National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention.
Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China.
Lancet Reg Health West Pac. 2022 Jun 16;24:100493. doi: 10.1016/j.lanwpc.2022.100493. eCollection 2022 Jul.
BACKGROUND: Non-optimal temperatures are associated with mortality risk, yet the heterogeneity of temperature-attributable mortality burden across subnational regions in a country was rarely investigated. We estimated the mortality burden related to non-optimal temperatures across all provinces in China in 2019. METHODS: The global daily temperature data were obtained from the ERA5 reanalysis dataset. The daily mortality data and exposure-response curves between daily temperature and mortality for 176 individual causes of death were obtained from the Global Burden of Disease Study 2019 (GBD 2019). We estimated the population attributable fraction (PAF) based on the exposure-response curves, daily gridded temperature, and population. We calculated the cause- and province-specific mortality burden based on PAF and disease burden data from the GBD 2019. FINDINGS: We estimated that 593·9 (95% UI:498·8, 704·6) thousand deaths were attributable to non-optimal temperatures in China in 2019 (PAF=5·58% [4·93%, 6·28%]), with 580·8 (485·7, 690·1) thousand cold-related deaths and 13·9 (7·7, 23·2) thousand heat-related deaths. The majority of temperature-related deaths were from cardiovascular diseases (399·7 [322·8, 490·4] thousand) and chronic respiratory diseases (177·4 [141·4, 222·3] thousand). The mortality burdens were observed significantly spatial heterogeneity for both high and low temperatures. For instance, the age-standardized death rates (per 100 000) attributable to low temperature were higher in Western China, with the highest in Tibet (113·7 [82·0, 155·5]), while for high temperature, they were greater in Xinjiang (1·8 [0·7, 3·3]) and Central-Southern China such as Hainan (2·5 [0·9, 5·4]). We also observed considerable geographical variation in the temperature-related mortality burden by causes of death at provincial level. INTERPRETATION: A substantial mortality burden was attributable to non-optimal temperatures across China, and cold effects dominated the total mortality burden in all provinces. Both cold- and heat-related mortality burden showed significantly spatial variations across China. FUNDING: National Key Research and Development Program.
背景：非适宜温度与死亡风险相关，但一个国家内不同次国家级区域温度归因的死亡负担异质性很少被研究。我们估计了2019年中国所有省份与非适宜温度相关的死亡负担。 方法：全球每日温度数据来自ERA5再分析数据集。176种个体死因的每日死亡率数据以及每日温度与死亡率之间的暴露-反应曲线来自《2019年全球疾病负担研究》（GBD 2019）。我们基于暴露-反应曲线、每日网格化温度和人口估计人群归因分数（PAF）。我们根据PAF和GBD 2019的疾病负担数据计算特定病因和特定省份的死亡负担。 结果：我们估计2019年中国有59.39（95%UI：49.88，70.46）万人死亡可归因于非适宜温度（PAF = 5.58% [4.93%，6.28%]），其中58.08（48.57，69.01）万人死于与寒冷相关的原因，1.39（0.77，2.32）万人死于与炎热相关的原因。大多数与温度相关的死亡来自心血管疾病（39.97 [32.28，49.04]万人）和慢性呼吸道疾病（17.74 [14.14，22.23]万人）。高温和低温的死亡负担均存在显著的空间异质性。例如，中国西部低温归因的年龄标准化死亡率（每10万人）较高，西藏最高（113.7 [82.0，155.5]），而高温归因的年龄标准化死亡率在新疆（1.8 [0.7，3.3]）以及中国中南部如海南（2.5 [0.9，5.4]）更高。我们还观察到省级层面按死因划分的温度相关死亡负担存在相当大的地理差异。 解读：中国各地相当一部分死亡负担可归因于非适宜温度，寒冷影响在所有省份的总死亡负担中占主导。寒冷和炎热相关的死亡负担在中国各地均呈现出显著的空间差异。 资助：国家重点研发计划。
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