• 中国精品科技期刊
  • 《中文核心期刊要目总览》收录期刊
  • RCCSE 中国核心期刊(5/114,A+)
  • Scopus收录期刊
  • 美国《化学文摘》(CA)收录期刊
  • WHO 西太平洋地区医学索引(WPRIM)收录期刊
  • 《中国科学引文数据库(CSCD)》核心库期刊 (C)
  • 中国科技核心期刊
  • 中国科技论文统计源期刊
  • 《日本科学技术振兴机构数据库(中国)》(JSTChina)收录期刊
  • 美国《乌利希期刊指南》(UIrichsweb)收录期刊
  • 中华预防医学会系列杂志优秀期刊(2019年)

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于死亡风险的中国不同气温带气温预警阈值研究

吴君乐, 俞敏, 周脉耕, 周纯良, 肖义泽, 黄彪, 许燕君, 孟瑞琳, 赵亮, 胡建雄, 何冠豪, 许晓君, 刘涛, 肖建鹏, 曾韦霖, 郭凌川, 李杏, 马文军. 基于死亡风险的中国不同气温带气温预警阈值研究[J]. 中华疾病控制杂志, 2021, 25(10): 1139-1146. doi: 10.16462/j.cnki.zhjbkz.2021.10.005
引用本文: 吴君乐, 俞敏, 周脉耕, 周纯良, 肖义泽, 黄彪, 许燕君, 孟瑞琳, 赵亮, 胡建雄, 何冠豪, 许晓君, 刘涛, 肖建鹏, 曾韦霖, 郭凌川, 李杏, 马文军. 基于死亡风险的中国不同气温带气温预警阈值研究[J]. 中华疾病控制杂志, 2021, 25(10): 1139-1146. doi: 10.16462/j.cnki.zhjbkz.2021.10.005
WU Jun-le, YU Min, ZHOU Mai-geng, ZHOU Chun-liang, XIAO Yi-ze, HUANG Biao, XU Yan-jun, MENG Rui-lin, ZHAO Liang, HU Jian-xiong, HE Guan-hao, XU Xiao-jun, LIU Tao, XIAO Jian-peng, ZENG Wei-lin, GUO Ling-chuan, LI Xing, MA Wen-jun. A study on the thresholds of temperature for early warning in different temperature zones of China based on the temperature-mortality relationships[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(10): 1139-1146. doi: 10.16462/j.cnki.zhjbkz.2021.10.005
Citation: WU Jun-le, YU Min, ZHOU Mai-geng, ZHOU Chun-liang, XIAO Yi-ze, HUANG Biao, XU Yan-jun, MENG Rui-lin, ZHAO Liang, HU Jian-xiong, HE Guan-hao, XU Xiao-jun, LIU Tao, XIAO Jian-peng, ZENG Wei-lin, GUO Ling-chuan, LI Xing, MA Wen-jun. A study on the thresholds of temperature for early warning in different temperature zones of China based on the temperature-mortality relationships[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(10): 1139-1146. doi: 10.16462/j.cnki.zhjbkz.2021.10.005

基于死亡风险的中国不同气温带气温预警阈值研究

doi: 10.16462/j.cnki.zhjbkz.2021.10.005
基金项目: 

国家重点研发计划 2018YFA0606200

国家自然科学基金 42075173

广东省医学科学技术研究基金 A2021340

详细信息
    通讯作者:

    马文军,E-mail:mawj@gdiph.org.cn

  • 中图分类号: R122.2+1;R195.1

A study on the thresholds of temperature for early warning in different temperature zones of China based on the temperature-mortality relationships

Funds: 

The National Key Research and Development Program of China 2018YFA0606200

National Natural Science Foundation of China 42075173

Guangdong Medical Science and Technology Research A2021340

More Information
  • 摘要:   目的  估算中国不同气温带气温健康预警的阈值,为发展基于死亡风险的气温健康预警系统提供科学依据。  方法  收集2006―2017年全国364个县、区作为研究点的死亡与气象数据,利用分布滞后非线性模型(distribution lag non-linear model, DLNM)和多变量Meta方法分析气温与死亡的暴露反应关系,划分气温预警阈值。  结果  研究期间日平均气温16.0 ℃,日平均相对湿度73.0%,日均死亡人数为8.3例。不同气温带的气温-死亡的暴露反应关系总体上呈倒“J”型。中温带、暖温带+北亚热带、中亚热带以及南亚热带冷效应低风险气温范围分别9.1~13.8 ℃、0.1~19.3 ℃、8.8~24.3 ℃以及9.9~25.3 ℃,中风险分别为1.8~9.1 ℃、-6.1~0.1 ℃、1.5~8.8 ℃以及4.8~9.9 ℃,高风险分别为<1.8 ℃、<-6.1 ℃、<1.5 ℃以及<4.8 ℃;热效应低风险气温范围分别为23.4~24.8 ℃、28.6~29.3 ℃、27.2~29.5 ℃以及28.2~28.6 ℃,中风险分别为24.8~26.1 ℃、29.3~30.1 ℃、29.5~31.0 ℃以及28.6~29.0 ℃,高风险分别为>26.1 ℃、>30.1 ℃、>31.0 ℃以及>29.0 ℃。所有气温带在高温端的日均死亡人数均随着风险等级增加而升高,而除了暖温带+北亚热带外,其他气温带在低温端的日均死亡人数随着风险等级增加而升高。  结论  基于死亡风险可以确定气温预警的阈值并进行预警等级划分,预警效果较好。
  • 图  1  364个研究县、区在各气温带的分布情况

    Figure  1.  The distribution of 364 Counties/Districts in each temperature zone

    图  2  四个气温带气温-死亡暴露反应关系曲线

    Figure  2.  Exposure response curve of air temperature and death in four climate zones

    图  3  各气温阈值风险等级的天数占比以及日均死亡人数

    Figure  3.  The proportion of day and daily death toll of each temperature threshold risk grade

    表  1  2006―2017年所有研究县、区气象资料、空气质量和居民日死亡人数情况[n (%)]

    Table  1.   Meteorological data, PM10 and daily mortality of all locations from 2006 to 2017 [n (%)]

    变量 全国 中温带 暖温带+北亚热带 中亚热带 南亚热带
    研究县、区数 364(100.00) 45(12.36) 49(13.46) 199(54.67) 71(19.51)
    日均死亡数
      总人群[(x±s),人] 8.3±6.3 5.2±4.3 8.1±6.3 9.1±6.5 7.7±5.7
      0~<65岁[(x±s),人] 2.1±2.0 1.8±1.8 2.2±2.2 2.2±2.0 2.1±1.9
      ≥65岁[(x±s),人] 6.2±5.0 3.4±3.1 5.9±4.9 7.0±5.3 5.6±4.5
    气象与污染物
      平均气温[(x±s),℃] 16.0±9.8 5.7±13.3 13.5±10.3 17.5±8.3 19.7±6.1
      相对湿度[(x±s),%] 73.0±13.9 62.7±16.3 67.3±16.8 75.8±11.6 74.9±12.6
      PM10[(x±s),μg/m3] 81.1±41.4 87.7±56.2 92.5±36.0 84.0±39.6 56.8±29.6
    下载: 导出CSV

    表  2  各风险级别RR

    Table  2.   RR of each risk grade

    类别 风险级别 RR(95% CI)值
    中温带 暖温带+北亚热带 中亚热带 南亚热带
    冷效应 高风险 >1.18(1.07~1.30) >1.67(1.46~1.91) >1.59(1.50~1.69) >1.67(1.43~1.94)
    中风险 1.11(1.03~1.19)~1.18(1.07~1.30) 1.35(1.21~1.51)~1.67(1.46~1.91) 1.30(1.24~1.37)~1.59(1.50~1.69) 1.34(1.20~1.49)~1.67(1.43~1.94)
    低风险 1.04(1.00~1.08)~1.11(1.03~1.19) 1.03(1.00~1.06)~1.35(1.21~1.51) 1.01(1.00~1.01)~1.30(1.24~1.37) 1.01(1.00~1.02)~1.34(1.20~1.49)
    舒适温度 很低 1.04(0.99~1.08) 1.03(0.99~1.06)~1.06(0.99~1.13) 1.00(0.99~1.01)~1.01(0.99~1.01) 1.01(0.99~1.02)~1.04(0.99~1.09)
    热效应 低风险 1.04(1.00~1.08)~1.07(1.01~1.13) 1.06(1.00~1.13)~1.08(1.00~1.16) 1.00(1.00~1.01)~1.04(1.02~1.06) 1.04(1.00~1.09)~1.06(1.01~1.12)
    中风险 1.07(1.01~1.13)~1.10(1.02~1.19) 1.08(1.00~1.16)~1.10(1.01~1.20) 1.04(1.02~1.06)~1.08(1.05~1.11) 1.06(1.01~1.12)~1.08(1.01~1.15)
    高风险 >1.10(1.02~1.19) >1.10(1.01~1.20) >1.08(1.05~1.11) >1.08(1.01~1.15)
    下载: 导出CSV

    表  3  各风险级别温度范围

    Table  3.   Temperature range of each risk grade

    类别 风险级别 温度范围(℃)
    中温带 暖温带+北亚热带 中亚热带 南亚热带
    冷效应 高风险 <1.8 <-6.1 <1.5 <4.8
    中风险 1.8~<9.1 -6.1~<0.1 1.5~<8.8 4.8~<9.9
    低风险 9.1~<13.8 0.1~<19.3 8.8~<24.3 9.9~<25.3
    舒适温度 很低 13.8~<23.4 19.3~<28.6 24.3~<27.2 25.3~<28.2
    热效应 低风险 23.4~<24.8 28.6~<29.3 27.2~<29.5 28.2~<28.6
    中风险 24.8~<26.1 29.3~<30.1 29.5~<31.0 28.6~<29.0
    高风险 ≥26.1 ≥30.1 ≥31.0 ≥29.0
    下载: 导出CSV

    表  4  同一温度带不同冷、热风险级别日均死亡人数差异的方差分析

    Table  4.   Analysis of variance of average daily death difference among cold and heat risk grade in the same temperature zone

    温度带 冷效应 热效应
    F P F P
    中温带 7.41 0.22 1.33×1029 <0.01
    暖温带+北亚热带 4.08 0.29 1.62×1030 <0.01
    中亚热带 17.28 0.15 75 0.07
    南亚热带 1 083 0.02 27 0.12
    下载: 导出CSV

    表  5  不同df值或去除PM10污染物项情况下日平均温度导致的人群健康风险RR值[RR(95% CI)]

    Table  5.   Mortality RR caused by daily average temperature with different df or removal of PM10 pollutants setting [RR(95% CI)]

    不同参数设置 中温带 暖温带+北亚热带 中亚热带 南亚热带
    冷效应
      本研究参数设置 1.04(1.00~1.08) 1.03(1.00~1.07) 1.01(1.00~1.01) 1.01(1.00~1.02)
      去除PM10 1.04(1.00~1.08) 1.03(1.00~1.07) 1.01(1.00~1.01) 1.01(1.00~1.02)
      df=7 1.02(1.00~1.04) 1.03(1.00~1.06) 1.01(1.00~1.01) 1.08(1.00~1.17)
      df=8 1.02(1.00~1.05) 1.03(1.00~1.06) 1.01(1.00~1.02) 1.02(1.00~1.05)
      df=10 1.05(1.00~1.11) 1.04(1.00~1.09) 1.00(1.00~1.01) 1.01(1.00~1.02)
      df=11 1.10(1.00~1.21) 1.06(1.00~1.12) 1.00(1.00~1.01) 1.03(1.00~1.05)
    热效应
      本研究参数设置 1.04(1.00~1.08) 1.06(1.00~1.13) 1.00(1.00~1.01) 1.04(1.00~1.09)
      去除PM10 1.04(1.00~1.08) 1.06(1.00~1.12) 1.00(1.00~1.01) 1.05(1.00~1.10)
      df=7 1.02(1.00~1.04) 1.03(1.00~1.05) 1.00(1.00~1.01) 1.00(1.00~1.00)
      df=8 1.03(1.00~1.06) 1.05(1.00~1.10) 1.01(1.00~1.01) 1.03(1.00~1.05)
      df=10 1.06(1.00~1.13) 1.11(0.99~1.25) 1.00(1.00~1.01) 1.03(1.00~1.05)
      df=11 1.03(0.92~1.15) 1.12(1.00~1.25) 1.00(1.00~1.01) 1.06(0.98~1.15)
    下载: 导出CSV
  • [1] 谢慧妍, 马文军, 张永慧, 等. 广州、长沙、昆明气温对非意外死亡的短期效应研究[J]. 中华预防医学杂志, 2014, 48(1): 38-43. DOI: 10.3760/cma.j.issn.0253-9624.2014.01.009.

    Xie HY, Ma WJ, Zhang YH, et al. Study on the short-term effect of temperature on non-accidental death in Guangzhou, Changsha and Kunming[J]. Chin J Prevent Med, 2014, 48(1): 38-43. DOI: 10.3760/cma.j.issn.0253-9624.2014.01.009.
    [2] Song X, Wang S, Hu Y, et al. Impact of ambient temperature on morbidity and mortality: an overview of reviews[J]. Sci Total Environ, 2017, 586: 241-254. DOI: 10.1016/j.scitotenv.2017.01.212.
    [3] Chen R, Yin P, Wang L, et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities[J]. BMJ, 2018, 363: k4306. DOI: 10.1136/bmj.k4306.
    [4] Gasparrini A, Guo Y, Hashizume M, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study[J]. Lancet, 2015, 386(9991): 369-375. DOI: 10.1016/s0140-6736(14)62114-0.
    [5] 曾韦霖, 李光春, 肖义泽, 等. 中国四城市温度对居民心脑血管疾病死亡影响的时间序列研究[J]. 中华流行病学杂志, 2012, 33(10): 1021-1025. DOI: 10.3760/cma.j.issn.0254-6450.2012.10.006.

    Zeng WL, Li GC, Xiao YZ, et al. A time series study on the effect of temperature on the death of cardiovascular and cerebrovascular diseases in four cities of China[J]. Chin J Epidemiol, 2012, 33(10): 1021-1025. DOI: 10.3760/cma.j.issn.0254-6450.2012.10.006.
    [6] Casanueva A, Burgstall A, Kotlarski S, et al. Overview of existing heat-health warning systems in Europe[J]. Int J Environ Res Public Health, 2019, 16(15): 2657. DOI: 10.3390/ijerph16152657.
    [7] Kalkstein LS, Jamason PF, Greene JS, et al. The philadelphia hot weather-health watch/warning system: development and application, summer 1995[J]. Bull Am Meteorol Soc, 1996, 77(7): 1519-1528. DOI: 10.1175/1520-0477(1996)0772.0.co;2.
    [8] Nicholls N, Skinner C, Loughnan M, et al. A simple heat alert system for Melbourne, Australia[J]. Int J Biometeorol, 2008, 52(5): 375-384. DOI: 10.1007/s00484-007-0132-5.
    [9] Lee KL, Chan YH, Lee TC, et al. The development of the Hong Kong heat index for enhancing the heat stress information service of the Hong Kong observatory[J]. Int J Biometeorol, 2016, 60(7): 1029-1039. DOI: 10.1007/s00484-015-1094-7.
    [10] Cheng YT, Shih-Chun CL, Hwang JS. New approach to identifying proper thresholds for a heat warning system using health risk increments[J]. Environ Res, 2019, 170: 282-292. DOI: 10.1016/j.envres.2018.12.059.
    [11] Chen YC, Matzarakis A. Modified physiologically equivalent temperature-basics and applications for Western European climate[J]. Theor Appl Climatol, 2018, 132(3): 1275-1289. DOI: 10.1007/s00704-017-2158-x.
    [12] 陈静, 韩军彩, 张素果, 等. 基于暑热指数的河北省中暑气象等级预报指标研究[J]. 气象与环境学报, 2013, 29(5): 86-91. DOI: 10.3969/j.issn.1673-503x.2013.05.014.

    Chen J, Han JC, Zhang SG, et al. Study on meteorological grade forecast indexes of heatstroke in Hebei Province based on summer heat index[J]. Journal of Meteorology and Environment, 2013, 29(5): 86-91. DOI: 10.3969/j.issn.1673-503x.2013.05.014.
    [13] 陈辉, 黄卓, 田华, 等. 高温中暑气象等级评定方法[J]. 应用气象学报, 2009, 20(4): 451-457. DOI: 10.11898/1001-7313.20090409.

    Chen H, Huang Z, Tian H, et al. Evaluation method of meteorological grade for high temperature heatstroke[J]. J Appl Meteor Sci, 2009, 20(4): 451-457. DOI: 10.11898/1001-7313.20090409.
    [14] Sheridan SC. A survey of public perception and response to heat warnings across four North American cities: an evaluation of municipal effectiveness[J]. Int J Biometeorol, 2007, 52(1): 3-15. DOI: 10.1007/s00484-006-0052-9.
    [15] Hutchinson X. Anusplin version 4.4 user guide[M]. Canberra: The Australian National University, 2013: 1-52.
    [16] Chen S, Xiao Y, Zhou M, et al. Comparison of life loss per death attributable to ambient temperature among various development regions: a nationwide study in 364 locations in China[J]. Environ Health, 2020, 19(1): 98. DOI: 10.1186/s12940-020-00653-3.
    [17] Liu T, Chen X, Xu Y, et al. Gut microbiota partially mediates the effects of fine particulate matter on type 2 diabetes: Evidence from a population-based epidemiological study[J]. Environ Int, 2019, 130: 104882. DOI: 10.1016/j.envint.2019.05.076.
    [18] Liu T, Xiao J, Zeng W, et al. A spatiotemporal land-use-regression model to assess individual level long-term exposure to ambient fine particulate matters[J]. MethodsX, 2019, 6: 2101-2105. DOI: 10.1016/j.mex.2019.09.009.
    [19] Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1): 5-32. DOI: 10.1023/A:1010933404324.
    [20] 郑景云, 尹云鹤, 李炳元. 中国气候区划新方案[J]. 地理学报, 2010, 65(1): 3-12. DOI: 10.11821/xb201001002.

    Zheng JY, Yin YH, Li BY. A new plan for climate regionalization in China[J]. Acta Geographcica Sinica, 2010, 65(1): 3-12. DOI: 10.11821/xb201001002.
    [21] Guo Y, Gasparrini A, Armstrong B, et al. Global variation in the effects of ambient temperature on mortality: a systematic evaluation[J]. Epidemiology, 2014, 25(6): 781-9. DOI: 10.1097/ede.0000000000000165.
    [22] Gasparrini A, Armstrong B. Reducing and meta-analysing estimates from distributed lag non-linear models[J]. BMC Med Res Methodol. 2013(13): 1. DOI: 10.1186/1471-2288-13-1.
    [23] Guo Y, Barnett AG, Pan X, et al. The impact of temperature on mortality in Tianjin, China: a case-crossover design with a distributed lag nonlinear model[J]. Environ Health Perspect, 2011, 119(12): 1719-1725. DOI: 10.1289/ehp.1103598.
    [24] 黄照, 刘涛, 许燕君, 等. 基于死亡数据用DLNM构建气象健康指数[J]. 环境卫生学杂志, 2018, 8(5): 368-373, 380. DOI: 10.13421/j.cnki.hjwsxzz.2018.05.002.

    Huang Z, Liu T, Xu YJ, et al. Using DLNM to construct meteorological health index based on mortality data[J]. Journal of Environmental Hygiene, 2018, 8(5): 368-373, 380. DOI: 10.13421/j.cnki.hjwsxzz.2018.05.002.
    [25] Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm[J]. J Stat Softw, 2011, 43(8): 1-20. DOI: 10.18637/jss.v043.i08.
    [26] Ma W, Chen R, Kan H. Temperature-related mortality in 17 large Chinese cities: how heat and cold affect mortality in China[J]. Environ Res, 2014, 134: 127-33. DOI: 10.1016/j.envres.2014.07.007.
    [27] Aström DO, Tornevi A, Ebi KL, et al. Evolution of minimum mortality temperature in Stockholm, Sweden, 1901-2009[J]. Environ Health Perspect, 2016, 124(6): 740-744. DOI: 10.1289/ehp.1509692.
    [28] Todd N, Valleron AJ. Space-time covariation of mortality with temperature: a systematic study of deaths in France, 1968-2009[J]. Environ Health Perspect, 2015, 123(7): 659-664. DOI: 10.1289/ehp.1307771.
    [29] Luo Q, Li S, Guo Y, et al. A systematic review and meta-analysis of the association between daily mean temperature and mortality in China[J]. Environ Res, 2019, 173: 281-299. DOI: 10.1016/j.envres.2019.03.044.
    [30] Yin Q, Wang J, Ren Z, et al. Mapping the increased minimum mortality temperatures in the context of global climate change[J]. Nat Commun, 2019, 10(1): 4640. DOI: 10.1038/s41467-019-12663-y.
    [31] Krummenauer L, Prahl BF, Costa L, et al. Global drivers of minimum mortality temperatures in cities[J]. Sci Total Environ, 2019, 695: 133560. DOI: 10.1016/j.scitotenv.2019.07.366.
  • 加载中
图(3) / 表(5)
计量
  • 文章访问数:  15
  • HTML全文浏览量:  14
  • PDF下载量:  6
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-04-09
  • 修回日期:  2021-07-21
  • 网络出版日期:  2021-11-17
  • 刊出日期:  2021-10-10

目录

    /

    返回文章
    返回