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中国不适环境温度对人群死亡影响的疾病负担分析和健康经济学评价

周璐 陈仁杰 阚海东

周璐,陈仁杰,阚海东. 2022. 中国不适环境温度对人群死亡影响的疾病负担分析和健康经济学评价. 气象学报,80(3):358-365 doi: 10.11676/qxxb2022.031
引用本文: 周璐,陈仁杰,阚海东. 2022. 中国不适环境温度对人群死亡影响的疾病负担分析和健康经济学评价. 气象学报,80(3):358-365 doi: 10.11676/qxxb2022.031
Zhou Lu, Chen Renjie, Kan Haidong. 2022. Mortality burden and related health economic assessment of non-optimal ambient temperature in China. Acta Meteorologica Sinica, 80(3):358-365 doi: 10.11676/qxxb2022.031
Citation: Zhou Lu, Chen Renjie, Kan Haidong. 2022. Mortality burden and related health economic assessment of non-optimal ambient temperature in China. Acta Meteorologica Sinica, 80(3):358-365 doi: 10.11676/qxxb2022.031

中国不适环境温度对人群死亡影响的疾病负担分析和健康经济学评价

doi: 10.11676/qxxb2022.031
基金项目: 上海市“科技创新行动计划” 国际科技合作伙伴项目(21230780200)、国家自然科学基金项目(92043301)
详细信息
    作者简介:

    周璐,主要从事环境流行病学研究。E-mail:20211020040@fudan.edu.cn

    通讯作者:

    陈仁杰,主要从事环境流行病学研究。E-mail:chenrenjie@fudan.edu.cn

  • 中图分类号: P49 R122

Mortality burden and related health economic assessment of non-optimal ambient temperature in China

  • 摘要: 气候变化对人群健康的影响不断加剧,亟待评价不适环境温度对健康的不良影响,量化与温度相关的死亡负担和对应的健康经济损失。本研究基于2013年1月1日至2015年12月31日中国272个主要城市的气温和人口死亡数据,采用时间序列方法建立温度与死亡的暴露-反应关系。同时,收集2020年中国大陆364个城市的气象、社会经济和人口数据,进一步估算31个省、自治区、直辖市低温和高温暴露的归因死亡人数和经济损失。结果表明,环境温度与死亡的暴露-反应关系近似呈反“J”型,环境低温和高温暴露均可引起死亡风险升高。2020年环境低温和高温暴露分别导致中国大陆84.24(95%置信区间(95%CI):65.93—102.20)万例和23.58(95%CI:14.69—32.17)万例死亡;相应健康的经济损失分别为17011.08(95%CI:13353.51—20597.72)亿元和5097.35(95%CI:3179.66—6945.93)亿元,共占国内生产总值(GDP)的2.18%。不适环境温度暴露已对中国造成了较大的死亡负担和健康经济损失。未来还需加强行动应对气候变化和不适环境温度的健康威胁,因地制宜采取适应措施保护人群健康。

     

  • 图 1  中国环境温度与总死亡的暴露-反应关系曲线 (a. 全国,b. 北方地区,c. 南方地区;阴影为95%置信区间)

    Figure 1.  Cumulative exposure-response curves for relationships between ambient temperature and total mortality in China (a. Nationwide,b. Northern China,c. Southern China;shade is 95% confidential interval)

    表  1  2020年中国31个省、自治区及直辖市的基本信息

    Table  1.   Basic information of 31 provinces,autonomous regions,and municipalities of China in 2020

    变量人口
    (万)
    死亡率(‰)人均年收入
    (万元)
    生产总值
    (亿元)
    年均温度
    (℃)
    统计生命价值(万元)
    安徽6104.86.02.838680.616.7180.5
    北京2189.05.56.936102.613.8446.0
    重庆3208.97.63.125002.819.2198.0
    福建4161.46.13.743903.921.3238.9
    甘肃2500.56.82.09016.78.7130.6
    广东12623.64.54.1110760.923.3263.5
    广西5018.76.12.522156.721.9157.8
    贵州3857.97.02.217826.616.2140.0
    海南1011.76.12.85532.425.3179.2
    河北7463.86.12.736206.912.8174.3
    河南9941.26.82.554997.115.6159.4
    黑龙江3170.96.72.513698.54.3159.9
    湖北5744.87.12.843443.516.7179.1
    湖南6645.37.32.941781.517.7188.7
    吉林2399.26.92.612311.36.5165.4
    江苏8477.37.04.3102719.016.8278.7
    江西4519.46.02.825691.518.7179.9
    辽宁4255.57.33.325115.010.1210.3
    内蒙古2402.85.73.117359.86.3202.3
    宁夏720.95.72.63920.69.9165.3
    青海592.86.12.43005.94.9154.4
    山东10164.57.53.373129.013.8211.2
    山西3490.45.92.517651.911.3161.9
    陕西3954.76.32.626181.913.0168.4
    上海2488.25.57.238700.617.8463.9
    四川8370.77.12.748598.815.8170.3
    天津1386.85.34.414083.713.8281.7
    西藏365.64.52.21902.77.6139.7
    新疆2590.54.52.413797.69.0153.2
    云南4722.26.22.324521.916.5149.6
    浙江6468.35.55.264613.318.5336.5
    全国141212.07.13.21015986.214.8206.7
    下载: 导出CSV

    表  2  中国不适环境温度相关的相对危险度

    Table  2.   Relative risks associated with non-optimal ambient temperatures in China

    变量城市数量(个)MMT (℃)极端低温 (℃)极端高温 (℃)相对危险度(均值及95%置信区间)
    极端低温极端高温
    全国27222.8−1.429.01.67 (1.56—1.79)1.16 (1.11—1.20)
    北方11919.6−9.227.31.29 (1.19—1.40)1.11 (1.07—1.16)
    南方15323.7 4.730.31.40 (1.32—1.49)1.19 (1.11—1.27)
     注:MMT,最低死亡率温度;极端低温为温度分布2.5%分位数;极端高温为温度分布97.5%分位数。
    下载: 导出CSV

    表  3  2020年全国31个省、自治区、直辖市的不适温度相关的死亡归因数 (均值及95%置信区间)

    Table  3.   Attributable number of deaths (mean value and the 95% confidential intervals) due to non-optimal ambient temperature in 31 provinces,autonomous regions and municipalities of China in 2020

    变量归因死亡数(万人)归因分数(%)
    低温高温低温高温汇总
    安徽3.63 (2.91—4.34)1.04 (0.64—1.42)9.842.8112.65
    北京0.82 (0.56—1.07)0.35 (0.21—0.49)6.812.949.75
    重庆2.45 (2.02—2.87)0.87 (0.56—1.17)10.093.5813.67
    福建1.75 (1.43—2.06)1.04 (0.66—1.41)6.884.1010.98
    甘肃1.67 (1.19—2.15)0.13 (0.08—0.18)9.910.7810.69
    广东2.74 (2.24—3.24)3.02 (1.91—4.08)4.875.3610.23
    广西1.93 (1.59—2.28)1.39 (0.88—1.89)6.274.5210.79
    贵州3.87 (3.22—4.50)0.22 (0.14—0.30)14.430.8215.25
    海南0.12 (0.10—0.15)0.40 (0.25—0.54)1.976.448.41
    河北3.23 (2.23—4.23)1.07 (0.65—1.48)7.072.359.42
    河南4.06 (2.85—5.27)2.15 (1.31—2.96)5.963.169.12
    黑龙江2.58 (1.82—3.30)0.20 (0.12—0.27)12.060.9212.98
    湖北4.99 (4.14—5.80)1.07 (0.67—1.45)12.262.6214.88
    湖南5.52 (4.59—6.43)1.60 (1.01—2.16)11.423.3014.72
    吉林1.82 (1.28—2.34)0.21 (0.13—0.29)11.001.2712.27
    江苏6.02 (4.88—7.12)1.58 (0.99—2.16)10.082.6612.74
    江西2.88 (2.39—3.36)1.05 (0.67—1.41)10.583.8314.41
    辽宁2.70 (1.88—3.51)0.52 (0.32—0.73)8.771.7010.47
    内蒙古1.50 (1.05—1.94)0.14 (0.08—0.19)11.051.0012.05
    宁夏0.36 (0.25—0.47)0.06 (0.03—0.08)8.721.3410.06
    青海0.42 (0.29—0.55)<0.0111.720.0211.74
    山东4.87 (3.34—6.41)1.84 (1.11—2.54)6.392.418.80
    山西1.93 (1.42—2.43)0.32 (0.19—0.44)9.471.5611.03
    陕西3.77 (3.07—4.45)0.28 (0.17—0.39)15.201.1316.33
    上海1.57 (1.30—1.83)0.38 (0.24—0.51)11.472.7714.24
    四川7.48 (6.22—8.71)1.00 (0.62—1.36)12.611.6814.29
    天津0.50 (0.34—0.65)0.21 (0.13—0.29)6.782.849.62
    西藏0.18 (0.12—0.23)<0.0110.920.1911.11
    新疆1.07 (0.75—1.38)0.19 (0.12—0.26)9.291.6610.95
    云南3.78 (3.12—4.42)0.09 (0.06—0.12)12.900.3013.20
    浙江4.02 (3.33—4.69)1.19 (0.75—1.60)11.253.3314.58
    全国84.24 (65.93—102.20)23.58 (14.69—32.17)8.362.3410.70
    北方31.69 (22.37—40.89)8.44 (5.11—11.67)8.042.1410.18
    南方52.55 (43.55—61.31)15.14 (9.57—20.50)10.503.0213.52
    下载: 导出CSV

    表  4  2020年全国31个省、自治区、直辖市不适温度相关的健康经济学损失 (均值及95%置信区间) 及其占GDP的比例

    Table  4.   Health economic loss (mean value and the 95% confidential intervals) and its proportion of local GDP due to non-optimal ambient temperature in 31 provinces,autonomous regions and municipalities of China in 2020

    变量健康经济损失(亿元)GDP比重(%)
    低温高温低温高温汇总
    安徽655.17 (525.16—782.63)186.93 (115.88—255.54)1.690.482.17
    北京365.21 (251.66—478.19)157.44 (95.66—217.01)1.010.441.45
    重庆485.37 (400.49—568.59)172.35 (109.95—231.46)1.940.692.63
    福建417.19 (341.79—492.17)248.69 (157.24—336.60)0.950.571.52
    甘肃218.53 (155.38—280.80)17.17 (10.34—23.94)2.420.192.61
    广东722.47 (590.24—854.76)794.62 (502.46—1075.39)0.650.721.37
    广西304.90 (250.36—358.92)219.78 (138.83—297.71)1.380.992.37
    贵州541.49 (450.21—629.79)30.78 (19.12—42.37)3.040.173.21
    海南21.78 (17.62—26.03)71.30 (45.10—96.46)0.391.291.68
    河北563.05 (388.44—736.48)186.75 (113.06—258.32)1.560.522.08
    河南646.19 (454.66—839.09)342.17 (208.45—471.01)1.170.621.79
    黑龙江412.26 (290.72—528.21)31.47 (18.91—43.86)3.010.233.24
    湖北893.03 (741.70—1039.37)190.96 (120.27—259.37)2.060.442.50
    湖南1042.28 (865.44—1213.75)301.63 (191.34—407.02)2.490.723.21
    吉林301.10 (211.25—387.59)34.77 (20.90—48.43)2.450.282.73
    江苏1677.22 (1361.11—1985.60)441.62 (275.99—600.97)1.630.432.06
    江西518.71 (430.06—604.96)188.07 (119.71—252.96)2.020.732.75
    辽宁568.79 (395.32—738.74)110.05 (66.33—152.87)2.260.442.70
    内蒙古304.01 (213.12—391.68)27.59 (16.57—38.47)1.750.161.91
    宁夏59.10 (40.93—77.03)9.11 (5.48—12.68)1.510.231.74
    青海65.24 (45.16—85.06)0.08 (0.05—0.12)2.17<0.012.17
    山东1029.06 (705.85—1353.60)388.57 (235.33—537.30)1.410.531.94
    山西313.03 (230.34—394.24)51.46 (30.97—71.59)1.770.292.06
    陕西635.71 (517.19—749.19)47.07 (28.61—65.20)2.430.182.61
    上海728.41 (604.45—848.73)175.73 (111.39—237.37)1.880.452.33
    四川1275.01 (1059.05—1484.46)169.60 (105.97—232.23)2.620.352.97
    天津140.46 (96.72—184.06)58.74 (35.67—81.00)1.000.421.42
    西藏24.88 (17.18—32.54)0.44 (0.28—0.60)1.310.021.33
    新疆164.07 (114.68—212.00)29.34 (17.78—40.55)1.190.211.40
    云南565.03 (466.24—662.05)13.30 (8.26—18.30)2.300.052.35
    浙江1352.33 (1120.96—1577.42)399.78 (253.76—539.22)2.090.622.71
    全国17011.08 (13353.51—20597.72)5097.35 (3179.66—6945.93)1.670.502.17
    北方5965.68 (4199.65—7710.45)1685.66 (1021.42—2329.79)0.590.170.76
    南方11045.40 (9153.85—12887.27)3411.69 (2158.24—4616.14)1.090.341.42
    下载: 导出CSV
  • [1] Adélaïde L,Chanel O,Pascal M. 2022. Health effects from heat waves in France:An economic evaluation. Eur J Health Econ,23(1):119-131 doi: 10.1007/s10198-021-01357-2
    [2] Ananthapavan J,Moodie M,Milat A J,et al. 2021. Systematic review to update 'value of a statistical life' estimates for Australia. Int J Environ Res Public Health,18(11):6168 doi: 10.3390/ijerph18116168
    [3] Cai D, Shi S, Jiang S, et al. 2021. Estimation of the cost-effective threshold of a quality-adjusted life year in China based on the value of statistical life. Eur J Health Econ, DOI: 10.1007/s10198-021-01384-2
    [4] Cai W J,Zhang C,Zhang S H,et al. 2021. The 2021 China report of the Lancet Countdown on health and climate change:Seizing the window of opportunity. Lancet Public Health,6(12):e932-e947 doi: 10.1016/S2468-2667(21)00209-7
    [5] Chen R J, Yin P, Wang L J, et al. 2018. Association between ambient temperature and mortality risk and burden: Time series study in 272 main Chinese cities. BMJ, 363: k4306
    [6] Ebi K L,Capon A,Berry P,et al. 2021. Hot weather and heat extremes:Health risks. Lancet,398(10301):698-708 doi: 10.1016/S0140-6736(21)01208-3
    [7] Gasparrini A,Guo Y M,Hashizume M,et al. 2015. Mortality risk attributable to high and low ambient temperature:A multicountry observational study. Lancet,386(9991):369-375 doi: 10.1016/S0140-6736(14)62114-0
    [8] Guo Y M,Gasparrini A,Armstrong B,et al. 2014. Global variation in the effects of ambient temperature on mortality:A systematic evaluation. Epidemiology,25(6):781-789 doi: 10.1097/EDE.0000000000000165
    [9] Hao Y,Zhao M Y,Lu Z N. 2019. What is the health cost of haze pollution? Evidence from China. Int J Health Plann Manage,34(4):1290-1303 doi: 10.1002/hpm.2791
    [10] Hoffmann S,Krupnick A,Qin P. 2017. Building a set of internationally comparable value of statistical life studies:Estimates of Chinese willingness to pay to reduce mortality risk. J Benefit-Cost Anal,8(2):251-289 doi: 10.1017/bca.2017.16
    [11] Keller E,Newman J E,Ortmann A,et al. 2021. How much is a human life worth? A systematic review. Value Health,24(10):1531-1541 doi: 10.1016/j.jval.2021.04.003
    [12] Liu Y,Saha S,Hoppe B O,et al. 2019. Degrees and dollars — health costs associated with suboptimal ambient temperature exposure. Sci Total Environ,678:702-711 doi: 10.1016/j.scitotenv.2019.04.398
    [13] Romanello M,McGushin A,Di Napoli C,et al. 2021. The 2021 report of the Lancet Countdown on health and climate change:Code red for a healthy future. Lancet,398(10311):1619-1662 doi: 10.1016/S0140-6736(21)01787-6
    [14] Song X P,Wang S G,Hu Y L,et al. 2017. Impact of ambient temperature on morbidity and mortality:An overview of reviews. Sci Total Environ,586:241-254 doi: 10.1016/j.scitotenv.2017.01.212
    [15] Taylor N A S. 2014. Human heat adaptation. Compr Physiol,4(1):325-365
    [16] Xia Y,Li Y,Guan D B,et al. 2018. Assessment of the economic impacts of heat waves:A case study of Nanjing,China. J Clean Prod,171:811-819 doi: 10.1016/j.jclepro.2017.10.069
    [17] Yang Q Q,Huang X,Tang Q H. 2019. The footprint of urban heat island effect in 302 Chinese cities:Temporal trends and associated factors. Sci Total Environ,655:652-662 doi: 10.1016/j.scitotenv.2018.11.171
    [18] Zhao Q,Guo Y M,Ye T T,et al. 2021. Global,regional,and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019:A three-stage modelling study. Lancet Planet Health,5(7):e415-e425 doi: 10.1016/S2542-5196(21)00081-4
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  • 收稿日期:  2021-12-21
  • 录用日期:  2022-04-21
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