中國(guó)不適環(huán)境溫度對(duì)人群死亡影響的疾病負(fù)擔(dān)分析和健康經(jīng)濟(jì)學(xué)評(píng)價(jià)
摘要: 氣候變化對(duì)人群健康的影響不斷加劇,亟待評(píng)價(jià)不適環(huán)境溫度對(duì)健康的不良影響,量化與溫度相關(guān)的死亡負(fù)擔(dān)和對(duì)應(yīng)的健康經(jīng)濟(jì)損失。本研究基于2013年1月1日至2015年12月31日中國(guó)272個(gè)主要城市的氣溫和人口死亡數(shù)據(jù),采用時(shí)間序列方法建立溫度與死亡的暴露-反應(yīng)關(guān)系。同時(shí),收集2020年中國(guó)大陸364個(gè)城市的氣象、社會(huì)經(jīng)濟(jì)和人口數(shù)據(jù),進(jìn)一步估算31個(gè)省、自治區(qū)、直轄市低溫和高溫暴露的歸因死亡人數(shù)和經(jīng)濟(jì)損失。結(jié)果表明,環(huán)境溫度與死亡的暴露-反應(yīng)關(guān)系近似呈反“J”型,環(huán)境低溫和高溫暴露均可引起死亡風(fēng)險(xiǎn)升高。2020年環(huán)境低溫和高溫暴露分別導(dǎo)致中國(guó)大陸84.24(95%置信區(qū)間(95%CI):65.93—102.20)萬(wàn)例和23.58(95%CI:14.69—32.17)萬(wàn)例死亡;相應(yīng)健康的經(jīng)濟(jì)損失分別為17011.08(95%CI:13353.51—20597.72)億元和5097.35(95%CI:3179.66—6945.93)億元,共占國(guó)內(nèi)生產(chǎn)總值(GDP)的2.18%。不適環(huán)境溫度暴露已對(duì)中國(guó)造成了較大的死亡負(fù)擔(dān)和健康經(jīng)濟(jì)損失。未來(lái)還需加強(qiáng)行動(dòng)應(yīng)對(duì)氣候變化和不適環(huán)境溫度的健康威脅,因地制宜采取適應(yīng)措施保護(hù)人群健康。
Abstract: With the increasing impact of climate change on public health, there is an urgent need to evaluate the detrimental effect of non-optimal ambient temperature on health and quantify the temperature-related mortality and corresponding economic losses. Based on the national database of weather conditions and mortality records in 272 main cities in China from 1 January 2013 to 31 December 2015, time-series analyses are conducted to estimate the exposure-response association between temperature and mortality. Besides, meteorological, socioeconomic, and demographic data for cities across China are collected to quantify the attributable deaths and corresponding economic losses due to low and high temperatures in 31 provinces, autonomous regions and municipalities of China. The exposure-response curve for the association between ambient temperature and mortality is J-shaped, with increased mortality risks for both low and high temperatures. As estimated, 842.4 (95%CI: 659.3—1022.0) thousand and 235.8 (95%CI: 146.9—321.7) thousand deaths are attributable to low and high temperatures in 2020 in China, respectively. The corresponding economic losses are 1701.11 (95%CI: 1335.35—2059.77) billion and 509.74 (95%CI: 317.97—694.59) billion Chinese yuan, respectively. The proportion of the overall economic loss to the gross domestic product (GDP) is 2.18%. Non-optimal ambient temperature exposure has led to substantial mortality and economic loss in China. It is necessary to strengthen actions to deal with the health threats of climate change and non-optimal ambient temperature, and local adaptation measures should be taken to protect public health in the future.
圖 1 中國(guó)環(huán)境溫度與總死亡的暴露-反應(yīng)關(guān)系曲線 (a. 全國(guó),b. 北方地區(qū),c. 南方地區(qū);陰影為95%置信區(qū)間)
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年中國(guó)31個(gè)省、自治區(qū)及直轄市的基本信息
Table 1 Basic information of 31 provinces,autonomous regions,and municipalities of China in 2020
變量人口(萬(wàn))死亡率(‰)人均年收入
(萬(wàn)元)生產(chǎn)總值
(億元)年均溫度
(℃)統(tǒng)計(jì)生命價(jià)值(萬(wàn)元) 安徽6104.86.02.838680.616.7180.5北京2189.05.56.936102.613.8446.0重慶3208.97.63.125002.819.2198.0福建4161.46.13.743903.921.3238.9甘肅2500.56.82.09016.78.7130.6廣東12623.64.54.1110760.923.3263.5廣西5018.76.12.522156.721.9157.8貴州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黑龍江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江蘇8477.37.04.3102719.016.8278.7江西4519.46.02.825691.518.7179.9遼寧4255.57.33.325115.010.1210.3內(nèi)蒙古2402.85.73.117359.86.3202.3寧夏720.95.72.63920.69.9165.3青海592.86.12.43005.94.9154.4山東10164.57.53.373129.013.8211.2山西3490.45.92.517651.911.3161.9陜西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全國(guó)141212.07.13.21015986.214.8206.7
表 2 中國(guó)不適環(huán)境溫度相關(guān)的相對(duì)危險(xiǎn)度
Table 2 Relative risks associated with non-optimal ambient temperatures in China
變量城市數(shù)量(個(gè))MMT (℃)極端低溫 (℃)極端高溫 (℃)相對(duì)危險(xiǎn)度(均值及95%置信區(qū)間)極端低溫極端高溫 全國(guó)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,最低死亡率溫度;極端低溫為溫度分布2.5%分位數(shù);極端高溫為溫度分布97.5%分位數(shù)。表 3 2020年全國(guó)31個(gè)省、自治區(qū)、直轄市的不適溫度相關(guān)的死亡歸因數(shù) (均值及95%置信區(qū)間)
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
變量歸因死亡數(shù)(萬(wàn)人)歸因分?jǐn)?shù)(%)低溫高溫低溫高溫匯總 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重慶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甘肅1.67 (1.19—2.15)0.13 (0.08—0.18)9.910.7810.69廣東2.74 (2.24—3.24)3.02 (1.91—4.08)4.875.3610.23廣西1.93 (1.59—2.28)1.39 (0.88—1.89)6.274.5210.79貴州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黑龍江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江蘇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遼寧2.70 (1.88—3.51)0.52 (0.32—0.73)8.771.7010.47內(nèi)蒙古1.50 (1.05—1.94)0.14 (0.08—0.19)11.051.0012.05寧夏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山東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陜西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全國(guó)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表 4 2020年全國(guó)31個(gè)省、自治區(qū)、直轄市不適溫度相關(guān)的健康經(jīng)濟(jì)學(xué)損失 (均值及95%置信區(qū)間) 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
變量健康經(jīng)濟(jì)損失(億元)GDP比重(%)低溫高溫低溫高溫匯總 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重慶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甘肅218.53 (155.38—280.80)17.17 (10.34—23.94)2.420.192.61廣東722.47 (590.24—854.76)794.62 (502.46—1075.39)0.650.721.37廣西304.90 (250.36—358.92)219.78 (138.83—297.71)1.380.992.37貴州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黑龍江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江蘇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遼寧568.79 (395.32—738.74)110.05 (66.33—152.87)2.260.442.70內(nèi)蒙古304.01 (213.12—391.68)27.59 (16.57—38.47)1.750.161.91寧夏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山東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陜西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全國(guó)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)1.090.341.42Adéla?de 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
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
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
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
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
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
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
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
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
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
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
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
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
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
Taylor N A S. 2014. Human heat adaptation. Compr Physiol,4(1):325-365
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
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
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
相關(guān)知識(shí)
我國(guó)心理健康與精神障礙疾病治療費(fèi)用與經(jīng)濟(jì)負(fù)擔(dān)分析
1990—2019年中國(guó)炎癥性腸病疾病負(fù)擔(dān)及變化趨勢(shì)分析
經(jīng)濟(jì)增長(zhǎng)影響健康的文獻(xiàn)綜述
垃圾污染對(duì)我們的健康、環(huán)境和經(jīng)濟(jì)的影響
清華大學(xué)聯(lián)合發(fā)布中國(guó)燃煤和其它主要空氣污染造成的疾病負(fù)擔(dān)報(bào)告
地鐵建設(shè)對(duì)人群健康影響如何?上海公布國(guó)內(nèi)首個(gè)完整健康影響評(píng)估制度建設(shè)方案
中國(guó)環(huán)境健康面臨的問(wèn)題及國(guó)外經(jīng)驗(yàn)借鑒
環(huán)境污染健康影響評(píng)價(jià)
中國(guó)人口健康模式變化?環(huán)境污染成威脅健康重要因素
環(huán)境污染對(duì)腸道菌群和免疫系統(tǒng)的影響
網(wǎng)址: 中國(guó)不適環(huán)境溫度對(duì)人群死亡影響的疾病負(fù)擔(dān)分析和健康經(jīng)濟(jì)學(xué)評(píng)價(jià) http://m.u1s5d6.cn/newsview198788.html
推薦資訊
- 1發(fā)朋友圈對(duì)老公徹底失望的心情 12775
- 2BMI體重指數(shù)計(jì)算公式是什么 11235
- 3補(bǔ)腎吃什么 補(bǔ)腎最佳食物推薦 11199
- 4性生活姿勢(shì)有哪些 盤點(diǎn)夫妻性 10425
- 5BMI正常值范圍一般是多少? 10137
- 6在線基礎(chǔ)代謝率(BMR)計(jì)算 9652
- 7一邊做飯一邊躁狂怎么辦 9138
- 8從出汗看健康 出汗透露你的健 9063
- 9早上怎么喝水最健康? 8613
- 10五大原因危害女性健康 如何保 7826