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
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。
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.
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
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。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）1.090.341.42Adélaïde 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
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