Effects of temperature and temperature change on mortality of residents in Qinhuangdao, China
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摘要: 气温及其变化是影响人群健康特别是死亡的重要环境危险因素。为了揭示气温对秦皇岛市居民死亡人数的影响,基于2014—2020年该市各区、县逐日气象资料和居民死亡资料,采用广义相加模型(GAM)和分布滞后非线性模型(DLNM),研究了气温、气温日较差和24小时变温对非意外死亡、循环系统疾病死亡、呼吸系统疾病死亡人数的影响。按性别、年龄分层建模,使用相对危险度(relative risk,RR)量化了暴露在特定气温变化状态下的死亡风险。采用非参数双变量响应模型分析了气温与变温的协同影响效应。结果显示:(1)秦皇岛市居民非意外死亡、循环系统疾病死亡、呼吸系统疾病死亡人数全年峰值出现在最冷的1月,气温对三类死亡人数的影响以冷效应为主且具有滞后效应,而高温具有即时效应。(2)气温日较差与非意外死亡、循环系统疾病死亡的总体暴露反应曲线呈U型分布,较大的气温日较差与上述两类死亡存在显著的风险效应,其中循环系统疾病死亡受影响最大,大幅气温日较差(19℃)累积3 d相对危险度为1.27,其95%的置信区间(95%CI)为1.15—1.4,而其对呼吸系统疾病死亡的风险效应未通过显著性检验。(3)24小时变温对非意外死亡、循环系统疾病死亡总体影响效应的暴露曲线呈非线性递增趋势,其中正变温呈现显著的风险效应。(4)性别、年龄分组结果显示,女性对气温变化更敏感,男性对气温变化存在一定的滞后效应,老年人群更容易受到气温变化的影响。(5)低温与变温的协同作用加剧了死亡风险。总体上,冬季低温背景与大幅气温变化相叠加对当地老年居民死亡影响风险最大,应予适时重点防护。Abstract: Temperature and temperature changes are important environmental risk factors that affect human health, especially mortality. This study investigates impacts of temperature and temperature changes on mortality of residents in Qinhuangdao based on daily meteorological data and resident mortality data for all districts and counties in Qinhuangdao from 2014 to 2020. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) are used to explore the impacts of temperature and temperature changes on the number of non-accidental mortality and cardiovascular and respiratory mortality from three perspectives of temperature, diurnal temperature change (DTR) and temperature change between neighboring days (TCN). The modeling study is stratified by sex and age, and the relative risk (RR) is used to quantify the mortality risk of exposure to specific temperature changes. In addition, a non-parametric bivariate response surface model is used to explore the interaction between temperature and temperature change. The results are as follows. (1) The annual peak of non-accidental mortality, cardiovascular mortality and respiratory mortality in Qinhuangdao occurs in January, the coldest month of the year. The impact of temperature on the three types of deaths is mainly dominated by lagged cold effects, while high temperature has immediate effects. (2) The overall exposure response curve of DTR and non-accidental and cardiovascular mortality shows a U-shaped distribution, and high DTR has significant risk effects on the above two types of deaths. Among them, cardiovascular mortality is most affected, and the cumulative 3 d relative risk of extremely large DTR (19°C) is 1.27 (95%CI: 1.15—1.4), while the risk effect of respiratory mortality cannot pass the significance test. (3) The exposure curve of the overall effect of TCN on non-accidental mortality and circulatory mortality shows a nonlinear increasing trend, and positive TCN has significant risk effects. (4) In terms of sex and age grouping, famale are more sensitive to temperature changes, while temperature changes have a lagged effect on male, and the elderly are more susceptible to temperature changes. (5) The synergistic effect of low temperature and temperature change exacerbates the mortality risk. The effect of temperature and its changes on the mortality of the three types is mainly the cold effect in Qinhuangdao. Particularly, the combination of low temperature background in winter and large temperature changes has the greatest impact on mortality risk of local elderly residents, and they should be protected in a timely manner.
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平均气温对死亡累积21 d的暴露-反应关系 (a、c、e) 和滞后效应 (b、d、f,色阶)(a、b. 非意外死亡,c、d. 循环系统疾病死亡,e、f. 呼吸系统疾病死亡)
The cumulative 21 d exposure-response relationship of average temperature to mortality (a,c,e) and lagged mortality due to average temperature (b,d,f;shaded)(a,b. non-accidental mortality;c,d. cardiovascular mortality;e,f. respiratory mortality)
图 2 平均气温对死亡累积21 d的暴露-反应关系 (a、c、e,灰色区域为RR的95%CI) 和滞后效应 (b、d、f,色阶)(a、b为非意外死亡,c、d为循环系统疾病死亡,e、f为呼吸系统疾病死亡)
Figure 2. The cumulative 21 d exposure-response relationship of average temperature to mortality (a,c,e;areas shaded in grey indicate the 95%CI of RR) and lagged mortality due to average temperature (b,d,f;shaded)(a,b. non-accidental mortality,c,d. cardiovascular mortality,e,f. respiratory mortality)
图 3 气温日较差与全部人群 (a1—c1) 及不同性别 (a2—c2. 男性,a3—c3. 女性)、年龄 (a4—c4. 0—64,a5—c5. ≥65) 居民死亡人数的总体暴露-反应曲线 (a. 非意外死亡,b. 循环系统疾病死亡,c. 呼吸系统疾病死亡;灰色区域为RR的95%置信区间)
Figure 3. Overall exposure–response relationships between DTR and mortality of residents stratified by all (a1—c1),sex (a2—c2. male,a3—c3. famale) and age (a4—c4. 0—64,a5—c5. ≥65)(areas shaded in grey indicate the 95%CI of RR)
图 4 24小时变温与全部人群 (a1—c1) 及不同性别 (a2—c2. 男性,a3—c3. 女性)、年龄 (a4—c4. 0—64,a5—c5. ≥ 65) 居民死亡人数的总体暴露-反应曲线 (a. 非意外死亡,b. 循环系统疾病死亡,c. 呼吸系统疾病死亡;灰色区域为RR的95%置信区间)
Figure 4. Overall exposure–response relationships between TCN and mortality of residents stratified by all (a1—c1),(a2—c2. male,a3—c3. famale) and sex age (a4—c4. 0—64,a5—c5. ≥65)(areas shaded in grey indicate the 95% confidence interval of RR)
图 5 气温与气温日较差 (a—c)、24小时变温 (d—f) 协同作用对非意外 (a、d)、循环 (b、e)、呼吸 (c、f) 系统死亡人数的影响 (气温、气温日较差、24小时变温均为当天)
Figure 5. Bivariate response surfaces of temperature with DTR (a—c) and TCN (d—f) on non-accidental mortality (a、d), cardiovascular mortality (b、e) and respiratory mortality (c、f) in Qinhuangdao (lag 0 for temperature,DTR and TCN)
1 不同性别、年龄在极高气温日较差(99%分位数,19℃)下不同滞后期的死亡风险
1. Table 1 Risk of mortality under extreme high DTR(99th,19℃)over different lagging periods by sex and age
lag0 lag3 lag7 lag14 lag0-3 lag0-7 lag0-14 非意外死亡 总体 1.04(0.99—1.1) 1.02(0.99—1.05) 0.99(0.97—1.01) 0.97(0.94—1) 1.16(1.06—1.28)* 1.13(1—1.28)* 1.01(0.87—1.18) 男性 1(0.94—1.07) 1.03(1—1.06)* 1(0.98—1.02) 0.96(0.93—1) 1.14(1.02—1.28)* 1.15(1—1.33)* 1.05(0.88—1.25) 女性 1.1(1.02—1.19)* 1.01(0.98—1.05) 0.98(0.95—1) 0.98(0.94—1.02) 1.2(1.05—1.36)* 1.09(0.93—1.29) 0.97(0.79—1.19) 年轻人群 1.03(0.94—1.13) 1(0.96—1.04) 0.98(0.95—1.01) 1.02(0.97—1.07) 1.12(0.96—1.31) 0.98(0.81—1.2) 1(0.79—1.28) 老年人群 1.05(0.98—1.11) 1.03(1—1.06)* 1(0.97—1.02) 0.96(0.92—0.99) 1.18(1.06—1.31)* 1.18(1.03—1.35)* 1.01(0.85—1.2) 循环系统疾病死亡 总体 1.07(1—1.14)* 1.03(1—1.06)* 1(0.98—1.02) 0.98(0.94—1.01) 1.26(1.13—1.4)* 1.23(1.07—1.41)* 1.16(0.98—1.38) 男性 1.02(0.94—1.1) 1.04(1.01—1.08)* 1.01(0.98—1.03) 0.98(0.94—1.02) 1.27(1.12—1.45)* 1.28(1.08—1.52)* 1.24(1.01—1.53)* 女性 1.14(1.05—1.23)* 1.01(0.97—1.05) 0.99(0.96—1.02) 0.97(0.93—1.02) 1.24(1.08—1.43)* 1.16(0.96—1.4) 1.07(0.85—1.35) 年轻人群 1.11(0.99—1.25) 1.03(0.98—1.09) 0.98(0.94—1.02) 1.01(0.95—1.08) 1.29(1.05—1.57)* 1.18(0.91—1.55) 1.12(0.8—1.56) 老年人群 1.06(0.99—1.13) 1.03(0.99—1.06) 1(0.98—1.03) 0.97(0.93—1.01) 1.25(1.12—1.4)* 1.24(1.07—1.44)* 1.17(0.97—1.41) 呼吸系统疾病死亡 总体 1.06(0.83—1.35) 1.06(0.93—1.2) 0.94(0.86—1.02) 0.95(0.82—1.1) 1.42(0.91—2.21) 1.25(0.7—2.22) 0.78(0.38—1.61) 男性 1.17(0.86—1.6) 0.99(0.85—1.16) 0.91(0.82—1.02) 0.93(0.77—1.12) 1.47(0.84—2.57) 1.17(0.57—2.43) 0.65(0.26—1.64) 女性 0.92(0.64—1.32) 1.15(0.96—1.39) 0.97(0.85—1.11) 0.98(0.79—1.22) 1.36(0.71—2.62) 1.38(0.59—3.25) 1.01(0.35—2.92) 年轻人群 0.65(0.39—1.08) 1.03(0.8—1.33) 0.93(0.78—1.12) 1(0.74—1.35) 0.88(0.36—2.16) 0.64(0.2—2.09) 0.53(0.12—2.33) 老年人群 1.2(0.91—1.58) 1.06(0.93—1.22) 0.94(0.85—1.03) 0.94(0.8—1.1) 1.59(0.98—2.6) 1.48(0.78—2.8) 0.86(0.39—1.9) 注:“*”表示通过α=0.05的显著性t检验,下同。 2 不同性别、年龄在极高24 h变温(99%分位数,4.8℃)下不同滞后期的的死亡风险
2. Risk of mortality under extreme high TCN(99th,4.8℃)over different lagging periods by sex and age
lag0 lag3 lag7 lag14 lag0-3 lag0-7 lag0-14 非意外死亡 总体 1.11(1.06—1.17)* 1.05(1.02—1.09)* 1.04(1.02—1.06)* 0.99(0.96—1.03) 1.21(1.1—1.34)* 1.48(1.28—1.71)* 1.75(1.43—2.15)* 男性 1.09(1.03—1.15)* 1.05(1.02—1.09)* 1.04(1.01—1.06)* 0.99(0.95—1.03) 1.24(1.11—1.39)* 1.48(1.25—1.75)* 1.71(1.34—2.17)* 女性 1.14(1.07—1.22)* 1.06(1.02—1.1)* 1.04(1.01—1.08)* 1(0.96—1.05) 1.17(1.02—1.33)* 1.48(1.22—1.8)* 1.82(1.38—2.39)* 年轻人群 1.08(1—1.17)* 1.02(0.97—1.07) 1.03(1—1.07)* 0.99(0.94—1.05) 1.18(1—1.38)* 1.26(1—1.6)* 1.51(1.08—2.11)* 老年人群 1.12(1.06—1.18)* 1.07(1.03—1.1)* 1.04(1.02—1.07)* 1(0.96—1.03) 1.22(1.09—1.36)* 1.56(1.33—1.83)* 1.84(1.46—2.3)* 循环系统疾病死亡 总体 1.14(1.07—1.21)* 1.09(1.06—1.14)* 1.06(1.03—1.09)* 0.98(0.94—1.02) 1.33(1.18—1.5)* 1.77(1.48—2.12)* 2.08(1.61—2.69)* 男性 1.12(1.04—1.21)* 1.11(1.06—1.16)* 1.06(1.02—1.09)* 0.98(0.93—1.03) 1.4(1.21—1.63)* 1.9(1.52—2.37)* 2.22(1.61—3.04)* 女性 1.17(1.08—1.26)* 1.07(1.02—1.13)* 1.05(1.01—1.09)* 0.98(0.92—1.03) 1.23(1.04—1.46)* 1.61(1.26—2.06)* 1.91(1.35—2.69)* 年轻人群 1.13(1.01—1.26)* 1.1(1.03—1.18)* 1.03(0.98—1.09) 0.96(0.89—1.05) 1.44(1.14—1.82)* 1.76(1.24—2.49)* 1.76(1.07—2.9)* 老年人群 1.14(1.07—1.22)* 1.09(1.05—1.14)* 1.06(1.03—1.09)* 0.98(0.94—1.03) 1.3(1.14—1.49)* 1.77(1.46—2.15)* 2.17(1.64—2.86)* 呼吸系统疾病死亡 总体 1.12(0.98—1.27) 1.02(0.95—1.11) 1(0.94—1.06) 1.03(0.94—1.12) 0.97(0.75—1.25) 1.13(0.77—1.66) 1.27(0.74—2.19) 男性 1.14(0.97—1.33) 0.99(0.9—1.1) 1(0.93—1.08) 1.01(0.91—1.12) 1.01(0.73—1.39) 1.2(0.74—1.93) 1.27(0.64—2.51) 女性 1.07(0.88—1.31) 1.06(0.95—1.19) 1(0.91—1.09) 1.05(0.92—1.19) 0.88(0.59—1.31) 1.01(0.56—1.8) 1.23(0.55—2.79) 年轻人群 0.96(0.72—1.28) 1.01(0.86—1.19) 0.96(0.85—1.09) 0.92(0.77—1.1) 0.78(0.44—1.39) 0.77(0.34—1.75) 0.53(0.17—1.65) 老年人群 1.16(1.01—1.33)* 1.03(0.94—1.12) 1.01(0.94—1.07) 1.05(0.96—1.15) 1.01(0.76—1.33) 1.22(0.81—1.86) 1.53(0.84—2.77) 表 1 2014—2020年秦皇岛市每日死亡情况及环境要素的统计
Table 1. Descriptive statistics of daily deaths and meteorological factors in Qinhuangdao during 2014—2020
变量 平均值±标准差 中位数 方差 最小值 最大值 气象要素 平均气温(℃) 11.72±11.24 13.02 126.418 −16.62 32.10 气温日较差(℃) 10.76±3.61 10.60 13.061 1.14 22.86 24小时变温(℃) 0±2.12 0.10 4.493 −8.42 7.04 相对湿度(%) 59.36±17.72 60.20 314.056 14.00 96.80 污染要素 PM2.5(μg/m3) 54.75±39.72 43.05 1577.671 7.09 326.14 PM10(μg/m3) 97.03±59.05 81.56 3487.067 11.89 501.21 SO2(μg/m3) 31.04±23.6 24.10 556.819 4.47 194.61 NO2(μg/m3) 37.96±17.02 34.64 289.783 7.21 123.25 CO(μg/m3) 1354.93±870.59 1128.00 757926.557 259.46 8498.44 死亡人数(人) 非意外死亡 35.16±13.89 34.00 193.046 5 102 性别 男性 20.21±8.43 19.00 71.056 3 59 女性 14.94±6.66 14.00 44.375 1 43 年龄 年轻人群(0—64岁) 8.62±3.7 8.00 13.727 0 24 老年人群(≥65岁) 26.54±11.62 25.00 135.052 2 80 循环系统疾病死亡 19.73±9.49 18.00 90.007 2 70 性别 男性 11.08±5.79 10.00 33.498 0 41 女性 8.66±4.69 8.00 22.034 0 30 年龄 年轻人群(0—64岁) 3.82±2.37 3.00 5.594 0 14 老年人群(≥65岁) 15.91±8.19 15.00 67.010 1 59 呼吸系统疾病死亡 3.27±2.1 3.00 4.420 0 16 性别 男性 1.92±1.52 2.00 2.303 0 11 女性 1.35±1.27 1.00 1.603 0 10 年龄 年轻人群(0—64岁) 0.65±0.84 0.00 0.712 0 5 老年人群(≥65岁) 2.62±1.84 2.00 3.381 0 12 表 2 不同性别、年龄不同滞后期极高气温日较差(99%分位数,19℃)的死亡风险
Table 2. Risk of mortality under extreme high DTR(99th,19℃) over different lagging periods by sex and age
lag0 lag3 lag7 lag14 lag0-3 lag0-7 lag0-14 非意外死亡 总体 1.04(0.99—1.1) 1.02(1—1.05)* 0.99(0.97—1.01) 0.97(0.94—1) 1.17(1.07—1.28)* 1.12(1—1.26)* 1(0.86—1.16) 男性 1.01(0.95—1.07) 1.03(1—1.06)* 1(0.98—1.02) 0.96(0.93—1) 1.16(1.05—1.28)* 1.16(1.01—1.33)* 1.04(0.87—1.23) 女性 1.09(1.02—1.16)* 1.02(0.98—1.05) 0.98(0.95—1) 0.98(0.94—1.02) 1.19(1.06—1.33)* 1.08(0.92—1.26) 0.95(0.78—1.16) 年轻人群 1.03(0.95—1.12) 0.99(0.95—1.04) 0.98(0.95—1.01) 1.02(0.97—1.07) 1.1(0.95—1.26) 0.95(0.79—1.15) 0.96(0.76—1.22) 老年人群 1.05(0.99—1.11) 1.03(1—1.06)* 0.99(0.97—1.02) 0.95(0.92—0.99) 1.2(1.08—1.32)* 1.19(1.04—1.35)* 1.01(0.86—1.19) 循环系统死亡 总体 1.06(1—1.12)* 1.03(1—1.06)* 1(0.98—1.02) 0.98(0.94—1.01) 1.27(1.15—1.4)* 1.23(1.08—1.41)* 1.16(0.98—1.37) 男性 1.03(0.96—1.11) 1.05(1.01—1.09)* 1.01(0.98—1.03) 0.98(0.94—1.02) 1.32(1.18—1.49)* 1.33(1.13—1.56)* 1.28(1.04—1.57)* 女性 1.1(1.02—1.19)* 1.01(0.97—1.05) 0.99(0.96—1.02) 0.97(0.93—1.02) 1.2(1.05—1.37)* 1.11(0.93—1.33) 1.02(0.82—1.28) 年轻人群 1.09(0.98—1.21) 1.03(0.98—1.09) 0.98(0.94—1.02) 1.01(0.94—1.08) 1.23(1.02—1.49)* 1.12(0.87—1.45) 1.05(0.76—1.44) 老年人群 1.05(0.99—1.12) 1.03(1—1.06)* 1(0.98—1.03) 0.97(0.93—1) 1.28(1.15—1.42)* 1.26(1.09—1.45)* 1.19(0.99—1.43) 呼吸系统死亡 总体 1.07(0.84—1.35) 1.06(0.94—1.2) 0.93(0.85—1.02) 0.94(0.81—1.09) 1.42(0.93—2.18) 1.22(0.69—2.14) 0.73(0.36—1.49) 男性 1.17(0.86—1.58) 0.99(0.85—1.16) 0.91(0.81—1.02) 0.91(0.76—1.1) 1.42(0.83—2.44) 1.1(0.54—2.23) 0.58(0.23—1.42) 女性 0.94(0.66—1.33) 1.17(0.97—1.4) 0.97(0.85—1.11) 0.96(0.77—1.2) 1.45(0.77—2.72) 1.45(0.63—3.36) 1.04(0.37—2.94) 年轻人群 0.6(0.37—0.98) 1.03(0.8—1.32) 0.94(0.78—1.12) 0.99(0.73—1.34) 0.76(0.32—1.79) 0.55(0.17—1.76) 0.46(0.11—1.96) 老年人群 1.24(0.95—1.61) 1.07(0.93—1.23) 0.93(0.85—1.03) 0.92(0.78—1.08) 1.66(1.04—2.67)* 1.49(0.8—2.78) 0.82(0.37—1.79) 注:“*”表示通过α=0.05的显著性t检验,下同。 表 3 不同性别、年龄在极高24 h变温(99%分位数,4.8℃)下不同滞后期的死亡风险
Table 3. Risk of mortality under extreme high TCN(99th,4.8℃) over different lagging periods by sex and age
lag0 lag3 lag7 lag14 lag0-3 lag0-7 lag0-14 非意外死亡 总体 1.11(1.06—1.17)* 1.06(1.03—1.09)* 1.04(1.02—1.06)* 1(0.96—1.03) 1.24(1.13—1.36)* 1.5(1.3—1.72)* 1.78(1.47—2.16)* 男性 1.09(1.04—1.16)* 1.05(1.02—1.09)* 1.04(1.01—1.06)* 0.99(0.95—1.03) 1.27(1.14—1.41)* 1.5(1.28—1.76)* 1.74(1.39—2.18)* 女性 1.14(1.07—1.21)* 1.06(1.02—1.1)* 1.04(1.01—1.08)* 1(0.96—1.05) 1.19(1.05—1.36)* 1.49(1.24—1.79)* 1.84(1.42—2.38)* 年轻人群 1.08(1—1.16)* 1.01(0.97—1.06) 1.03(0.99—1.07) 0.99(0.94—1.05) 1.17(1.01—1.36)* 1.23(0.98—1.54) 1.45(1.06—1.99)* 老年人群 1.12(1.07—1.18)* 1.07(1.04—1.1)* 1.04(1.02—1.07)* 1(0.96—1.04) 1.26(1.14—1.4)* 1.59(1.37—1.85)* 1.91(1.54—2.36)* 循环系统死亡 总体 1.15(1.08—1.21)* 1.1(1.06—1.14)* 1.06(1.03—1.09)* 0.98(0.94—1.02) 1.37(1.22—1.55)* 1.81(1.52—2.15)* 2.17(1.7—2.76)* 男性 1.13(1.05—1.22)* 1.12(1.07—1.17)* 1.06(1.03—1.1)* 0.98(0.93—1.03) 1.47(1.27—1.69)* 1.98(1.6—2.44)* 2.37(1.76—3.19)* 女性 1.16(1.08—1.26)* 1.07(1.02—1.13)* 1.05(1.01—1.09)* 0.98(0.93—1.04) 1.25(1.07—1.47)* 1.6(1.27—2.02)* 1.92(1.39—2.66)* 年轻人群 1.12(1.01—1.25)* 1.1(1.02—1.17)* 1.03(0.98—1.09) 0.96(0.89—1.05) 1.44(1.15—1.8)* 1.7(1.23—2.37)* 1.73(1.08—2.77)* 老年人群 1.15(1.08—1.23)* 1.1(1.06—1.14)* 1.06(1.03—1.1)* 0.99(0.94—1.03) 1.36(1.19—1.54)* 1.83(1.52—2.21)* 2.29(1.76—2.97)* 呼吸系统死亡 总体 1.14(1.01—1.29)* 1.04(0.96—1.12) 1.01(0.95—1.07) 1.03(0.95—1.13) 1.05(0.82—1.35) 1.24(0.86—1.79) 1.5(0.9—2.5) 男性 1.15(0.99—1.34) 1(0.91—1.1) 1.01(0.94—1.09) 1.01(0.91—1.13) 1.07(0.79—1.46) 1.25(0.79—1.97) 1.37(0.72—2.6) 女性 1.11(0.91—1.35) 1.09(0.98—1.22) 1.01(0.93—1.1) 1.06(0.93—1.2) 0.99(0.68—1.46) 1.19(0.69—2.07) 1.65(0.77—3.57) 年轻人群 0.96(0.73—1.27) 1.02(0.87—1.2) 0.98(0.87—1.1) 0.93(0.78—1.11) 0.82(0.48—1.41) 0.82(0.37—1.78) 0.65(0.22—1.88) 老年人群 1.19(1.04—1.36)* 1.04(0.96—1.13) 1.01(0.95—1.08) 1.06(0.97—1.16) 1.11(0.84—1.45) 1.35(0.91—2.02) 1.79(1.02—3.13)* -
[1] 樊星,秦圆圆,高翔. 2021. IPCC第六次评估报告第一工作组报告主要结论解读及建议. 环境保护,49(17):44-48Fan X,Qin Y Y,Gao X. 2021. Interpretation of the Main Conclusions and Suggestions of IPCC AR6 Working Group I Report. Environ Prot,49(17):44-48 (in Chinese) [2] 卫生部卫生统计信息中心,北京协和医院世界卫生组织疾病分类合作中心. 2001. 国际疾病分类(ICD-10)应用指导手册. 北京:中国协和医科大学出版社,411-435Health Statistics and Information Center of the Ministry of Health,Who Cooperative Center for Disease Classification,Peking Union Medical College Hospital. 2001. International Classification of Diseases (ICD-10) Application Guidance Manual. Beijing:China Union Medical College Press,411-435 (in Chinese) [3] 杨军. 2017. 气温变化对我国15城市人群心血管疾病死亡影响的研究[D]. 北京: 中国疾病预防控制中心. Yang J. 2017. Impact of temperature variation on cardiovascular mortality in 15 Chinese cities[D]. Beijing: Chinese Center for Disease Control and Prevention (in Chinese) [4] 张莹,王式功,贾旭伟等. 2017. 气温与PM2.5协同作用对疾病急诊就诊人数的影响. 中国环境科学,37(8):3175-3182 doi: 10.3969/j.issn.1000-6923.2017.08.044Zhang Y,Wang S G,Jia X W,et al. 2017. Synergetic effect of mean temperature and PM2.5 on emergency room visits for different diseases. China Environ Sci,37(8):3175-3182 (in Chinese) doi: 10.3969/j.issn.1000-6923.2017.08.044 [5] Chen J M,Gao Y,Jiang Y X,et al. 2022. Low ambient temperature and temperature drop between neighbouring days and acute aortic dissection:A case-crossover study. Eur Heart J,43(3):228-235 doi: 10.1093/eurheartj/ehab803 [6] 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 (in Chinese) [7] Cheng J,Xu Z W,Zhu R,et al. 2014a. Impact of diurnal temperature range on human health:A systematic review. Int J Biometeorol,58(9):2011-2024 doi: 10.1007/s00484-014-0797-5 [8] Cheng J,Zhu R,Xu Z W,et al. 2014b. Temperature variation between neighboring days and mortality:A distributed lag non-linear analysis. Int J Public Health,59(6):923-931 doi: 10.1007/s00038-014-0611-5 [9] Gasparrini A,Armstrong B,Kenward M G. 2010. Distributed lag non-linear models. Stat Med,29(21):2224-2234 doi: 10.1002/sim.3940 [10] Gasparrini A. 2014. Modeling exposure–lag–response associations with distributed lag non-linear models. Stat Med,33(5):881-899 doi: 10.1002/sim.5963 [11] 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 [12] 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 [13] Guo Y M,Gasparrini A,Armstrong B G,et al. 2016. Temperature variability and mortality:A multi-country study. Environ Health Perspect,124(10):1554-1559 doi: 10.1289/EHP149 [14] IPCC. 2021. Summary for policymakers∥IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press [15] Lim Y H,Park A K,Kim H. 2012. Modifiers of diurnal temperature range and mortality association in six Korean cities. Int J Biometeorol,56(1):33-42 doi: 10.1007/s00484-010-0395-0 [16] Lim Y H,Reid C E,Mann J K,et al. 2015. Diurnal temperature range and short-term mortality in large US communities. Int J Biometeorol,59(9):1311-1319 doi: 10.1007/s00484-014-0941-2 [17] Lin H L,Zhang Y H,Xu Y J,et al. 2013. Temperature changes between neighboring days and mortality in summer:A distributed lag non-linear time series analysis. PLoS One,8(6):e66403 doi: 10.1371/journal.pone.0066403 [18] Ma P,Zhang Y,Wang X Z,et al. 2021. Effect of diurnal temperature change on cardiovascular risks differed under opposite temperature trends. Environ Sci Pollut Res,28(29):39882-39891 doi: 10.1007/s11356-021-13583-5 [19] Ma W J,Chen R J,Kan H D. 2014. Temperature-related mortality in 17 large Chinese cities:How heat and cold affect mortality in China. Environ Res,134:127-133 doi: 10.1016/j.envres.2014.07.007 [20] Tang M N,He Y,Zhang X C,et al. 2021. The acute effects of temperature variability on heart rate variability:A repeated-measure study. Environ Res,194:110655 doi: 10.1016/j.envres.2020.110655 [21] Wang C Z,Zhang Z,Zhou M G,et al. 2017. Nonlinear relationship between extreme temperature and mortality in different temperature zones:A systematic study of 122 communities across the mainland of China. Sci Total Environ,586:96-106 doi: 10.1016/j.scitotenv.2017.01.218 [22] Xiao Y,Meng C Z,Huang S L,et al. 2021. Short-term effect of temperature change on non-accidental mortality in Shenzhen,China. Int J Environ Res Public Health,18(16):8760 doi: 10.3390/ijerph18168760 [23] Zanobetti A,O'Neill M S,Gronlund C J,et al. 2012. Summer temperature variability and long-term survival among elderly people with chronic disease. Proc Natl Acad Sci USA,109(17):6608-6613 doi: 10.1073/pnas.1113070109 [24] Zhan Z Y,Zhao Y,Pang S J,et al. 2017. Temperature change between neighboring days and mortality in United States:A nationwide study. Sci Total Environ,584-585:1152-1161 doi: 10.1016/j.scitotenv.2017.01.177 [25] Zhang Y,Wang S G,Zhang X L,et al. 2020. Association between moderately cold temperature and mortality in China. Environ Sci Pollut Res Int,27(21):26211-26220 doi: 10.1007/s11356-020-08960-5 [26] Zheng S,Zhu W Z,Wang M Z,et al. 2020. The effect of diurnal temperature range on blood pressure among 46,609 people in Northwestern China. Sci Total Environ,730:138987 doi: 10.1016/j.scitotenv.2020.138987 [27] Zhou X D,Zhao A,Meng X,et al. 2014. Acute effects of diurnal temperature range on mortality in 8 Chinese cities. Sci Total Environ,493:92-97 doi: 10.1016/j.scitotenv.2014.05.116 -