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Preplanned Studies: Projection of Temperature-Related Excess Mortality by Integrating Population Adaptability Under Changing Climate — China, 2050s and 2080s

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  • Summary

    What is already known about this topic?

    An increasing number of studies have projected temperature-related mortality, but few consider the change of population’s adaptability to future temperature and mortality burden from cold and heat effects.

    What is added by this report?

    This study offers a comprehensive characterization of human adaptability and excess mortality burden of temperature across various regions of China.

    What are the implications for public health practice?

    The temperature-related excess mortality was projected to increase in the 2050s and decrease in the 2080s. Heat adaptability was projected to increase in the future, but along with the rising temperatures, the heat-related excess mortality continuously rose, except for the low-speed rising scenario. Although the excess mortality of cold was projected to decrease in the nearer future, it might not keep declining in the long run, due to the decreasing cold-adaptability, which deserves more attention.

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  • Funding: The Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China (Grant No. 2017FY101204) and National High-level Talents Special Support Plan of China for Young Talents.
  • [1] Wang L, Chen W. A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int J Climatol 2014; 34(6): 2059-78. http://dx.doi.org/10.1002/joc.3822CrossRef
    [2] Climate Change Center of China Meteorological Administration. China blue book on climate change (2019). Beijing: Climate Change Center of China Meteorological Administration; 2019. http://www.cma.gov.cn/root7/auto13139/201905/t20190524_525556.html. (In Chinese). http://www.cma.gov.cn/root7/auto13139/201905/t20190524_525556.html
    [3] Gosling SN, Hondula DM, Bunker A, Ibarreta D, Liu JG, Zhang XX, et al. Adaptation to climate change: a comparative analysis of modeling methods for heat-related mortality. Environ Health Perspect 2017; 125(8): 087008. http://dx.doi.org/10.1289/EHP634CrossRef
    [4] Kinney PL, O’Neill MS, Bell ML, Schwartz J. Approaches for estimating effects of climate change on heat-related deaths: challenges and opportunities. Environ Sci Policy 2008; 11(1): 87-96. http://dx.doi.org/10.1016/j.envsci.2007.08.001CrossRef
    [5] Sanderson M, Arbuthnott K, Kovats S, Hajat S, Falloon P. The use of climate information to estimate future mortality from high ambient temperature: a systematic literature review. PLoS One 2017; 12(7): e0180369. http://dx.doi.org/10.1371/journal.pone.0180369CrossRef
    [6] Zhang J, Cao LG, Li XC, Zhan MJ, Jiang T. Advances in shared socio-economic pathways in IPCC AR5. Progr Inquisit Mutat Climatis 2013; 9(3): 225-8. http://dx.doi.org/10.3969/j.issn.1673-1719.2013.03.012 (In Chinese). CrossRef
    [7] Li YX, Li GX, Zeng Q, Liang FC, Pan XC. Projecting temperature-related years of life lost under different climate change scenarios in one temperate megacity, China. Environ Pollut 2018; 233: 1068-75. http://dx.doi.org/10.1016/j.envpol.2017.10.008CrossRef
    [8] Bobb JF, Peng RD, Bell ML, Dominici F. Heat-related mortality and adaptation to heat in the United States. Environ Health Perspect 2014; 122(8): 811-6. http://dx.doi.org/10.1289/ehp.1307392CrossRef
    [9] Jenkins K, Hall J, Glenis V, Kilsby C, McCarthy M, Goodess C, et al. Probabilistic spatial risk assessment of heat impacts and adaptations for London. Climatic Change 2014; 124(1): 105-17. http://dx.doi.org/10.1007/s10584-014-1105-4CrossRef
    [10] Hajat S, Vardoulakis S, Heaviside C, Eggen B. Climate change effects on human health: projections of temperature-related mortality for the UK during the 2020s, 2050s and 2080s. J Epidemiol Community Health 2014; 68(7): 641-8. http://dx.doi.org/10.1136/jech-2013-202449CrossRef
    [11] Li TT, Horton RM, Kinney PL. Projections of seasonal patterns in temperature- related deaths for Manhattan, New York. Nat Climate Change 2013; 3: 717-21. http://dx.doi.org/10.1038/nclimate1902CrossRef
    [12] Huang CR, Barnett AG, Wang XM, Vaneckova P, FitzGerald G, Tong SL. Projecting future heat-related mortality under climate change scenarios: a systematic review. Environ Health Perspect 2011; 119(12): 1681-90. http://dx.doi.org/10.1289/ehp.1103456CrossRef
  • FIGURE 1.  Future relationships between temperature and mortality in three periods (baseline, 2050s, and 2080s) in China under 3 united scenarios. (A) The low-speed scenario; (B) The medium-speed scenario; (C) The high-speed scenario.

    FIGURE 2.  Changes (%) in projected annual death number caused by temperature in 3 periods (baseline, 2050s, and 2080s) in China under three united scenarios. (A) Net effect under low-speed scenario; (B) Net effect under medium-speed scenario; (C) Net effect under high-speed scenario; (D) Cold effect under low-speed scenario in China; (E) Cold effect under medium-speed scenario; (F) Cold effect under high-speed scenario in China; (G) Heat-effect under low-speed scenario; (H) Heat-effect under medium-speed scenario; (I) Heat-effect under high-speed scenario.

    Abbreviations: GCM=general circulation model; GFDL=GFDL-ESM2M; HAD=HadGEM2-ES; MIROC=MIROC-ESM-CHEM; NOR=NorESM1-M; IPSL=IPSL-CM5A-LR.

    FIGURE 3.  Changes (%) in temperature-related annual death number in seven regions of China in 3 periods (baseline, 2050s, and 2080s) under 3 united scenarios. (A) The cold-related mortality burden under the low-speed scenario; (B) The heat-related mortality burden under the low-speed scenario; (C) The temperature-related mortality burden under the low-speed scenario; (D) The cold-related mortality burden under the medium-speed scenario; (E) The heat-related mortality burden under the medium-speed scenario; (F) The temperature-related mortality burden under the medium-speed scenario; (G) The cold-related mortality burden under the high-speed scenario; (H) The heat-related mortality burden under the high-speed scenario; (I) The temperature-related mortality burden under the high-speed scenario.

    TABLE S1.  Describe of meteorological and death data in 105 counties of China, 2013–2017.

    ItemNumber of daysMeanSDMinimumP25P50P75Maximum
    Daily death data
    Total1,8269.546.8305.008.0013.00221.00
    Male1,8265.434.1002.005.008.00121.00
    Female1,8264.113.4102.003.006.00100.00
    0–64 years1,8262.382.0801.002.003.0047.00
    65–74 years1,8261.941.8601.002.003.0053.00
    ≥751,8265.224.4002.004.007.00121.00
    Daily Meteorological data
    Mean temperature (°C)1,82614.2610.94−29.56.7015.9023.1036.50
    Maximum temperature (°C)1,82619.5010.97−24.311.7021.3028.2042.60
    Minimum temperature (°C)1,8269.8911.36−34.62.1011.4019.0032.40
    Mean relative humidity (%)1,82664.8418.934.0052.0067.0079.00100.00
    Download: CSV

    TABLE S2.  Sensitivity analysis of the relationship between temperature and mortality of 105 counties in China, 2013–2017.

    Sensitivity analysis I2Q (P)
    Core model46.8%977.43 (P<0.05)
    Add PM2.546.8%993.10 (P<0.05)
    Change degree of time
    Time/df=648.8%1,016.36 (P<0.05)
    Time/df=552.2%1,087.91 (P<0.05)
    Add relative humidity
    + ns (rh/df=3)46.9%979.69 (P<0.05)
    + ns (rh/df=5)46.8%977.82 (P<0.05)
    Download: CSV

    TABLE S3.  The result of meta-regression including different factors.

    FactorsI2Q(P)
    /39.70%838.33 (P<0.05)
    The total population39.40%842.14 (P<0.05)
    Birth rate38.10%823.47 (P<0.05)
    Mortality38.20%825.33 (P<0.05)
    GDP38.70%832.03 (P<0.05)
    Air-conditioning ownership37.40%813.23 (P<0.05)
    Heating35.40%789.22 (P<0.05)
    Latitude36.00%796.27 (P<0.05)
    Province38.90%834.68 (P<0.05)
    Note: “/”=meta-regression without adding any factors.
    Abbreviations: GDP=gross domestic product
    Download: CSV

    TABLE S4.  Correlation between Shared Socio-economic Pathways (SSP) and representative concentration paths (RCP).

    SSPRCP
    SSP14.5
    SSP26.0
    SSP38.5
    SSP48.5
    SSP5/
    Source: Zhang J, Cao LG, Li XC, Zhan MJ, Jiang T. Advances in shared socio-economic pathways in IPCC AR5. Advances in Climate Change Research, IPCC AR5. Advances in Climate Change Research 2013;9(3):225−8. http://www.climatechange.cn/EN/Y2013/V9/I3/225.
    Download: CSV

    TABLE S6.  Future united scenario settings*.

    United ScenariosLow-speed scenarioMedium-speed scenarioHigh-speed scenario
    TemperatureRCP8.5, 5GCMsRCP8.5, 5GCMsRCP4.5, 5GCMs
    PopulationSSP3SSP4SSP1
    Birth rateHighMediumLow
    MortalityHighMediumLow
    GDP2050s and 2080s increase by 410% and 420%, respectively2050s and 2080s increase by 570% and 500%, respectively2050s and 2080s increase by 660% and 680%, respectively
    Air conditioning possession rate2050s and 2080s increase by 50% and 100%, respectively2050s and 2080s increase by 100% and 150%, respectively2050s and 2080s increase by 150% and 200%, respectively
    * The three colors represent low, medium, and high levels from light to dark, respectively.
    Abbreviations: SSP=shared socioeconomic pathways; RCP=representative concentration pathways.
    Download: CSV

    TABLE S7.  The heat and cold effects in different scenarios and periods in China.

    PeriodsScenariosHeat effects-RR (95% CI) 32 °C
    (99%) vs. MMT (%)
    Cold effect-RR (95% CI) −15 °C
    (1%) vs. MMT (%)
    MMT (°C)
    Baseline period/17.09 (14.99, 9.23)12.04 (9.81, 14.31) 23.8
    2050sLow-speed6.36 (5.33, 7.41)28.16 (23.65, 32.85)25.6
    Medium-speed6.77 (5.61, 7.95)38.58 (32.14, 45.34)25.4
    High-speed7.36 (6.13, 8.60)39.39 (32.15, 47.04)25.5
    2080sLow-speed6.47 (5.25, 7.69)34.60 (29.55, 39.87)24.6
    Medium-speed6.77 (5.67, 7.89)42.13 (35.28, 49.32)25.5
    High-speed6.35 (5.15, 7.57)47.17 (38.17, 56.75)25.4
    Note: "/"=No scenarios in the baseline period.
    Abbreviation: CI=confidence interval; MMT=the minimum mortality temperature.
    Download: CSV

    TABLE S8.  Future temperature-related annual death number with 5 GCMs in 3 periods (baseline, 2050s, and 2080s) in China under 3 united scenarios (the low, medium, and high-speed scenarios).

    GCMBaseline period
    (95%CI)
    2050s2080s
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Temperature-related annual death
    GFDL18,178
    (14,716−21,592)
    26,791
    (19,725−33,726)
    24,961
    (16,850−32,905)
    28,219
    (16,391−39,765)
    23,580
    (17,993−29,060)
    12,594
    (7,414−17672)
    15,629
    (5,597−25,404)
    HAD18,178
    (14,716−21,592)
    23,057
    (16,205−29,786)
    19,740
    (11,876−27,451)
    21,944
    (10,623−33,015)
    15,953
    (10,852−20,967)
    11,922
    (6,650−17,090)
    13,787
    (3,921−23,415)
    MIROC18,178
    (14,716−21,592)
    22,945
    (16,239−29,531)
    17,663
    (10,129−25,054)
    19,341
    (8,488−29,959)
    16,286
    (11,176−21,306)
    9,499
    (4,600−14,309)
    12,393
    (3,069−21,501)
    NOR18,178
    (14,716−21,592)
    25,522
    (18,559−32,357)
    21,057
    (13,083−28,870)
    23,195
    (11,669−34,456)
    18,511
    (13,216−23,709)
    9,851
    (4,802−14,805)
    11,619
    (1,719−21,275)
    IPSL18,178
    (14,716−21,592)
    23,308
    (16,596−29,899)
    19,136
    (11,481−26,642)
    21,211
    (10,143−32,034)
    16,808
    (11,721−21,805)
    9,707
    (4,740−14,582)
    12,456
    (2,934−21,752)
    Cold-related annual death
    GFDL15,271
    (12,224−18,275)
    25,125
    (18,428−31,692)
    22,701
    (15,135−30,102)
    24,909
    (13,892−35,651)
    22,858
    (17,483−28,128)
    10,321
    (5,712−14,833)
    11,808
    (2,897−20,472)
    HAD15,271
    (12,224−18,275)
    19,365
    (13,194−25,420)
    13,604
    (6,831−20,235)
    13,251
    (3,459−22,810)
    12,977
    (8,516−17,357)
    5,062
    (784−9,250)
    3
    (-7,989−7,782)
    MIROC15,271
    (12,224−18,275)
    19,938
    (13,808−25,953)
    12,076
    (5,618−18,400)
    11,378
    (2,031−20,507)
    13,619
    (9,095−18,060)
    1,554
    (-2,262−5,293)
    -1,042
    (-8,570−6,293)
    NOR15,271
    (12,224−18,275)
    23,568
    (17,056−29,956)
    18,009
    (10,739−25,123)
    18,750
    (8,259−28,987)
    17,187
    (12,261−22,020)
    5,924
    (1,744−10,018)
    5,012
    (-3,417−13,213)
    IPSL15,271
    (12,224−18,275)
    20,650
    (14,510−26,674)
    14,408
    (7,719−20,956)
    14,413
    (4,708−23,885)
    14,850
    (10,251−19,363)
    2,791
    (-1,139−6,641)
    797
    (-6,994−8,383)
    Heat-related annual death
    GFDL2,906
    (2,491−3,397)
    1,667
    (1,297−2,034)
    2,261
    (1,715−2,803)
    3,310
    (2,499−4,115)
    722
    (510−932)
    2,273
    (1,701−2,840)
    3,821
    (2,700−4932)
    HAD2,906
    (2,491−3,397)
    3,692
    (3,011−4,366)
    6,136
    (5,045−7,216)
    8,693
    (7,164−10,205)
    2,976
    (2,336−3,610)
    6,860
    (5,867−7,840)
    13,784
    (11,910−15,634)
    MIROC2,906
    (2,491−3,397)
    3,007
    (2,431−3,578)
    5,587
    (4,510−6,654)
    7,962
    (6,457−9,453)
    2,667
    (2,082−3,247)
    7,946
    (6,863−9,016)
    13,435
    (11,639−15,208)
    NOR2,906
    (2,491−3,397)
    1,953
    (1,503−2,401)
    3,048
    (2,344−3,747)
    4,444
    (3,411−5,469)
    1,323
    (955−1,689)
    3,927
    (3,058−4,787)
    6,606
    (5,135−8,062)
    IPSL2,906
    (2,491−3,397)
    2,657
    (2,086−3,225)
    4,728
    (3,762−5,687)
    6,798
    (5,435−8,149)
    1,958
    (1,470−2,442)
    6,915
    (5,879−7,941)
    11,658
    (9,928−13,369)
    Abbreviation: GCM=general circulation model; GFDL=GFDL-ESM2M; HAD=HadGEM2-ES; MIROC=MIROC-ESM-CHEM ; NOR=NorESM1-M; IPSL=IPSL-CM5A-LR..
    Download: CSV

    TABLE S9.  Future temperature-related annual deaths in 3 periods (baseline, 2050s and 2080s) in 7 regions of China under 3 united scenarios (the low, medium, and high-speed scenarios).

    RegionsBaseline period
    (95%CI)
    2050s2080s
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Temperature-related annual death
    Northeast3,601
    (2963−4,229)
    3,008
    (2,348−3,653)
    3,004
    (2,211−3,776)
    3,647
    (2,488−4,770)
    2,380
    (1,880−2,869)
    1,419
    (899−1,925)
    1,783
    (730−2,802)
    North1,872
    (1,528−2,212)
    3,062
    (2,218−3,889)
    2,613
    (1,642−3,562)
    2,784
    (1,403−4,128)
    2,305
    (1,685−2,912)
    1,132
    (488−1,762)
    997
    (-290−2,247)
    East6,476
    (5,252−7684)
    11,400
    (7,982−14,758)
    9,601
    (5,542−13,584)
    10,230
    (4,422−15,915)
    8,683
    (5,996−11,323)
    5,256
    (2,599−7,866)
    6,378
    (1,342−11,304)
    South516
    (398−632)
    738
    (541−933)
    843
    (593−1,090)
    1,049
    (718−1,376)
    520
    (368−670)
    706
    (529−880)
    1,223
    (917−1,525)
    Central1,783
    (1,443−2,118)
    2,231
    (1,600−2,851)
    1,840
    (1,149−2,519)
    2,191
    (1,130−3,230)
    1,582
    (1,110−2,046)
    1,154
    (705−1,596)
    1,654
    (817−2,473)
    Northwest2,189
    (1,771−2,601)
    1,958
    (1,431−2,474)
    1,381
    (867−1,883)
    1,580
    (777−2,361)
    1,457
    (1,071−1,835)
    489
    (182−789)
    388
    (-232−991)
    Southwest1,741
    (1,362−2,116)
    1,928
    (1,346−2,501)
    1,231
    (680−1,772)
    1,303
    (524−2,066)
    1,300
    (880−1,714)
    559
    (239−873)
    753
    (166−1,328)
    Cold-related annual death
    Northeast3,538
    (2,915−4152)
    2,990
    (2,337−3,629)
    2,942
    (2,169−3,694)
    3,555
    (2,426−4,649)
    2,361
    (1,869−2,841)
    1296
    (805−1773)
    1576
    (575−2542)
    North1,689
    (1,375−1,999)
    2,957
    (2,140−3,758)
    2,406
    (1,486−3,304)
    2,495
    (1,186−3,770)
    2,227
    (1,631−2,811)
    811
    (230−1379)
    444
    (-732−1584)
    East4,912
    (3,900−5,910)
    9,900
    (6,796−12,946)
    7,029
    (3,495−10,491)
    6,533
    (1,473−11,478)
    7,553
    (5,141−9,921)
    1900
    (-213−3972)
    444
    (-3637−4424)
    South257
    (181−333)
    430
    (295−563)
    305
    (158−449)
    301
    (112−487)
    284
    (186−380)
    98
    (18,177)
    151
    (12,288)
    Central1,326
    (1,048−1,600)
    1,715
    (1,174−2,246)
    1,100
    (537−1,652)
    1,114
    (241−1,968)
    1,220
    (822−1,611)
    297
    (-30−618)
    121
    (-502−730)
    Northwest2,091
    (1,690−2,487)
    1,950
    (1,425−2,464)
    1,362
    (854−1,859)
    1,550
    (756−2,323)
    1,451
    (1,068−1,827)
    454
    (156−746)
    329
    (-277−916)
    Southwest1,458
    (1,116−1,795)
    1,788
    (1,233−2,334)
    1,016
    (509−1,515)
    991
    (275−1,693)
    1,203
    (805−1,593)
    275
    (2−543)
    251
    (-253−745)
    Heat-related annual death
    Northeast62
    (47−77)
    18
    (11−25)
    62
    (41−82)
    92
    (62−121)
    20
    (11−28)
    123
    (94−152)
    207
    (155−260)
    North183
    (153−213)
    105
    (78−131)
    207
    (156−258)
    288
    (217−359)
    78
    (55−101)
    321
    (258−383)
    553
    (442−664)
    East1,564
    (1,351−1,775)
    1,500
    (1,186−1,812)
    2,572
    (2,047−3,093)
    3,697
    (2,949−4,437)
    1,129
    (855−1,401)
    3,356
    (2,812−3,895)
    5,934
    (4,978−6,879)
    South259
    (218−299)
    308
    (246−370)
    539
    (435−642)
    748
    (606−889)
    236
    (182−290)
    607
    (511−702)
    1,072
    (905−1,237)
    Central457
    (395−518)
    516
    (426−605)
    740
    (612−867)
    1,077
    (889−1,262)
    362
    (289−435)
    858
    (735−978)
    1,532
    (1,319−1,743)
    Northwest98
    (82−115)
    8
    (5−10)
    19
    (13−24)
    29
    (21−38)
    6
    (4−8)
    35
    (27−43)
    60
    (45−75)
    Southwest284
    (246−321)
    140
    (113−167)
    214
    (171−257)
    311
    (249−373)
    98
    (75−120)
    284
    (237−330)
    501
    (419−583)
    Download: CSV

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Projection of Temperature-Related Excess Mortality by Integrating Population Adaptability Under Changing Climate — China, 2050s and 2080s

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Summary

What is already known about this topic?

An increasing number of studies have projected temperature-related mortality, but few consider the change of population’s adaptability to future temperature and mortality burden from cold and heat effects.

What is added by this report?

This study offers a comprehensive characterization of human adaptability and excess mortality burden of temperature across various regions of China.

What are the implications for public health practice?

The temperature-related excess mortality was projected to increase in the 2050s and decrease in the 2080s. Heat adaptability was projected to increase in the future, but along with the rising temperatures, the heat-related excess mortality continuously rose, except for the low-speed rising scenario. Although the excess mortality of cold was projected to decrease in the nearer future, it might not keep declining in the long run, due to the decreasing cold-adaptability, which deserves more attention.

  • 1. China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Institute of Environment and Health, Tianjin Center for Disease Control and Prevention, Tianjin, China
  • 3. Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
  • 4. School of Ecology and Environment, Beijing Technology and Business University, Beijing, China
  • 5. Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
  • 6. Environmental Research Center, Duke Kunshan University, Durham, NC, USA
  • Corresponding author:

    Tiantian Li, litiantian@nieh.chinacdc.cn

  • Funding: The Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China (Grant No. 2017FY101204) and National High-level Talents Special Support Plan of China for Young Talents.
  • Online Date: August 13 2021
    doi: 10.46234/ccdcw2021.174
  • Rising temperatures due to changing climate is a major public health concern. There is a great need to better estimate the disease burden related to temperature in China, considering changing population adaptation. A two-stage analysis was used in this study to obtain effect-modifiers in temperature-mortality relationships in 105 counties of China, then 3 united scenarios were constructed, future curves were fitted, and the numbers of attributable deaths in the 2050s and 2080s were estimated. Compared with the baseline period, the future cold effects show an upward trend, and the heat effects downward, indicating adaptability to cold would reduce while to heat would increase. The future temperature-related excess mortality was projected to increase in the 2050s and decrease in the 2080s, and cold-related mortality had a similar trend; however, heat-related mortality generally showed a continuously rising trend. Developed areas had greater cold effects and faced an increase of heat effects, while developing regions faced different situations, indicating different mitigation measures were needed. The medium-speed scenario could be the most appropriate developing scenario for China in the future, providing important sustainable policy implications.

    Increasing temperatures under a changing climate are one of the major public health concerns in the 21st century. According to the Fifth Report of the Intergovernmental Panel on Climate Change (2013), the future global temperature will increase by 0.3 °C–4.8 °C under the different representative concentration paths (RCPs) by 2100. In 2070–2099, the annual averaged temperature was projected to increase by 1.9 °C–3.3 °C in China (1). China may face a larger burden of disease from a warming climate in the near future (2).

    Studies have shown that human adaptability and adaptation measures can greatly affect the health effects of temperature (3). The adaptability to heat has been well-studied and will probably increase according to these previous studies, but the adaptability to cold remains unclear, although it is critical in projecting the temperature-related mortality burden (3-4). Additionally, in recent years, many studies have established a matrix of shared socioeconomic pathways-representative concentration paths (SSP-RCPs) to represent future human adaptability to temperature (5). However, there is a certain relationship between SSPs and RCPs, and each RCP in some aspects is corresponding with certain socioeconomic development pathways, and it may be unreasonable to simply use the matrix (6).

    To address these gaps, this report constructed three united scenarios by linking the social economic development and climate change scenarios and projected the future mortality burden caused by high and low temperature in China under each scenario.

    This paper included 105 counties distributed over the 7 geographical regions of the mainland of China (Supplementary Figure S1 and Supplementary Table S1) and defined the baseline period as 2013–2017 and two future periods as 2050s (2041–2070) and 2080s (2071–2099) based on the literature. This report conducted the project through three main steps (Supplementary Figure S2). First, the report modeled the exposure-response relationship between temperature and mortality in 105 counties through distributed lag nonlinear model (DLNM) and a meta-regression. Through the Wald test, Cochran Q test, and I2 in the regression, the report determined the effect modifiers, including the population size, birth rate, mortality, gross domestic product (GDP), air conditioning possession rate, heating in winter, latitude, and provinces (Supplementary Tables S2S3). Second, predicting the temperature-morality relationship curves in the 2050s and 2080s. Three united scenarios (low, medium, and high-speed scenarios) were established by integrating the effect-modifiers and based on the mapping relationship between SSPs and the RCPs for future temperature, birth rate, mortality, and gross GDP (Supplementary Tables S4S6, Supplementary Material). All these analyses were carried out using the packages “dlnm” and “mvmeta” in R (version 3.3.2, R Foundation for Statistical Computing, Vienna, Austria).

    ItemNumber of daysMeanSDMinimumP25P50P75Maximum
    Daily death data
    Total1,8269.546.8305.008.0013.00221.00
    Male1,8265.434.1002.005.008.00121.00
    Female1,8264.113.4102.003.006.00100.00
    0–64 years1,8262.382.0801.002.003.0047.00
    65–74 years1,8261.941.8601.002.003.0053.00
    ≥751,8265.224.4002.004.007.00121.00
    Daily Meteorological data
    Mean temperature (°C)1,82614.2610.94−29.56.7015.9023.1036.50
    Maximum temperature (°C)1,82619.5010.97−24.311.7021.3028.2042.60
    Minimum temperature (°C)1,8269.8911.36−34.62.1011.4019.0032.40
    Mean relative humidity (%)1,82664.8418.934.0052.0067.0079.00100.00

    Table S1.  Describe of meteorological and death data in 105 counties of China, 2013–2017.

    Sensitivity analysis I2Q (P)
    Core model46.8%977.43 (P<0.05)
    Add PM2.546.8%993.10 (P<0.05)
    Change degree of time
    Time/df=648.8%1,016.36 (P<0.05)
    Time/df=552.2%1,087.91 (P<0.05)
    Add relative humidity
    + ns (rh/df=3)46.9%979.69 (P<0.05)
    + ns (rh/df=5)46.8%977.82 (P<0.05)

    Table S2.  Sensitivity analysis of the relationship between temperature and mortality of 105 counties in China, 2013–2017.

    FactorsI2Q(P)
    /39.70%838.33 (P<0.05)
    The total population39.40%842.14 (P<0.05)
    Birth rate38.10%823.47 (P<0.05)
    Mortality38.20%825.33 (P<0.05)
    GDP38.70%832.03 (P<0.05)
    Air-conditioning ownership37.40%813.23 (P<0.05)
    Heating35.40%789.22 (P<0.05)
    Latitude36.00%796.27 (P<0.05)
    Province38.90%834.68 (P<0.05)
    Note: “/”=meta-regression without adding any factors.
    Abbreviations: GDP=gross domestic product

    Table S3.  The result of meta-regression including different factors.

    SSPRCP
    SSP14.5
    SSP26.0
    SSP38.5
    SSP48.5
    SSP5/
    Source: Zhang J, Cao LG, Li XC, Zhan MJ, Jiang T. Advances in shared socio-economic pathways in IPCC AR5. Advances in Climate Change Research, IPCC AR5. Advances in Climate Change Research 2013;9(3):225−8. http://www.climatechange.cn/EN/Y2013/V9/I3/225.

    Table S4.  Correlation between Shared Socio-economic Pathways (SSP) and representative concentration paths (RCP).

    United ScenariosLow-speed scenarioMedium-speed scenarioHigh-speed scenario
    TemperatureRCP8.5, 5GCMsRCP8.5, 5GCMsRCP4.5, 5GCMs
    PopulationSSP3SSP4SSP1
    Birth rateHighMediumLow
    MortalityHighMediumLow
    GDP2050s and 2080s increase by 410% and 420%, respectively2050s and 2080s increase by 570% and 500%, respectively2050s and 2080s increase by 660% and 680%, respectively
    Air conditioning possession rate2050s and 2080s increase by 50% and 100%, respectively2050s and 2080s increase by 100% and 150%, respectively2050s and 2080s increase by 150% and 200%, respectively
    * The three colors represent low, medium, and high levels from light to dark, respectively.
    Abbreviations: SSP=shared socioeconomic pathways; RCP=representative concentration pathways.

    Table S6.  Future united scenario settings*.

    Figure 1 and Supplementary Table S7 respectively showed the curves and the quantitative results between temperature and mortality at the median of the 5 general circulation models (GCM) under different united scenarios. The future cold effects were projected to increase with time, but the heat effects would decrease with time. Among the three scenarios, the future cold effects and heat effects both would be the greatest under the high-speed scenario.

    PeriodsScenariosHeat effects-RR (95% CI) 32 °C
    (99%) vs. MMT (%)
    Cold effect-RR (95% CI) −15 °C
    (1%) vs. MMT (%)
    MMT (°C)
    Baseline period/17.09 (14.99, 9.23)12.04 (9.81, 14.31) 23.8
    2050sLow-speed6.36 (5.33, 7.41)28.16 (23.65, 32.85)25.6
    Medium-speed6.77 (5.61, 7.95)38.58 (32.14, 45.34)25.4
    High-speed7.36 (6.13, 8.60)39.39 (32.15, 47.04)25.5
    2080sLow-speed6.47 (5.25, 7.69)34.60 (29.55, 39.87)24.6
    Medium-speed6.77 (5.67, 7.89)42.13 (35.28, 49.32)25.5
    High-speed6.35 (5.15, 7.57)47.17 (38.17, 56.75)25.4
    Note: "/"=No scenarios in the baseline period.
    Abbreviation: CI=confidence interval; MMT=the minimum mortality temperature.

    Table S7.  The heat and cold effects in different scenarios and periods in China.

    The excess mortality from temperature under all three scenarios was projected to generally increase in the 2050s and decrease in the 2080s compared with the baseline period (Figure 2 and Supplementary Figure S3). Cold-related mortality would increase in the 2050s and decrease in the 2080s under the low-speed scenario and decrease in both the 2050s and the 2080s under the medium- and high-speed scenarios. Heat-related mortality had different trends: decreasing in the low-speed scenario, and increasing in the medium- and the high-speed scenarios. Using the mid-level IPSL model and the medium-speed scenario as an example, the excess mortality from cold-and-net effects of temperature were projected to have minor changes (decrease by 5.7% and increase by 5.3%, respectively) in the 2050s and decrease by 81.7% and 46.6% in the 2080s, compared with the baseline period. The excess mortality from heat was projected to increase by 62.7% and 138.0% in the 2050s and 2080s, respectively. Taken together, the medium speed scenario was estimated to have the least temperature-related excess mortality (Supplementary Table S8). The number of cold-related mortality was generally larger than the heat-related excess mortality, except for the numbers in the 2080s under the medium and high-speed scenarios.

    GCMBaseline period
    (95%CI)
    2050s2080s
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Temperature-related annual death
    GFDL18,178
    (14,716−21,592)
    26,791
    (19,725−33,726)
    24,961
    (16,850−32,905)
    28,219
    (16,391−39,765)
    23,580
    (17,993−29,060)
    12,594
    (7,414−17672)
    15,629
    (5,597−25,404)
    HAD18,178
    (14,716−21,592)
    23,057
    (16,205−29,786)
    19,740
    (11,876−27,451)
    21,944
    (10,623−33,015)
    15,953
    (10,852−20,967)
    11,922
    (6,650−17,090)
    13,787
    (3,921−23,415)
    MIROC18,178
    (14,716−21,592)
    22,945
    (16,239−29,531)
    17,663
    (10,129−25,054)
    19,341
    (8,488−29,959)
    16,286
    (11,176−21,306)
    9,499
    (4,600−14,309)
    12,393
    (3,069−21,501)
    NOR18,178
    (14,716−21,592)
    25,522
    (18,559−32,357)
    21,057
    (13,083−28,870)
    23,195
    (11,669−34,456)
    18,511
    (13,216−23,709)
    9,851
    (4,802−14,805)
    11,619
    (1,719−21,275)
    IPSL18,178
    (14,716−21,592)
    23,308
    (16,596−29,899)
    19,136
    (11,481−26,642)
    21,211
    (10,143−32,034)
    16,808
    (11,721−21,805)
    9,707
    (4,740−14,582)
    12,456
    (2,934−21,752)
    Cold-related annual death
    GFDL15,271
    (12,224−18,275)
    25,125
    (18,428−31,692)
    22,701
    (15,135−30,102)
    24,909
    (13,892−35,651)
    22,858
    (17,483−28,128)
    10,321
    (5,712−14,833)
    11,808
    (2,897−20,472)
    HAD15,271
    (12,224−18,275)
    19,365
    (13,194−25,420)
    13,604
    (6,831−20,235)
    13,251
    (3,459−22,810)
    12,977
    (8,516−17,357)
    5,062
    (784−9,250)
    3
    (-7,989−7,782)
    MIROC15,271
    (12,224−18,275)
    19,938
    (13,808−25,953)
    12,076
    (5,618−18,400)
    11,378
    (2,031−20,507)
    13,619
    (9,095−18,060)
    1,554
    (-2,262−5,293)
    -1,042
    (-8,570−6,293)
    NOR15,271
    (12,224−18,275)
    23,568
    (17,056−29,956)
    18,009
    (10,739−25,123)
    18,750
    (8,259−28,987)
    17,187
    (12,261−22,020)
    5,924
    (1,744−10,018)
    5,012
    (-3,417−13,213)
    IPSL15,271
    (12,224−18,275)
    20,650
    (14,510−26,674)
    14,408
    (7,719−20,956)
    14,413
    (4,708−23,885)
    14,850
    (10,251−19,363)
    2,791
    (-1,139−6,641)
    797
    (-6,994−8,383)
    Heat-related annual death
    GFDL2,906
    (2,491−3,397)
    1,667
    (1,297−2,034)
    2,261
    (1,715−2,803)
    3,310
    (2,499−4,115)
    722
    (510−932)
    2,273
    (1,701−2,840)
    3,821
    (2,700−4932)
    HAD2,906
    (2,491−3,397)
    3,692
    (3,011−4,366)
    6,136
    (5,045−7,216)
    8,693
    (7,164−10,205)
    2,976
    (2,336−3,610)
    6,860
    (5,867−7,840)
    13,784
    (11,910−15,634)
    MIROC2,906
    (2,491−3,397)
    3,007
    (2,431−3,578)
    5,587
    (4,510−6,654)
    7,962
    (6,457−9,453)
    2,667
    (2,082−3,247)
    7,946
    (6,863−9,016)
    13,435
    (11,639−15,208)
    NOR2,906
    (2,491−3,397)
    1,953
    (1,503−2,401)
    3,048
    (2,344−3,747)
    4,444
    (3,411−5,469)
    1,323
    (955−1,689)
    3,927
    (3,058−4,787)
    6,606
    (5,135−8,062)
    IPSL2,906
    (2,491−3,397)
    2,657
    (2,086−3,225)
    4,728
    (3,762−5,687)
    6,798
    (5,435−8,149)
    1,958
    (1,470−2,442)
    6,915
    (5,879−7,941)
    11,658
    (9,928−13,369)
    Abbreviation: GCM=general circulation model; GFDL=GFDL-ESM2M; HAD=HadGEM2-ES; MIROC=MIROC-ESM-CHEM ; NOR=NorESM1-M; IPSL=IPSL-CM5A-LR..

    Table S8.  Future temperature-related annual death number with 5 GCMs in 3 periods (baseline, 2050s, and 2080s) in China under 3 united scenarios (the low, medium, and high-speed scenarios).

    The temperature-related excess mortality would increase in the 2050s and decline in the 2080s (the East, the North, and the Central) or keep decreasing (Northeast, Northwest, and Southwest), and all the regions have the temperature-related excess mortality decreasing at last, except for South China, where it shows a rising trend (Figure 3, Supplementary Figure S4, and Supplementary Table S9).

    RegionsBaseline period
    (95%CI)
    2050s2080s
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Low-speed
    (95%CI)
    Medium-speed
    (95%CI)
    High-speed
    (95%CI)
    Temperature-related annual death
    Northeast3,601
    (2963−4,229)
    3,008
    (2,348−3,653)
    3,004
    (2,211−3,776)
    3,647
    (2,488−4,770)
    2,380
    (1,880−2,869)
    1,419
    (899−1,925)
    1,783
    (730−2,802)
    North1,872
    (1,528−2,212)
    3,062
    (2,218−3,889)
    2,613
    (1,642−3,562)
    2,784
    (1,403−4,128)
    2,305
    (1,685−2,912)
    1,132
    (488−1,762)
    997
    (-290−2,247)
    East6,476
    (5,252−7684)
    11,400
    (7,982−14,758)
    9,601
    (5,542−13,584)
    10,230
    (4,422−15,915)
    8,683
    (5,996−11,323)
    5,256
    (2,599−7,866)
    6,378
    (1,342−11,304)
    South516
    (398−632)
    738
    (541−933)
    843
    (593−1,090)
    1,049
    (718−1,376)
    520
    (368−670)
    706
    (529−880)
    1,223
    (917−1,525)
    Central1,783
    (1,443−2,118)
    2,231
    (1,600−2,851)
    1,840
    (1,149−2,519)
    2,191
    (1,130−3,230)
    1,582
    (1,110−2,046)
    1,154
    (705−1,596)
    1,654
    (817−2,473)
    Northwest2,189
    (1,771−2,601)
    1,958
    (1,431−2,474)
    1,381
    (867−1,883)
    1,580
    (777−2,361)
    1,457
    (1,071−1,835)
    489
    (182−789)
    388
    (-232−991)
    Southwest1,741
    (1,362−2,116)
    1,928
    (1,346−2,501)
    1,231
    (680−1,772)
    1,303
    (524−2,066)
    1,300
    (880−1,714)
    559
    (239−873)
    753
    (166−1,328)
    Cold-related annual death
    Northeast3,538
    (2,915−4152)
    2,990
    (2,337−3,629)
    2,942
    (2,169−3,694)
    3,555
    (2,426−4,649)
    2,361
    (1,869−2,841)
    1296
    (805−1773)
    1576
    (575−2542)
    North1,689
    (1,375−1,999)
    2,957
    (2,140−3,758)
    2,406
    (1,486−3,304)
    2,495
    (1,186−3,770)
    2,227
    (1,631−2,811)
    811
    (230−1379)
    444
    (-732−1584)
    East4,912
    (3,900−5,910)
    9,900
    (6,796−12,946)
    7,029
    (3,495−10,491)
    6,533
    (1,473−11,478)
    7,553
    (5,141−9,921)
    1900
    (-213−3972)
    444
    (-3637−4424)
    South257
    (181−333)
    430
    (295−563)
    305
    (158−449)
    301
    (112−487)
    284
    (186−380)
    98
    (18,177)
    151
    (12,288)
    Central1,326
    (1,048−1,600)
    1,715
    (1,174−2,246)
    1,100
    (537−1,652)
    1,114
    (241−1,968)
    1,220
    (822−1,611)
    297
    (-30−618)
    121
    (-502−730)
    Northwest2,091
    (1,690−2,487)
    1,950
    (1,425−2,464)
    1,362
    (854−1,859)
    1,550
    (756−2,323)
    1,451
    (1,068−1,827)
    454
    (156−746)
    329
    (-277−916)
    Southwest1,458
    (1,116−1,795)
    1,788
    (1,233−2,334)
    1,016
    (509−1,515)
    991
    (275−1,693)
    1,203
    (805−1,593)
    275
    (2−543)
    251
    (-253−745)
    Heat-related annual death
    Northeast62
    (47−77)
    18
    (11−25)
    62
    (41−82)
    92
    (62−121)
    20
    (11−28)
    123
    (94−152)
    207
    (155−260)
    North183
    (153−213)
    105
    (78−131)
    207
    (156−258)
    288
    (217−359)
    78
    (55−101)
    321
    (258−383)
    553
    (442−664)
    East1,564
    (1,351−1,775)
    1,500
    (1,186−1,812)
    2,572
    (2,047−3,093)
    3,697
    (2,949−4,437)
    1,129
    (855−1,401)
    3,356
    (2,812−3,895)
    5,934
    (4,978−6,879)
    South259
    (218−299)
    308
    (246−370)
    539
    (435−642)
    748
    (606−889)
    236
    (182−290)
    607
    (511−702)
    1,072
    (905−1,237)
    Central457
    (395−518)
    516
    (426−605)
    740
    (612−867)
    1,077
    (889−1,262)
    362
    (289−435)
    858
    (735−978)
    1,532
    (1,319−1,743)
    Northwest98
    (82−115)
    8
    (5−10)
    19
    (13−24)
    29
    (21−38)
    6
    (4−8)
    35
    (27−43)
    60
    (45−75)
    Southwest284
    (246−321)
    140
    (113−167)
    214
    (171−257)
    311
    (249−373)
    98
    (75−120)
    284
    (237−330)
    501
    (419−583)

    Table S9.  Future temperature-related annual deaths in 3 periods (baseline, 2050s and 2080s) in 7 regions of China under 3 united scenarios (the low, medium, and high-speed scenarios).

  • This report offers a comprehensive characterization of human adaptability and excess mortality burden of temperature across various regions of China in three future united scenarios, which fully considered the mapping relationships between the demographic characteristics of China and the SSPs, which provide more realistic estimates than previous studies.

    The finding of an increase in heat adaptability is consistent with numerous previous studies (7-9), which may be caused by changes or differences in human physiological mechanism adjustment (3), socioeconomic development, the usage rate of air conditioning, and early warning systems (10-11). However, our study also indicated a declining trend in cold adaptability, which has not been reported before. Along with global warming, heat adaptability is increasing, and at the same time, cold adaptability may decrease due to reduced exposure to cold environments (6).

    The report also found a generally increasing trend of heat-related excess mortality, and the cold-related and the total excess mortality were generally increasing in the 2050s and followed by a more obvious decrease in the 2080s. The trend is inconsistent with previous studies in the US, Europe, and the Republic of Korea (5,12). The inconsistency may be due to the differences in the sets of adaptation scenarios. Gosling et al. found that the adaptive modeling is a key part of future temperature-related mortality risk projection studies (3-4). Compared with previous studies, this report introduced more socioeconomic variables as adaptation indicators that may modify the temperature-mortality relationship, and the report fully considered the mapping relationship between birth rate, mortality, and SSP, and constructed three united scenarios instead of a simple matrix of the SSPs and RCPs.

    There were differences in the changing trends of the temperature-related excess mortality in different geographical regions of China under different united scenarios, which may be caused by both temperature and socioeconomic differences. First, the northwestern, southwestern, and northeastern regions have relatively lower temperatures, but the temperature will rise along with global warming, more attention should be paid to the disease burden caused by high temperatures in the future. Second, Central China, South China, North China, and East China generally had higher development levels, better medical resources, and better resilience to health risks from temperature changes. In these regions, it is worth paying attention to both the high and low temperatures in the 2050s, and more attention should be paid to the impact of heat in the 2080s. South China should prioritize responding to the health risk associated with temperatures, as it is the only region in China that has the temperature-related mortality increasing in all three scenarios.

    This study was subject to some limitations. First, the estimation relied heavily on future projection data, which brought some uncertainties. Second, this projection did not consider the change of population age structure, so the future mortality burden due to high and low temperature might be underestimated as China is experiencing a stage of population aging and the elderly are more vulnerable to non-optimal temperatures. Future studies should pay attention to these aspects and provide more insights for policymaking.

    Figure 1.  Future relationships between temperature and mortality in three periods (baseline, 2050s, and 2080s) in China under 3 united scenarios. (A) The low-speed scenario; (B) The medium-speed scenario; (C) The high-speed scenario.
    Figure 2.  Changes (%) in projected annual death number caused by temperature in 3 periods (baseline, 2050s, and 2080s) in China under three united scenarios. (A) Net effect under low-speed scenario; (B) Net effect under medium-speed scenario; (C) Net effect under high-speed scenario; (D) Cold effect under low-speed scenario in China; (E) Cold effect under medium-speed scenario; (F) Cold effect under high-speed scenario in China; (G) Heat-effect under low-speed scenario; (H) Heat-effect under medium-speed scenario; (I) Heat-effect under high-speed scenario. Abbreviations: GCM=general circulation model; GFDL=GFDL-ESM2M; HAD=HadGEM2-ES; MIROC=MIROC-ESM-CHEM; NOR=NorESM1-M; IPSL=IPSL-CM5A-LR.
    Figure 3.  Changes (%) in temperature-related annual death number in seven regions of China in 3 periods (baseline, 2050s, and 2080s) under 3 united scenarios. (A) The cold-related mortality burden under the low-speed scenario; (B) The heat-related mortality burden under the low-speed scenario; (C) The temperature-related mortality burden under the low-speed scenario; (D) The cold-related mortality burden under the medium-speed scenario; (E) The heat-related mortality burden under the medium-speed scenario; (F) The temperature-related mortality burden under the medium-speed scenario; (G) The cold-related mortality burden under the high-speed scenario; (H) The heat-related mortality burden under the high-speed scenario; (I) The temperature-related mortality burden under the high-speed scenario.

    In summary, the findings of this report suggest that the temperature-related excess mortality was projected to increase in the 2050s and decrease in the 2080s. Heat adaptability was projected to increase in the future, but along with rising temperatures, the heat-related excess mortality showed a continuously rising trend, except for low-speed scenarios; although the mortality burden due to the cold was projected to decrease in the nearer future, it might not keep declining in the long run due to the decreasing cold-adaptability, which deserves more attention. Different regions would need different adaptation policies according to the patterns of temperature-related health risk and considering the differences in geographic, climate, demographic, and economic characteristics.

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