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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 S2–S3). 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 S4–S6, 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).
Item Number of days Mean SD Minimum P25 P50 P75 Maximum Daily death data Total 1,826 9.54 6.83 0 5.00 8.00 13.00 221.00 Male 1,826 5.43 4.10 0 2.00 5.00 8.00 121.00 Female 1,826 4.11 3.41 0 2.00 3.00 6.00 100.00 0–64 years 1,826 2.38 2.08 0 1.00 2.00 3.00 47.00 65–74 years 1,826 1.94 1.86 0 1.00 2.00 3.00 53.00 ≥75 1,826 5.22 4.40 0 2.00 4.00 7.00 121.00 Daily Meteorological data Mean temperature (°C) 1,826 14.26 10.94 −29.5 6.70 15.90 23.10 36.50 Maximum temperature (°C) 1,826 19.50 10.97 −24.3 11.70 21.30 28.20 42.60 Minimum temperature (°C) 1,826 9.89 11.36 −34.6 2.10 11.40 19.00 32.40 Mean relative humidity (%) 1,826 64.84 18.93 4.00 52.00 67.00 79.00 100.00 Table S1. Describe of meteorological and death data in 105 counties of China, 2013–2017.
Sensitivity analysis I2 Q (P) Core model 46.8% 977.43 (P<0.05) Add PM2.5 46.8% 993.10 (P<0.05) Change degree of time Time/df=6 48.8% 1,016.36 (P<0.05) Time/df=5 52.2% 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.
Factors I2 Q(P) / 39.70% 838.33 (P<0.05) The total population 39.40% 842.14 (P<0.05) Birth rate 38.10% 823.47 (P<0.05) Mortality 38.20% 825.33 (P<0.05) GDP 38.70% 832.03 (P<0.05) Air-conditioning ownership 37.40% 813.23 (P<0.05) Heating 35.40% 789.22 (P<0.05) Latitude 36.00% 796.27 (P<0.05) Province 38.90% 834.68 (P<0.05) Note: “/”=meta-regression without adding any factors.
Abbreviations: GDP=gross domestic productTable S3. The result of meta-regression including different factors.
SSP RCP SSP1 4.5 SSP2 6.0 SSP3 8.5 SSP4 8.5 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 Scenarios Low-speed scenario Medium-speed scenario High-speed scenario Temperature RCP8.5, 5GCMs RCP8.5, 5GCMs RCP4.5, 5GCMs Population SSP3 SSP4 SSP1 Birth rate High Medium Low Mortality High Medium Low GDP 2050s and 2080s increase by 410% and 420%, respectively 2050s and 2080s increase by 570% and 500%, respectively 2050s and 2080s increase by 660% and 680%, respectively Air conditioning possession rate 2050s and 2080s increase by 50% and 100%, respectively 2050s and 2080s increase by 100% and 150%, respectively 2050s 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.
Periods Scenarios Heat 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 2050s Low-speed 6.36 (5.33, 7.41) 28.16 (23.65, 32.85) 25.6 Medium-speed 6.77 (5.61, 7.95) 38.58 (32.14, 45.34) 25.4 High-speed 7.36 (6.13, 8.60) 39.39 (32.15, 47.04) 25.5 2080s Low-speed 6.47 (5.25, 7.69) 34.60 (29.55, 39.87) 24.6 Medium-speed 6.77 (5.67, 7.89) 42.13 (35.28, 49.32) 25.5 High-speed 6.35 (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.
GCM Baseline period
(95%CI)2050s 2080s 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 GFDL 18,178
(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)HAD 18,178
(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)MIROC 18,178
(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)NOR 18,178
(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)IPSL 18,178
(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 GFDL 15,271
(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)HAD 15,271
(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)MIROC 15,271
(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)NOR 15,271
(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)IPSL 15,271
(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 GFDL 2,906
(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)HAD 2,906
(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)MIROC 2,906
(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)NOR 2,906
(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)IPSL 2,906
(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).
Regions Baseline period
(95%CI)2050s 2080s 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 Northeast 3,601
(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)North 1,872
(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)East 6,476
(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)South 516
(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)Central 1,783
(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)Northwest 2,189
(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)Southwest 1,741
(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 Northeast 3,538
(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)North 1,689
(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)East 4,912
(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)South 257
(181−333)430
(295−563)305
(158−449)301
(112−487)284
(186−380)98
(18,177)151
(12,288)Central 1,326
(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)Northwest 2,091
(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)Southwest 1,458
(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 Northeast 62
(47−77)18
(11−25)62
(41−82)92
(62−121)20
(11−28)123
(94−152)207
(155−260)North 183
(153−213)105
(78−131)207
(156−258)288
(217−359)78
(55−101)321
(258−383)553
(442−664)East 1,564
(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)South 259
(218−299)308
(246−370)539
(435−642)748
(606−889)236
(182−290)607
(511−702)1,072
(905−1,237)Central 457
(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)Northwest 98
(82−115)8
(5−10)19
(13−24)29
(21−38)6
(4−8)35
(27−43)60
(45−75)Southwest 284
(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).
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