Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jun;11(6):492-500.
doi: 10.1038/s41558-021-01058-x. Epub 2021 May 31.

The burden of heat-related mortality attributable to recent human-induced climate change

A M Vicedo-Cabrera  1   2   3 , N Scovronick  4 , F Sera  3   5 , D Royé  6   7 , R Schneider  3   8   9   10 , A Tobias  11   12 , C Astrom  13 , Y Guo  14 , Y Honda  15 , D M Hondula  16 , R Abrutzky  17 , S Tong  18   19   20   21 , M de Sousa Zanotti Stagliorio Coelho  22 , P H Nascimento Saldiva  22 , E Lavigne  23   24 , P Matus Correa  25 , N Valdes Ortega  25 , H Kan  26 , S Osorio  27 , J Kyselý  28   29 , A Urban  28   29 , H Orru  30 , E Indermitte  30 , J J K Jaakkola  31   32 , N Ryti  31 , M Pascal  33 , A Schneider  34 , K Katsouyanni  35   36 , E Samoli  35 , F Mayvaneh  37 , A Entezari  37 , P Goodman  38 , A Zeka  39 , P Michelozzi  40 , F de'Donato  40 , M Hashizume  41 , B Alahmad  42 , M Hurtado Diaz  43 , C De La Cruz Valencia  43 , A Overcenco  44 , D Houthuijs  45 , C Ameling  45 , S Rao  46 , F Di Ruscio  46 , G Carrasco-Escobar  47 , X Seposo  48 , S Silva  49 , J Madureira  50   51 , I H Holobaca  52 , S Fratianni  53 , F Acquaotta  53 , H Kim  54 , W Lee  54 , C Iniguez  7   55 , B Forsberg  13 , M S Ragettli  56   57 , Y L L Guo  58   59 , B Y Chen  59 , S Li  14 , B Armstrong  3   9 , A Aleman  60 , A Zanobetti  42 , J Schwartz  42 , T N Dang  61 , D V Dung  61 , N Gillett  62 , A Haines  3   8 , M Mengel  63 , V Huber  63   64 , A Gasparrini  3   9   65
Affiliations

The burden of heat-related mortality attributable to recent human-induced climate change

A M Vicedo-Cabrera et al. Nat Clim Chang. 2021 Jun.

Abstract

Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991-2018. Across all study countries, we find that 37.0% (range 20.5-76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change.

PubMed Disclaimer

Conflict of interest statement

Competing interests The authors declare no competing interests.

Figures

Extended Data Fig. 1
Extended Data Fig. 1. Time series plots of the warm-season mean daily temperatures in each scenario provided by each model.
The time series plots depict the temporal trends in average warm-season temperatures across the 732 locations included in the study. Factual scenario (with natural and anthropogenic forcings) is depicted in brown, while counterfactual scenario (with natural forcings only) in orange. The grey dark area corresponds to the study period 1991–2018. (ACC: ACCESS-ESM1–5, CAN: CanESM5, CNR: CESM2, FGO: FGOALS-g3, GFD: GFDL-ESM4, HAD: HadGEM3-GC31-LL, IPS: IPSL-CM6A-LR, MIR: MIROC6, MRI: MRI-ESM2–0, Nor: NorESM2-LM).
Extended Data Fig. 2
Extended Data Fig. 2. Time series plots of the warm-season mean daily temperatures in each scenario in the 43 countries included in the study.
As Fig.1, factual scenario (with natural and anthropogenic forcings) is depicted in brown, while counterfactual scenario (with natural forcings only) in orange. The shaded area corresponds to 1 standard deviation across model-specific average estimates. The dashed line shows the start of the study period (1991–2018).
Extended Data Fig. 3
Extended Data Fig. 3. Country-averaged warm-season temperature distributions modelled in each scenario.
As Fig.1, factual scenario (with natural and anthropogenic forcings) is depicted in brown, while counterfactual scenario (with natural forcings only) in orange.
Extended Data Fig. 4
Extended Data Fig. 4. Location-specific heat-related mortality attributed to human-induced climate change (CC) between 1991–2018.
Map with the location-specific estimates of heat-related mortality fractions attributed to human-induced climate change (expressed in %). Estimates ranged between 0.2% and 0.8%, corresponding to the interquartile range, with a maximum value of 3.8%, and 23 locations reported an estimate below 0 (minimum value of −0.1%).
Extended Data Fig. 5
Extended Data Fig. 5. Proportion of heat-related mortality attributed to human-induced climate change (CC), between 1991–2018.
Map with the location-specific estimates of the proportion of heat-related mortality attributed to human-induced climate change (expressed in %). Estimates ranged between 28.6% and 54.2%, corresponding to the interquartile range, with a maximum value of 92%, and 1 location with estimates below 0 (minimum value of −0.1%).
Extended Data Fig. 6
Extended Data Fig. 6. Model-specific estimates of the heat-related mortality attributed to human-induced climate change (CC) for each country, expressed as mortality fraction (%).
The plot shows the model-specific estimates of heat-related mortality fraction attributed to human-induced climate change for each country (1991–2018). ACC: ACCESS-ESM1-5, CAN: CanESM5, CNR: CESM2, FGO: FGOALS-g3, GFD: GFDL-ESM4, HAD: HadGEM3-GC31-LL, IPS: IPSL-CM6A-LR, MIR: MIROC6, MRI: MRI-ESM2-;0, Nor: NorESM2-LM.
Fig. 1
Fig. 1. Temperature modelled under the factual (with both anthropogenic and natural forcings) and counterfactual (with only natural forcings) scenarios.
a, Warm-season average temperature since 1900, including the 1991–2018 study period (shaded) across the 732 locations. b, Temperature differences between scenarios in the 43 study countries, respectively, during the study period (warm season only). Country results are based on included locations only. c, Average temperature difference between scenarios during the study period in the 732 study locations (warm season only).
Fig. 2
Fig. 2. Heat-mortality associations in 16 representative locations.
Exposure-response associations are estimated as best linear unbiased predictions (BLUPs; Methods) and reported as relative risks (with 95% CI, shaded grey) for a cumulative 10-d lag of warm-season temperature, versus the optimum temperature (corresponding to the temperature of minimum mortality). For comparison across locations, vertical red dotted lines indicate the 99th percentile of location-specific warm-season temperature.
Fig. 3
Fig. 3. Heat-related mortality associations in the 732 locations.
These are expressed as the estimated relative risk at the 99th percentile of the location-specific warm-season temperature distribution using the temperature of minimum mortality as reference. Estimates are represented by the location-specific BLUPs (Methods).
Fig. 4
Fig. 4. Heat-related mortality and the contribution of human-induced climate change, 1991–2018.
a, Heat-related mortality as a percentage of total mortality during warm season (mortality fraction (%)) estimated in the 43 countries under the factual (all anthropogenic and natural forcings (shaded)) and counterfactual (natural forcings only (unshaded)) climate scenarios. b, Percentage of total deaths during warm season attributable to heat-related human-induced climate change, estimated as the difference in heat-related mortality in the factual compared to the counterfactual scenario, with the corresponding 95% CI. c, Proportion of heat-related mortality attributable to human-induced climate change estimated as the fraction of heat-related mortality in the factual scenario that results from the contribution of anthropogenic forcings.
Fig. 5
Fig. 5. Heat-related mortality rate attributable to human-induced climate change, 1991–2018.
The estimated rate in each country is based on the attributable fractions for the location(s) within the country. The rates indicate the total burden in the population and are thus a complementary measure of impact to that of Fig. 4b, which reports the attributable fraction. For example, the rate shown here for Brazil is relatively modest, whereas the fraction is high; the opposite is true in a country like Greece.
See this image and copyright information in PMC

References

    1. IPCCMasson-Delmotte V, et al., editors. Special Report on Global Warming of 1.5 °C. WMO; 2018.
    1. Gasparrini A, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet. 2015;386:369–375. - PMC - PubMed
    1. Hsiang S, et al. Estimating economic damage from climate change in the United States. Science. 2017;356:1362–1369. - PubMed
    1. Ye X, et al. Ambient temperature and morbidity: a review of epidemiological evidence. Environ Health Perspect. 2012;120:19–28. - PMC - PubMed
    1. Pachauri RK, Meyer LA, IPCC Core Writing Team . Climate Change 2014: Synthesis Report. IPCC; 2014.