Quantifying excess deaths related to heatwaves under climate change scenarios: A multicountry time series modelling study

Background Heatwaves are a critical public health problem. There will be an increase in the frequency and severity of heatwaves under changing climate. However, evidence about the impacts of climate change on heatwave-related mortality at a global scale is limited. Methods and findings We collected historical daily time series of mean temperature and mortality for all causes or nonexternal causes, in periods ranging from January 1, 1984, to December 31, 2015, in 412 communities within 20 countries/regions. We estimated heatwave–mortality associations through a two-stage time series design. Current and future daily mean temperature series were projected under four scenarios of greenhouse gas emissions from 1971–2099, with five general circulation models. We projected excess mortality in relation to heatwaves in the future under each scenario of greenhouse gas emissions, with two assumptions for adaptation (no adaptation and hypothetical adaptation) and three scenarios of population change (high variant, median variant, and low variant). Results show that, if there is no adaptation, heatwave-related excess mortality is expected to increase the most in tropical and subtropical countries/regions (close to the equator), while European countries and the United States will have smaller percent increases in heatwave-related excess mortality. The higher the population variant and the greenhouse gas emissions, the higher the increase of heatwave-related excess mortality in the future. The changes in 2031–2080 compared with 1971–2020 range from approximately 2,000% in Colombia to 150% in Moldova under the highest emission scenario and high-variant population scenario, without any adaptation. If we considered hypothetical adaptation to future climate, under high-variant population scenario and all scenarios of greenhouse gas emissions, the heatwave-related excess mortality is expected to still increase across all the countries/regions except Moldova and Japan. However, the increase would be much smaller than the no adaptation scenario. The simple assumptions with respect to adaptation as follows: no adaptation and hypothetical adaptation results in some uncertainties of projections. Conclusions This study provides a comprehensive characterisation of future heatwave-related excess mortality across various regions and under alternative scenarios of greenhouse gas emissions, different assumptions of adaptation, and different scenarios of population change. The projections can help decision makers in planning adaptation and mitigation strategies for climate change.


Ireland
Daily non-accidental deaths were obtained from the Irish Central Statistics Office for data in the republic of Ireland (ROI), and Northern Ireland Social Research Agency for data in Northern Ireland (NI) for the period of January 1st 1984 and December 31st 2007. Daily timeseries weather data for the study period were obtained from Met Eireann, the Irish Meteorological Service, for ten weather stations in the ROI: Birr, Clones, Casement Aerodrome, Cork, Dublin, Kilkenny, Malin Head, Rosslare, Shannon and Valentia. The weather for NI was obtained from the United Kingdom Meteorological Office for four weather stations with full time-series data: Aldergrove, Armagh, Ballywatticock, and Banagher. The data included daily maximum, minimum, and mean temperatures, relative humidity and air pressure.

Italy
We obtained daily data on mortality from all causes among the resident population dying within the city for Palermo, Bari, Latina, Frosinone, Roma, Viterbo, Bologna and Brescia; no-accidental causes were collected for Genova and Torino. Data were extracted from local mortality registries and from the rapid mortality surveillance system operational since 2004.
Meteorological data referring to the airport station located closest to the city centre were obtained from the Meteorological Service of the Italian Air Force.

Japan
Data on daily deaths for non-external causes only (ICD-9 codes: 1-799; ICD-10 codes: A00-R99) in 47 prefectures (see full list in Table S1 below) between 1st of January 1985 and 31st of December 2012 were collected. Data on daily minimum, mean (computed as the 24-hours average based on hourly measurements) and maximum temperatures and relative humidity were obtained for the same study period.

Moldova
Data on daily deaths for non-external causes only (ICD-10 codes: A00-R99) in 4 cities (see full list in Table S1 below) between 1st of January 2001 and 31st of December 2010 were collected from National Centre of Public Health Management. Data on daily minimum, mean (computed as the 24-hours average based on hourly measurements) and maximum temperatures and relative humidity were obtained by State Hydrometeorological Service for the same study period.

Philippines
Data on daily deaths for non-external causes only (ICD-10 codes: A00-R99) in 4 cities (see full list in Table S1 below) between 1st of January 2006 and 31st of December 2010 were collected from the Philippine Statistics Authority. Data on daily minimum, mean (computed as the 24-hours average based on hourly measurements) and maximum temperatures and relative humidity were obtained by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) for the same study period.

South Korea
Data on daily deaths for non-external causes only (ICD-9 codes: 1-799; ICD-10 codes: A00-R99) in 7 cities (see full list in Table S1 below) between 1st of January 1992 and 31st of December 2010 were collected. Data on daily minimum, mean (computed as the 24-hours average based on hourly measurements) and maximum temperatures and relative humidity were obtained for the same study period.

Spain
We obtained daily data on non-accidental causes for the 51 capital cities from the Spain National Institute of Statistics for summer months (from 1st June to 30th September) from 1990 to 2014. Daily minimum, mean and maximum temperatures for the 51 capital cities were collected from the Spain National Meteorology Agency for the same study period. We did not get the data on relative humidity, because it is not available.

Sweden
We collected data from the county of Stockholm between 1st of January 1990 and 31st of December 2002. Daily mortality, obtained from the Swedish Cause of Death Register at the Swedish National Board of Health and Welfare, is represented by counts of deaths for nonexternal causes only (ICD-9: 0-799; ICD-10: A00-R99). Mean daily temperature (in ˚C) and relative humidity (in %), computed as the 24-hour average based on hourly measurements, were obtained from the Environment and Health Administration. A single weather station, located at Torkel Knutssongatan in Central Stockholm, was selected. Measures ozone (O3, in ppb) and nitrogen oxides (NOx, in ppb) were available in the same period. Daily level of pollutants were computed as the 24-hour mean based on hourly measurements. In total, missing data amount for 0.00% and 6.59% of the mortality and temperature series, respectively. These data were used and described in previous publications.

Taiwan
Data on daily deaths for non-external causes (ICD-9 codes: 1-799; ICD-10 codes: A00-R99) in Kaohsiung, Taipei and Taichung between 1st of January 1994 and 31st of December 2007 were collected. Data on daily minimum, mean (computed as the 24-hours average based on hourly measurements) and maximum temperatures and relative humidity were obtained for the same study period.

Thailand
We obtained daily data on non-accidental deaths from the Ministry of Public Health, Thailand for 62 provinces during 1999-2008. The daily weather data (daily minimum, mean, and maximum temperatures and mean relative humidity) were obtained from the Meteorological Department, Ministry of Information and Communication Technology. There were 117 weather stations in 62 provinces, with at least one weather monitoring station in each province.

UK
We obtained daily data on non-accidental mortality from the Office of National Statistics The postcodes were used to divide deaths into 10 government regions and date to make daily series of counts for each region. The daily weather data (daily minimum, mean, and maximum temperatures and mean relative humidity) were downloaded from the British Atmospheric Data Centre. There was a mean of 29 stations contributing data to each regional series, from a minimum of 7 in London to a maximum of 44 in Wales.

USA
We collected data from 135 cities (see full list in Table S1)

Vietnam
Data on daily deaths for non-external causes only (ICD-10 codes: A00-R99) in 2 cities (Ho Chi Minh City and Hue) between 1st of January 2009 and 31st of December 2013 were collected from the A6 mortality reporting system, Vietnam. Data on daily minimum, mean (computed as the 24-hours average based on hourly measurements) and maximum temperatures and relative humidity were obtained by the US National Oceanic and Atmospheric Administration's National Climate Data Center for the same study period.