Heat induced mortality

Sector Health
Description Number of deaths associated with temperatures above the 75th percentile of daily mean temperature during summer months (Apr-Sep). Relative risks extracted from a European multi-city study (de’ Donato et al. 2015) are used to describe the effect of high temperatures on mortality.
End User Health authorities, environmental authorities, general public
Calculation method Temperature 75th percentile is calculated from Harmonie model output at the location of an official weather station and then used in the evaluation of each grid cell. The determination of the temperature 75th percentile is made separately for the historical period and for the present window of the climate scenario (for the future window of the climate scenario, the same temperature 75th percentile as calculated in the present window is used). The evaluation period for health impacts of temperatures above the 75th percentile is the full year (this since  for the future climate scenario there are temperatures above the thresholds also outside the period Apr-Sep). 

Relative risks (RR) are recalculated to represent the risk associated with a 1°C increase in daily mean temperature. These risk coefficients were aggregated to two regions (Southern and Northern Europe) as well as the mean for Europe. Thus, each city must be classified as belonging to one of these groups.

Population data have been obtained for each city, region or country. For Stockholm national data for 2012, with a spatial resolution of 100×100 m2, have been obtained from Swedish statistics. For Bologna and Amsterdam/Rotterdam,  a 1 * 1 km2 population grid disaggregated data has been applied (Gallego 2010).

The data on baseline mortality are from national official sources, for Stockholm from Swedish statistics, for Bologna from the Bologna province statistics and for Amsterdam from Centraal Bureau voor de Statistiek.

The estimated number of deaths are calculated as
∆Y = (Y0 * P) * (RR * Tdd)
where Y0 is the baseline rate; P the number of exposed persons; RR the relative risk associated with a 1°C increase in temperature above the 75th percentile and Tdd is the number of degree days above the 75th percentile.
The RR is scaled so that the total number of extra deaths for the entire city is equal to the number of deaths you would get if you used the daily temperatures from the location of the weather station for all the city population. This means that for present climate Urban SIS will just distribute spatially the impact to be stronger in more heated urban areas and lower in colder areas of the city. The scaling and the determination of the 75th  percentile determined for the present climate is maintained for the future climate, thus allowing raising temperatures in the future to give a stronger health impact.

ID Title Period Statistical processing Unit Threshold Comment
heatdeaths Annual heatrelated deaths yearly See above deaths/year  


Requires population data

hatdeathsnorm Annual heatrelated deaths per 100,000 inhabitants yearly Assume 100 000 inhabitants in each grid deaths/(year *100,000
Provenance Theese indicators are based on output from the Harmonie meteorological modell.
Validation The simulations made by HARMONIE-AROME in Urban SIS has been validated against observations in Urban SIS deliverable 5.1, where an overview is given in Table 4.  
Calculation caveats Spatial representation:
Other caveats: O1, O2
Could be compared to:
Could be used with:

Derivation of risk estimates

Given the many studies showing a connection between high ambient temperatures and health, it is undisputable. However, scientific consensus as to the best climatological metric to describe or explain the connection between heat and health is missing. Studies use daily mean, maximum and minimum temperature (e.g. Medina-Ramon et al. 2006, de’ Donato et al. 2015, Oudin et al. 2016) or a combination (Rocklov et al. 2011) to describe the temperature mortality relationship. Many combine temperature with humidity, given the human body’s inability to cool in humid conditions but again the metrics differ (e.g. HUMIDEX, THOM index and apparent temperature). Although different temperature metrics are used to get the “best” predictor few evaluate the metric or the implications of the choice.

Barnett et al. (2010) consider a range of metrics (mean, minimum and maximum temperature with and without humidity, apparent temperature and HUMIDEX) with mortality but found none to be consistently the best predictor. They conclude the modelling method can be of greater importance than the metric itself and therefore the choice should be based on practical constraints. Similarly, Foroni et al. (2007) found the choice of Thom index if based on the maximum temperature or mean and maximum Thom wasn’t critical.

Temperature mortality impact has been studied in a range of cities across Europe. However, Hajat and Kosatky’s (2010) review found only Baccini et al. (2008) had multiple (15) European cities (e.g. Stockholm and Helsinki in the north and Athens and Valencia in the south). Baccini et al. (2008) established temperature thresholds for each city and a change in mortality for per degree increase above that threshold. These range from 1.84% K-1 (northcontinental) to 3.12% K-1 in the Mediterranean region. However, the temperature thresholds in each city have different percentiles making it hard to generalize or extrapolate from, so unsuitable for this project.

Fortunately, newer studies have addressed multiple European cities. Guo et al. (2014) analysed 306 communities in 12 countries (e.g., Spain, Italy and United Kingdom). They conclude that Italy and Spain have higher temperature mortality risks than other countries based on accumulated risk over a 21-day lag of daily mean temperatures. Similarly, with Sweden (Stockholm) also included, an analysis of deaths attributable to the warm and cold season Gasparrini et al. (2015) found the lowest mortality was in the 80-90th percentile of annual mean temperatures for communities in a temperate region.

The health effects from high temperatures in 9 European cities across a wide geographical distribution using daily mean temperature were considered using cumulative risk over 40 days (de’ Donato et al. 2015). The risk ratio (RR) used was the difference in risk for days with temperatures at the 75th percentile of summer temperatures compared to the 99th percentile. This use of relative increases in temperature to estimate the health effects makes the results more comparable between cities and easier to extrapolate beyond the study cities. The study controlled for factors such as barometric pressure, wind speed and NO2 as confounders. The risks were estimated for two time periods to assess the possible effects of the 2003 heatwave. Here, the later period is used.

de’ Donato et al. (2015) risks range from an 11% increase in mortality in Paris to a 35% increase in Athens. As these are associated with a relative increase in mortality comparison with similar studies is hard. If a linear increase in mortality between the 75th and 99th percentile is assumed, the increase per 1 K is from 1.7% (Paris) to 7.9% (Barcelona (mean increase of 4.6% all cities) is similar to previous studies of European cities.

If Europe is divided into two (North and South) a risk increase per 1 K above the 75th percentile based on the areal mean based on the similarity in estimated risks for the cities in the suggested regions (rather than geographical location per se). The suggested relative risks associated with 1 K increase above the 75th percentile are:

Region RR (range within region)
Europe 4,6% (1.7%-7.9%)
Northern 2,5% (1.7% – 3.5%)
Southern 6,2% (4.7% – 7.9%)

Using these RR for the future scenario assumes no adaption. Whereas, it is reasonable to expect individuals and populations will over time adapt to a changing climate. Temperature mortality relationships for a specific location change with adaptation, changes in population mortality rates or changing prevalence of chronic diseases, amongst other factors.

Adaptation over time to regional temperatures has been observed using historical registers for the 20th century. For Europe, declining vulnerability to heat, and cold, are observed in Germany (Lerchl, 1998), London, UK (Carson et al. 2006), Zeeland, The Netherlands (Ekamper et al. 2009) and Stockholm, Sweden (Astrom et al. 2013). Contributing factors include: medical and technological advances, demographical and epidemiological changes, improvements in the public health and health care sectors, improvements in housing standards with increased use of air conditioners and central heating. Individual physiological adaptation to higher than normal temperatures may occur through increased sweating and improved cardiovascular capacity (Parsons 2002). Furthermore, behavioural changes among population may alter the temperature mortality relationship as people may actively take measures to avoiding the heat when extremes occur. These relationships can change within a summer, with the impact of heat being higher earlier in summer than later (Gasparrini et al. 2016).

Impacts of heat and cold are regional, with heat-related mortality occurring at higher temperatures in warmer regions (Anderson and Bell 2009). Reduced vulnerability to heat before and after the 2003 heat wave was found in most cities but in northern cities (e.g. Stockholm, Helsinki) heat vulnerability increased (de’Donato et al. 2015).

Demographic change can be a driver of changing impacts on population health (Huang et al. 2011). The expected increase in elderly and other potentially vulnerable groups could make temperature extremes impact on human health more severe (Sierra et al. 2009), as the elderly and chronically ill are more vulnerable to high temperature (Basu 2009, Oudin Åström et al. 2011, Åström et al. 2015). Changing prevalence of chronic diseases (e.g. diabetes and Chronic Obstructive Pulmonary Disease (COPD)) and (in and out) migration must be considered. For example, in Italy the region of birth has been associated with heat sensitivity in adulthood (Vigotti et al. 2006).

Future winter mortality may modify the impacts on future summer mortality. High winter mortality reduces the effect of high temperatures the following summer in Stockholm (Rocklöv et al. 2009) and in warmer climates (Stafoggia et al. 2009). The mechanism may be that an increasing mortality during winter depletes the susceptible individuals pool who are most vulnerable to summer heat. Ebi and Mills (2013) suggest winter mortality rates are unlikely to decrease significantly. Future heat waves may also be more intense and have longer duration (Field 2012). There may be increasing risks for more extreme heat waves but no increase in cold spells (Barnett et al. 2012). Gasparrini and Armstrong (2011) separate the risk during elevated temperature into a main effect due to the daily high temperatures and the added effect of the duration of the heat wave. The latter, found to occur after 4-days, was rather small compared to the main effect (Gasparrini and Armstrong 2011).

Todd and Valeron (2015) and Oudin Åström et al. (2016) reported that the minimum mortality temperatures were increasing over time in France and Sweden. This suggests that using a fixed percentile of current or future temperature distribution may be inappropriate.

Observed changes over time of the temperature mortality relationship as well as changes in population demographics, prevalence of chronic disease with a changing climate indicates estimating impacts of extreme temperatures on mortality is highly complex. Although it may be inappropriate to assume present relationships are representative of future responses at the European scale it may be necessary, as a limit to adaptation may exist among European countries that have recently experienced reduced risks and increased awareness in the northern regions, may reduce the risk in the future.

Experience user  

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