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Evolution of the threshold temperature definition of a heat wave vs. evolution of the minimum mortality temperature: a case study in Spain during the 1983–2018 period

Environmental Sciences Europe volume 33, Article number: 101 (2021) Cite this article

Abstract

Background

An area of current study concerns analysis of the possible adaptation of the population to heat, based on the temporal evolution of the minimum mortality temperature (MMT). It is important to know how is the evolution of the threshold temperatures (Tthreshold) due to these temperatures provide the basis for the activation of public health prevention plans against high temperatures. The objective of this study was to analyze the temporal evolution of threshold temperatures (Tthreshold) produced in different Spanish regions during the 1983–2018 period and to compare this evolution with the evolution of MMT. The dependent variable used was the raw rate of daily mortality due to natural causes ICD X: (A00-R99) for the considered period. The independent variable was maximum daily temperature (Tmax) during the summer months registered in the reference observatory of each region. Threshold values were determined using dispersion diagrams (annual) of the prewhitened series of mortality temperatures and Tmax. Later, linear fit models were carried out between the different values of Tthreshold throughout the study period, which permitted detecting the annual rate of change in Tthreshold.

Results

The results obtained show that, on average, Tthreshold has increased at a rate of 0.57 ºC/decade in Spain, while Tmax temperatures in the summer have increased at a rate of 0.41 ºC/decade, suggesting adaptation to heat. This rate of evolution presents important geographic heterogeneity. Also, the rate of evolution of Tthreshold was similar to what was detected for MMT.

Conclusions

The temporal evolution of the series of both temperature measures can be used as indicators of population adaptation to heat. The temporal evolution of Tthreshold has important geographic variation, probably related to sociodemographic and economic factors, that should be studied at the local level.

Introduction

In recent years, studies in different countries have observed a decrease in the mortality attributable to heat waves [2, 3, 9, 27, 28]. This could be interpreted as a progressive process of population adaptation to high temperatures, due to a variety of factors [19, 31], among which the efficiency of heat prevention plans in different countries is worth mentioning [12].

The decrease in the impact of heat is generally measured in terms of the decrease in the relative risks of daily mortality associated with extremely hot temperatures. This process can be visualized as an evolution over time towards higher values for the temperature thresholds for heat waves (Tthreshold) [18, 30] (Díaz et al. 2019). The threshold temperature for a heat wave can be generally defined as the epidemiological threshold at which the effects of heat begin to provoke excess mortality attributable to heat. These thresholds also mark the activation of prevention plans based on public health action to respond to high temperatures. These Tthreshold values are dynamic, they vary in time as the as well as the sociodemographic and economic dynamics also makes it. The Tthreshold values can be used as an indicator of the adaptation to extremely high temperatures [18, 30] (Díaz et al. 2019). From the point of view of population adaptation to heat waves, adaptation is complete when the rate of increase in maximum daily temperature as a consequence of global warming is less than the rate of increase in Tthreshold (Díaz et al. 2019) [15], not including summer mortality excesses.

In order to analyze whether a process of population adaptation is in fact occurring, there is research that investigates the evolution of another epidemiological indicator that defines the traditional functional relationship that exists, in the “V” form, between daily mortality and temperature. This indicator is known as minimum mortality temperature (MMT) [2, 8, 33].

The evolution of MMT has also been used as an indicator of possible population adaptation to heat [15, 16]. From a conceptual point of view, MMT and Tthreshold represent two different indicators. In a graphic representation (Fig. 1) of the temperature–mortality relationship, MMT represents the temperature at which mortality reaches its minimum value. Thus, mortality attributable to heat is represented to the right of MMT, while mortality attributable to cold is represented to the left [1]. However, Tthreshold values represent the temperature at which mortality begins to increase due to heat waves. It is evident that mortality due to heat includes mortality due to heat waves [29], however, the behavior and temporal evolution are not necessarily similar.

Fig. 1
figure 1

Temperature–mortality relationship in Madrid, 1983–2018 period. Minimum mortality temperature (MMT) and temperature threshold for heat waves (Tthreshold)

Full size image

In the report “Heat Health in the WHO European Region: Updated Evidence for Effective Prevention” [34], the WHO established that the activation of prevention plans to address high temperatures should have an epidemiological basis. That is to say, they should be based on a determination of Tthreshold for each geographic and sub-climatic area of study, based on the increase in mortality with high temperatures. Also, these plans should be revised periodically, given that Tthreshold varies across time. Despite the important role of Tthreshold in the process of population adaptation to high temperatures, there are few studies that analyze its temporal evolution and that also establish variation in time as an indicator of the process of population adaptation to heat waves.

The first objective of this study was to analyze the temporal evolution of Tthreshold temperatures across a period of 36 years (1983–2018) in Spanish regions that are representative of the different impacts of heat waves, and to evaluate whether Tthreshold constitutes a good indicator of population adaptation to high temperatures. Second, this study aimed to compare the rate of evolution of Tthreshold with the rate of evolution observed in MMT during the same time period studied, to analyze the possible relationship and possible implications for future adaptation.

Materials and methods

Mortality rates

From among all Spanish provinces, 10 were selected as representative of the behavior of Spanish regions in terms of thermal extremes, according to previous studies [9, 11, 32].

The dependent variable was made up of the rate of daily mortality due to natural causes (ICD X: A00-R99) in municipalities with over 10,000 inhabitants in selected Spanish regions during the 1983–2018 period. These data were provided by the National Statistics Institute (INE). Based on daily mortality data, and using population data also supplied by INE, the rate of daily mortality per 100,000 inhabitants was calculated.

Temperature data

The data were provided by the State Meteorological Agency (AEMET). Maximum daily temperature in the summer months (Tmax) was the independent variable, registered in the meteorological observatory of reference in each region during the analyzed period corresponding to 1983–2018.

Tmax was used, because it is the variable that presents the best statistical association with daily mortality during heat waves [11, 18].

In addition, we used the rate of evolution of maximum daily temperature (Tmax) in the summer months for the 1983–2018 period and for future Tmax foreseen for the 2051–2100 time horizon under an RCP8.5 emissions scenario. Data were taken from previous papers: [16] and Díaz et al. 2019, respectively.

Determination of threshold temperatures (Tthreshold)

In order to eliminate the analogous components of trend, seasonality and autoregressive character in the series of temperature and mortality, we used a pre-whitening procedure with the Box–Jenkins’ methodology [4].

These prewhitened series constitute the residuals obtained through ARIMA modeling and represent the anomalies that correspond to the mortality rate. The series was modeled for the entire 1983–2018 period.

Find below the equation of the ARIMA regression model in the general form:

$$\begin{array}{*{20}c} {Y_{t} = b + \beta_{1p} \varphi_{pt} + \beta_{2q} \theta_{qt} + \beta_{3P} s\varphi_{Pt} + \beta_{4Q} s\theta_{Qt} + \beta_{5} n1_{t} + \beta_{6\alpha } \cos \left( {\alpha t} \right) + \beta_{7\alpha } {\text{sen}}\left( {\alpha t} \right) + \varepsilon_{t} ,} \\ {\varepsilon_{t} \sim N\left( {0,\sigma } \right),} \\ \end{array}$$

where \(Y_{t}\) is mortality on day t; \(b\) is the intercept; \(\beta\) are the coefficient of each variable in each case; \(\varphi\) is the non-seasonal autoregressive parameter of order p on day t; \(\theta\) is the non-seasonal mobile average of order q on day t; \(s\varphi\) is the seasonal autoregressive parameter of order P on day t;\(s\theta_{Qt}\) is the seasonal mobile average of order Q on day t; n1 is the trend on day t; \(\cos \left( {\alpha t} \right){\text{and sin }}\left( {\alpha t} \right) \,\) are seasonal functions of \(\upalpha\) {365, 180, 120, 90, 60, 30} periods on day t; and \(\varepsilon\) is the residuals which performs a normal distribution of mean = 0, and \(\sigma\) is the standard deviation of the \(\varepsilon\). Since trend was included as an independent variable, the integrated parameter was I = 0. Lastly, it were fixed a period of 7 days for seasonal part of the regression model.

Later, for each year, a dispersion diagram (scatter plot) was constructed such that the X-axis represents maximum daily temperatures in 2 ºC intervals, and the Y-axis represents the value corresponding to these residuals, averaged for these intervals, with the corresponding confidence intervals. Using this methodology, it was possible to relate statistically significant mortality anomalies that were detected at a determined temperature. The value of Tmax, the point at which mortality increases in an anomalous way, was denominated Tthreshold. This methodology has been used in multiple other studies [6, 7, 11, 23, 30].

By way of example, Fig. 2 shows the process by which residuals were obtained and the later determination of Tthreshold in the case of Barcelona for the 1983–2018 period.

Fig. 2
figure 2

a Temporal evolution of the daily mortality rate for Barcelona during the 1983–2018 period; b temporal evolution of the daily mortality rate prewhitened series for this period, and c graphic illustration of the threshold temperature for the 1983–1988 period

Full size image

Calculation of the rate of temporal evolution of Tthreshold

Once Tthreshold was calculated for each year and region, a linear fit process was carried out for the results obtained. The values on the X-axis represent the years between 1983 and 2018, and the Y-axis show the values of Tthreshold for each year, when it was possible to calculate this value. The slope of the line obtained in the linear fit model represents the rate of evolution of Tthreshold during the period of analysis.

Comparison with the evolution of MMT

In other recent studies in Spain for the same period (1983–2018), the rate of evolution of MMT was calculated [16]. If both rates are compared and bivariate correlations are established between the annual series of Tthreshold and MMT during the study period, it is possible to describe a potential association between them.

Also, cross-correlation functions (CCF) were calculated between the series, which allowed for the analysis of a possible time lag between the values of MMT and Tthreshold.

Determination of the increase in Tthreshold

Given that we were working with spatial data, the time evolution of the results was analyzed using a linear mixed model (link = identity). In this model, the Tthreshold values were used, calculated as a dependent variable, the independent variable of fixed effects was the year, and region was used as a factor of random effects, by way of the following equation:

$${\text{geeglm}}\left( {{\text{formula }} = {\text{ d}}\$ {\text{Tumbral }}\sim {\text{ d}}\$ {\text{year}},{\text{ data }} = {\text{ data}},{\text{ id }} = {\text{ d}}\$ {\text{Provincia}}} \right).$$

This analysis was carried out using the statistical software package SPSS 27. The linear mixed models used the geeglm() function of the geepack package of free R software.

Results

Figure 3 shows the graphs that correspond to the linear fit models for Tthreshold, for the total of the 10 regions analyzed. As can be observed, in all of the cases except Badajoz, there was an increasing temporal evolution in terms of the slopes of all of the fit lines.

Fig. 3
figure 3

Linear fit based on the threshold temperature in the years of the study period in each of the regions considered

Full size image

Table 1 shows the average values that correspond to the daily mortality rate and the maximum daily temperature (Tmax) for the summer months in different Spanish regions for the 1983–2018 period. It also shows the average values of the rate of change in the minimum mortality temperatures (MMT) obtained previously [16] and the average values of threshold temperatures (Tthreshold) corresponding to the linear fit models shown in Fig. 3. The values of the slopes are expressed in terms of ºC/decade, both in the case of Tthreshold as well as for the values of MMT. Table 1 also shows the Pearson correlation coefficients of the bivariate correlations obtained between the series of the annual values of Tthreshold and MMT. In general, no correlation exists between the series, except in three regions (Alicante, Barcelona and Zaragoza), and in three of the regions (Badajoz, Orense and Valladolid) the correlations have a negative sign.

Table 1 Average values of daily mortality, daily maximum temperature (Tmax) of the summer months; rate of change in minimum mortality temperatures (MMT) and threshold temperatures (Tthreshold) by region during the 1983–2018 period
Full size table

The CCF calculated between the series of MMT and Tthreshold values did not show statistically significant lags, except in Barcelona, Alicante and Zaragoza, in which case the significant associations were established in lag zero, as shown in Fig. 4.

Fig. 4
figure 4

Cross-correlation functions (CCF) between the annual series of minimum mortality temperatures (MMT) and the threshold temperatures (Tthreshold) for the 1983–2018 period for the regions of Barcelona, Alicante and Zaragoza, respectively

Full size image

On the other hand, Table 2 shows the results obtained in the linear mixed model, where for all of the regions analyzed, there was a statistically significant, increasing trend in Tthreshold values.

Table 2 Results of the mixed model to determine the annual increase (d$year) in Tthreshold for all of the regions considered
Full size table

Table 3 shows the rate of increase in Tmax in the summer months in each of the regions analyzed during the 1983–2018 period and the future rate of increase in Tmax values foreseen for the 2051–2100 time horizon under an RCP8.5 emissions scenario.

Table 3 Rate of evolution of maximum daily temperature (Tmax) in the summer months for the 1983–2018 period and for future Tmax foreseen for the 2051–2100 time horizon under an RCP8.5 emissions scenario
Full size table

Discussion

The primary result of this study is that, at the global level, Tthreshold has increased in Spain over the 36-year period of analysis (1983–2018), which indicates a gradual process of population adaptation to heat waves. These results agree with those of studies of relative attributable risks that analyze the impact of heat waves in Spain [9] and with the results obtained from studies in other locations both in Europe and in the United States [2, 3, 27, 28].

The rate of evolution of Tthreshold observed here is around 0.57 ºC/decade and is similar to the rate of evolution of MMT for all of Spain, established at 0.64 ºC/decade [16]. Despite this, the rate of increase in Tthreshold is greater than that of MMT in 8 of the 10 considered regions, which indicates that the population adapts more rapidly to the more extreme values of Tthreshold than to the lower temperature values that correspond to MMT. This could be related to the measures put into place specifically related to heat waves (prevention plans to address high temperatures, air conditioning, health alerts) [31].

Similar to the evolution of MMT [16], there are great geographic differences in the evolution of Tthreshold values. Table 1 shows a contrast between the rate of increase of up to 1.32 ºC/decade, such as occurs in Bizkaia, and other regions which may even show a decline, such as in the case of Badajoz (−0.25 ºC/decade). There are diverse factors, as the mean age of the population per each region, that could help to explain these variations between regions with different climatic and demographic contexts, some of which could potentially be influenced, such as health spending [20] or the level of income [22]. Others, such as demographic structure [25] or the rural/urban character, also influence the different impacts of heat [23], but would be difficult to modify.

However, there are other factors that operate at a sub-regional level that are probably important in explaining the different behavior of heat with respect to mortality; for example, the age of built structures [21], their quality and insulation [24] and even the access to air conditioning [14]. The existence of green roofs and walls [5] and the accessibility of green zones could also influence mortality due to heat [26] and, therefore, could change the relationship between temperature and mortality. Explanations of the differences in the evolution of MMT should also take place at the sub-provincial level considered here.

The average maximum temperatures in the summer months in Spain have increased at a rate of 0.41 ºC/decade [16]. Therefore, at the global level it can be said that a process of adaptation to heat waves exists in Spain, in accordance with the hypothesis that adaptation to heat waves exists when the rate of increase in Tthreshold is greater than the rate of growth in Tmax [10, 17]. However, the regional differences mean that this is not the case for the regions as a whole. Table 3 shows a comparison of the rate of increase in Tthreshold (Table 1) with the increase in Tmax for the summer months during the 1983–2018 period. These findings show that adaptation can be said to be taking place in the regions as a whole, except in Badajoz.

Table 3 also shows the potential future increase in Tmax for the summer months for the 2051–2100 time horizon, considering a high emissions scenario RCP8.5 (Díaz et al. 2019). A similar process to that described here would suggest that if the rate of increase in Tthreshold is sustained, there will be a process of adaptation in the future to temperatures in all regions, with the exception of Alicante and Badajoz.

Despite the fact that there is similar behavior in the evolution of the MMT series and Tthreshold series, they represent different conceptualizations. This highlights that a statistically significant correlation exists between both annual series in only three of the regions analyzed. In the cases in which this association exists, both series are in sync, that is, MMT changes in the same year as does Tthreshold.

One of the limitations of this study is that it considered, at most, a 36-year series. Given that there was only one Tthreshold value per year, only 36 values of Tthreshold were included. This precluded carrying out the sensitivity analyses that are typical of time series methodologies, such as Jack-Knife [13]. The use of a relatively short data series (36 years, 36 values) could provide uncertainty in the determinations of the slopes of the linear fits. This uncertainty is inherent in this type of estimations with such scarce number of data. In addition, in some of the regions considered, there were years without a heat wave, therefore it was not possible to determine a Tthreshold value, which also removed data from the series of Tthreshold values.

A representative observatory was used as a reference for an entire region, which could give rise to bias in the assignment of exposure temperatures of the population [7]. The possible bias due to not controlling for air pollution variables was minimized through the use of prewhitened series of mortality rates and through directly relating mortality anomalies with temperature anomalies to determine Tthreshold values.

Conclusions

The temporal evolution of the series of both, MMT and Tthreshold temperatures, can be used as indicators of the population adaptation to heat. The temporal evolution of Tthreshold has important geographic variation, probably related to sociodemographic and economic factors that should be studied at the local level. It is important to keep in mind that the activation of heat prevention plans should take place based on these heat wave definition threshold temperatures, and should be implemented at the local level [34]. An analysis of the temporal evolution of Tthreshold is key not only in updating these threshold levels periodically, as suggested by the WHO [34], but also as an indicator of the population adaptation to heat. Knowing which variables influence changes in Tthreshold levels and modifying them to favor adaptation processes could be a key tool in adaptation to climate change. If this population adaptation to heat is achieved, attributable mortality could be dramatically reduced [18, 30] (Díaz et al. 2019).

Availability of data and materials

It is an ecological analysis so the study does not involve human subjects.

Abbreviations

MMT:

Minimum mortality temperature

Tthreshold:

Threshold temperature

Tmax:

Maximum daily temperature

ICD:

International Classification of Diseases

CCF:

Cross-correlation functions

References

  1. Alberdi JC, Díaz J, Montero JC, Mirón IJ (1998) Daily mortality in Madrid Community (Spain) 1986–1991: relationship with atmospheric variables. Eur J Epidemiol 14:571–578

    Article  CAS  Google Scholar 

  2. Åström DO, Tornevi A, Ebi KL, Rocklöv J, Forsberg B (2016) Evolution of minimum mortality temperature in Stockholm, Sweden, 1901–2009. Environ Health Perspect 124(6):740–744

    Article  Google Scholar 

  3. Barreca A, Clay K, Deschenes O, Greenstone M, Shapiro JS (2016) Adapting to climate change: the remarkable decline in the US temperature-mortality relationship over the twentieth century. J Politic Economic 124(1):105–109

    Article  Google Scholar 

  4. Box GE, Jenkins GM, Reinsel C (1994) Time series analysis. Forecasting and control. Prentice Hall, Englewood

    Google Scholar 

  5. Buchin O, Hoelscher MT, Meier F, Nehls T, Ziegler F (2016) Evaluation of the health-risk reduction potential of countermeasures to urban heat islands. Energy Buildings 114:27–37

    Article  Google Scholar 

  6. Carmona R, Díaz J, Mirón IJ, Ortíz C, León I, Linares C (2016) Geographical variation in relative risks associated with cold waves in Spain: the need for a cold wave prevention plan. Environ Int 88:103–111

    Article  CAS  Google Scholar 

  7. Carmona R, Linares C, Ortiz C, Mirón IJ, Luna MY, Díaz J (2017) Spatial variability in threshold temperatures during extreme heat days: Impact assessment on prevention plans. Int J Environ Health Res 27:463–475

    Article  CAS  Google Scholar 

  8. Chung Y, Noh H, Honda Y, Hashizume M, Bell ML, Guo YL, Kim H (2017) Temporal changes in mortality related to extreme temperatures for 15 cities in Northeast Asia: adaptation to heat and mal adaptation to cold. Am J Epidemiol 185(10):907–913

    Article  Google Scholar 

  9. Díaz J, Carmona R, Mirón IJ, Luna MY, Linares C (2018) Time trend in the impact of heat waves on daily mortality in Spain for a period of over thirty years (1983–2013). Environ Int 116:10–17

    Article  Google Scholar 

  10. Díaz J, Sáez M, Carmona R, Mirón IJ, Barceló MA, Luna MY, Linares C (2019) Mortality attributable to high temperaturesover the 2021–2050 and 2051–2100 time horizons in Spain: adaptation and economic estimate. Environmental Research. 172:475–485

    Google Scholar 

  11. Díaz J, Carmona R, Mirón IJ, Ortiz C, León I, Linares C (2015) Geographical variation in relative risks associated with heat: update of Spain’s Heat Wave Prevention Plan. Environ Int 85:273–283

    Article  Google Scholar 

  12. de Donato F, Scortichini M, De Sario M, De Martino A, Michelozzi P (2018) Temporal variation in the effect of heat and the role of the Italian heat prevention plan. Public Health 161:154–162

    Article  Google Scholar 

  13. Efron B. Bootstrap methods: another look at the jackknife. The annals of Statistics, 1979; pp 1–26

  14. Flouris AD, McGinn R, Poirie MP, Louie JC et al (2018) Screening criteria for increased susceptibility to heat stress during work or leisure in hot environments in healthy individuals aged 31–70 years. Temperature (Austin) 5(1):86–99. https://doi.org/10.1080/23328940.2017.1381800.eCollection

    Article  Google Scholar 

  15. Follos F, Linares C, Vellón JM, López-Bueno JA, Luna MY, Martínez GS, Díaz J (2020) The evolution of minimum mortality temperatures as an indicator of heat adaptation: the cases of Madrid and Seville (Spain). Sci Tot Environ 747:141259

    Article  CAS  Google Scholar 

  16. Follos F, Linares C, López-Bueno JA, Navas MA, Vellón JM, Luna MY, Sánchez-Martínez G, Díaz J (2021) Evolution of the minimum mortality temperature (1983–2018): is Spain adapting to heat? Sci Total Environ 784:147233

    Article  CAS  Google Scholar 

  17. Guo Y, Gasparrini A, Armstrong BG, Tawatsupa B, Tobias A, Lavigne E et al (2017) Heat wave andmortality: a multicountry, multicommunity study. Environ Health Perspect. 125(8): 087006

    Article  Google Scholar 

  18. Guo Y, Gasparrini A, Li S, Sera F, Vicedo-Cabrera AM, de Coelho SZSM, Saldiva PHN et al (2018) Quantifying excess deaths related to heatwaves under climate change scenarios: a multicountry time series modelling study. PLoS Med 15(7):e1002629

    Article  Google Scholar 

  19. Kazmierczak A, Bittner S, Breil M, Coninx I, Johnson K, Kleinenkuhnen L, Zandersen M (2020) Urban adaptation in Europe: how cities and towns respond to climate change

  20. Leone M, D’Ippoliti D, De Sario M, Analitis A, Menne B, Katsouyanni K, Dörtbudak Z (2013) A time series study on the effects of heat on mortality and evaluation of heterogeneity into European and Eastern-Southern Mediterranean cities: results of EU CIRCE project. Environ Health 12(1):55

    Article  Google Scholar 

  21. López-Bueno JA, Díaz J, Linares C (2019) Differences in the impact of heat waves according to urban and peri-urban factor in Madrid. Int J Biometeorol 63:371–380

    Article  Google Scholar 

  22. López-Bueno JA, Díaz J, Sánchez-Guevara C, Sánchez-Martínez G, Franco M, Gullón P, Linares C (2020) The impact of heat waves on daily mortality in districts in Madrid: the effect of sociodemographic factors. Environ Res 190:109993

    Article  Google Scholar 

  23. López-Bueno JA, Navas-Martín MA, Linares C, Mirón IJ, Luna MY, Sánchez-Martínez G, Culqui D, Díaz J (2021) Analysis of the impact of heat waves on daily mortality in urban and rural areas in Madrid. Environ Res 195:110892

    Article  Google Scholar 

  24. Matthies, F, Bickler, G, Cardeñosa N, Hales S, Editors., World Health Organization. Regional Office for Europe. et al. (2008). Heat-health action plans: guidance. In: Franziska Matthies, ed. et al. Copenhagen: WHO Regional Office for Europe. https://apps.who.int/iris/handle/10665/107888

  25. Montero JC, Miron IJ, Criado-Alvarez JJ, Linares C, Diaz J (2012) Influence of local factors in the relationship between mortality and heat waves: castile-La Mancha (1975–2003). Sci Total Environ 414:73–80

    Article  CAS  Google Scholar 

  26. Murage P, Kovats S, Sarran C, Taylor J, McInnes R (2020) What individual and neighbourhood-level factors increase the risk of heat-related mortality? A case-crossover study of over 185,000 deaths in London using high-resolution climate datasets. Environ Int 134:105292

    Article  Google Scholar 

  27. Petkova EP, Gasparrini A, Kinney PL (2014) Heat and mortality in New York City since the beginning of the 20th century. Epidemiology 25(4):554–560

    Article  Google Scholar 

  28. Ragettli MS, Vicedo-Cabrera AM, Schindler C, Röösli M (2017) Exploring the association between heat and mortality in Switzerland between 1995 and 2013. Environ Res 158:703–709

    Article  CAS  Google Scholar 

  29. Sánchez-Guevara C, Gayoso M, Núñez-Peiró M, Sanz A, Neila FJ Alesanco P, et al. Feminización de la pobreza energética en Madrid. Exposición a extremos térmicos. Fundación General de la UPM. ISBN: 978-84-09-20538-7. Madrid, 2020

  30. Sánchez-Martínez G, Díaz J, Linares C, Nieuwenhuyse A, Hooyberghs H, Lauwaet D, De Ridder K, Carmona R, Ortiz C, Kendrovski V, Aerts R, Dunbar M (2018) Heat and health under climate change in Antwerp: projected impacts and implications for prevention. Environ Int 111:135–143

    Article  Google Scholar 

  31. Sánchez-Martínez G, Linares C, Ayuso A, Kendrovski V, Boeckmann M, Díaz J (2019) Heat-Health Action Plans in Europe: challenges ahead and how to tackle them. Environ Res 176:108548

    Article  Google Scholar 

  32. Tobías A, Armstrong B, Gasparrini A, Díaz J (2014) Effects of high summer temperatures on mortality in 50 Spanish cities. Environ Health 13:48

    Article  Google Scholar 

  33. Todd N, Valleron A-J (2015) Space-time covariation of mortality with temperature: a systematic study of deaths in France, 1968–2009. Environ Health Perspect 123(7):659–664

    Article  Google Scholar 

  34. WHO Regional Office for Europe (2021) Heat and health in the Who European Region: update evidence for effective prevention. Copenhagen

Download references

Acknowledgements

This research project was funded by the Carlos III Health Institute (ISCIII) under file number ENPY 470/19 and is supported by the Biodiversity Foundation of the Ministry for Ecological Transition and Demographic Challenge, in addition to the research projects ISCIII: ENPY107/18 and ENPY 376/18.

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This paper reports independent results and research. The views expressed are those of the authors and not necessarily those of the Carlos III Institute of Health (Instituto de Salud Carlos III).

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Authors and Affiliations

  1. National School of Public Health, Carlos III Institute of Health, Escuela Nacional de Sanidad, Avda. Monforte de Lemos 5, 28029, Madrid, Spain

    J. A. López-Bueno, J. Díaz, M. A. Navas, D. Culqui & C. Linares

  2. Tdot Soluciones Sostenibles, SL, Ferrol, A Coruña, Spain

    F. Follos & J. M. Vellón

  3. State Meteorological Agency, Madrid, Spain

    M. Y. Luna

  4. The UNEP DTU Partnership, Copenhagen, Denmark

    G. Sánchez-Martínez

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  1. J. A. López-Bueno
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  4. J. M. Vellón
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Contributions

LJA: providing and analysis of data; elaboration and revision of the manuscript. DJ: original idea of the study. Study design: elaboration and revision of the manuscript. FF: providing and analysis of data; elaboration and revision of the manuscript. VJM: providing and analysis of data; elaboration and revision of the manuscript. NMÁ: providing and analysis of data; elaboration and revision of the manuscript. CD: providing and analysis of data; elaboration and revision of the manuscript. LMY: providing and analysis of data; elaboration and revision of the manuscript. SMG: epidemiological study design. Elaboration and revision of the manuscript. LC: original idea of the study. Study design; elaboration and revision of the manuscript. All authors read and approved the final manuscript.

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Correspondence to J. Díaz.

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López-Bueno, J.A., Díaz, J., Follos, F. et al. Evolution of the threshold temperature definition of a heat wave vs. evolution of the minimum mortality temperature: a case study in Spain during the 1983–2018 period. Environ Sci Eur 33, 101 (2021). https://doi.org/10.1186/s12302-021-00542-7

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  • DOI: https://doi.org/10.1186/s12302-021-00542-7

Keywords

  • Temperature threshold
  • Minimum mortality temperature
  • Adaptation
  • Mortality attributable