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Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities
Journal Pre-proof Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities Dominic Royé, Carmen Íñiguez, Aurelio Tobías PII:
S0013-9351(20)30129-8
DOI:
https://doi.org/10.1016/j.envres.2020.109237
Reference:
YENRS 109237
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Environmental Research
Received Date: 13 November 2019 Revised Date:
16 January 2020
Accepted Date: 5 February 2020
Please cite this article as: Royé, D., Íñiguez, C., Tobías, A., Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities, Environmental Research (2020), doi: https://doi.org/10.1016/j.envres.2020.109237. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.
Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities
Dominic Royé (1, 2), Carmen Íñiguez (2, 3), Aurelio Tobías (4)
1. Department of Geography, University of Santiago de Compostela, Santiago de Compostela, Spain. 2. Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Spain. 3. Department of Statistics and Computational Research, University of Valencia, Valencia, Spain. 4. Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain.
Running title: Temperature-mortality for observation and reanalysis data.
Conflict of interest: None.
Corresponding author: Dr. Dominic Royé Facultade de Xeografía e Historia, Universidade de Santiago de Compostela Praza da Universidade 1, 15782 Santiago de Compostela de Compostela, Spain. E-mail: [email protected]
ABSTRACT
Background: Most studies use temperature observation data from weather stations near the analyzed region or city as the reference point for the exposure-response association. Climatic reanalysis data sets have already been used for climate studies, but are not yet used routinely in environmental epidemiology. Methods: We compared the mortality-temperature association using weather station temperature and ERA-5 reanalysis data for the 52 provincial capital cities in Spain, using time-series regression with distributed lag non-linear models. Results: The shape of temperature distribution is very close between the weather station and ERA-5 reanalysis data (correlation from 0.90 to 0.99). The overall cumulative exposure-response curves are very similar in their shape and risks estimates for cold and heat effects, although risk estimates for ERA-5 were slightly lower than for weather station temperature. Conclusions: Reanalysis data allow the estimation of the health effects of temperature, even in areas located far from weather stations or without any available.
Keywords: Temperature, weather station, reanalysis, mortality, Spain, distributed lag non-linear models.
Introduction Low and high ambient temperatures are associated with an increased risk of mortality and morbidity in a wide range of climates.1-8 The temperature-related mortality is of great concern for public health in the world, especially, for the potential estimates of future mortality under a warming climate.9 However, when analyzing the impact of the thermal environment, most studies use temperature observation data from weather stations near the analyzed city or region, as the reference point for the exposure-response association. The problem here is that good quality and complete weather station temperature time-series are not available for all regions in the world. In atmospheric science, climatic reanalysis data is used for many climate studies.10-13 These data sets are combinations of forecast models and data assimilation systems (supplemented and corrected by satellite‐borne instruments from the 1970s onwards, by increasing numbers of observations from aircraft, ocean‐buoys and other surface platforms), which allow creating corrected global grids of the recent history of the atmosphere, land surface, and oceans.14-15 Considering the great potential in the use of climatic reanalysis, it is necessary to examine which differences could be found in the temperature-mortality association by using reanalysis time-series instead of weather station data. In this study, we tested and compared the mortalitytemperature association using the ERA-5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) and weather station temperature for the 52 provincial capital cities in Spain.
Data and methods Weather station temperature data Average daily air temperature for the 52 capital cities was provided by the European Climate Assessment & Dataset (ECAD) project.16 We collected the observed temperature data from ECAD for the study period between 1st January 1990 to 31st December 2014. ERA-5 reanalysis data The most recent of the state-of-the-art global atmospheric reanalysis model is the fifth next-generation ERA‐5 from the European Centre for Medium-Range Weather Forecasts (ECMWF).17-18 The ERA-5 reanalysis has a temporary coverage from 1979 until today at a horizontal resolution of 30 km globally with 137 levels from the surface up to a height of 80 km. An important difference to the previous ERA-Interim is the temporal resolution with hourly data. For each weather station of the ECA dataset, the nearest reanalysis point (to the center of raster cells) of 24-hourly surface air temperature was extracted. Then the average daily temperature was calculated. Mortality data Data on daily all-natural cause mortality (International Classification of Diseases, 9th Revision (ICD-9) codes 001-799; and 10th Revision (ICD-10) codes A00-R99) for the 52 capital cities was provided by the Spain National Institute of Statistics, for the same study period. Statistical analysis A standard time-series quasi-Poisson regression was applied to each city and temperature variable in absolute scale. The model included a natural cubic
spline of time with 10 degrees of freedom per year to control for long-term trend and seasonality and indicator variables for weekdays. The mortalitytemperature association was modelled by using distributed lag-non-linear models able to describe complex nonlinear and lagged dependencies.19 Specifically, we modelled the exposure-response curve with a natural cubic spline with three internal knots placed at the 10th, 75th and 90th centiles of cityspecific temperature distributions, and the lag-exposure curve with a natural cubic spline with three internal knots placed at equally spaced values in the log scale. The lag period was extended to 21 to capture the long delay in the effects of cold.20 For each city, and temperature model we estimated the overall cumulative relative risks of death associated with cold and heat, respectively defined as the risk increment at percentiles 2.5th and 97.5th of each temperature distribution relative to the minimum-mortality temperature (MMT). As a summary at the country level, we pooled the city-specific curves to represent the overall cumulative exposure-response relationship using a relative approach.21-22 Since the natural scale for temperature makes for difficulties when combining curves across cities with non-overlapping temperature ranges, we standardized the original temperature series, working with a relative scale. In order to make easy the interpretation, the results are expressed in terms of country temperature percentiles, which correspond to different city-specific absolute temperatures. We calculated a country average minimum-mortality temperature percentile and their empirical confidence intervals by using semiparametric bootstrap.20 Finally, we estimated the overall cumulative relative risks of death associated with cold and heat, respectively defined, as the risk increment at percentiles 2.5th and 97.5th of the joint temperature distribution
relative to the MMT. Finally, we used Bland-Altman plots23 to evaluate the agreement of heat-cold effects as well as MMT between the mean differences of weather temperature and ERA-5 reanalysis data.
Results Table 1 shows the daily mean and range values of both air temperature data sources, and the overall number of deaths and population, for the 52 capital cities during the study period. The average difference between weather station temperature and ERA-5 reanalysis data is of 1.4 ºC, ranging from -2.1 ºC to 4.3 ºC, and showing slightly lower temperatures for the ERA-5 reanalysis data. The shape of temperature distribution is very close between both air temperature data sources, with correlation coefficients ranging from 0.90 to 0.99. However, some cities show shifts in average and extreme values (Figure 1). Looking at city-specific geographical characteristics, the difference between weather temperature and ERA-5 reanalysis data decreases significantly by 1.1 ºC (95% confidence interval (CI)=[-1.7, -0.5]) for an interquartile range (IQR) increase of the altitude of 600 meters, and by 0.5 ºC (95%CI=[-1.0, 0.2]) for an IQR of 150 kilometers of distance to the sea, although this was not statistically significant (Appendix Figure 1). The beta coefficients for cold and heat effects, and for the minimum mortality temperature (MMT), estimated using weather station temperature and ERA-5 reanalysis data show high correlation coefficients of 0.88, 0.79 and 0.77, respectively (Figure 2). The corresponding linear regression slopes show an unitary association between estimates using weather station temperature and
ERA-5 reanalysis data: 0.96 (95%CI=[0.86, 1.05]) for cold, 1.00 (95%CI=[0.85, 1.14]) for heat, and 0.89 (95%CI=[0.76, 1.03]) for the MMT. We observed slightly higher risk estimates for weather station than using ERA-5 reanalysis data, in 60% of the cities for the cold effect and 55% for the heat effect. These differences are more remarkable for the MMT, where 80% of the cities showing a higher threshold using weather station data. The city-specific overall cumulative exposure-response associations can be found in Appendix Figure 2. The agreement between the mortality-temperature associations using weather temperature and ERA-5 reanalysis through Bland-Altman plots shows a small average error of 0.016 (95%CI=[-0.075, 0.108]) for cold, 0.002 (95%CI=[-0.159, 0.165]) for heat, and 1.4 ºC (95%CI=[-5.5, 8.3]) for the MMT (Appendix Figure 3). However, the larger differences mainly come from the smallest capital cities. Figure 3 shows the pooled overall cumulative exposure-response curves for weather station temperature and ERA-5 reanalysis data as percentiles distribution. Both exposure curves are very similar in their shape and risk estimates. The pooled overall effects for cold are 1.19 (95%CI=[1.14, 1.24]) and 1.17 (95%CI=[1.12, 1.22]), respectively. While for the heat effect, the pooled risk estimates result in 1.15 (95%CI=[1.13, 1.18]) and 1.14 (95%CI=[1.11, 1.17]).
Discussion In this study, using data from 52 Spanish capital cities with different climate and wide range of temperature, we found a very similar shape of the overall
cumulative exposure-response curves using weather station temperature and ERA-5 reanalysis data across the full range of temperatures. However, the risk estimates for heat and cold effects tended to be smaller when estimated using ERA-5 reanalysis temperature data. The results are consistent with previous studies comparing temperature-mortality associations generated with weather station temperature data.4-5,7 To our knowledge, this is the first study where ERA-5 reanalysis data has been compared with observed single point temperature data to model temperaturemortality associations nationwide. Adeyeye et al.26 compared reanalysis temperature data from the National Land Data Assimilation System with weather stations temperature data for hospital admissions in New York State, reporting similar risk for health effects of heat. Other studies compared gridded temperature data sets with single-point temperature data.3,24,25 The advantage of using gridded data instead of a single time-series from a weather station is that the spatial heterogeneity is considered more representative.24,25 However, the gridded data is geographically modelled, usually at high-resolution grids (5 km), while the reanalysis temperature data we used in our study, ERA-5 from ECMWF, is the combination of forecast models corrected by multiple observation sources. Here, Weinberger et al.25 also found lower risks estimates for gridded temperature for cold effects and the entire range of temperatures, but the opposite for heat effects. In most of the cities included in our study, the nearest grid point of ERA-5 reanalysis data agreed well with the observed weather station temperature data. Although it should be realized that, even small, differences at extremes could affect the identification of absolute thresholds. The observed shift and the
under/overestimation of extremes between both temperature data sets could be explained by the complex orography and particularly by the altitude. This agrees with previously published studies to estimate uncertainties and sources of error for the ERA-5 reanalysis temperature data.27,28 Moreover, Corners and Jones29 also stated that the success of the reanalysis data (in this case ERA-Interim) depends in the season, which would make necessary to investigate possible seasonal differences in the exposure-response estimation. In future studies, other places and the behavior of maximum and minimum temperatures should also be analyzed for ERA-5 reanalysis data. However, we should notice some limitations. Firstly, when using ERA-5 reanalysis temperature data, the error could be higher when using a resolution of 30 km, because the complex orography or urban heat island effects predominate. Additionally, the quality of the data also depends on good observational coverage. The resolution of ERA-5 is a disadvantage in comparison to high resolution gridded temperature datasets based on observation data25,30. Many issues in reanalysis are related to aspects of processes and systems that drive regional and local climate variability, and areas of complex geomorphology, like irregular coastlines or regions with heterogeneous land cover31. Secondly, in many coastal cities, the nearest ERA5 grid point is on the sea or show land-sea fraction below 50%. The land-sea mask of the ERA-5 indicates that the median land fraction for coastal cities is 0.66 and for inland 0.99 for our analyzed cities. The land-sea proportion implies different calculation for the fluxes of heat, moisture and momentum at these grid points. In our study, we did not limit the proportion, but maybe it could be a possible way to reduce the error. Finally, the weather monitoring stations are
usually located at airports or at least outside the city, and not evenly distributed throughout all analyzed cities3. This issue could affect the closest grid point selected for the ERA-5 reanalysis. In conclusion, reanalysis data allow the estimation of the health effects of temperature, even in areas located far from weather stations or without any station. Nonetheless, it is important to recognize the specific limitations both in the analysis and the interpretation of results obtained by using reanalysis data32.
Legend for Tables and Figures
Table 1. Descriptive statistics for weather station temperature and ERA-5 reanalysis data, mortality counts and population for the 52 provincial capital cities in Spain, 1990-2014.
Figure 1. Correlation between the distribution of weather station temperature and ERA-5 reanalysis data for the 52 provincial capital cities in Spain, 19902014.
Figure 2. Association between beta coefficients for cold and heat effects (percentiles 2.5th and 97.5th, respectively) and the minimum mortality temperature (MMT) using weather station temperature and ERA-5 reanalysis data, in natural scale.
Figure 3. Pooled cumulative exposure-response curves for associations between daily temperature and all-natural cause mortality, using weather station temperature and ERA-5 reanalysis data, in relative scale.
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City A Coruña Albacete Alicante Almería Ávila Badajoz Barcelona Bilbao Burgos Cáceres Cádiz Castellón Ceuta Córdoba Ciudad Real Cuenca Girona Granada Guadalajara Huelva Huesca Jaén León Lleida Logroño Lugo Madrid Málaga Melilla Murcia Pamplona Ourense Oviedo Palencia Las Palmas Palma Pontevedra Salamanca Santander Tenerife Segovia Sevilla Soria
Weather temperature data Mean (Min. , Max.) 15.0 14.5 18.5 19.2 11.3 17.2 16.5 14.9 10.9 16.4 18.6 17.9 18.2 18.3 15.8 13.4 15.0 15.6 13.3 18.3 14.3 17.1 11.1 15.3 14.1 12.1 15.2 18.8 19.1 18.9 13.2 15.1 13.4 12.0 21.3 16.8 14.8 12.3 14.7 21.6 12.4 19.5 11.2
San Sebastián
13.7
Tarragona Teruel Toledo Valencia Vitoria Valladolid Zamora Zaragoza
17.9 12.3 16.0 18.6 11.9 12.9 13.3 15.8
(-2.4 (10.7 (-4.0 (-5.4 (6.3 (-1.6 (-1.4 (0.4 (9.8 (-0.1 (-3.9 (-3.4 (-6.4 (-1.0 (3.2 (4.4 (2.3 (3.0 (3.8 (-3.8 (5.5 (3.1 (8.5 (7.6 (4.9 (4.1 (1.8 (-4.4 (-3.8 (-2.2 (5.2 (1.3 (1.6 (6.1 (-13.7 (-1.0 (-2.4 (5.1 (-1.1 (-12.6 (6.3 (-2.7 (7.2
ERA-5 reanalysis data Mean (Min. , Max.)
Population
, 26.7) , 28.9) , 32.0) , 29.5) , 31.4) , 33.2) , 29.5) , 29.4) , 27.3) , 33.3) , 34.2) , 28.2) , 29.3) , 32.6) , 31.7) , 30.3) , 30.0) , 33.1) , 30.3) , 33.4) , 27.9) , 31.5) , 28.1) , 28.5) , 28.2) , 26.0) , 30.8) , 30.3) , 36.1) , 31.3) , 27.1) , 27.6) , 28.2) , 27.1) , 28.5) , 29.3) , 28.2) , 29.8) , 28.3) , 28.9) , 29.5) , 33.4) , 30.0)
54,375 25,183 57,825 31,718 10,456 24,373 393,885 85,143 34,905 15,075 30,384 29,637 11,914 59,896 13,156 11,197 15,083 51,709 12,284 27,290 11,444 20,458 31,433 25,335 26,511 20,716 635,447 105,276 10,134 65,346 38,222 24,237 48,946 18,591 66,933 68,971 15,005 35,218 43,721 39,113 11,901 141,794 7,682
244,850 173,050 336,577 196,851 57,697 150,530 1,620,343 345,821 175,921 96,098 116,979 170,888 85,144 325,708 74,743 54,898 100,266 232,208 84,910 144,258 52,463 113,457 124,772 137,856 151,113 98,025 3,223,334 571,026 86,384 447,182 199,066 105,505 220,020 78,629 378,517 409,661 82,802 143,978 172,144 204,856 51,683 688,711 39,112
41,490
186,665
21,942 7,674 12,742 172,452 38,531 62,127 14,658 137,572
132,299 35,691 84,282 791,413 249,176 298,866 61,827 666,880
, 29.0) , 31.9) , 32.2) , 36.2) , 28.6) , 33.9) , 30.9) , 32.2) , 29.8) , 34.1) , 32.9) , 32.0) , 31.8) , 36.3) , 33.7) , 30.5) , 30.4) , 33.0) , 30.0) , 36.2) , 32.0) , 35.3) , 27.9) , 31.0) , 31.5) , 29.1) , 32.8) , 34.3) , 36.1) , 34.5) , 31.6) , 31.5) , 28.4) , 29.4) , 34.5) , 32.1) , 30.4) , 29.1) , 28.4) , 34.3) , 31.4) , 36.8) , 28.1)
12.7 16.5 17.5 18.5 13.4 16.4 16.5 12.4 11.6 15.9 17.9 14.8 17.9 14.8 14.5 12.3 15.0 16.4 12.7 17.1 12.4 15.1 11.1 13.9 12.1 11.2 12.5 17.9 17.2 18.0 10.9 10.8 11.9 11.0 20.8 18.2 11.4 12.7 14.0 20.5 12.3 16.4 13.2
(2.6 , 30.3)
13.0
(2.9 , 29.3)
15.6 12.2 15.0 15.1 12.1 11.1 12.3 14.1
(1.6 (6.7 (2.5 (1.0 (4.5 (5.6 (4.6 (3.4
(0.1 (10.9 (2.0 (-3.4 (6.4 (4.8 (4.3 (4.8
, 32.4) , 28.4) , 34.0) , 33.8) , 30.6) , 30.9) , 30.8) , 32.9)
(1.2 (-1.5 (-1.6 (-4.3 (3.8 (0.7 (-0.7 (4.9 (4.8 (1.1 (-2.2 (1.4 (-4.7 (3.2 (3.5 (5.3 (1.8 (1.4 (4.6 (0.8 (5.5 (2.7 (5.2 (4.0 (4.9 (3.9 (5.1 (-3.5 (-0.5 (-2.4 (8.2 (4.5 (2.7 (5.3 (-14.5 (-3.7 (3.1 (3.5 (0.8 (-13.9 (4.5 (2.0 (4.5
Num. of deaths
, 29.3) , 30.0) , 33.2) , 29.4) , 28.6) , 27.5) , 29.2) , 30.7)
S0013-9351(20)30129-8
DOI:
https://doi.org/10.1016/j.envres.2020.109237
Reference:
YENRS 109237
To appear in:
Environmental Research
Received Date: 13 November 2019 Revised Date:
16 January 2020
Accepted Date: 5 February 2020
Please cite this article as: Royé, D., Íñiguez, C., Tobías, A., Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities, Environmental Research (2020), doi: https://doi.org/10.1016/j.envres.2020.109237. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.
Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities
Dominic Royé (1, 2), Carmen Íñiguez (2, 3), Aurelio Tobías (4)
1. Department of Geography, University of Santiago de Compostela, Santiago de Compostela, Spain. 2. Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Spain. 3. Department of Statistics and Computational Research, University of Valencia, Valencia, Spain. 4. Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain.
Running title: Temperature-mortality for observation and reanalysis data.
Conflict of interest: None.
Corresponding author: Dr. Dominic Royé Facultade de Xeografía e Historia, Universidade de Santiago de Compostela Praza da Universidade 1, 15782 Santiago de Compostela de Compostela, Spain. E-mail: [email protected]
ABSTRACT
Background: Most studies use temperature observation data from weather stations near the analyzed region or city as the reference point for the exposure-response association. Climatic reanalysis data sets have already been used for climate studies, but are not yet used routinely in environmental epidemiology. Methods: We compared the mortality-temperature association using weather station temperature and ERA-5 reanalysis data for the 52 provincial capital cities in Spain, using time-series regression with distributed lag non-linear models. Results: The shape of temperature distribution is very close between the weather station and ERA-5 reanalysis data (correlation from 0.90 to 0.99). The overall cumulative exposure-response curves are very similar in their shape and risks estimates for cold and heat effects, although risk estimates for ERA-5 were slightly lower than for weather station temperature. Conclusions: Reanalysis data allow the estimation of the health effects of temperature, even in areas located far from weather stations or without any available.
Keywords: Temperature, weather station, reanalysis, mortality, Spain, distributed lag non-linear models.
Introduction Low and high ambient temperatures are associated with an increased risk of mortality and morbidity in a wide range of climates.1-8 The temperature-related mortality is of great concern for public health in the world, especially, for the potential estimates of future mortality under a warming climate.9 However, when analyzing the impact of the thermal environment, most studies use temperature observation data from weather stations near the analyzed city or region, as the reference point for the exposure-response association. The problem here is that good quality and complete weather station temperature time-series are not available for all regions in the world. In atmospheric science, climatic reanalysis data is used for many climate studies.10-13 These data sets are combinations of forecast models and data assimilation systems (supplemented and corrected by satellite‐borne instruments from the 1970s onwards, by increasing numbers of observations from aircraft, ocean‐buoys and other surface platforms), which allow creating corrected global grids of the recent history of the atmosphere, land surface, and oceans.14-15 Considering the great potential in the use of climatic reanalysis, it is necessary to examine which differences could be found in the temperature-mortality association by using reanalysis time-series instead of weather station data. In this study, we tested and compared the mortalitytemperature association using the ERA-5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) and weather station temperature for the 52 provincial capital cities in Spain.
Data and methods Weather station temperature data Average daily air temperature for the 52 capital cities was provided by the European Climate Assessment & Dataset (ECAD) project.16 We collected the observed temperature data from ECAD for the study period between 1st January 1990 to 31st December 2014. ERA-5 reanalysis data The most recent of the state-of-the-art global atmospheric reanalysis model is the fifth next-generation ERA‐5 from the European Centre for Medium-Range Weather Forecasts (ECMWF).17-18 The ERA-5 reanalysis has a temporary coverage from 1979 until today at a horizontal resolution of 30 km globally with 137 levels from the surface up to a height of 80 km. An important difference to the previous ERA-Interim is the temporal resolution with hourly data. For each weather station of the ECA dataset, the nearest reanalysis point (to the center of raster cells) of 24-hourly surface air temperature was extracted. Then the average daily temperature was calculated. Mortality data Data on daily all-natural cause mortality (International Classification of Diseases, 9th Revision (ICD-9) codes 001-799; and 10th Revision (ICD-10) codes A00-R99) for the 52 capital cities was provided by the Spain National Institute of Statistics, for the same study period. Statistical analysis A standard time-series quasi-Poisson regression was applied to each city and temperature variable in absolute scale. The model included a natural cubic
spline of time with 10 degrees of freedom per year to control for long-term trend and seasonality and indicator variables for weekdays. The mortalitytemperature association was modelled by using distributed lag-non-linear models able to describe complex nonlinear and lagged dependencies.19 Specifically, we modelled the exposure-response curve with a natural cubic spline with three internal knots placed at the 10th, 75th and 90th centiles of cityspecific temperature distributions, and the lag-exposure curve with a natural cubic spline with three internal knots placed at equally spaced values in the log scale. The lag period was extended to 21 to capture the long delay in the effects of cold.20 For each city, and temperature model we estimated the overall cumulative relative risks of death associated with cold and heat, respectively defined as the risk increment at percentiles 2.5th and 97.5th of each temperature distribution relative to the minimum-mortality temperature (MMT). As a summary at the country level, we pooled the city-specific curves to represent the overall cumulative exposure-response relationship using a relative approach.21-22 Since the natural scale for temperature makes for difficulties when combining curves across cities with non-overlapping temperature ranges, we standardized the original temperature series, working with a relative scale. In order to make easy the interpretation, the results are expressed in terms of country temperature percentiles, which correspond to different city-specific absolute temperatures. We calculated a country average minimum-mortality temperature percentile and their empirical confidence intervals by using semiparametric bootstrap.20 Finally, we estimated the overall cumulative relative risks of death associated with cold and heat, respectively defined, as the risk increment at percentiles 2.5th and 97.5th of the joint temperature distribution
relative to the MMT. Finally, we used Bland-Altman plots23 to evaluate the agreement of heat-cold effects as well as MMT between the mean differences of weather temperature and ERA-5 reanalysis data.
Results Table 1 shows the daily mean and range values of both air temperature data sources, and the overall number of deaths and population, for the 52 capital cities during the study period. The average difference between weather station temperature and ERA-5 reanalysis data is of 1.4 ºC, ranging from -2.1 ºC to 4.3 ºC, and showing slightly lower temperatures for the ERA-5 reanalysis data. The shape of temperature distribution is very close between both air temperature data sources, with correlation coefficients ranging from 0.90 to 0.99. However, some cities show shifts in average and extreme values (Figure 1). Looking at city-specific geographical characteristics, the difference between weather temperature and ERA-5 reanalysis data decreases significantly by 1.1 ºC (95% confidence interval (CI)=[-1.7, -0.5]) for an interquartile range (IQR) increase of the altitude of 600 meters, and by 0.5 ºC (95%CI=[-1.0, 0.2]) for an IQR of 150 kilometers of distance to the sea, although this was not statistically significant (Appendix Figure 1). The beta coefficients for cold and heat effects, and for the minimum mortality temperature (MMT), estimated using weather station temperature and ERA-5 reanalysis data show high correlation coefficients of 0.88, 0.79 and 0.77, respectively (Figure 2). The corresponding linear regression slopes show an unitary association between estimates using weather station temperature and
ERA-5 reanalysis data: 0.96 (95%CI=[0.86, 1.05]) for cold, 1.00 (95%CI=[0.85, 1.14]) for heat, and 0.89 (95%CI=[0.76, 1.03]) for the MMT. We observed slightly higher risk estimates for weather station than using ERA-5 reanalysis data, in 60% of the cities for the cold effect and 55% for the heat effect. These differences are more remarkable for the MMT, where 80% of the cities showing a higher threshold using weather station data. The city-specific overall cumulative exposure-response associations can be found in Appendix Figure 2. The agreement between the mortality-temperature associations using weather temperature and ERA-5 reanalysis through Bland-Altman plots shows a small average error of 0.016 (95%CI=[-0.075, 0.108]) for cold, 0.002 (95%CI=[-0.159, 0.165]) for heat, and 1.4 ºC (95%CI=[-5.5, 8.3]) for the MMT (Appendix Figure 3). However, the larger differences mainly come from the smallest capital cities. Figure 3 shows the pooled overall cumulative exposure-response curves for weather station temperature and ERA-5 reanalysis data as percentiles distribution. Both exposure curves are very similar in their shape and risk estimates. The pooled overall effects for cold are 1.19 (95%CI=[1.14, 1.24]) and 1.17 (95%CI=[1.12, 1.22]), respectively. While for the heat effect, the pooled risk estimates result in 1.15 (95%CI=[1.13, 1.18]) and 1.14 (95%CI=[1.11, 1.17]).
Discussion In this study, using data from 52 Spanish capital cities with different climate and wide range of temperature, we found a very similar shape of the overall
cumulative exposure-response curves using weather station temperature and ERA-5 reanalysis data across the full range of temperatures. However, the risk estimates for heat and cold effects tended to be smaller when estimated using ERA-5 reanalysis temperature data. The results are consistent with previous studies comparing temperature-mortality associations generated with weather station temperature data.4-5,7 To our knowledge, this is the first study where ERA-5 reanalysis data has been compared with observed single point temperature data to model temperaturemortality associations nationwide. Adeyeye et al.26 compared reanalysis temperature data from the National Land Data Assimilation System with weather stations temperature data for hospital admissions in New York State, reporting similar risk for health effects of heat. Other studies compared gridded temperature data sets with single-point temperature data.3,24,25 The advantage of using gridded data instead of a single time-series from a weather station is that the spatial heterogeneity is considered more representative.24,25 However, the gridded data is geographically modelled, usually at high-resolution grids (5 km), while the reanalysis temperature data we used in our study, ERA-5 from ECMWF, is the combination of forecast models corrected by multiple observation sources. Here, Weinberger et al.25 also found lower risks estimates for gridded temperature for cold effects and the entire range of temperatures, but the opposite for heat effects. In most of the cities included in our study, the nearest grid point of ERA-5 reanalysis data agreed well with the observed weather station temperature data. Although it should be realized that, even small, differences at extremes could affect the identification of absolute thresholds. The observed shift and the
under/overestimation of extremes between both temperature data sets could be explained by the complex orography and particularly by the altitude. This agrees with previously published studies to estimate uncertainties and sources of error for the ERA-5 reanalysis temperature data.27,28 Moreover, Corners and Jones29 also stated that the success of the reanalysis data (in this case ERA-Interim) depends in the season, which would make necessary to investigate possible seasonal differences in the exposure-response estimation. In future studies, other places and the behavior of maximum and minimum temperatures should also be analyzed for ERA-5 reanalysis data. However, we should notice some limitations. Firstly, when using ERA-5 reanalysis temperature data, the error could be higher when using a resolution of 30 km, because the complex orography or urban heat island effects predominate. Additionally, the quality of the data also depends on good observational coverage. The resolution of ERA-5 is a disadvantage in comparison to high resolution gridded temperature datasets based on observation data25,30. Many issues in reanalysis are related to aspects of processes and systems that drive regional and local climate variability, and areas of complex geomorphology, like irregular coastlines or regions with heterogeneous land cover31. Secondly, in many coastal cities, the nearest ERA5 grid point is on the sea or show land-sea fraction below 50%. The land-sea mask of the ERA-5 indicates that the median land fraction for coastal cities is 0.66 and for inland 0.99 for our analyzed cities. The land-sea proportion implies different calculation for the fluxes of heat, moisture and momentum at these grid points. In our study, we did not limit the proportion, but maybe it could be a possible way to reduce the error. Finally, the weather monitoring stations are
usually located at airports or at least outside the city, and not evenly distributed throughout all analyzed cities3. This issue could affect the closest grid point selected for the ERA-5 reanalysis. In conclusion, reanalysis data allow the estimation of the health effects of temperature, even in areas located far from weather stations or without any station. Nonetheless, it is important to recognize the specific limitations both in the analysis and the interpretation of results obtained by using reanalysis data32.
Legend for Tables and Figures
Table 1. Descriptive statistics for weather station temperature and ERA-5 reanalysis data, mortality counts and population for the 52 provincial capital cities in Spain, 1990-2014.
Figure 1. Correlation between the distribution of weather station temperature and ERA-5 reanalysis data for the 52 provincial capital cities in Spain, 19902014.
Figure 2. Association between beta coefficients for cold and heat effects (percentiles 2.5th and 97.5th, respectively) and the minimum mortality temperature (MMT) using weather station temperature and ERA-5 reanalysis data, in natural scale.
Figure 3. Pooled cumulative exposure-response curves for associations between daily temperature and all-natural cause mortality, using weather station temperature and ERA-5 reanalysis data, in relative scale.
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City A Coruña Albacete Alicante Almería Ávila Badajoz Barcelona Bilbao Burgos Cáceres Cádiz Castellón Ceuta Córdoba Ciudad Real Cuenca Girona Granada Guadalajara Huelva Huesca Jaén León Lleida Logroño Lugo Madrid Málaga Melilla Murcia Pamplona Ourense Oviedo Palencia Las Palmas Palma Pontevedra Salamanca Santander Tenerife Segovia Sevilla Soria
Weather temperature data Mean (Min. , Max.) 15.0 14.5 18.5 19.2 11.3 17.2 16.5 14.9 10.9 16.4 18.6 17.9 18.2 18.3 15.8 13.4 15.0 15.6 13.3 18.3 14.3 17.1 11.1 15.3 14.1 12.1 15.2 18.8 19.1 18.9 13.2 15.1 13.4 12.0 21.3 16.8 14.8 12.3 14.7 21.6 12.4 19.5 11.2
San Sebastián
13.7
Tarragona Teruel Toledo Valencia Vitoria Valladolid Zamora Zaragoza
17.9 12.3 16.0 18.6 11.9 12.9 13.3 15.8
(-2.4 (10.7 (-4.0 (-5.4 (6.3 (-1.6 (-1.4 (0.4 (9.8 (-0.1 (-3.9 (-3.4 (-6.4 (-1.0 (3.2 (4.4 (2.3 (3.0 (3.8 (-3.8 (5.5 (3.1 (8.5 (7.6 (4.9 (4.1 (1.8 (-4.4 (-3.8 (-2.2 (5.2 (1.3 (1.6 (6.1 (-13.7 (-1.0 (-2.4 (5.1 (-1.1 (-12.6 (6.3 (-2.7 (7.2
ERA-5 reanalysis data Mean (Min. , Max.)
Population
, 26.7) , 28.9) , 32.0) , 29.5) , 31.4) , 33.2) , 29.5) , 29.4) , 27.3) , 33.3) , 34.2) , 28.2) , 29.3) , 32.6) , 31.7) , 30.3) , 30.0) , 33.1) , 30.3) , 33.4) , 27.9) , 31.5) , 28.1) , 28.5) , 28.2) , 26.0) , 30.8) , 30.3) , 36.1) , 31.3) , 27.1) , 27.6) , 28.2) , 27.1) , 28.5) , 29.3) , 28.2) , 29.8) , 28.3) , 28.9) , 29.5) , 33.4) , 30.0)
54,375 25,183 57,825 31,718 10,456 24,373 393,885 85,143 34,905 15,075 30,384 29,637 11,914 59,896 13,156 11,197 15,083 51,709 12,284 27,290 11,444 20,458 31,433 25,335 26,511 20,716 635,447 105,276 10,134 65,346 38,222 24,237 48,946 18,591 66,933 68,971 15,005 35,218 43,721 39,113 11,901 141,794 7,682
244,850 173,050 336,577 196,851 57,697 150,530 1,620,343 345,821 175,921 96,098 116,979 170,888 85,144 325,708 74,743 54,898 100,266 232,208 84,910 144,258 52,463 113,457 124,772 137,856 151,113 98,025 3,223,334 571,026 86,384 447,182 199,066 105,505 220,020 78,629 378,517 409,661 82,802 143,978 172,144 204,856 51,683 688,711 39,112
41,490
186,665
21,942 7,674 12,742 172,452 38,531 62,127 14,658 137,572
132,299 35,691 84,282 791,413 249,176 298,866 61,827 666,880
, 29.0) , 31.9) , 32.2) , 36.2) , 28.6) , 33.9) , 30.9) , 32.2) , 29.8) , 34.1) , 32.9) , 32.0) , 31.8) , 36.3) , 33.7) , 30.5) , 30.4) , 33.0) , 30.0) , 36.2) , 32.0) , 35.3) , 27.9) , 31.0) , 31.5) , 29.1) , 32.8) , 34.3) , 36.1) , 34.5) , 31.6) , 31.5) , 28.4) , 29.4) , 34.5) , 32.1) , 30.4) , 29.1) , 28.4) , 34.3) , 31.4) , 36.8) , 28.1)
12.7 16.5 17.5 18.5 13.4 16.4 16.5 12.4 11.6 15.9 17.9 14.8 17.9 14.8 14.5 12.3 15.0 16.4 12.7 17.1 12.4 15.1 11.1 13.9 12.1 11.2 12.5 17.9 17.2 18.0 10.9 10.8 11.9 11.0 20.8 18.2 11.4 12.7 14.0 20.5 12.3 16.4 13.2
(2.6 , 30.3)
13.0
(2.9 , 29.3)
15.6 12.2 15.0 15.1 12.1 11.1 12.3 14.1
(1.6 (6.7 (2.5 (1.0 (4.5 (5.6 (4.6 (3.4
(0.1 (10.9 (2.0 (-3.4 (6.4 (4.8 (4.3 (4.8
, 32.4) , 28.4) , 34.0) , 33.8) , 30.6) , 30.9) , 30.8) , 32.9)
(1.2 (-1.5 (-1.6 (-4.3 (3.8 (0.7 (-0.7 (4.9 (4.8 (1.1 (-2.2 (1.4 (-4.7 (3.2 (3.5 (5.3 (1.8 (1.4 (4.6 (0.8 (5.5 (2.7 (5.2 (4.0 (4.9 (3.9 (5.1 (-3.5 (-0.5 (-2.4 (8.2 (4.5 (2.7 (5.3 (-14.5 (-3.7 (3.1 (3.5 (0.8 (-13.9 (4.5 (2.0 (4.5
Num. of deaths
, 29.3) , 30.0) , 33.2) , 29.4) , 28.6) , 27.5) , 29.2) , 30.7)