Research
All EHP content is accessible to individuals with disabilities. A fully accessible (Section 508–compliant) HTML version of this article is available at http://dx.doi.org/10.1289/ehp.1306954.
Variability in Temperature-Related Mortality Projections under Climate Change Tarik Benmarhnia,1,2,3 Marie-France Sottile,4,5 Céline Plante,6 Allan Brand,3,7 Barbara Casati,4 Michel Fournier,6 and Audrey Smargiassi 1,3,7 1Département
de santé environnementale et santé au travail (DSEST), Université de Montréal, Montréal, Québec, Canada; 2EHESP School of Public Health (École des hautes études en santé publique), Rennes, Sorbonne-Paris Cité, Rennes, France; 3Chaire sur la pollution de l’air, les changements climatiques et la santé, DSEST, Université de Montréal, Montréal, Québec, Canada; 4Consortium Ouranos, Montréal, Québec, Canada; 5Ministère du Développement durable, de l’Environnement et des Parcs, Québec, Canada; 6Direction de santé publique de l’Agence de la santé et des services sociaux de Montréal, Québec, Canada; 7Institut National de Santé Publique du Québec, Montréal, Québec, Canada
Background: Most studies that have assessed impacts on mortality of future temperature increases have relied on a small number of simulations and have not addressed the variability and sources of uncertainty in their mortality projections. Objectives: We assessed the variability of temperature projections and dependent future mortality distributions, using a large panel of temperature simulations based on different climate models and emission scenarios. Methods: We used historical data from 1990 through 2007 for Montreal, Quebec, Canada, and Poisson regression models to estimate relative risks (RR) for daily nonaccidental mortality in association with three different daily temperature metrics (mean, minimum, and maximum temperature) during June through August. To estimate future numbers of deaths attributable to ambient temperatures and the uncertainty of the estimates, we used 32 different simulations of daily temperatures for June–August 2020–2037 derived from three global climate models (GCMs) and a Canadian regional climate model with three sets of RRs (one based on the observed historical data, and two on bootstrap samples that generated the 95% CI of the attributable number (AN) of deaths). We then used analysis of covariance to evaluate the influence of the simulation, the projected year, and the sets of RRs used to derive the attributable numbers of deaths. Results: We found that 0.05; Lobato and Velasco 2004). We also visually inspected the plots of modeled Pearson residuals against the predicted values to verify that there was no important overdispersion. Generation of 95% CIs of the ANs. There is no standard analytical way to estimate the 95% CI bounds of the distribution of a set of RRs (i.e., when there is one RR per degree temperature due to the nonlinear relation). We thus estimated the statistical uncertainty of the ANs calculated using the observed historical temperatures with 1,000 bootstrap samples, from which we selected the 2.5% and the 97.5% of the ANs based on the observed data, and the corresponding sets of RRs that produced them (i.e., corresponding point estimates per degree temperature produced with the same parameters as the Poisson model developed with the historical data). The bootstrap samples were developed from the observed daily data as follows. For each of the 1,000 samples, we drew with replacement 18 times from day ones, the day twos, and so on (bootstrap samples were stratified by day of “summer”). Thus each of the 1,000 samples contained 1,656 days (92 days × 18 years).
Environmental Health Perspectives • volume 122 | number 12 | December 2014
Calculation of deaths attributable to temperatures. We first calculated the attributable fraction of daily deaths (AF) for each daily temperature metric value T i (mean, maximum, or minimum) using the three sets of RRs described above (i.e., one based on the observed historical data, and two based on individual bootstrap samples). We used Equation 1 to calculate the AF only when the RR was > 1 and the daily minimum, mean, and maximum temperatures were above 15°C, 20°C, and 20°C, respectively.
AF(Ti ) = [RR(Ti ) – 1]/RR(Ti ). [1]
The total number of ANs per year for the 1990–2007 and 2020–2037 periods for a given temperature metric (mean, maximum, or minimum) was then estimated using Equation 2: AN = Σ[AF(Ti ) × MDC × ND(Ti )], [2] where MDC is the mean observed daily death count for the period 1990–2007 and ND(Ti ) is the number of days with the temperature metric (observed or simulated) of Ti; values are summed from the minimum value of T i (i.e., 15°C, 20°C, and 20°C for daily minimum, mean, and maximum temperatures, respectively) for which the estimated RR for the temperature metric and mortality based on historical data was > 1 up to the maximum value of Ti. In our calculation of deaths attributable to future temperatures, we assumed that there would be no change in the mean daily death count in the future, no demographical change, no change in O 3 levels, and no adaptation to heat from populations. Variability analysis. We studied the influence of the different simulations on the future temperature and mortality distributions (2020–2037) focusing on daily mean temperature (not on daily maximum or minimum). Temperature projection variability. We performed an ANCOVA in which the variable to be explained was the simulated daily mean temperature and the predictors were the simulation (n = 32) and the year modeled (n = 18). Variability in ANs. We also used an ANCOVA to evaluate the influence of the set of RRs used to represent the temperature– mortality association, the simulation used to project future temperatures, and the year of the simulation on the estimated number of deaths attributable to temperature during each future year of 2020–2037. The three sets of RRs used included the set based on the observed historical data and the two sets of RRs based on the bootstrap data samples that generated the 95% CIs of ANs.
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Benmarhnia et al.
Observations. For the 18-year period 1990– 2007 in Montreal, 61,356 nona ccidental deaths occurred during June–August, of which 79.9% were among people > 65 years of age. The average observed values for daily mean, maximum, and minimum temperatures were 20.4°C, 24.9°C, and 15.6°C for June, July, and August of 1990–2007. For the distributions of observed daily temperatures and O3 levels for the summers (i.e., June–August) of 1990–2007, see Supplemental Material, Table S1. The average daily O3 concentration for the same period was 25.3 μg/m3 (range, 0.83–76.1 μg/m3) (see Supplemental Material, Table S1). Simulated temperatures. Historical temperatures. Average values of simulated daily mean temperatures for 1990–2007 over the 32 simulations were lower than observed values before the DT correction was applied (16.6°C compared with 20.4°C), whereas simulated mean daily temperatures were closer to the observed values after correction (e.g., mean 20.1°C) (Table 2). We found similar results with daily maximum and minimum temperatures (data not shown).
Future temperatures. All DT-corrected simulations for June–August 2020–2037 suggested an increase in daily mean temperatures in Montreal compared with observed temperatures (Table 2): The average daily mean temperature from the 32 future simulations was 20.9°C (range: 20.3–21.3°C), compared with 20.4°C for the observed temperatures for June–August 1990–2007. We observed notable differences between the 32 DT-corrected and uncorrected values (uncorrected average daily mean, 17.6°C). However, the effect of the correction was not constant at all percentiles; for the future simulations, we observed greater differences in the ranges at higher and lower percentiles. For example, the average (range) of the corrected and uncorrected future simulated daily mean temperatures for the lowest percentile were 8.2°C (5.2–10.5°C) and 5.5°C (3.8–8.7°C), respectively, and for the 50th percentile were 21.1°C (20.3–21.6°C) and 17.7°C (17.1–18.1°C), respectively. Results were similar for daily maximum and minimum temperatures (data not shown). Variability in temperature projections. Table 3 presents the percent of variance in
Table 2. Distribution of observed (1990–2007), historical simulated (1990–2007), and future simulated (2020–2037) average daily mean temperatures (°C) (n = 1,656 days). Simulated [average (range)] Time period, quantile 1990–2007 Minimum 1% 5% 25% 50% 75% 95% 99% Maximum Average SD 2020–2037 Minimum 1% 5% 25% 50% 75% 95% 99% Maximum Average SD
Observed
Uncorrecteda
DT-correcteda
9.6 12.5 15.0 18.3 20.5 22.7 24.5 27.4 29.2 20.4 3.2
4.55 (3.96–8.08) 7.41 (6.94–7.99) 9.80 (8.94–10.22) 13.57 (13.17–13.89) 16.56 (16.01–17.03) 19.32 (18.79–20.06) 23.71 (23.10–24.13) 26.27 (25.44–27.82) 28.77 (27.74–30.09) 16.57 (16.02–16.97) 4.21 (4.08–4.36)
7.62 (5.75–9.23) 11.46 (10.58–12.36) 14.02 (13.61–14.66) 17.68 (17.33–18.03) 20.22 (19.91–20.54) 22.59 (22.42–22.89) 25.62 (25.04–26.13) 27.72 (26.84–28.83) 31.37 (28.81–37.29) 20.06 (19.85–20.34) 3.56 (3.33–3.76)
NA NA NA NA NA NA NA NA NA NA NA
5.50 (3.78–8.65) 8.43 (8.08–8.86) 10.55 (10.16–11.02) 14.38 (13.78–14.90) 17.68 (17.12–18.09) 20.70 (20.14–21.05) 24.80 (23.87–25.44) 29.27 (28.32–30.65) 29.71 (28.12–32.18) 17.62 (17.27–17.89) 4.33 (4.25–4.39)
8.15 (5.24–10.48) 12.07 (11.31–12.92) 14.69 (13.91–15.38) 18.47 (17.92–18.92) 21.10 (20.30–21.59) 23.57 (22.74–24.22) 26.58 (25.48–27.18) 28.76 (27.37–29.89) 32.21 (29.39–35.75) 20.94 (20.31–21.32) 3.66 (3.39–3.89)
NA, not applicable. aAverage (range) of the 32 simulations.
Table 3. Effect of simulation and year on daily mean temperature projections from an ANCOVA model (n = 52,992). Variable Year (n = 18; 2020–2037) Simulation (n = 32) Residuals
Partial sum of squares 1,476 3,269 708,380
df 1 31 52,991
Mean squares 1,476 105 13
F 110.4 7.9 —
p-Value
All EHP content is accessible to individuals with disabilities. A fully accessible (Section 508–compliant) HTML version of this article is available at http://dx.doi.org/10.1289/ehp.1306954.
Variability in Temperature-Related Mortality Projections under Climate Change Tarik Benmarhnia,1,2,3 Marie-France Sottile,4,5 Céline Plante,6 Allan Brand,3,7 Barbara Casati,4 Michel Fournier,6 and Audrey Smargiassi 1,3,7 1Département
de santé environnementale et santé au travail (DSEST), Université de Montréal, Montréal, Québec, Canada; 2EHESP School of Public Health (École des hautes études en santé publique), Rennes, Sorbonne-Paris Cité, Rennes, France; 3Chaire sur la pollution de l’air, les changements climatiques et la santé, DSEST, Université de Montréal, Montréal, Québec, Canada; 4Consortium Ouranos, Montréal, Québec, Canada; 5Ministère du Développement durable, de l’Environnement et des Parcs, Québec, Canada; 6Direction de santé publique de l’Agence de la santé et des services sociaux de Montréal, Québec, Canada; 7Institut National de Santé Publique du Québec, Montréal, Québec, Canada
Background: Most studies that have assessed impacts on mortality of future temperature increases have relied on a small number of simulations and have not addressed the variability and sources of uncertainty in their mortality projections. Objectives: We assessed the variability of temperature projections and dependent future mortality distributions, using a large panel of temperature simulations based on different climate models and emission scenarios. Methods: We used historical data from 1990 through 2007 for Montreal, Quebec, Canada, and Poisson regression models to estimate relative risks (RR) for daily nonaccidental mortality in association with three different daily temperature metrics (mean, minimum, and maximum temperature) during June through August. To estimate future numbers of deaths attributable to ambient temperatures and the uncertainty of the estimates, we used 32 different simulations of daily temperatures for June–August 2020–2037 derived from three global climate models (GCMs) and a Canadian regional climate model with three sets of RRs (one based on the observed historical data, and two on bootstrap samples that generated the 95% CI of the attributable number (AN) of deaths). We then used analysis of covariance to evaluate the influence of the simulation, the projected year, and the sets of RRs used to derive the attributable numbers of deaths. Results: We found that 0.05; Lobato and Velasco 2004). We also visually inspected the plots of modeled Pearson residuals against the predicted values to verify that there was no important overdispersion. Generation of 95% CIs of the ANs. There is no standard analytical way to estimate the 95% CI bounds of the distribution of a set of RRs (i.e., when there is one RR per degree temperature due to the nonlinear relation). We thus estimated the statistical uncertainty of the ANs calculated using the observed historical temperatures with 1,000 bootstrap samples, from which we selected the 2.5% and the 97.5% of the ANs based on the observed data, and the corresponding sets of RRs that produced them (i.e., corresponding point estimates per degree temperature produced with the same parameters as the Poisson model developed with the historical data). The bootstrap samples were developed from the observed daily data as follows. For each of the 1,000 samples, we drew with replacement 18 times from day ones, the day twos, and so on (bootstrap samples were stratified by day of “summer”). Thus each of the 1,000 samples contained 1,656 days (92 days × 18 years).
Environmental Health Perspectives • volume 122 | number 12 | December 2014
Calculation of deaths attributable to temperatures. We first calculated the attributable fraction of daily deaths (AF) for each daily temperature metric value T i (mean, maximum, or minimum) using the three sets of RRs described above (i.e., one based on the observed historical data, and two based on individual bootstrap samples). We used Equation 1 to calculate the AF only when the RR was > 1 and the daily minimum, mean, and maximum temperatures were above 15°C, 20°C, and 20°C, respectively.
AF(Ti ) = [RR(Ti ) – 1]/RR(Ti ). [1]
The total number of ANs per year for the 1990–2007 and 2020–2037 periods for a given temperature metric (mean, maximum, or minimum) was then estimated using Equation 2: AN = Σ[AF(Ti ) × MDC × ND(Ti )], [2] where MDC is the mean observed daily death count for the period 1990–2007 and ND(Ti ) is the number of days with the temperature metric (observed or simulated) of Ti; values are summed from the minimum value of T i (i.e., 15°C, 20°C, and 20°C for daily minimum, mean, and maximum temperatures, respectively) for which the estimated RR for the temperature metric and mortality based on historical data was > 1 up to the maximum value of Ti. In our calculation of deaths attributable to future temperatures, we assumed that there would be no change in the mean daily death count in the future, no demographical change, no change in O 3 levels, and no adaptation to heat from populations. Variability analysis. We studied the influence of the different simulations on the future temperature and mortality distributions (2020–2037) focusing on daily mean temperature (not on daily maximum or minimum). Temperature projection variability. We performed an ANCOVA in which the variable to be explained was the simulated daily mean temperature and the predictors were the simulation (n = 32) and the year modeled (n = 18). Variability in ANs. We also used an ANCOVA to evaluate the influence of the set of RRs used to represent the temperature– mortality association, the simulation used to project future temperatures, and the year of the simulation on the estimated number of deaths attributable to temperature during each future year of 2020–2037. The three sets of RRs used included the set based on the observed historical data and the two sets of RRs based on the bootstrap data samples that generated the 95% CIs of ANs.
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Benmarhnia et al.
Observations. For the 18-year period 1990– 2007 in Montreal, 61,356 nona ccidental deaths occurred during June–August, of which 79.9% were among people > 65 years of age. The average observed values for daily mean, maximum, and minimum temperatures were 20.4°C, 24.9°C, and 15.6°C for June, July, and August of 1990–2007. For the distributions of observed daily temperatures and O3 levels for the summers (i.e., June–August) of 1990–2007, see Supplemental Material, Table S1. The average daily O3 concentration for the same period was 25.3 μg/m3 (range, 0.83–76.1 μg/m3) (see Supplemental Material, Table S1). Simulated temperatures. Historical temperatures. Average values of simulated daily mean temperatures for 1990–2007 over the 32 simulations were lower than observed values before the DT correction was applied (16.6°C compared with 20.4°C), whereas simulated mean daily temperatures were closer to the observed values after correction (e.g., mean 20.1°C) (Table 2). We found similar results with daily maximum and minimum temperatures (data not shown).
Future temperatures. All DT-corrected simulations for June–August 2020–2037 suggested an increase in daily mean temperatures in Montreal compared with observed temperatures (Table 2): The average daily mean temperature from the 32 future simulations was 20.9°C (range: 20.3–21.3°C), compared with 20.4°C for the observed temperatures for June–August 1990–2007. We observed notable differences between the 32 DT-corrected and uncorrected values (uncorrected average daily mean, 17.6°C). However, the effect of the correction was not constant at all percentiles; for the future simulations, we observed greater differences in the ranges at higher and lower percentiles. For example, the average (range) of the corrected and uncorrected future simulated daily mean temperatures for the lowest percentile were 8.2°C (5.2–10.5°C) and 5.5°C (3.8–8.7°C), respectively, and for the 50th percentile were 21.1°C (20.3–21.6°C) and 17.7°C (17.1–18.1°C), respectively. Results were similar for daily maximum and minimum temperatures (data not shown). Variability in temperature projections. Table 3 presents the percent of variance in
Table 2. Distribution of observed (1990–2007), historical simulated (1990–2007), and future simulated (2020–2037) average daily mean temperatures (°C) (n = 1,656 days). Simulated [average (range)] Time period, quantile 1990–2007 Minimum 1% 5% 25% 50% 75% 95% 99% Maximum Average SD 2020–2037 Minimum 1% 5% 25% 50% 75% 95% 99% Maximum Average SD
Observed
Uncorrecteda
DT-correcteda
9.6 12.5 15.0 18.3 20.5 22.7 24.5 27.4 29.2 20.4 3.2
4.55 (3.96–8.08) 7.41 (6.94–7.99) 9.80 (8.94–10.22) 13.57 (13.17–13.89) 16.56 (16.01–17.03) 19.32 (18.79–20.06) 23.71 (23.10–24.13) 26.27 (25.44–27.82) 28.77 (27.74–30.09) 16.57 (16.02–16.97) 4.21 (4.08–4.36)
7.62 (5.75–9.23) 11.46 (10.58–12.36) 14.02 (13.61–14.66) 17.68 (17.33–18.03) 20.22 (19.91–20.54) 22.59 (22.42–22.89) 25.62 (25.04–26.13) 27.72 (26.84–28.83) 31.37 (28.81–37.29) 20.06 (19.85–20.34) 3.56 (3.33–3.76)
NA NA NA NA NA NA NA NA NA NA NA
5.50 (3.78–8.65) 8.43 (8.08–8.86) 10.55 (10.16–11.02) 14.38 (13.78–14.90) 17.68 (17.12–18.09) 20.70 (20.14–21.05) 24.80 (23.87–25.44) 29.27 (28.32–30.65) 29.71 (28.12–32.18) 17.62 (17.27–17.89) 4.33 (4.25–4.39)
8.15 (5.24–10.48) 12.07 (11.31–12.92) 14.69 (13.91–15.38) 18.47 (17.92–18.92) 21.10 (20.30–21.59) 23.57 (22.74–24.22) 26.58 (25.48–27.18) 28.76 (27.37–29.89) 32.21 (29.39–35.75) 20.94 (20.31–21.32) 3.66 (3.39–3.89)
NA, not applicable. aAverage (range) of the 32 simulations.
Table 3. Effect of simulation and year on daily mean temperature projections from an ANCOVA model (n = 52,992). Variable Year (n = 18; 2020–2037) Simulation (n = 32) Residuals
Partial sum of squares 1,476 3,269 708,380
df 1 31 52,991
Mean squares 1,476 105 13
F 110.4 7.9 —
p-Value
