Research

A Section 508–conformant HTML version of this article
is available at https://doi.org/10.1289/EHP9835.

Nationwide Analysis of the Heat- and Cold-Related Mortality Trends in Switzerland
between 1969 and 2017: The Role of Population Aging
Evan de Schrijver,1,2,3 Marvin Bundo,1,2,3 Martina S. Ragettli,4,5 Francesco Sera,6,7
Oscar H. Franco,1 and Ana M. Vicedo-Cabrera1,2

Antonio Gasparrini,7,8,9

1

Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
Oeschger Center for Climate Change Research (OCCR), University of Bern, Bern, Switzerland
3
Graduate school of Health Sciences (GHS), University of Bern, Bern, Switzerland
4
Swiss Tropical and Public Health Institute, Allschwil, Switzerland
5
University of Basel, Basel, Switzerland
6
Department of Statistics, Informatics, Applications, University of Florence, Florence, Italy
7
Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
8Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK
9Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
2

BACKGROUND: Because older adults are particularly vulnerable to nonoptimal temperatures, it is expected that the progressive population aging will
amplify the health burden attributable to heat and cold due to climate change in future decades. However, limited evidence exists on the contributionof population aging on historical temperature–mortality trends.
OBJECTIVES: We aimed to a) assess trends in heat- and cold-related mortality in Switzerland between 1969 and 2017 and b) to quantify the contribution of population aging to the observed patterns.
METHODS: We collected daily time series of all-cause mortality by age group (<65, 65–79, and 80 y and older) and mean temperature for each Swiss
municipality (1969–2017). We performed a two-stage time-series analysis with distributed lag nonlinear models and multivariate longitudinal metaregression to obtain temperature–mortality associations by canton, decade, and age group. We then calculated the corresponding excess mortality attributable to nonoptimal temperatures and compared it to the estimates obtained in a hypothetical scenario of no population aging.
RESULTS: Between 1969 and 2017, heat- and cold-related mortality represented 0.28% [95% conﬁdence interval (CI): 0.18, 0.37] and 8.91% (95% CI:
7.46, 10.21) of total mortality, which corresponded to 2.4 and 77 deaths per 100,000 people annually, respectively. Although mortality rates for heatslightly increased over time, annual number of deaths substantially raised up from 74 (12;125) to 181 (39;307) between 1969–78 and 2009–17, mostly
driven by the ≥80-y-old age group. Cold-related mortality rates decreased across all ages, but annual cold-related deaths still increased among the≥80, due to the increase in the population at risk. We estimated that heat- and cold-related deaths would have been 52.7% and 44.6% lower, respectively, in the most recent decade in the absence of population aging.
DISCUSSION: Our ﬁndings suggest that a substantial proportion of historical temperature-related impacts can be attributed to population aging. We
found that population aging has attenuated the decrease in cold-related mortality and ampliﬁed heat-related mortality. https://doi.org/10.1289/
EHP9835

Background
Exposure to nonoptimal ambient temperatures is considered to beamong the most important environmental health hazards (Murray
et al. 2020). It has been associated with approximately 9.4% of
the total mortality burden globally, corresponding to 74 deaths
per 100,000 people a year (Zhao et al. 2021). This estimate issimilar to the relative contribution of other environmental risk
factors, such as long-term exposure to air pollution, which has
been associated with 7.3% of total mortality globally (Murray
et al. 2020). Cold-related deaths, usually deﬁned as deaths associated with nonoptimal temperature on days below the temperatureof minimum mortality (MMT), represent the largest contributionof nonoptimal temperature-related mortality [8.5% vs. 0.9% ofheat-related deaths (days with temperature above the MMT),
globally (Zhao et al. 2021)]. However, recent projection studies
suggest that climate change will further amplify the mortality

Address correspondence to Ana M. Vicedo-Cabrera, Mittelstrasse 43, 3012
Bern, Switzerland. Email: anamaria.vicedo@ispm.unibe.ch
Supplemental Material is available online (https://doi.org/10.1289/EHP9835).
No competing interest to declare.
Received 17 June 2021; Revised 27 January 2022; Accepted 31 January2022; Published 9 March 2022.
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Environmental Health Perspectives

burden attributed to heat and eventually outweigh current coldrelated deaths in the future in most locations worldwide(Gasparrini et al. 2017; Zhao et al. 2021).
Along with the threatening projections of a warmer climate,
the world’s population is projected to grow older. Current demographic trajectories reveal that the world’s population age 65 yand older will increase substantially from 9% to 16% by 2050
(United Nations 2019). This increase would pose additional societal challenges in the future, in particular for public health,because older adults are considered to be a population subgroupthat is particularly susceptible to diﬀerent environmental hazards,
including heat and cold (Achebak et al. 2019). This increased
vulnerability to heat and cold can be explained by physiologicaldiﬀerences in thermoregulation (Basu and Samet 2002), as well
as housing (Kim et al. 2019), prevalence of comorbidities(Bunker et al. 2016; Schneider et al. 2017), and social isolation,
among others (Kim et al. 2020; Son et al. 2019). Projection studies published so far concluded that the trends in future burden
attributed to nonoptimal temperatures are largely driven by theincrease in the pool of susceptible population (i.e., population>65 y) (Chen et al. 2020; Lee et al. 2018; Lee and Kim 2016; Liet al. 2016; Marsha et al. 2018; Vardoulakis et al. 2014).
Therefore, understanding the role of future changes in the demographic structure of the population on projected health impacts is
critical to formulating eﬃcient strategies for adaptation to climatechange. Additionally, quantifying the contribution of these factors in the observed impacts in the past would provide useful
insights on the potential mechanisms of action, help to interpretthe projected impacts, and help to assess the viability of the proposed adaptation policies.

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When assessing the heat-related mortality during the last decades, studies found a decline in heat-related vulnerability, suggesting partial adaptation of the population, whereas for cold theevidence remains unclear (Achebak et al. 2019; Arbuthnott et al.
2016; Bobb et al. 2014; Chung et al. 2018; Sera et al. 2020;
Vicedo-Cabrera et al. 2018). As reviewed by Sheridan and Allen,
previous studies have attempted to assess the role of several factors as potential drivers of the attenuation of vulnerability, including socioeconomic indicators, changes in infrastructure, anddemographic factors, including population aging (Sheridan andAllen 2018). In particular, the role of population aging has mostly
been assessed through the evaluation of temporal trends in agespeciﬁc mortality fractions attributable to nonoptimal temperatures (Achebak et al. 2019). However, evidence from theseassessments only reﬂect relative changes in the mortality burden
across time and age groups and neither disentangle nor quantify
the contribution of underlying changes in the populations at risk
(i.e., population aging) and the corresponding societal impacts.
In Switzerland, progressive aging of the population is alreadyevident, with almost 20% of the population over age 65 y, a sharethat is set to further increase up to 30% by 2060 (Lewis andOllivaud 2020). Besides progressive population aging, the eﬀects
of climate change are directly visible, because 9 out of the 10 hottest years have occurred in the 21st century (National Centre for
Climate Services 2018; Suarez-Gutierrez et al. 2018, 2020; Watts
et al. 2021). Moreover, a recent international assessment found that
37% of heat-related deaths can be attributed to human-induced climate change (Vicedo-Cabrera et al. 2021). Thus, this study aimed
to assess the role of the progressive aging of the population on thetrends in heat- and cold-related mortality impacts in Switzerland
over the past 49 y. Speciﬁcally, we a) comprehensively exploredheat- and cold-related mortality trends in Switzerland during thelast ﬁve decades using high-resolution mortality and weather data
and b) quantiﬁed the contribution of population aging to theobserved patterns. The main methodological novelty of this study
is that we accounted for the changes in the demographic structureof the population across time (i.e., aging) when assessing the trends
in excess mortality due to nonoptimal temperature, which was
unlike approaches taken in previous studies. By doing so, we wereable to disentangle and quantify the contribution of aging to thetemporal trends driven by adaptation and/or acclimatization.

Methods and Data
Data
Mortality data. We collected all-cause mortality data in Switzerland
between the 1 January 1969 and 31 December 2017 from theFederal Statistical Oﬃce (BFS). Ethical approval was not required
because data were gathered from administrative sources through thecorresponding agreement between parties. For each Swiss municipality, we computed the daily count of all-cause deaths by age category (which was divided into <65, 65–79 y, and ≥80 y). We usedthe 2019 Swiss Oﬃcial Commune Register as the reference for themunicipality boundaries (Federal Statistical Oﬃce 2020). From the2,212 municipalities, we merged 156 smaller municipalities with<100 recorded deaths in 49 y with neighboring municipalities basedon similar neighborhood types as characterized by the BFS(Bundesamt für Statistik Statistischer Atlas der Schweiz 2020),
which ultimately amounted to 2,056 units of analysis (Figure S1).
Temperature data. We obtained daily mean temperatures on a
2-km grid across the full Swiss geography from a gridded climate
data set (MeteoSwiss-grid-product) provided by MeteoSwiss(Federal Oﬃce of Meteorology and Climatalogy MeteoSwiss
2020). This high-resolution gridded data allowed us to capture thetemperature exposures in areas with a sparse monitor network (i.e.,
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rural and mountainous areas) and assign a unique exposure to eachof the 2,056 municipalities in Switzerland (de Schrijver et al. 2021;
Spangler et al. 2019). We included the grid cells that intersected theboundaries of the municipality to create the municipality-speciﬁc
temperature series. To properly account for the heterogeneous distribution of the population due to the irregular orography (Figures
S2 and S3), we derived population-weighted daily mean temperature series across the study period for each Swiss municipality, as
explained in a previous study (de Schrijver et al. 2021).

Statistical Analysis
Overview of the statistical method and rationale. We assessed
the temporal trends in heat- and cold-related mortality impacts in
Switzerland and the inﬂuence of population aging in three steps. In
brief, we ﬁrst performed a two-stage time-series analysis to derive
the decadal temperature–mortality associations for each canton
and age category. We estimated the canton-speciﬁc association
using temperature–mortality series in each municipality by decade
while accounting for heterogeneity between municipalities andcantons. Second, we estimated the corresponding overall anddecade-speciﬁc heat- and cold-related excess mortality for eachcanton and age category. The statistical framework for these two
ﬁrst steps has been previously applied in recent works (e.g.,
Gasparrini et al. 2015a). Finally, we quantiﬁed the excess heat- andcold-related mortality attributed to population aging by rescaling
the observed impacts in each decade to the age structure of the population in the ﬁrst decade (1969–1978), thus assuming a hypothetical scenario of no population aging. Diﬀerently from previous
assessments of temporal trends, we accounted for changes in the
demographic structure of the population across time by estimating
the age-speciﬁc excess mortality using the corresponding baseline
mortality in each age category, decade, and canton. This approachmimics the method used in assessments of projections of healthimpacts to account for socioeconomic and demographic scenarios(Vicedo-Cabrera et al. 2019).
Estimation of the heat- and cold-related mortality associations. We applied an extension of the usual two-stage time-series
approach to estimate the geographical and temporal variation inage-speciﬁc heat- and cold-related mortality association using highresolution data. First, we performed separate ﬁrst-stage models foreach combination of canton, age category, and decade (1969–1978,
1979–1988, 1989–1998, 1999–2008, and 2009–2017). We applied
the novel case time-series design, a method which allows the application of ﬂexible time series techniques without the need of aggregating the data in a single series (Gasparrini 2021). Speciﬁcally, wedeﬁned multiple series of mortality and temperature at the municipality level within each canton and modeled them all together using
a conditional quasi-Poisson regression (Armstrong et al. 2014;
Gasparrini et al. 2010). We deﬁned matching strata deﬁned by year,
month, and day of the week, which is similar to the idea of the bidirectional time-stratiﬁed case-crossover design, and municipality.
This analytical approach provides a common exposure–response
function for all municipalities in a canton using temperature andmortality data at a higher resolution (i.e., municipality level), thusincreasing the precision of the estimates.
We modeled the temperature–mortality association using distributed lag nonlinear models. We deﬁned a quadratic B-spline withthree internal knots placed at the 10th, 75th, and 90th percentile ofthe canton-speciﬁc temperature distribution in the exposure dimension of the so-called cross-basis term of temperature (Gasparrini
et al. 2010). We modeled the lag-response curve using a natural
cubic B-spline with three internal knots placed at equally spaced values on the log scale up to 21 d to capture the long-lagged associations and short-term harvesting. We reduced the bidimensional
exposure-lag-response curve to a one-dimensional overall

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cumulative exposure–response association to reduce the number ofparameters while preserving complexity of the dependencies.
Then, we pooled the overall cumulative exposure–response
associations derived in the previous step by decade, age category,and canton through a multivariate multilevel longitudinal metaregression model (Sera et al. 2019a). This model included as ﬁxedeﬀect predictors a factor variable of time (i.e., decade) and age category, and mean temperature (as numeric variable) in each cantonand period. We tested for other variables (i.e., time-variant andinvariant contextual variables); however, these did not end up
being relevant based on the Wald test, Akaike criterion, and theassessment of the heterogeneity (Cochran Q bidimensional test
and the I 2 statistic) (see the Supplementary Material for details,
Table S1). We derived from the meta-analytic model the best linear
unbiased predictions (BLUPs), which are improved estimates oftemperature–mortality associations (Gasparrini et al. 2012; Sera
et al. 2019a). For each combination of canton, age, and decade we
derived the MMT, used as the reference, and the corresponding
minimum mortality percentile (MMP) (deﬁned between the 60thand 98th percentile of the decade-speciﬁc temperature distribution). The MMT corresponds to the temperature value for which
the temperature-mortality risk is minimum, usually considered as
optimal temperature (Gasparrini et al. 2015a).
Estimation of excess mortality impacts. We calculated theexcess mortality attributed to nonoptimal temperatures using thedecade- and age-speciﬁc BLUPs and corresponding baseline
mortality and temperature series in each canton, as described
elsewhere (Gasparrini and Leone 2014). We computed the coldand heat-related excess mortality by summing the daily contributions on days with temperatures lower or higher than the decadeage-canton–speciﬁc MMT. We calculated the corresponding 95%
empirical conﬁdence interval (eCI) using Monte Carlo simulations (Gasparrini and Leone 2014). We ﬁrst reported the impacts
as the total number of excess deaths attributed to cold and heat,and the average annual per subperiod. We also calculated theheat and cold mortality rates (per 100,000 people) by age category and decade. We collected historical population data per canton and age group from Historical Statistics of Switzerland(HSSO 2012). We used the population in the ﬁrst year of the decade for each of the subperiods (e.g., 1970 for the 1969–1978 period, 1980 for the 1979–1988). To obtain comparable estimatesacross cantons, we calculated the heat- and cold-related agestandardized mortality rates using the average population distribution in each decade. We also estimated the overall mortality
rates by age group (i.e., for the full study period) and the overall
rate as an average of the corresponding decade-speciﬁc estimates.
Assessment of the impact of population aging on the observed
trends. We assessed the role of population aging in two ways.
First, we compared the trends in average annual number of deaths
and mortality rates by age group to disentangle the inﬂuence ofchanges in the size of the population at risk in each age group.

Second, we compared the estimated all-age heat/cold mortality
rates (observed) in each decade with the corresponding mortality
rates calculated assuming the demographic structure of the population in the ﬁrst decade, which is assuming that no changes in
the population age composition had occured (i.e., no aging). In
brief, we restructured the decade-speciﬁc population according to
the relative age-group contribution in the ﬁrst decade (1969–1978). In this way, we rescaled the observed population for each
decade according to the age structure of 1969–1978 (i.e., assuming no population aging), while keeping constant the total population for the corresponding decade. We then calculated the heatand cold-related deaths over the rescaled population by using thecorresponding age- and decade-speciﬁc mortality rates estimatedin the main analysis. This approach yielded the number of heatand cold-related excess mortality in the hypothetical scenario of
no population aging. We then expressed the diﬀerence percent
between the excess mortality calculated with and without the
population aging to quantify the contribution of population aging
to the observed trend. As the population between the observed
and the hypothetical scenario in each decade remained constant,the diﬀerences would only reﬂect the inﬂuence of the changes indemographic structure across decades (i.e., distribution of the
population across age groups).
We used R software (version 4.01; R Development CoreTeam) for this data analysis. The dlnm package was used to create distributed lag nonlinear model (Gasparrini 2011), and themixmeta package was used to ﬁt the longitudinal multivariate
meta-analysis (Sera et al. 2019a).

Results
Description of the Mortality and Temperature Series
Table 1 provides a summary description of the mortality and temperature data by decade from 1969 to 2017 aggregated by region in
Switzerland (Table S2 reports the corresponding summary description for each canton; for further details on the spatial boundaries
see Figure S1 and Table S3). We analyzed 2,986,581 death records
throughout the 2,212 municipalities (2,056 aggregated units) covering the full Swiss geography. The age distribution of the total
mortality changed over time, with the ≥80-y-old age group contributing 27.8% of the total annual deaths in 1969 and 60.8% in2017 (Figure S4), which equates to an increase from 16,000 to
40,000 annual deaths in this age group. However, for the <65 and
65–79-y-old age groups this proportion decreased from 29.8% to13.6% and from 42.4% to 25.6%, respectively. The population sizeof the ≥80-y-old age group also substantially increased from111,285 to 372,894 people, the equivalent of 235%, between 1970
and 2010 (Figures S5 and S6). As shown in Table 1, we observe
the warmest mean temperatures in the regions of northwest
Switzerland and Ticino, and the coldest temperatures wereTable 1. Descriptive statistics by region for the total number of all-cause deaths, number of municipalities, and average daily mean temperature distributions(median and IQR) for the overall study period and by decade.

1. Lake Geneva
2. Espace Mitteland
3. Northwest
Switzerland
4. Zurich
5. Eastern Switzerland
6. Central Switzerland
7. TicinoOverall

Average daily mean temperature by period + IQRDeaths(n)Municipalities(n)511,754
747,935
403,450

418
617
288

7.9 (−3:7, 19.3) 6.7 (−4:6, 18.1) 6.9 (−4:8, 18.7) 8.0 (−3:0, 19.0) 8.6 (−3:0, 20.3) 8.7 (−2:8, 20.2)
8.1 (−3:4, 19.5) 7.2 (−4:3, 18.6) 7.6 (−4:1, 19.2) 8.4 (−2:7, 19.5) 8.9 (−2:8, 20.6) 9.1 (−2:5, 20.7)
9.6 (−1:9, 21.2) 8.4 (−3:9, 19.8) 8.9 (−2:7, 20.5) 9.8 (−1:4, 21.0) 10.5 (−1:2, 22.2) 10.6 (−1:0, 22.2)

506,099
439,438
200,599
131,7632,986,581

163
304
158
108
2,056

9.1 (−2:8, 21.1) 8.0 (−3:9, 19.8) 8.5 (−3:4, 20.5) 9.3 (−2:4, 20.9) 10.0 (−2:1, 22.2) 10.1 (−1:9, 22.2)
7.0 (−4:4, 18.4) 6.0 (−5:3, 17.3) 6.4 (−5:2, 18.0) 7.1 (−4:0, 18.2) 7.7 (−3:8, 19.3) 7.9 (−3:4, 19.3)
6.4 (−4:8, 17.6) 5.4 (−5:8, 16.5) 5.9 (−5:5, 17.2) 6.6 (−4:2, 17.3) 7.1 (−4:4, 18.6) 7.5 (−3:8, 18.7)
9.2 (−2:7, 21.1) 8.2 (−3:5, 19.8) 8.3 (−3:9, 20.5) 9.3 (−2:1, 20.8) 9.9 (−2:2, 22.0) 10.3 (−1:8, 22.3)
8.1 (−3:4, 19.7) 7.0 (−4:4, 18.5) 7.3 (−4:4, 19.1) 8.3 (−2:9, 19.4) 8.8 (−3:0, 20.6) 9.0 (−2:7, 20.7)

Region1969–2017

1969–1978

1979–1988

1989–1998

1999–2008

2009–2017

Note: IQR, interquartile range.

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Table 2. Excess all-cause mortality related to nonoptimal temperature (heat, cold, and total), by age group and decade between 1969 and 2017 in Switzerland[number of deaths and fractions (%), and 95% confidence interval]. Ntot represents the corresponding total all-cause mortality for each study period and age
group.
Sub-category

Measure of impact

Heat-related mortality(95% CI)

Cold-related mortality(95% CI)

Non-optimal temperature-related
mortality (95% CI)

Total

1969–2017(Ntot = 2,986,581)

Total (n)Fraction (%)*
Rate (per 100,000)*

8,385 (5,482, 10,937)
0.28 (0.18, 0.37)
2.4 (1.4, 3.4)

266,193 (222,842, 304,815)
8.91 (7.46, 10.21)
77 (63, 88)

274,578 (230,657, 312,761)
9.19 (7.72, 10.47)
79 (66, 90)

Age

<65 y
(Ntot = 603,540)

Total (n)Fraction (%)*
Rate (per 100,000)*
Total (n)Fraction (%)*
Rate (per 100,000)*
Total (n)Fraction (%)*
Rate (per 100,000)*953 (158, 1,645)
0.16 (0.03, 0.27)
0.3 (0.1, 0.5)
1,508 (572, 2,530)
0.17 (0.06, 0.26)
4.3 (1.5, 6.6)
5,794 (4,003, 7,423)
0.42 (0.29, 0.53)
48.2 (33.3, 61.8)

21,418 (8,354, 32,540)
3.55 (1.38, 5.38)
7 (3, 11)
92,903 (74,085, 107,898)
9.26 (7.49, 10.90)
239 (193, 281)
153,185 (131,787, 171,906)
10.99 (9.46, 12.34)
1,275 (1,097, 1,431)

22,372 (9,625, 33,316)
3.71 (1.59, 5.52)
8 (3, 11)
93,227 (75,520, 109,623)
9.42 (7.63, 11.07)
243 (197, 286)
158,797 (137,495, 177,816)
11.41 (9.87, 12.76)
1,323 (1,145, 1,480)

Annual (n)Fraction (%)
Rate (per 100,000)
Annual (n)Fraction (%)
Rate (per 100,000)
Annual (n)Fraction (%)
Rate (per 100,000)
Annual (n)Fraction (%)
Rate (per 100,000)
Annual (n)Fraction (%)
Rate (per 100,000)

74 (12, 125)0.13 (0.02, 0.22)
1.2 (0.2, 2.0)
158 (76, 236)
0.27 (0.13, 0.40)
2.5 (1.2, 3.7)
104 (10, 193)
0.17 (0.02, 0.31)
1.5 (0.1, 2.8)338 (239, 432)
0.55 (0.39, 0.70)
4.7 (3.3, 6.0)
181 (39, 307)0.28 (0.06, 0.48)
2.1 (0.4, 3.5)

6,559 (5,009, 7,985)
11.52 (8.80, 14.03)
105 (80, 127)
4,780 (3,780, 5,789)
8.03 (6.36, 9.73)
75 (59, 91)
5,145 (3,839, 6,325)
8.22 (6.13, 10.11)
75 (56, 92)
6,306 (5,052, 7,533)
10.23 (8.20, 12.23)
87 (70, 105)
4,254 (2,642, 5,757)
6.59 (4.10, 8.92)
49 (30, 66)

6,633 (5,041, 8,096)
11.65 (8.86, 14.22)
106 (80, 129)
4,939 (3,941, 5,954)
8.30 (6.63, 10.01)
78 (62, 94)
5,250 (3,918, 6,386)
8.39 (6.26, 10.21)
76 (57, 93)
6,645 (5,360, 7,918)
10.78 (8.70, 12.85)
92 (74, 110)
4,435 (2,807, 5,976)
6.87 (4.35, 9.26)
51 (32, 68)

Category

65–79 y(Ntot = 989,574)≥80 y(Ntot = 1,393,467)
Decade1969–1978(Ntot = 569,226)
1979–1988(Ntot = 594,733)
1989–1998(Ntot = 625,706)
1999–2008(Ntot = 616,199)
2009–2017(Ntot = 580,717)*

The results of the excess mortality fractions and rates (per 100,000 people) over the total study period (1969–2017) should be interpreted with caution because these have been averaged over the total deaths and total population, respectively.

registered in less populated and mountainous regions of easternand central Switzerland. The daily mean temperature distribution
progressively shifted toward warmer temperatures in all regions,with an average of approximately 2°C increase in 2009–2017 vs.
1969–1978.

Overall Heat- and Cold-Related Excess Mortality
Total all-cause excess mortality associated with nonoptimal temperatures was 9.19% [95% conﬁdence interval (CI): 7.72, 10.47], whichtranslates to 274,578 (95% CI: 230,657, 312,761) temperaturerelated excess deaths in Switzerland between 1969 and 2017 (Table
2; Table S4). Cold-related mortality represented a larger fraction incomparison with heat, with 8.91% (95% CI: 7.46, 10.21) vs. 0.28%(95% CI: 0.18, 0.37). The corresponding annual all-age rates were
2.4 deaths (95% CI: 1.4, 3.4) and 77 deaths (95% CI: 63, 88) per
100,000 people, with heterogeneous impacts across age groups. The
average heat-related mortality rate per 100,000 people between
1969 and 2017 was lower in the <65 age group, with 0.3 deaths(95% CI: 01, 0.5), followed by the 65–79 age group with 4.3 deaths(95% CI: 1.5, 6.6), and 48.2 deaths (95% CI: 33.3, 61.8) per 100,000
people for the ≥80-y-old age group. For cold, the ≥80-y-old age
group is also most vulnerable, with on average 1,275 deaths (95%
CI: 1,097, 1,431) per 100,000 people, although this was substantially lower for the 65–79-y-old and <65-y-old age groups, with
239 deaths (95% CI: 193, 281) and 7 deaths (95% CI: 3, 11) per
100,000 people, respectively. The mortality fractions by age groupare shown in Table 2.

Temporal Trends in Heat- and Cold-Related Mortality
For heat, the average annual excess deaths more than doubled
between 1969–1978 and 2009–2017 [from 74 (95% CI: 12, 125) to181 deaths (95% CI: 39, 307)], whereas the overall heat-related
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mortality rate also increased but at a lower magnitude from 1.2
deaths (95% CI: 0.2, 2.0) to 2.1 deaths (95% CI: 0.4, 3.5) per
100,000 people. The 1999–2008 decade reported substantiallyhigher annual heat-related deaths with 338 deaths (95% CI: 239,432), which corresponds to 4.7 (95% CI: 3.3, 6.0) deaths per
100,000 people (Table 2; Table S4). When excluding the excess
mortality in the year 2003 (when the massive European heat wave
occured), annual heat-related mortality burden in this decadedecreased to 234 deaths (95% CI: 153, 298), thus suggesting thatthe steep increase could partially be attributed to the 2003 heatwave (Table S5). Similar patterns can be observed in the ≥80 age
group (Figure 1A and B), with the largest increase in annual excess
deaths from 33 deaths (95% CI: −2, 59) in 1969–1978 to 162 (95%
CI: 36, 278) deaths in 2009–2017, whereas the increase in mortality rates was not as substantial, from 29.4 deaths (95% CI: −1:9,52.9) in 1969–1978 to 39.1 deaths (95% CI: 8.7, 67.1) per 100,000
people in 2009–2017. For the <65 and 65–79 age groups, both theannual excess mortality as well as the mortality rates remained constant over time (See Table S4 for the corresponding ﬁgures).
Conversely, cold-related mortality decreased in Switzerland
during the study period, from 6,559 (95% CI: 5,009, 7,985) in
1969–1978 to 4,254 (95% CI: 2,642, 5,757) annual deaths in 2009–2017, corresponding to a decrease in the overall rate from 105
deaths (95% CI: 80, 127) to 49 deaths (95% CI: 30, 66) per 100,000
people, respectively (Figure 1C,D; Table 2). All numbers illustrated in Figure 1 have been reported in the supplementary ﬁle(Table S4). The most substantial decrease in the cold-related
excess mortality was among the 65–79 y age group, from 2,999(95% CI: 1,955, 4,020) in 1969–1978 to 1,076 annual deaths (95%
CI: 37, 1,722) in the 2009–2017 decade (Table S4). Similarly, mortality rates decreased from 497 deaths (95% CI: 324, 666) to 101
deaths (95% CI: 35, 161) in 2009–2017. Interestingly, among the≥80-y-old age category, although mortality rates decreased from

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Figure 1. Annual excess mortality for heat (A,B) and cold (C,D) by decade and age group. Panels A and C report the age-speciﬁc annual excess deaths forheat and cold respectively, and panels B and D show the corresponding heat and cold-related mortality rates per 100,000 people. The point estimates with thecorresponding 95% conﬁdence interval are provided in Table S7.

2,339 deaths (95% CI: 1,663; 2,995) in 1969–1978 to 1,408 deaths(95% CI: 1,070, 1,725) per 100,000 people per year in 1999–2008,
the corresponding annual number of deaths substantially increased
from 2,603 (95% CI: 1,850, 3,333) to 4,102 deaths (95% CI: 3,119,
5,026) in the fourth decade (1999–2008). Both rates and number ofdeaths decreased from the fourth to the last decade. Furthermore, atthe beginning of the study period, there were more annual coldrelated deaths of the 65–79 y age group [2,999 annual deaths (95%
CI: 1,955, 4,020)], whereas at the end of the period, the ≥80 y age
group reported the largest number, with 3,059 annual deaths (95%
CI: 1,787, 4,283) in 2009–2017. For the <65 age group, the coldrelated mortality rate has remained small and even close to null inthe 2009–2017 decade, with 2 deaths (95% CI: −5, 11) per 100,000
people.
The observed trends in excess mortality align with thechanges in risk and evolution of the MMT/MMP across decadesin each age group (Figures S7–S8; Tables S6–S8). Overall, heatrelated mortality risk remained fairly stable in all age groups
across all decades, except for the 1999–2008 decade in which therisk substantially increased. For cold, mortality risk decreased in
all age groups across the study period. Additionally, we foundthat the MMT for the <65 and 65–79 age groups has increased
over time, whereas the MMP has remained constant (Figure S7).
For the ≥80 age group, the MMT has remained constant overtime, whereas the MMPs decreased.
Furthermore, as shown in Figure 2, the geographical distribution of the heat-related excess mortality rates changed over the
study period. Speciﬁcally, we can see that rates were similar
across cantons in the ﬁrst decades and started to spatially diverge
in the 1980s with larger rates in northern and western
Switzerland and lower rates in central Switzerland. For cold, a
more homogenous decline among all cantons can be observed,
with large spatial heterogeneity.

Impact of Population Aging
First, we found that the relative contribution of heat- and coldrelated deaths in the ≥80 age group (over the all-age heat- andcold-related deaths) substantially increased across the study period
Environmental Health Perspectives(from 36.3% to 90.2% and from 39.0% to 70.8% for heat and cold,
respectively) (Table S9). Figure 3 illustrates the contribution of
population aging in the observed trend over the study period, as
depicted by the diﬀerence between the dark and bright bars and theshown percentage on top. The contribution of population aging tothe overall heat- and cold-related mortality trend increased from
21.7% in the 1979–1988 decade to 52.7% in the most recent decadeand from 23.9% to 44.6%, respectively.

Discussion
In this nationwide study, we found evidence that population
aging could have altered the temporal patterns in heat- and coldrelated mortality between 1969 and 2017 in Switzerland. In particular, our results ﬁrst conﬁrm that a large proportion of heatand cold-related mortality happens among the age groups above
65 y of age. We estimated that 0.28% and 8.91% of all-cause
mortality, which corresponds to 2.4 and 77 deaths per 100,000
people per year, could be attributed to heat and cold, respectively.
More than half of these were in the population of ≥80 y old.
Second, vulnerability to heat increased for the ≥80 age groupover the past 50 y, whereas the <65 and 65–79-y-old populations
showed signs of resilience. In contrast, although vulnerability tocold decreased across all age groups, the cold-related mortality
burden, in terms of number of deaths, has continuously increased
among the ≥80-y-old age group amid the increase in the pool ofvulnerable individuals due to population aging. Finally, our ﬁndings suggest that a substantial proportion of temperature–mortality impacts can be attributed to population aging, amounting to
52.7% and 44.6% of heat- and cold-related mortality for the mostrecent decade.
We estimated that 9.2% of all-cause mortality during the lastﬁve decades in Switzerland can be attributed to nonoptimal temperatures. This amounted to 8,385 and 266,193 excess deaths intotal for heat and cold, of which 64% were among the ≥80 age
group. Additionally, we found that heat-related deaths almost
tripled between 1969–1978 and 2009–2017, which was largely aresult of an increase in the ≥80 age group, whereas it slightly
decreased in the <65 y and 65–79 y age groups. The observed

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Figure 2. Age-standardized (std.) excess mortality rate for heat (A) and cold (B) by decade per 100,000 people.

Figure 3. Panels A and B compare the observed annual heat- and cold-related number of excess deaths, respectively, with the excess mortality estimated in a
scenario of no population aging. The percent reported on the “observed” bars illustrate the corresponding decade-speciﬁc percent diﬀerence between theobserved annual heat and cold-related excess mortality and the scenario of no population aging in comparison with 1969–1978. The point estimates and diﬀerence percent between the observed and counter-factual scenario are provided in Table S9.

Environmental Health Perspectives

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increase in heat vulnerability contradicts ﬁndings in other retrospective studies, which found a reduction of risks, suggesting apartial adaptation of the population to the increase in temperature(Achebak et al. 2019; Gasparrini et al. 2015b; Nordio et al. 2015;
Vicedo-Cabrera et al. 2018). In particular, a recent multicountry
study found an overall decline in heat-related excess mortality in
Switzerland (Vicedo-Cabrera et al. 2018). However, it should be
noted that this study included a shorter study period (1995–
2012), coinciding with the period between 1999–2008 and 2009–2017 decades in which we found a similar decline in heat-related
mortality. In our analysis, the 2003 heat wave was shown to partially drive the exceptionally high annual heat-related mortality
for the 1999–2008 decade (Grize et al. 2003). However, whenexcluding the year 2003, we still observed higher excess heatrelated mortality in comparison with the period 2009–2017. For
cold, we observed an overall decreasing trend in excess mortality
rates between 1969–1978 and 2009–2017, which is consistent
with existing ﬁndings (Achebak et al. 2019; Vicedo-Cabrera
et al. 2018). This decline is a result of changes in vulnerability
and relative risk for cold temperatures, commonly attributable tochanges in infrastructure, changes in health care access, and otherimproved vulnerability factors (Arbuthnott et al. 2016).
Our ﬁndings support the conclusion that climate change andaging populations constitute a dual challenge for future generationsin terms of health burden attributed to nonoptimal temperatures.
We found that population aging could have been responsible for an
increase in annual mortality burden due to cold in the ≥80-y-old
age group, despite the decrease in vulnerability (i.e., decrease inrelative risk and mortality rates). The progressive increase in thepool of vulnerable individuals could have partially counteracted
the potential beneﬁts of the progressive adaption of the population
to cold temperatures. In the case of heat, population aging could
have led to an ampliﬁcation of the impacts. We observe that rates
slightly increased over the past 50 y, whereas the annual number ofdeaths is 5 times more in the recent decade among the ≥80 age
group. No previous study has attempted to compare the contribution of both changes in vulnerability and population at risk to thehistorical temperature–mortality burden. If current aging trends
persist in Switzerland, the pool of susceptible population at risk for
extreme temperatures (i.e., elders) will double by 2060 (Lewis andOllivaud 2020). Thus, the combination of a warming climate andpopulation aging will likely exacerbate the impact of heat and possibly attenuate the potential decrease in cold-related deaths infuture decades (Chen et al. 2020). For instance, when projection
studies assumed a constant population over time, they estimated an
overall net decrease (or lower increase) in temperature-related
mortality burden; however, when accounting for demographic
change, a larger net increase in heat- and cold-related mortality
was observed (Åström et al. 2017; Chen et al. 2020; Lee et al.
2018; Lee and Kim 2016; Li et al. 2016; Liu et al. 2019; Marsha
et al. 2018; Vardoulakis et al. 2014).
The observed progressive population aging we observed in
Switzerland follows the current trends in developed northern
regions of the globe. Thus, it is likely that the impact of population aging on historical temperature–mortality trends in theseregions could be similar. However, these ﬁndings should not bedirectly extrapolated to these geographical locations, because
other factors driving changes in vulnerability are highly heterogeneous and diverge from one place to another. Although we
assessed several potential factors driving changes in vulnerability
over time (e.g., percent of population above 80 y of age and population density), none of them resulted in relevance in the statistical model. However, we acknowledge that other relevant timevarying factors were not assessed due to the lack of data, such asaccess to air conditioning (Nordio et al. 2015; Sera et al. 2020),
Environmental Health Perspectives

socioeconomic status (Ng et al. 2016; Sera et al. 2019a), orchanges in land use (Murage et al. 2020; Sera et al. 2019b).
Similarly, regions currently having a large proportion of young
population, such as in some low- and middle-income countries,
will also likely experience population aging in the future, according to current population projections (United Nations 2019).
Thus, our ﬁndings suggest that the impact of population aging
that we observed in past decades in Switzerland could be potentially relevant for the coming decades in these regions.
Our ﬁndings also suggest large diﬀerences in vulnerability
between age groups, with populations <65 and 65–79 y of age
being more resilient to nonoptimal temperatures in comparison
with the ≥80-y-old age group. Considerably larger risks were
found in the oldest age category for both heat and cold (FigureS8). Another proxy for vulnerability often used is the diﬀerencein MMT between age groups and over time (Achebak et al. 2019;Åström et al. 2016; Todd and Valleron 2015). First, we observed
higher MMPs among the <65 and 65–79-y-old age groups compared to ≥80-y-old category (Figure S7). This observation would
suggest that the eﬀect of heat is restricted to more extreme temperature ranges in the two younger subpopulations, whereas
increased risks are already present in milder temperatures in the≥80-y age category. Second, MMTs increased among the<65-y-old and 65–79-y-old age groups in time, whereas MMPs
remained constant, which would suggest resilience to the progressive shift toward warmer temperatures due to climate change.
In contrast, the ≥80-y-old age group showed no signs of adaptation because the MMT has remained constant despite the increasein temperature. The trends in the <65-y-old and 65–79-y-old age
groups are consistent with ﬁndings reported in previous literature;
however, a stagnation in the MMT among the ≥80-y-old age
group has not been reported before (Achebak et al. 2019).
This study has several strengths. We assessed temperature–mortality trends by properly accounting for changes in the age structureof population, as reported in health impact projection studies inwhich diﬀerent socioeconomic development pathways are compared. Previous studies estimated the overall temperature–mortality
associations (i.e., across all ages) and applied them over the all-agesbaseline daily mortality (Gasparrini et al. 2016; Vicedo-Cabrera
et al. 2018). This approach does not take into account thattemperature–mortality risks and baseline mortality (or population atrisk) change in time but also between population subgroups (i.e., agegroups), because of diﬀerent degrees of adaptation/acclimatizationand demographic changes. Additionally, this study used the stateof-the-art methodologies recently developed in climate change epidemiology, which allowed us to obtain robust estimations ofimpacts while accounting for complex temporal and geographicalpatterns in the risks. We also made use of historical high-quality
daily mortality and temperature data deﬁned at each municipality in
Switzerland along almost 50 y of time. Regarding the latter,municipality-level series were derived from the combination ofhigh-resolution temperature and population maps. As shown in aprevious publication (de Schrijver et al. 2021), this methodology
provides more accurate exposure series and reduced exposure misclassiﬁcation, in particular for large municipalities located in rural
areas with a sparse population distribution. By assessing almost 50 yof data, we were able to better capture temporal patterns and potential drivers happening at a longer time scale (i.e., adaptation andpopulation aging).
Some limitations must be acknowledged. We could not assess
temporal pattern by combinations of sex and age category due to thelack of statistical power. Although previous studies found heterogeneous trends by sex (Achebak et al. 2019), we assume that the roleof population aging might have been the same in the temperature–mortality impacts for both sexes. Apart from the role of population

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aging, we assessed other potential time-varying factors (e.g., population density, spatial terms, and interaction terms) in the metaanalytical model, which could have partially explained the observed
trends, although none of the tested variables were found to be relevant. It should be noted that other relevant factors such as air conditioning or other infrastructural measures (Sera et al. 2020) were notconsidered, because data was not available for the whole country at
diﬀerent time points. Understanding changes in the temperature vulnerability over time and its drivers is critical for projection studies
and public health (Hess and Ebi 2016). Thus, future studies should
aim to speciﬁcally identify those relevant factors that could help todesign eﬀective adaptation strategies in the future. We did not testfor other environmental factors as potential confounders of the temperature–mortality, such as air pollution, humidity, and inﬂuenza
epidemics. However, we believe that their impact would be, if present, minimal because the role of these variables as confounders
remains debatable (Armstrong et al. 2019; Buckley et al. 2014; vonKlot et al. 2012). Last, although we explored only trends in mortality, mortality is hypothesized to be only the tip of the iceberg withlarge morbidity and economic costs associated with nonoptimal
temperatures; yet, underlying changes in morbidity and other societal costs have remained largely unexplored in the current literature(Wellenius et al. 2017; Wondmagegn et al. 2021).

Conclusion
In conclusion, our ﬁndings suggest that population aging could
have been a relevant driver of historic trends in temperaturerelated mortality impacts during the last ﬁve decades in
Switzerland. In particular, we found that progressive aging of the
population might have attenuated the decrease in cold-related
mortality expected from the warming of the climate and exacerbated the burden attributed to heat. This evidence supports the
hypothesis that population aging combined with climate change
is likely to further amplify the mortality burden attributed to heatand cold if no further adaptation measures are introduced, putting
an additional strain on health care systems across countries withan aging population. Therefore, protecting elders from the eﬀects
of climate change could be crucial to reducing the health impacts
and building populations resilient to the threatening adverse
health impacts of a warming climate.

Acknowledgments
The authors would like to thank the BFS for providing data on
the daily mortality in Switzerland used in this study.
E.S. has received funding from The European Union’s Horizon
2020 research and innovation program under the Marie SkłodowskaCurie grant agreement No. 801076, through the Swiss School of
Public Health (SSPH) Global PhD Fellowship Programme in Public
Health Sciences (GlobalP3HS) of the SSPH. A.G. has received
funding from The Medical Research Council-UK (Grant ID: MR/
R013349/1), The Natural Environment Research Council UK (Grant
ID: NE/R009384/1), The European Union’s Horizon 2020 Project
Exhaustion (Grant ID: 820655), and The Joint Research Center ofthe European Union (JRC/SVQ/2020/MVP/1654).

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037001-9130(3) March 2022