Exploring vulnerability to heat and cold across urban and rural populations in Switzerland

Heat- and cold-related mortality risks are highly variable across different geographies, suggesting a differential distribution of vulnerability factors between and within countries, which could partly be driven by urban-to-rural disparities. Identifying these drivers of risk is crucial to characterize local vulnerability and design tailored public health interventions to improve adaptation of populations to climate change. We aimed to assess how heat- and cold-mortality risks change across urban, peri-urban and rural areas in Switzerland and to identify and compare the factors associated with increased vulnerability within and between different area typologies. We estimated the heat- and cold-related mortality association using the case time-series design and distributed lag non-linear models over daily mean temperature and all-cause mortality series between 1990-2017 in each municipality in Switzerland. Then, through multivariate meta-regression, we derived pooled heat and cold-mortality associations by typology (i.e. urban/rural/peri-urban) and assessed potential vulnerability factors among a wealth of demographic, socioeconomic, topographic, climatic, land use and other environmental data. Urban clusters reported larger pooled heat-related mortality risk (at 99th percentile, vs. temperature of minimum mortality (MMT)) (relative risk=1.17(95%CI:1.10;1.24, vs peri-urban 1.03(1.00;1.06), and rural 1.03 (0.99;1.08)), but similar cold-mortality risk (at 1st percentile, vs. MMT) (1.35(1.28;1.43), vs rural 1.28(1.14;1.44) and peri-urban 1.39 (1.27-1.53)) clusters. We found different sets of vulnerability factors explaining the differential risk patterns across typologies. In urban clusters, mainly environmental factors (i.e. PM2.5) drove differences in heat-mortality association, while for peri-urban/rural clusters socio-economic variables were also important. For cold, socio-economic variables drove changes in vulnerability across all typologies, while environmental factors and ageing were other important drivers of larger vulnerability in peri-urban/rural clusters, with heterogeneity in the direction of the association. Our findings suggest that urban populations in Switzerland may be more vulnerable to heat, compared to rural locations, and different sets of vulnerability factors may drive these associations in each typology. Thus, future public health adaptation strategies should consider local and more tailored interventions rather than a one-size fits all approach. size fits all approach.


Introduction
There is a well-established relationship between exposure to non-optimal temperatures and a wide range of adverse health outcomes 1,2 . Currently, non-optimal temperatures are associated with approximately 9.4% of the total mortality burden globally, which corresponds to 74 deaths per 100,000 people, of which the largest part can be attributed to cold (8.5% versus 0.9% for heat) 2 . Evidence suggests that climate change is already substantially affecting populations leading to an additional heat-mortality burden which is likely to increase further in the future and even overtake the current cold-related mortality under various climate change scenarios 3,4 . Even with full implementation of the Paris agreement and reaching net-zero carbon emissions by 2050, the inherent inertia of the climate system will continue to increase temperatures for several more decades after 5,6 , yielding a substantial additional health burden 7,8 . Thus, accelerated adaptation to non-optimal temperature is essential to reduce the heat-related mortality burden 9,10 . Moreover, besides the expected increase in heat-related mortality, cold-related mortality is likely to further increase due to population ageing, showing the necessity to identify further adaptation strategies and vulnerability factors 11,12 A large body of literature has shown that the temperature-mortality association can substantially vary across different geographical units and population sub-groups [13][14][15][16][17][18][19][20][21][22] , which are driven by small area characteristics such as access to air conditioning (AC), ageing, greenness and socioeconomic level amongst others [19][20][21][23][24][25] . However, most of the existing evidence of temperature-mortality risks and corresponding vulnerability factors rely on impact assessments conducted in urban locations alone, since smaller cities and rural locations have barely been assessed due to a lack of valid exposure data 26,27 , low statistical power 28,29 , or have been considered as part of larger regions lacking the local dimension of the risks.
Even though urban areas tend to be warmer than rural regions due to the Urban Heat Island effect (UHI), rural regions have found to be at least equally vulnerable to temperature and climate change 14,17,18,22,[30][31][32][33] . Moreover, the association between urbanicity and heat-vulnerability has been hypothesised to follow a U-shape curve, with larger risks in extremely urban or extremely rural regions 17 , while for cold, rural regions tend to be more vulnerable due to lower access to health care, lower baseline health or poverty amongst others 12, 16 . Although there is agreement on spatial variability of the temperature-mortality association, little is known regarding differential drivers of temperature-vulnerability between typologies. Moreover, in Switzerland, previous studies have observed large variation in the heat-and cold-related mortality impacts between cities and cantons with larger heat-mortality impacts in urban regions (i.e., Zurich, Basel and Geneva), and larger cold impacts in rural regions. However, thus far the underlying mechanisms for this large spatial variation has remained unexplored and it is not known which social (i.e., climate injustice), biological (i.e., ageing) or environmental vulnerability factors (i.e., particulate matter concentrations and temperature) explain the variation of non-optimal temperature-mortality impacts between regions in Switzerland. Understanding the mechanisms and factors driving vulnerability in urban/rural locations can help to identify the most vulnerable populations and aid the design of tailored public health interventions to modulate heat and cold-related vulnerability.
In this assessment, we hypothesise that vulnerability to heat and cold vary across urban and rural locations driven by different sets of vulnerability factors. First, vulnerability to non-optimal temperature is usually dependent on small-area level characteristics of the population and environment, which are highly heterogeneous within and between urban and rural regions. Second, these characteristics or factors can have different effects in each type of area (i.e., level of greenness in urban vs. rural locations). Therefore, we aimed to assess how heat-and cold-mortality risks differ across urban, peri-urban and rural regions between 1990 and 2017 in Switzerland, and to explore what factors are associated with increased vulnerability to non-optimal temperatures in each type of region. The novelty of this assessment is the application of a recently developed statistical framework to study the effect modification of individual variables in a complex multivariable regression model 16,34 , using a wealth of sociodemographic and environmental data available at high resolution.

Study setting
Switzerland is a country with a particularly sparse population which is unevenly distributed throughout the country (Fig S1). In particular, North and West Switzerland are more highly populated (where the main cities such as Zurich, Basel and Geneva are located) compared to Central and East Switzerland where the Swiss Alps are (Fig S2), creating stark differences in climate, orography and population distribution. Additionally, characteristics and composition of the populations in terms of social, demographic, and environmental factors are widely heterogeneously across Switzerland, with strong differences between urban, peri-urban and rural (Table 1, Fig S3)).

Temperature and mortality data
resolution temperature grid cells has shown to be a valid alternative to monitor stations to assess temperature-mortality impacts. It also has many advantages as opposed to monitor stations, as it allows us to assign an exposure to remote areas regardless the presence or not of weather stations (i.e., rural districts). 35,36

Vulnerability factors
We initially compiled an integrated dataset of 42 variables characterizing the population and environment for each municipality which we believed could modulate the vulnerability to non-optimal temperatures. We included several socioeconomic variables (social index (SES), percentage of new houses, ageing index), topographic variables (access to health care, population density), climatic variables (annual mean temperature and temperature range) as well as land use and environmental data (impervious surfaces, constructed area, Enhanced Vegetation Index (EVI), PM 2.5 annual concentration, percentage of water area). The full list of variables with the corresponding definition and source is provided in Table  S1. These variables were derived for each municipality and then aggregated to a new higher agglomerative cluster level (defined in Section 2.4). The spatial distribution for each variable at municipality level resolution is illustrated in Figure S1 with the correlation between all variables at the district level in Figure S4.
Since many of the 42 variables showed a large degree of multi-collinearity ( Figure S3), we reduced dimensionality in two ways. For the assessment of individual effect modification by vulnerability variable (Section 2.6), we selected 9 variables that we considered representative of different features based on the coordinates of the principal component and correlation matrix( Figure S5) (using the correlation matrix between variables by urban/ peri-urban/rural clusters is illustrated in Figure S6). The 9 selected variables were used as single vulnerability variables in our assessment as elaborated upon in section 2.6. Then, we conducted a principal component analysis (PCA) over the 9 selected vulnerability factors and created two principal components (PC), explaining the heterogeneity between urban, peri-urban and rural districts, respectively. These two components were then used to account for within-area typology-specific confounders when predicting the pooled urban, peri-urban and rural temperature-mortality association (as discussed in section 2.5).

Definition of urban, peri-urban and rural clusters
We defined a set of urban, peri-urban and rural clusters by combining all of the 2,054 municipalities into 94 higher agglomerative clusters using the Ward-like hierarchical clustering method with geographical constraints using municipality-level information on several vulnerability variables 37 . The Ward-like algorithm is a constrained hierarchical clustering method that aims to optimize a convex combination using two dissimilarity matrices and a mixing parameter to create a new higher agglomerative layer consisting of municipalities that are both similar and proximal to each other ( Figure S6) 37 . Methods S1 provides a more elaborate explanation. Subsequently, we developed a new agglomerative level consisting of 94 clusters, which was based on municipalities that were both similarand proximal to each other and had a minimum of 1,000 deaths. We then classified each high agglomerative cluster as "urban", "peri-urban" or "rural" according to the following criteria: each municipality was defined as urban or rural based on the official definition of BFS. When <50% of the population in each cluster lived in urban municipalities, we considered the cluster rural, when 50-80% resided in an urban municipality it was considered peri-urban and when >80% of the population resided in an urban municipality, we considered the cluster urban. We consider this ad hoc definition of clusters more appropriate for this study purposes, compared to the administrative upper level (i.e. district) defined by BFS, and also more appropriate than using an ad hoc definition based on clustering variables as used for the Ward-like hierarchical clustering method (i.e. EVI, where highly urbanised regions can have a similar value as mountainous regions). In particular, we believe that the differential effects of vulnerability factors by typology on the temperature-mortality association could be diluted as the Swiss orography, population characteristics (demography, environment) and distribution are highly heterogeneous within districts. Using the proposed approach, the municipalities included in the derived high agglomeration clusters are more homogeneous, thus allowing for a better characterisation of the vulnerability of the population and it would help to better capture the signal of potential effect modification of vulnerability factors.

Estimation of the temperature-mortality associations
To estimate the temperature-mortality association in each cluster, we performed a case time series analysis with conditional quasi-Poisson regression and distributed lag nonlinear models using municipality-specific temperature-mortality series 15,38 . The case time series design allowed us to estimate the exposure-response function within a cluster, but still use the high-resolution municipality level data, therefore, reducing exposure misclassification and increasing the precision of the estimates. This design also controls for temporal trends using a matching stratum defined by year, month and day of the week by municipality. We modelled the cluster-specific temperature-mortality association using the distributed lag nonlinear framework, a flexible technique to model non-linear exposure-response associations and lagged dependencies 39 . We defined a quadratic B-spline with three internal knots placed at the 10 th , 75 th and 90 th percentile of the cluster-specific temperature distribution (Table S2). We modelled the lag-response curve using a natural spline with three internal knots equally placed on the log scale up to 21 days to capture the long-lagged effects of heat and cold and to account for short-term harvesting, as done in previous studies 15 . We then reduced the bi-dimensional temperature-lag response curve to the one-dimensional overall cumulative exposure-response association.
In a second stage, we derived the pooled cumulative exposure-response associations for each cluster type through a multivariate meta-regression model 40 . We included an indicator of the typology to predict the pooled urban/peri-urban/rural-specific exposure-response curves. To account for specific within-typology variation of spatial and socio-demographic variables, we included the two principal components (PC1 and PC2) summarizing the 10 cluster-level selected variables in the meta-regression model. We assessed the heterogeneity using the likelihood-ratio test and the Cochran Q-test and the I 2 statistic (Table S3). We then predicted the pooled urban/peri-urban/rural temperature-mortality association expressed as a relative risk (RR), with the temperature of minimum mortality (MMT) as reference 40 .
The MMT corresponds to the temperature value for which the temperature-mortality risk is minimum, with days with a mean temperature below the MMT are considered cold and above the MMT are considered hot.

Assessment of the vulnerability factors
To assess vulnerability factors across Switzerland by typology, we applied the same multivariate meta-regression framework used before but separately for each typology and by including each of the 9 vulnerability factors as predictors in univariable models 34 . In this instance, in each of the univariable meta-analytical models, we separately tested how each predictor modifies the heat and cold-related temperature-mortality association by typology. We predicted the pooled exposure-response curves at the 5 th percentile (corresponding to a "low" value) and 95 th percentile (a "high" value) value for each of the 9 selected district-specific meta vulnerability factors. Thus, for each typology (urban/peri-urban/rural) we aimed to compare the heat-and cold-related mortality association for the hypothetical high and low levels of the vulnerability factor and subsequently calculated the corresponding p-value between "high" and "low" exposure for each vulnerability factor using the Waldtest. For example, "high" level of exposure to travel time to health care means longer travel time to health care in that specific cluster (corresponding to the 95th percentile), whilst "low" exposure represents short travel time to healthcare for a given cluster by typology (5th percentile). Similarly, exposure to "high" ageing represents a higher ratio of population aged over 65 years compared to the 20-64 age group present in a cluster compared "low" exposure to ageing for a given typology, which has a smaller proportion of people aged over 65 compared to the 20-64 age group, and thus can be characterised as a younger population. We did this for all vulnerability factors. Then, we extracted the RR at the 1 st percentile of the temperature distribution for cold and the 99 th percentile for heat for each variable with the corresponding 95% confidence interval. Lastly, to ease interpretability, we computed for each vulnerability factor the absolute relative risk difference between "high" and "low" exposure of the RR estimate. Table 1 provides a summary description of the mortality data, temperature series and the 9 selected vulnerability factors by urban, peri-urban and rural clusters in Switzerland. We analysed 1,775,178 deaths throughout 2,212 municipalities (2,054 aggregated units), covering the full Swiss geography between 1990 and 2017. 48.1% of the deaths occurred in urban clusters (854,077 deaths), followed by peri-urban (577,978) (32.6%) and rural clusters (343,123 (19.3%)) ( Figure 1). The urban clusters are mainly located in the North and West of Switzerland, while rural clusters tend to be clustered in Central and East Switzerland, which coincides with the mountainous area of the Swiss Alps ( Figure  S1-2). Additionally, warmer median temperatures were registered in urban clusters (9.2°C) compared to peri-urban (8.6°C) and rural clusters 7.4°C). Urban clusters also show higher population density compared to rural clusters ( )), although the confidence intervals partly overlap. There is some evidence for differential patterns of overall non-optimal temperature-mortality association across urban, peri-urban and rural clusters based on the Wald test (p-value = 0.13).

Results
For illustrative purposes, Figure 3 shows the temperature-mortality association by typology predicted at high and low levels of annual mean PM 2.5 concentration (defined as the 95 th and 5 th percentile, in purple and pink, respectively) using the univariable metaregression model (i.e., including only PM 2.5 concentration as predictor  figure 5A illustrate the cold and heat-mortality risks predicted at low (5 th percentile) and high (95 th percentile) levels for the selected vulnerability factors, respectively. The full exposure-response functions for each vulnerability factor (as shown in Figure 3) are reported in Figure S8-S10 and the complete list of estimates is reported in Table S4-S6. The heat and cold-mortality risks for low exposure to vulnerability factors are indicated with a light pink and orange cube, respectively, while high exposure is indicated with a purple and red triangle, with the corresponding 95% confidence intervals. Figure  4B and figure 5B illustrate the absolute relative risk difference between "high" and "low" exposure to a vulnerability factor. A high exposure to a vulnerability factor associated with lower risk is indicated in blue, while a high risk associated with a higher risk is illustrated in red. Figure 4 shows that the most influential drivers for cold-related vulnerability across all typologies are social factors while for peri-urban and rural clusters also environmental factors and variables related to urban characteristics are important effect modifiers. High SES and low % of foreign population in urban and rural clusters are associated with a reduction in risk, whilst for peri-urban clusters mixed associations are observed. In urban clusters, high SES is associated with a reduction in risk (1. 16

Discussion
This nationwide study aimed to assess how vulnerability to heat and cold varies across urban, peri-urban and rural clusters, and more importantly, to identify which factors are driving such vulnerability patterns. Our results suggest that urban clusters are at increased vulnerability to non-optimal temperatures, mainly to heat, compared to rural and peri-urban clusters in Switzerland. Therefore, health impacts derived at national or large regional level may be under-or overestimated if ignoring differential vulnerability between urban and rural regions. This may be relevant for the evaluation of historical and future health impacts of climate change. More importantly, our findings challenge the assumption that urban/peri-urban/rural regions share similar vulnerability drivers in terms of characteristics of the population, geographic and socio-economic factors. The main driver for the heatmortality association across all urban/peri-urban/rural clusters are environmental factors (i.e. temperature and PM 2.5 ), however, for peri-urban and rural regions other factors also modify the association such as socio-economic factors as well as population ageing. For cold, across all urban/peri-urban/rural clusters social factors (% of foreign population, SES and travel time to nearest health care facility) modify the cold-mortality association, while for peri-urban and rural clusters also environmental factors and biological factors affected the cold-mortality association, with heterogeneity in the direction of the association between typologies. Although not all identified vulnerability factors such as biological factors are modifiable, this study can help identify vulnerable subpopulations in Switzerland in specific tasks like vulnerability mapping 25,41 . Moreover, future public health adaptation strategies which aim to attenuate heat and cold-related health impacts should account for heterogeneity and implement more tailored interventions according to the local characteristics of the population.
Overall, we observed a larger heat-related mortality risk in urban clusters, followed by peri-urban and then rural clusters, similar to findings from recent assessments 16,32,[42][43][44] , whilst other studies reported larger vulnerability in rural areas 14,17,18,30,31,33,45,46 . Possibly, these contradictory results could be explained by differences in the baseline health and/or characteristics of the population of urban and rural populations between countries assessed with different baseline characteristics on access to healthcare, population ageing and SES 17,21 . For cold, however, we found some evidence that rural/low-density peri-urban regions yielded lower risks.
We evaluated for the first time the vulnerability factors by different types of regions for both heat-and cold-related mortality in a nationwide study setting. Thus far, previous assessments have primarily aimed to identify heat-related vulnerability factors in single 19,20,25 or multi-location analyses 21,47 while disregarding the potential heterogeneity in vulnerability and associated drivers by type of area. In this study, we applied the novel extended two-stage design recently developed and we observed substantial differences in vulnerability factors between types of areas driving the temperature-mortality association 34 . could be in part driven by the urban heat island effect as our findings suggest that environmental vulnerability related to urban characteristics (high mean temperature and high PM 2.5 concentrations) were associated with increased vulnerability which is in accordance with literature 32,48,49 . However, we cannot disentangle the contribution of the UHI and/or any of the other drivers due to the complex correlation between them and the lack of specific UHI metrics 50 . Regarding social vulnerability, previous assessments found that low SES, social isolation and population ageing could increase vulnerability in urban areas, for the former we observed good evidence for effect modification for both heat (peri-urban clusters) and cold (urban and rural clusters), where higher SES is associated with a reduction in risk 13,19,21,25,44,[51][52][53] . Despite increased risk for heat mortality with lower SES, we did not observe patterns in urban heat exposure and climate injustice between clusters, which usually is present on an intra-city level [54][55][56] . For peri-urban clusters, where besides environmental factors, also social (i.e., SES, travel time to healthcare) and biological factors (i.e., ageing) were found to be important effect modifiers. In contrast to many previous studies, we did not observe evidence for greenness as an effect modifier for the heat-mortality risk 20,21,25,52,57,58 . This may be explained by the limited variability in the EVI values across urban and peri-urban clusters in Switzerland (IQR of 0.41-0.45 and 0.42-0.49, respectively). Although for urban clusters we found a negative association between greenness and temperature, for peri-urban and rural clusters, temperature was positively correlated with greenness (possibly since the level of greenness at high altitude in the Alps is missing, therefore, reducing the spread of EVI) (Fig S1-2  the limitation of using EVI as a universal indicator for greenness without making regional distinctions. Unlike heat vulnerability, evidence on cold-related vulnerability factors remains limited and inconclusive in the literature 16 21 . Our findings suggest that cold-related vulnerability in urban clusters was mainly driven by socio-economic factors (e.g., long travel time to time health care, % of foreign population and SES) as well as population density, consistent with previous studies 21,22 , while more relevant drivers were identified in peri-urban and rural clusters such as % of new houses, PM 2.5 , ageing and population density. To note, although many previous studies found that low-housing quality exacerbates the risk of heat 22,52 , this is one of the more recent studies that also report housing as an effect modifier for cold-related mortality, particularly in peri-urban regions 59,60 . It has been hypothesised that the reason for the existing inconclusive findings or complex patterns on cold vulnerability might be due to the more complex pathways of how cold exposure can affect health (i.e., infectious diseases, public health interventions). Future research should aim to study coldrelated vulnerability factors and clarify the links between factors and mechanisms driving increased risks.
A result worth highlighting is the heterogeneity in the direction of effect modifiers of the heat and cold-mortality association by urban/peri-urban/rural clusters. We found that environmental factors (i.e. PM 2.5 concentration and mean annual temperature), as well as population density, are negatively associated with cold-related mortality in rural clusters while positively associated in peri-urban clusters. Meaning that peri-urban clusters that are more similar to urban clusters have higher risks for cold-related mortality than periurban clusters which are more similar to rural clusters. Moreover, rural clusters with higher temperatures, higher PM 2.5 concentrations and higher population density that are generally associated with increased urbanisation have a lower risk than rural clusters with low temperatures and low population density. Therefore, we believe that for cold-related mortality the lowest risk can be observed in rural/low-density peri-urban areas, a finding shared by a recent study that observed this association for heat 17 . For heat, however, we only observed increased vulnerability for urban clusters, which might be due to the urban heat island, while in peri-urban and rural no differences were found, possibly because outside of the main cities, Switzerland is very sparsely populated ( Figure S6).
This study has several strengths. First, we used advanced statistical methods recently developed in climate epidemiology to maximize the power of the available data and increase the precision of our estimates and the reliability of our conclusions. In particular, we applied the novel case time series design which allowed us to use temperature and mortality data at a high resolution and thereby increase the precision of the risk estimates 38 . We used DLNMs to account for the complexity of the temperature-mortality association, in terms of potential non-linearities and delayed effects up to 21 days. To pool the risks and assess the proposed vulnerability factors, we then applied a complex meta-analytical model which properly accounted for the hierarchical structure and heterogeneity of the risks (Sera et al., 2020). Finally, to assess the effect modification of vulnerability factors we applied a novel extended second-stage time series which allowed us to test vulnerability factors in the complex non-linear mixed meta regression which has not been applied to nationwide data before 34 . Then, to assess vulnerability factors in both urban and rural areas we used gridded climate datasets which allowed us to assign temperature exposure at municipality level, which is considered an unprecedented high resolution in many ecological studies investigating temperature and mortality 36 . Lastly, using the Ward-like hierarchical clustering with geographical constraints 37 , we defined ad hoc clusters of municipalities with similar characteristics, as an alternative to the administrative district boundaries (i.e., an upper geographical unit above the municipality) defined by the BFS (BFS, 2021). The Swiss orography and population distribution are highly heterogeneous, with large variation in demographic and environmental variables within administrative clusters and thus the effect modification of vulnerability factors by cluster typology can be diluted if heterogenous municipalities are grouped in the same cluster.
Some limitations should be acknowledged. First, our findings suggest vulnerability patterns according to levels of specific vulnerability factors but do not remove effects from correlated variables. That is, risks at different levels of the vulnerability factor were derived using univariate models, thus, not accounting for other (correlated) factors that might partly explain differences in the temperature-mortality association within typologies (i.e., by the UHI). Second, the low statistical power in rural clusters hindered the assessment of vulnerability factors. Additionally, we observed limited variability for some effect modifiers by urban/peri-urban/rural typologies, which could have limited the power to detect effect modification by variable. Then, we did not include humidity, influenza and air pollution concentrations as confounding variables, as we believe that their impact would be, if present, minimal, as their role as an confounding variable remain debated [63][64][65] . Lastly, this is an ecological assessment conducted at the municipality level. Thus, our results would not necessarily correlate with evidence on vulnerability factors driving differences at a finer resolution within municipalities (i.e., neighbourhood).

Conclusion
Our findings suggest larger temperature vulnerability in urban clusters, particularly for heat compared to rural regions, while cold-related vulnerability was similar across typologies. More importantly, this study has shown that drivers of temperature vulnerability can considerably vary by urban-rural typology in Switzerland. Therefore, future public health adaptation strategies aimed at mitigating the adverse impacts of climate change on population health should consider tailored interventions according to the characteristics of the target population.

Supplementary Material
Refer to Web version on PubMed Central for supplementary material. The data that support the findings of this study are available upon request from the authors. Urban clusters are indicated in red, peri-urban cluster in orange and rural clusters in yellow, with the main cities indicated in green.  The temperature-mortality association is expressed as relative risk with the corresponding 95% empirical confidence intervals (shaded area), with the temperature of minimum mortality as reference.   The cold-related relative risk of "low" exposure (5 th percentile) is indicated as a pink cube while for "high" exposure (95 th percentile) for each vulnerability factor is indicated as a purple triangle together with the corresponding 95% confidence interval ( Figure 4A). The absolute Relative Risk difference between "high" and "low" exposure to vulnerability factor is indicated in Figure 4B. A high exposure to a vulnerability factor associated with lower risk is indicated in blue, while a high exposure associated with a higher risk is illustrated in red. High exposure to each vulnerability factor is a high air pollution concentration, high temperature, high proportion of new houses, high ageing index, high proportion of foreign population present, high socio-economic status, long travel time to health care, high population density and high values for EVI for urban, peri-urban and rural clusters.  The heat-related relative risk of low exposure (5 th percentile) is indicated as an orange cube while the RR for high exposure (95 th percentile) is indicated as a red triangle together with the corresponding 95% confidence interval ( Figure 5A). The absolute Relative Risk difference between "high" and "low" exposure to vulnerability factor is indicated in Figure  5B. A high exposure to a vulnerability factor associated with lower risk is indicated in blue, while a high exposure associated with a higher risk is illustrated in red. High exposure to each vulnerability factor is a high air pollution concentration, high temperature, high proportion of new houses, high ageing index, high proportion of foreign population present,  Table 1 Number of clusters, municipalities, total all-cause deaths and average daily mean temperature between 1990 and 2017 and range and mean value and inter quartile range of the selected vulnerability factors (see Table S1 for the complete list of variables, source and description) by type of cluster in Switzerland.