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Spatial differentials in mortality in Switzerland: How do contexts explain the differences between natives and migrants?

Inégalités spatiales de mortalité en Suisse : l’influence des contextes sur les différentiels entre natifs et migrants
Jonathan Zufferey et Michel Oris
Traduction de Richard Hillman
Cet article est une traduction de :
Inégalités spatiales de mortalité en Suisse : l’influence des contextes sur les différentiels entre natifs et migrants [fr]

Résumés

Dans les sociétés postindustrielles contemporaines, les migrants ont généralement des risques de mortalité plus faibles que les natifs malgré des positions sociales tendanciellement plus basses. La recherche académique peine toujours à expliquer pleinement les origines de ce paradoxe. Bien que les facteurs individuels soient les causes fondamentales des inégalités face à la mort, l’influence de l’environnement social, économique et culturel est aussi décisif. En prenant l’exemple de la Suisse, cet article détermine dans quelle mesure les contextes socioéconomique, culturel et géographique parviennent expliquer le différentiel entre les natifs et les migrants. Par des modèles bayésiens multiniveaux, les auteurs décomposent, pour chaque population, les inégalités spatiales de mortalité dans les communes et les quartiers Suisses.

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Texte intégral

This research was supported by the NCCR ‘On the move’ and the NCCR ‘LIVES’ financed by the Fonds National Suisse. The authors are grateful to the Swiss Federal Statistical Office for having furnished the data on mortality and from the population census. They are also happy to acknowledge the support of the Swiss National Cohort, which rendered this study possible.

  • 1 See Markides and Coreil [1986] for the Hispanic communities in the United States, Khlat and Darmon (...)

1Academic research has demonstrated the existence, almost universal, of an advantage with respect to mortality for migrants, compared with the native born.1 In Switzerland, a study has established that the life expectancy of foreign men was almost two years higher than that of Swiss nationals, and that the difference was 1.2 years for women [Zufferey, 2017]. This lower mortality constitutes a paradox because migrants tend to find themselves in lower social positions than native-born Swiss; positions where mortality is known to be higher. If the selection biases at immigration (healthy migrant effect) and emigration (salmon bias) interfere, they are not sufficient to explain the constant advantage of migrant populations [Abraido-Lanza et al., 1999; Deboosere and Gadeyne, 2005; Palloni and Arias, 2004]. Other approaches are necessary to be able to completely understand this phenomenon.

2In this respect, this paper considers the differential impact of the environment on the mortality risks of migrants and the native-born. Since the early 2000s, a number of researchers have studied the effects of context, the interactions between individuals and the framework of their lives. Environment both reflects and produces inequalities as it creates the opportunities and the means which generate social, psychological and economic resources to the profit, or the detriment, of the individuals who compose the social environment in a given place [Takeuchi, Walton and Leung, 2010]. It is therefore important to separate the proper impact of social and economic living contexts from what may belong to other spatial elements, such as topography, hydrography, and the distance to infrastructures. That is our objective in this article.

3In the next section, a review of the literature on the spatial determinants of mortality leads us to posit four research hypotheses concerning the relations between local context and the mortality of migrants, compared with the native born, in a country, Switzerland, which presents a certain number of specificities. In the following section, the data exploited for this study are introduced, as well as the methodological approach applied, namely multilevel Bayesian models, which make possible, in particular, to determine the influence of each component on the all-cause mortality. After a presentation of the results, the final section discusses them and identifies the contributions of this article.

Theory and contexts

The influence of socio-economic and socio-cultural environments on mortality

4Although the research agrees that individual factors are the most determinant causes of inequalities in mortality, the nearby environment, and particularly the socio-economic context, also influence health. Living in a poor neighbourhood, all other things being equal, increases the risks of a degradation of one’s health, as has been shown by Pickett and Pearl [2001] in their review of some thirty articles. The causes singled out are multiple, with poor economic resources at the local level able to create exposure of individuals to more significant risks (poor infrastructure, medical services of lesser quality), to result in weak social capital or insecurity (deficient social support, drug, violence), or indeed to confront individuals with a physically degraded environment (pollution, absence of green spaces, etc.) [Robert, 1999]. Certain studies nuance this picture of causality, for it has not been possible to demonstrate a systematic influence of the socio-economic context, either because it is difficult to put into quantitative terms or because the observed differences in health have appeared minimal [Meijer et al., 2012].

5The question of the influence of social context on health has been thoroughly investigated by studies which have focused on the aspects of confidence and cohesion, more particularly on the social capital of the neighbourhood [Kawachi et al., 1997]. In the Bourdieusian view, the concept of social capital refers to the network of relations which an individual can mobilise and transform into other forms of capital, cultural or economic. Other theorists, such as Coleman, also see in this a facilitating role in the performance of actions, but situate it within the inherent structure of social relations, which functions notably by way of confidence, norms and exchanges [Coleman, 1988]. The political scientist Putnam has gone still further in the collective definition of this capital and considers it as public property which depends above all on confidence and on civic and civil participation. Social capital has been related to health by way of mechanisms of support and cohesion, whether in an individual perspective [Szreter and Woolcock, 2004] or a contextual one [Kawachi et Berkman, 2000]. In the present study, we have privileged this view, judging that social cohesion manifests itself through interaction between the individual and society.

6The social context is not only the environment in which differentials are inscribed; it also transmits modes of behaviour, values and collective representations of surroundings. There is a social learning process: an interaction between the individual who observes, imitates and serves as a model for the peers who compose his social environment [Bandura, 1971]. Thus, we can formulate a dialectic relation between the context and the individual who learns and modifies his behaviour in reaction to those around him, but who at the same time is himself capable of exerting influence [Levy et al., 1997].

7Following this logic, Robert [1999] stresses that to live in a poor neighbourhood increases high-risk behaviour (consumption of cigarettes, food poisoning) but also affects attitudes towards the body (less openness to prevention). Marsden and Friedkin [1994] arrived at exactly the same conclusion by considering the inadequacy, not of economic resources, but of contacts and the social network. This suggests, unsurprisingly, that the two aspects are linked. For Ellen, Mijanovich and Dillman [2001], the neighbourhood has the effect, in the short term, of modulating behaviour and attitudes toward health, as well as the use of the health system. But the living space also has a longer-term influence, with, for the most disadvantaged neighbourhoods, a process of erosion of the quality of life: little by little, an accumulation of social stress, a depressing environment, and limited social resources render individuals more vulnerable and negatively affect their state of health.

Migration and contextual effects

8On arriving in their host country, migrants enter into a local environment, a system with its particular dynamics and constraints, a new social space. What is more, they enter it with their own system of relations, which is called upon to change, but which may also remain stable. The context is fundamental in this respect, because it expresses economic, cultural and social attributes, but also historical and political factors. The latter have a particular importance in a country as decentralised as Switzerland, where the application of social policies, and policies of integration especially, is generally taken on the municipality or cantonal level. These important contextual and institutional variations may be a source of fragility in populations of foreign origin. Thus, if research has shown that the differentials of mortality among migrants were less marked by social stratification than among those of Swiss origin [Zufferey, 2016], we would expect, by contrast, the social and cultural environment to have greater importance. The tendency of people newly arrived to enter specific segments of the housing market and specific spaces reinforces this expectation.

9With regard to social capital, the preceding section has highlighted its protective effects by way of the networks of relations that provide support and mutual aide. According to Portes [1999], strong supportive links emerge among migrants because of the uncertainty that dominates their condition. As a result of their very condition, foreigners are likely to develop a denser and more intense social network. Puyat [2012] goes further, supposing that these social networks have a stronger impact on health for migrants than for the native born. Simich and his co-authors [2005] insist on the importance of social support upon the arrival of new-comers, whether through the members of their family or the community that welcomes them out of a feeling of shared identity, for these relations make it possible to diminish the stress of the migration and facilitate installation in a new environment. Van Kemenade, Roy and Bouchard [2006] maintain, in turn, that access to these social networks based on ethnicity favour economic and social integration, but also the well-being of the immigrants. These results conduce to the conclusion that the state of migrants’ health may be affected by the strength or weakness of the community spirit in neighbourhoods; some American studies also make this argument. They have, in fact, established that ethnic density has a protective effect on health, thanks to the buffering effects of cohesion and social support: there is said to be a stronger feeling of belonging to the community and less stigmatisation and discrimination respecting minorities [Bécares, Nazroo and Stafford, 2009; Pickett and Wilkinson, 2008]. Studies about the European context are rarer, but one may expect to find similar logics operating in Europe.

The Swiss context

10Switzerland is a country of immigration: in 2000, 21% of its 7.3 million inhabitants were of foreign nationality. At the end of the second world war, the country appealed to workers from southern Europe (Italy, Spain, then Portugal, Yugoslavia and Turkey) to respond to the demand for less qualified employment in the Swiss economy. In the 1990s, the origin of these flows and the reasons for migration became more diverse. Today, the new arrivals include more and more highly qualified workers. By far the great majority still come from European countries [Zufferey et al., 2021].

11The foreign population is not uniformly distributed throughout the territory, as is illustrated in Figure 1. The cities and agglomerations, essentially the regions of Zurich and Basel, the Lake Geneva region (connecting Geneva to Lausanne), and the canton of Tessin, are the most attractive for migrants because of favourable employment opportunities. In 2000, the proportion of foreigners in the urban sones was 28%, as opposed to 13% in the rural regions. High proportions of foreigners are also found in several peripheral localities of central Switzerland or the Alpine regions of the Grisons or the Valais. This type of localisation corresponds to workers in the tourism sector, but also to rich foreigners in search of a comfortable living space and optimal fiscal conditions.

Figure 1: Proportion of foreigners in Swiss communes and neighbourhoods, 2000 (in %)

Figure 1: Proportion of foreigners in Swiss communes and neighbourhoods, 2000 (in %)

Source: FSO/SNC; Map background: FSO ThemaKart 2010

12As we previously noted, the spatial settlement of foreigners is fixed according to complex territorialisations. Previous studies have demonstrated that the general mortality – but this is still more true of causes of death – is structured geographically. It can be observed at the level of regions within the European Union [Jougla et al., 2002], as well as on a much smaller scale, as in France, where the geographic regimes of mortality stand out very clearly [Rican, Jougla and Salem, 2003]. Although Switzerland is one of the smallest states on the European continent, it is distinguished not only by an important political decentralisation, but also by four different languages and strong regionalisms. Nevertheless, the few studies on life-expectancy (or mortality by age-groups) according to cantons and regions have pointed up relatively marginal differences [Wanner, Fei and Cotter, 1997; Wanner, Lerch and Kohli, 2012]. In a recent study, Lerch, Oris and Wanner [2017] have tried to find out whether there has not been a resurgence of mortality in Swiss cities over the last decades. They have, in fact, observed a concentration of marginality in the large urban centres, while the most well-off individuals and families abandon these localities for the peri-urban zones. At a finer level, Panczak and his colleagues [2012] have confirmed a gradient of mortality according to the socio-economic position of the neighbourhood, even after controlling for individual characteristics.

13Crossing these different empirical and theoretical observations, our research proposes to test the four following hypotheses, which are (at least partially) incompatible:

  • H1: The regional differences of mortality in Switzerland are marginal, if one takes into account the composition and the context.

  • H2: Foreigners are more vulnerable than Swiss to the environment in which they live; that is, the contextual variables will have greater influence on their survival.

  • H3: The socio-economic context is an important factor in inequalities of mortality, even when the individual socio-economic position are considered.

  • H4: The context of social relations (community social capital) has a positive influence on the health of populations, whatever their origin.

Data and methods

14The data used in this study comes from the Swiss National Cohort (SNC), an interdisciplinary research platform which consists of an exhaustive longitudinal follow-up of the Swiss population. For its research in public health, the SNC has linked in a probabilistic manner the deaths occurring during the period 2000 to 2008 with the population derived from the federal census of 2000 [Bopp et al., 2009], as collected by the Swiss Federal Statistical Office (FSO), thus permitting longitudinal follow up of a national cohort. The population census data are extremely rich and detail the characteristics of the population. They also include precise information on the place of residence. This makes possible to determine the individual factors of mortality but also, at the same time, to understand the relation between the place of residence and the risks of death.

  • 2 Swiss statistics do not permit, prior to 2010, individual determination of the emigrations of natio (...)

15We have selected for our analyses the population aged from 25 to 64 years, followed from the 2000 census (5 December) to the occurrence of death or of emigration (only for the foreigners2), or otherwise until the end of our observation on 31 December 2008. This represents 24.8 and 5.3 million person-years at risk for the Swiss and 64,900 and 9,400 deaths for the foreigners. The attributes of the population under study and individual variables are detailed in Table 1.

Table 1: Descriptive statistics of the population under study

Person-years

Deaths

Foreigners

Swiss

Foreigners

Swiss

Total

5,336,938

24,82,933

9,369

64,930

sex

Women

45.8%

51.3%

28.5%

37.2%

Men

54.2%

48.7%

71.5%

62.8%

age

25-29

11.4%

10.2%

2.4%

2.2%

30-34

14.9%

11.7%

3.9%

2.9%

35-39

17.3%

13.9%

5.9%

4.7%

40-44

16.2%

14.5%

8.1%

7.2%

45-49

13.1%

13.6%

11.4%

10.8%

50-54

10.7%

12.9%

16.3%

16.1%

55-59

8.9%

12.5%

21.1%

24.6%

60-64

7.5%

10.7%

30.8%

31.5%

Marital Status

unmarried

17.8%

29.3%

14.6%

22.4%

married

75.8%

61.1%

71.3%

59.4%

widowed

1.0%

1.5%

2.5%

3.2%

divorced

5.5%

8.2%

11.6%

15.0%

education

mandatory

40.4%

12.8%

43.4%

19.6%

secondary II

33.4%

61.1%

37.0%

59.0%

superior

18.3%

24.4%

15.2%

19.8%

unknown

7.9%

1.7%

4.5%

1.5%

Socio-professional category (CSP)

Executive

2.2%

3.0%

1.5%

2.3%

manager

3.8%

8.6%

4.5%

9.0%

independent

5.2%

7.6%

3.0%

5.0%

emp

23.8%

40.7%

17.3%

27.9%

unqualified

18.2%

6.0%

15.1%

6.3%

other

25.2%

16.0%

21.6%

17.7%

not working

21.5%

18.2%

37.0%

31.8%

Source : FSO/SNC

16To define the place of residence, we have chosen to base ourselves on the municipalities, while descending, however, for the 17 largest cities in Switzerland, to the level of the 295 neighbourhoods defined by Schuler, Dessemontet and Joye [2005]. It is, therefore, on the basis of 3,174 spaces that we analyse the spatial distribution of mortality. In the absence of a true measure of context, social environment is here accounted for by the collection of the attributes of the individuals, while positing the hypothesis of a dialectic relation between the person (ego) and those who live near him in a given place: ‘People make places and places make people’ [Subramanian, 2004]. We therefore measure the social environment by the location quotients, the over- or under-representation of a characteristic in an area in relation to its distribution in the country as a whole.

17Applying this method, we approach the socio-economic context with a focus on the implantation of the socio-professional categories without qualifications, thereby putting the emphasis on the most marginal. Other measures were also tested, notably the level of unemployment or several levels of socio-professional categories; but the results were not as conclusive, for in Switzerland the geographic disparities of unemployment are more marked at the regional than the local level. With regard to the socio-cultural context, we have selected the location quotients of foreigners and of charitable activities. The first indicates the level of concentration of foreign communities in a given place. It permits to detect whether a social and multicultural network has positive effects on the populations who live there. The second approximates an environmental social capital. Indeed, at the collective level, Kawachi [1999] argues that the individuals who are prepared to help others will intervene more when harmful forms of behaviour are found in their neighbourhood, such as abuse of alcohol or consumption of drugs, or of cigarettes by the young. The study of Blakely and his colleagues [2006], however, has not supported this assertion, since those New Zealand researchers did not detect an association between mortality and social capital as measured by charitable activity. Table 2 records the spatial attributes of the territorial units which we will use in this study.

Table 2: Descriptive statistics of the 3,174 geographic areas studied

minimum

average

median

maximum

Person-years

16

9,502

10,785

120,412

Deaths

0

23

10

337

Location quotient unqualified

0.09

0.93

0.88

3.30

Location quotient charitable activity

0.13

1.06

1.08

3.31

Location quotient foreigner

0.01

0.62

0.48

2.86

Source: FSO/SNC

18The analytic approach chosen, which has been fairly well exploited in epidemiology but very little in demography and the other social sciences, consists in modelling, for each spatial unit, the standardised mortality rate (SMR), that is, the relation between the number of deaths expected and the number observed. The analysis takes place in two steps. In a first step, it defines the mortality expected in each area studied using an individual model, and in a second step, it models the environmental and geographic mortality with the a Bayesian model [Lawson, 2009]. Bayesian statistics differs from frequentist methods; it is based on the theorem of Bayes and defines the distribution of the parameters to estimate, known as distribution a posteriori, as the product of a a priori distribution of the parameters and of the data plausibility. The a priori distribution makes it possible either to integrate knowledge concerning the object of study or to be neutral (non informative).

19First of all, on the individual level, the mortality predicted for each area has been estimated using generalised linear models, with a Poisson link function, by modelling the risks of mortality on the basis of all the variables presented in Table 1. Since the explanatory variables do not have the same effect by sex and origin, the models have been stratified by sex and nationality (Swiss vs. foreign). This first step therefore permits prediction, for each geographic unit, of the number of deaths expected among Swiss and foreigners, according to the demographic and socio-economic composition. Since this study does not deal with individual mortality, the results are not presented, but an interested reader can find this information in Zufferey [2017].

20Next, at the level of the neighbourhoods and communes, we model the logarithm of the over or under-mortality of each spatial unit as a linear combination of different explanatory factors. Since the social processes are not independent between adjacent spaces, we integrate an auto-regressive conditional model to take account of the spatial correlation, that is, of regional variations. These models were developed in the Bayesian framework by Besag, York and Mollié [1991], and they integrate three components in order to model the SMR: the coefficients of the contextual explanatory variables (explained heterogeneity); the effects that vary in structured faction within the space (spatially correlated heterogeneity); and the residual effects, which have no structure between the regions (residual heterogeneity).

21Let us first of all define the number of deaths by spatial unit, yi. These deaths are distributed according to a Poisson law whose mean is expressed by the multiplication of the deaths expected, ei, and of the SMR, θi, the excess or sub-mortality in the area i.

22The a priori distribution of the relative risk θi is a log-normal defined by:

23With x1iβ1 + x2iβ2 + ⋯ + xkiβk, a linear combination of k explanatory variables, ui the spatially correlated heterogeneity, and vi the residual heterogeneity. Each parameter βk as well as vi has an a priori normal distribution:

24The spatially correlated heterogeneity, ui, is, for its part, distributed according to an autoregressive CAR model, whose expectancy ūi , is a weighted estimation of the relative risk in the neighbouring areas j [Besag, York and Mollié, 1991] :

25To estimate these geographic effects, we conscientiously prepared the data by defining, for each area, all of the neighbouring territories. Indeed, since for a space like Switzerland, where the natural barriers are physical obstacles, we cannot consider even that two territories with a frontier boundary, but separated by a mountain, are adjacent. It is with the help of a territorial map of productive areas [Swiss Federal statistical office, 2010], which determines the contiguity of the Alpine valleys on the basis of the physical particularities of the territories and the watersheds, that we have defined, for each commune and administrative neighbourhood, all of the adjacent territories.

26The models have been estimated by Markov Chain Monte Carlo (MCMC) with the programme OpenBUGS12 [Lunn et al., 2009] and the R library R2OpenBUGS[Sturtz, Ligges and Gelman, 2005]; the preparation of the data and all the preliminary analyses were done with the R software [R Core Team, 2017]. The distributions of hyperpriors (the parameters of a priori distributions for τv, τu, τβ) systematically have high variances to be as non informative as possible. The results proposed, moreover, are produced by three Markov chains having different starting values and converging on the a posteriori distribution.

27This approach, although a little complex, enables a break-down of the geography of mortality into contextual components (expressed by socio-economic and cultural level, measured by location quotients), spatial and residual. In order to compare the distribution of relative risk between indigenous and foreign populations, we defined these three components within a single model for the foreigners and the Swiss, while specifying the contextual effects of the foreigners as a difference by comparison with those of the Swiss,. This allows testing if there is a differential impact of social factors between the two populations.

28This quantitative study of the environmental determinants of mortality must nevertheless acknowledge certain limitations. First of all, the data used follow one cohort from the population census of 2000 to the end of December 2008 or the occurrence of a death or an emigration. Although the data are longitudinal, the individual and geographic attributes are fixed in time, since they are drawn from the 2000 census . Consequently, the logics of internal mobility and peri-urbanisation could not be taken into account in the analyses. Next, the geographical outputs are modelled according to the common mortality levels between an area and its neighbours. For remote areas with a small number of inhabitants area, the smoothing becomes more effective, and the results tends to move strongly towards the average. Thus the geographic variations illustrated by the maps are not systematically statistically different from the mean and need to be interpreted more for their descriptive value. Finally, we have selected in this study a population of working age, for migrants, still today, are only slightly represented among the elderly. The study from Lerch, Oris and Wanner [2017] on mortality in Swiss urban spaces has demonstrated the impacts of very different contexts by comparing workers with the elderly.

Results

29The relative risks of over or under-mortality, measured as the ratio between the predictions of the individual model and the observations, are presented in Figure 2. Very high variability is evident, especially among the foreigners (Figure 2b), where the small numbers necessarily lead to over- or under-representations. Thus, if the individual model predicts 2 deaths but 3 are observed in a neighbourhood, the excess mortality rises to 150%, while if only 1 death is observed, the under-mortality is 50% instead. But these random variations in small areas are smoothed by the model which we presented in the preceding sections; thus, if these two neighbourhoods are situated side-by-side, the model will move these values towards a relative risk close to 1.

Figure 2: Relative risks of mortality by commune and neighbourhood before the modelling. Swiss and foreign populations, 2000-2008

Figure 2: Relative risks of mortality by commune and neighbourhood before the modelling. Swiss and foreign populations, 2000-2008

Source: FSO/SNC; Map background: FSO ThemaKart 2010

30In Figure 3 the results of the relative risks estimated by the model are represented. The high variability which rendered the preceding maps difficult to interpret has been smoothed by the modelling: the new representations are more homogeneous, and the regional regimes of mortality stand out, although small-scale variations remain. The latter are explicable by effects of context, socio-economic or socio-cultural, or by elements not taken into account (residual heterogeneity). However, we can observe clear differences between the spatial distribution of relative risks for foreigners, on the one hand, Swiss, on the other. This suggests a differential influence of the explanatory variables.

Figure 3: Relative risks of mortality by commune and neighbourhood after the modelling, Swiss and foreign populations, 2000-2008 (in %)

Figure 3: Relative risks of mortality by commune and neighbourhood after the modelling, Swiss and foreign populations, 2000-2008 (in %)

Source: FSO/SNC; Map background: FSO ThemaKart 2010

31The analysis shows, in fact, a significant influence of almost all the contextual variables taken into account on the risks of death in the Swiss municipalities and neighbourhoods (see Table 3, for the coefficients). For the Swiss, a neighbourhood’s low socio-economic level raises the risks of death: a rise of one point in the location quotient of unqualified workers increases the relative risk of death by 9% (exp(0.084)=1.09). The influence of the socio-economic environment for the foreigners does not differ significantly from the level for the Swiss, although the coefficient is slightly reduced. With regard to social capital, it is protective, as expected: the more volunteers there are in the immediate environment, the lower the risks of death. But this is true only for the Swiss; For the foreigners, the effect is lower by half (-0.138+0,063=-0.075). Moreover, in a model which would include only foreigners, this effect is not significantly different from zero. Finally, the more foreigners there are in the immediate environment, the higher are the risks of death for the Swiss. For the foreigners, the result is the contrary, but a productive effect of ‘among themselves’ does not really appear (0.051-0.087=-0.036) and, moreover, is not significant in a modelling which takes only foreigners into account. These surprising results will be discussed below. Before, we break down the influence of the different components of the geographic heterogeneity of mortality.

Table 3: Estimated coefficients of the contextual variables, Swiss and foreigners, 2000-2008

Coef. Swiss

Coef. Foreigners

Location quotient, unqualified workers

0.084

*

-0.006

Location quotient, charitable action

-0.138

*

0.063

*

Location quotient, foreign

0.051

*

-0.087

*

Notes: The coefficient of the foreigners indicates the difference relative to the value of the Swiss.
Credibility level: * < 0.05. In the Bayesian system, one speaks not of signification but of credibility, because the estimated parameters have a distribution. One can be 95% sure that the true value of the coefficient is not equal to 0.

Source: FSO/SNC

32The Bayesian approach used in this study has the advantage of being about to account for the geographic, contextual and residual effects. Figure 4 illustrates the importance of each of these components in determining the overall relative risks. The distribution of the spatially corelated mortality (4a et 4b), which reflects the regional or local mortality regimes, shows similarities between the map of the risks for Swiss and that for foreigners. Zones with a slightly excessive mortality are found in the peripheral regions of the Lake Geneva area, in north-eastern Switzerland, as well as in the lower Valais. Although only slightly pronounced, excess mortality is also found in the three large cities of German-speaking Switzerland: Zurich, Basel and Bern (but only for the Swiss in the last case). The Engadine (a valley in Grisons) and Tessin, for their part, have levels of mortality generally lower than expected. In contrast with the Swiss, the foreigners are distinguished by a high level of excess mortality in the upper Valais.

33The heterogeneity accounted for by the contextual variables causes a vast mosaic to appear, which expresses the economic and social differentiation previously presented. The relative risks here are less extreme for the foreigners than for the Swiss, for, as already pointed out, the magnitude of the effects is lesser for the former, as is the spatial variability.

34The residual heterogeneity (4e et 4f) appears marginal in these graphics. Only certain communes and certain neighbourhoods are characterised by a regime of over or under-mortality. This does not mean that there are no differences, but that because of the small numbers, the residual differences have been smoothed by the model.

Figure 4: Breakdown of the relative risks of mortality into contextual, spatial and residual components, Swiss and foreigners, 2000-2008

Figure 4: Breakdown of the relative risks of mortality into contextual, spatial and residual components, Swiss and foreigners, 2000-2008

Source: FSO/SNC; Map background: FSO ThemaKart 2010

Discussion and conclusions

35Our analyses show an influence both of space and contexts on the spatial inequalities of mortality in Switzerland. Differences appear, however, between the foreign and Swiss populations. In the following section, we return to the research hypotheses and discuss the results.

36Our first hypothesis postulated, in line with the conclusions of Wanner, Fei and Cotter [1997], that the geographic variations of longevity in Switzerland are minimal, if the overall picture and contextual dimensions are taken into account. Indeed, our analyses show little differentiation within the space: some 7.5% of the population (Swiss and foreign combined) lives in an area where the spatial component of mortality is 10% above or below the average. This also signifies that the economic and social environment is, after the individual determinants, the second cause of inequalities in mortality across Switzerland.

37In focusing on the differentials between Swiss and foreigners, we have been able to establish that, contrary to what was expected from our second hypothesis, contextual factors have less importance for the foreigners than for the Swiss, and this according to all our indicators. Indeed, we expected that the economic and social environments would play a determining role for the migrants. Now, it seems that, as for the socio-economic gradient at the individual level [Goldman et al., 2006; Zufferey, 2016], the effect of a concentration, heavy or light, of workers without qualification in ego’s living space is smaller among the migrant populations. The origin of this phenomenon may be found in the selection process at immigration and emigration (migrants are selected from a population in good health, and migrants in poor health tend to return to their country of origin), which would make the state of health of the foreign population more homogeneous and therefore showing less random variations.

38Our third hypothesis suggested an influence on mortality of the local socio-economic context. If, in Switzerland, there are no real concentrations of poverty in neglected suburbs, the spatial distribution of social classes is nevertheless not neutral. Our study shows that, despite a relatively slight differentiation, the socio-economic environment plays a determining role in the state of health of the populations. It is not only in contexts where the spatial inequalities are big, as in the United States for example, that one can observe significant differences in longevity.

39Finally, the last hypothesis postulated the importance of social context, arguing, on the basis of previous research, that community social capital has a protective effect on health. We selected volunteering activities to test the density of the social bonds at the local level. Our results show that, the more social activity there is in the communities, the lower are the risks of death. But a distinct difference appears between the Swiss and the foreigners: the latter benefit hardly at all from the reduction of risks associated with this form of social capital. This result is revealing. The social fabric, a strong and mutually supportive community, benefits the Swiss but leaves the foreigners out of the picture. Studies on volunteering activities in Switzerland have shown that they are more concerned with individuals of Swiss nationality [Schön-Bühlmann, 2008], and that effects of solidarity thus tend to be oriented toward the ‘local’ community in the narrow sense. In other words, volunteering activities are essentially performed by the Swiss for the Swiss.

40If the foreigners participate less in charitable activities and scarcely benefit from the community social capital which is produced by them, other logics of solidarity may express themselves. In a provocative article entitled, ‘Why not ghettos?’, Cattacin [2006] wonders whether ghettoisation would not be favourable to the regulation and stabilisation of cities. In considering the importance of the foreign communities on the local level, we have attempted to gauge whether the effects of concentration could produce support, hence social capital, for the foreigners and thereby diminish their risks of death. The results are not conclusive; the impacts on the foreigners are minor, while for the Swiss, a greater density of the foreign population significantly increases the risks of death.

41To explain this unexpected result, some lines of thought enter the field of political studies. They lead us back to the debate presented at the beginning of this article on the association between the environmental factors influencing mortality and its inequalities. Leigh [2006] maintains that in Australia, confidence is lower in communities that are heterogenous ethnically and linguistically. In a polemical contribution, Putnam goes further, maintaining that diversity erodes social capital. According to him, interpersonal confidence, as well as formal and informal interactions are distorted in heterogenous communities: ‘diversity, at least in the short run, seems to bring out the turtle in all of us’ [Putnam, 2007, p. 151]. He concedes, however, that over the long term, diversity has positive virtues for society. This view has received considerable criticism, and a number of authors show that it is above all the socio-economic position of the neighbourhood which influences social capital, rather than ethnic diversity [Gesthuizen, Van der Meer and Scheepers, 2009; Letki, 2008]. Portes and Vickstrom [2011] go further and show that diversity has a negative influence only when it is accompanied by inequalities and spatial segregation. The two characteristics are thus very closely linked; a high concentration of foreigners goes together with a neighbourhood’s low socio-economic level and so is perceived as a symptom of an insecure environment. From this perspective, the higher mortality of Swiss in the sectors strongly dominated by foreigners would be a token of inequalities. The Swiss who are the most vulnerable because of the paucity of their resources find themselves in positions of isolation and exposure to risks by being confined to poor neighbourhoods, which compound the disadvantages. These spaces, because of their low rents, also attract migrant populations, but the individuals who make up the latter are largely selected from the point of view of their health and therefore more able to resist these living conditions.

42This should impel us to pursue the work of Portes and Vickstrom [2011] with truly longitudinal data. Areas of poverty and heavy population concentrations may be accentuated by phenomena of deprivation, a reverse ‘gentrification’ characterised by the departure of the least marginal. Eggerickx and his colleagues [2002] observe such a phenomenon in Namur, where Belgian nationals and those with a high professional status have a higher chance to achieve upward mobility and abandon the poorest neighbourhoods. Now, this mobility within the city is very closely linked with the question of health [Norman, Boyle and Rees, 2005]. In Switzerland, the social fracture seems little apparent because it concerns small areas of marginality in cities and whose processes can only imperfectly be captured in a cross-sectional approach. On the basis of population registers, which since 2010 have replaced the census, it will soon be possible to test these hypotheses using a longitudinal approach.

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Notes

1 See Markides and Coreil [1986] for the Hispanic communities in the United States, Khlat and Darmon [2003] for the migrants from the Mediterranean region in Europe, Razum et al. [1998] for the Turks in Germany, as well as Deboosere and Gadeyne [2005] for Belgium.

2 Swiss statistics do not permit, prior to 2010, individual determination of the emigrations of nationals.

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Table des illustrations

Titre Figure 1: Proportion of foreigners in Swiss communes and neighbourhoods, 2000 (in %)
Crédits Source: FSO/SNC; Map background: FSO ThemaKart 2010
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Titre Figure 2: Relative risks of mortality by commune and neighbourhood before the modelling. Swiss and foreign populations, 2000-2008
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Titre Figure 3: Relative risks of mortality by commune and neighbourhood after the modelling, Swiss and foreign populations, 2000-2008 (in %)
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Titre Figure 4: Breakdown of the relative risks of mortality into contextual, spatial and residual components, Swiss and foreigners, 2000-2008
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Référence électronique

Jonathan Zufferey et Michel Oris, « Spatial differentials in mortality in Switzerland: How do contexts explain the differences between natives and migrants? »Espace populations sociétés [En ligne], Hors-série | 2021, mis en ligne le 06 novembre 2021, consulté le 29 novembre 2021. URL : http://journals.openedition.org/eps/12203 ; DOI : https://doi.org/10.4000/eps.12203

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Auteurs

Jonathan Zufferey

Université de Genève, Institut de démographie et socioéconomie, NCCR « On the Move », jonathan.zufferey@unige.ch

Michel Oris

Université de Genève, centre interfacultaire de gérontologie et d’études des vulnérabilités, NCCR « LIVES », michel.oris@unige.ch

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