braubach-residential conditions and impact on satisfaction

384 Int. J. Environment and Pollution, Vol. 30, Nos. 3/4, 2007

Copyright © 2007 Inderscience Enterprises Ltd.

Residential conditions and their impact on residential environment satisfaction and health: results of the WHO large analysis and review of European housing and health status (LARES) study

Matthias Braubach WHO Regional Office for Europe, European Centre for Environment and Health, Bonn, Germany E-mail: [email protected]

Abstract: Recent research into urban patterns and residential conditions has shown that neighbourhood conditions are associated with both residential environment satisfaction, and health and well-being. Multivariate analyses performed on the LARES database confirmed that various environmental characteristics (noise, lack of recreational areas, perception of fear, low maintenance, etc.) show significant associations with residential environment satisfaction. An increased likelihood for sleep disturbance is identified for noise exposure, lack of recreational areas and the perception of fear, while depression is related to noise exposure and safety perceptions. Cardiovascular symptoms only showed a significant association with the perception of safety.

Keywords: housing and health; residential environment; residential satisfaction; sleep; depression; cardiovascular symptoms; neighbourhoods; urban planning.

Reference to this paper should be made as follows: Braubach, M. (2007) ‘Residential conditions and their impact on residential environment satisfaction and health: results of the WHO large analysis and review of European housing and health status (LARES) study’, Int. J. Environment and Pollution, Vol. 30, Nos. 3/4, pp.384–403.

Biographical notes: Matthias Braubach holds a Degree in Geography (focusing on urban and environmental issues) and a Master in Public Health. He works for the WHO Regional Office for Europe (European Centre for Environment and Health, Bonn office) as a Technical Officer for Housing and Health. He works on the health relevance of the residential environment, the housing challenge for ageing societies, the health effects of housing insulation, and the development of a burden of disease assessment for inadequate housing conditions. Since July 2006, he is the head of the housing and health program.

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1 Introduction: urban settings, housing environments and well-being

The considerable differences in the health status of populations that exist within cities may partially be explained by the environmental conditions provided in the different neighbourhoods. Empirical research has shown that within cities and neighbourhoods, health differences can be extreme (e.g., Cohen et al., 2003; Macintyre et al., 2003; Stafford and Marmot, 2003; Mackenbach and Howden-Chapman, 2002). It has been argued that

“… individual or household level socio-economic factors can explain most of these area differences in health. On the other hand, other studies hypothesise that ecological or environmental effects on health exist, independent of individual or household level factors.” (Chandola, 2001 p.105)

One of the main concerns in current research on the relationship between neighbourhood quality and health effects is therefore, to identify the association between the quality and perception of physical and social characteristics of the residential area and the health status of residents (Wilson et al., 2004). Various research projects and publications show that next to the housing quality, the impact of the residential environment is a relevant health determinant (Ellaway et al., 2001; van Poll, 1997). In addition to the mere physical and architectural quality, the neighbourhood is also affected by social and community level parameters such as social cohesion or safety. The common use of public spaces leads to social and functional conflicts and thereby brings a new dimension into the residential environment that is fundamentally different from the residential experience within one’s four walls, where no compromises and negotiations with neighbours are required.

The approach of the LARES survey – and the general WHO approach to housing and health – is based on four dimensions of housing:

• the home as a refuge and safe haven

• the building as the physical shelter

• the neighbourhood/community as the social climate surrounding the residential place

• the immediate housing and residential environment as the physical infrastructure of the residential place.

The LARES survey intentionally defines the surrounding areas around the house as a part of the residential experience. As well, it includes data on the subjectively perceived social and psychological dimension of the residential environment and does not limit data collection to information on the physical surrounding, exclusively.

Assuming that individual health and well-being is the result of the interaction between

a the characteristics of physical and psychosocial environments

b the subjective perceptions and evaluations of these surroundings by the individual (see Figure 1), both objective and subjective factors are shaping the residential satisfaction (Amerigo and Aragones, 1997) and affect the health of the residents of a given area.

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Figure 1 Objective and subjective factors for health and well-being

The same objective level of noise or air pollution can either be interpreted by a resident as a normal consequence of urban life – thereby indirectly accepting it – or as a limiting factor for the health related quality of life that has to be counteracted. In the latter case, inadequate residential settings may represent a pathogenic residential health context that may be expressed in the residents through stress, mental imbalance or direct and indirect health effects.

Although urban planning processes are – and always have been – strongly informed by public health arguments (WHO Regional Office for Europe, 1999), a variety of stressors and health risks still exists in residential environments. The closeness of people and the complexity of urban living make cities a place of constant negotiation of living conditions. In addition, it has been found that architects and building professionals show less interest in evaluating the performance of their urban buildings and neighbourhoods than in the development of new (Brown, 2003), implying that there is still space for improvement.

It is a challenge to create residential environments that support the health and wellbeing of the residents and provide high levels of satisfaction (van Kamp et al., 2003; Amerigo and Aragones, 1997; Sime, 1986). Residential environments may have their strongest impacts on children and sick or disabled persons, who spend most of their time in the neighbourhood, and are more vulnerable to health threats within that setting. It has been acknowledged by many public health researchers that environmental prevention should receive increased attention (Stokols et al., 2003; WHO Regional Office for Europe, 1986) instead of relying on health care systems. In this context, the increased interest in the health relevance of the place raises the question as to whether healthy residential settings can support and maintain the health status of healthy people and prevent ill health (Bistrup, 1991).

The development of healthy residential settings would be an ideal way to implement the objectives of public health, which have been defined as (a) the ‘reduction of social and health inequalities’ (b) the ‘striving for health sustaining environments’ (McMichael and Beaglehole, 2000, p.495).

2 Residential quality and health: empirical findings

Recent work carried out by researchers such as Ellaway and Macintyre (2000), Cattell (2001), Dunn (2002), Stafford and Marmot (2003), Evans (2003) and Latkin and Curry (2003) provided mounting evidence that the health status of residents is affected by the quality of the residential conditions and the built environment. This influence is based on two mechanisms: Firstly, improved residential quality and reduced residential stressors directly lead to lower exposure to pathogenic factors. Secondly, improved


residential quality and attractiveness of neighbourhoods increase the residential satisfaction of the residents and provide a more positive interpretation and evaluation of neighbourhood life and living conditions, while residential problems that cannot be escaped from, provide dissatisfaction and frustration.

It seems natural that the first mechanism will have a direct bearing on the physical health of residents, although the latter pathway may be more relevant for social and mental health.

Despite the diversity of areas, and the uniqueness of local problems, there are five general types of features of local areas that appear to have a direct or indirect influence on health, and are of relevance for any neighbourhood (Macintyre and Ellaway, 2000). These overarching features are

• physical characteristics of the shared environment

• healthy environments in general, referring to the conditions at, and the functionality of, home, work, school or recreational settings

• services provided to support the daily life of residents

• sociocultural features of neighbourhoods

• the reputation of the neighbourhood.

These five points indicate that residential neighbourhood quality covers a whole range of aspects, including physical and social characteristics as well as subjective dimensions.

Neighbourhood effects on health and well-being have received increasing interest in recent years and various research projects have attempted to identify the health impact of contextual and/or environmental factors. Urban and residential health research therefore mostly deals with the mental and social effects of the physical and built environments (Evans, 2003; Jackson, 2003; Twiss, 2003). In urban planning and landscape architecture, numerous publications also emphasise the meaning and importance of public and social places (Thompson, 2002), green and open spaces, and vegetation in urban settings (Attwell, 2000; Botkin and Beveridge, 1997). Urban ecology is a major field within urban planning, and concepts such as the urban green structure aim at improving the liveability of cities (Jensen et al., 2000; WHO Regional Office for Europe, 1997), including ecological as well as urban and recreational functions. Urban design discusses issues of accessibility and participation while trying to make cities integrative spaces for all groups of society (Imrie, 2000). Evidence from various studies indicates that the view of natural scenery positively affects mental capacity and physiological indicators such as mood states and stress hormones (Kaplan, 1995; Ulrich, 1991). Next to the effects on health, residential satisfaction studies and projects on the neighbourhood quality have been able to link almost all physical and social aspects of neighbourhoods to the residents’ subjective assessment of the quality and the satisfaction with the residential environment (Türkoglu, 1997; Anderson and Weidemann, 1997). It is therefore likely that residential environment satisfaction is closely related to health related quality of life, but so far this has only been tested in relation to the mobility of residents (Kahlmeier et al., 2001).

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The availability and quality of green open spaces and places for physical exercise has been associated with the rising epidemic of obesity (Hume et al., 2005; de Bourdeaudhuij et al., 2003; Emery et al., 2003; Giles-Corti and Donovan, 2003), as well as the perception of safety and the conditions of playgrounds etc. In addition to the decreased use of neighbourhood amenities in unsafe and deteriorating areas, the perception of insecurity has been identified as a health threat per se (Latkin and Curry, 2003; Austin et al., 2002; Green et al., 2002, Chandola, 2001) that is capable of strongly affecting the life of residents and impacting their mental balance.

Environmental pollution – present in the neighbourhood through litter, bad smells and fumes, and animal excrements (Ellaway et al., 2001; van Poll, 1997) – limits not only the aesthetic value of the neighbourhood and its usability, it also threatens physical and mental health by posing the risk of allergies, respiratory effects, and general discomfort and irritation. Noise exposure, probably the largest reason for environmental pollution in urban settings, is increasing in most European cities (Sharp, 2002) and has been linked with sleep disturbance, emotional and hormonal effects and increased risk of cardiovascular effects (Stansfeld and Matheson, 2003; Lercher et al., 2002; Babisch, 2001). Studies on children also found cognitive and social effects of night noise exposure, e.g., expressed by an impaired ability for learning in school (Maschke et al., 2001; Stansfeld et al., 2000).

The location and the general structure of neighbourhoods are relevant factors for exposure to traffic flows – at the origin of noise as well as air pollution – and the inclusion of city quarters into public life and their proximity to public services and amenities (Thomson et al., 2003). Furthermore, the exposure to traffic flows also increases the necessity for security measures in order to avoid accidents (Nakahara et al., 2004).

Stress and related health effects arise most often from a lack of one or several of the necessary features of residential neighbourhoods (Halpern, 1995). Severe limitations in the quality of life result from the collision of too many discordant expectations towards residential environments, possibly leading to social tension and conflicts (Thompson, 2002).

3 Analytical objectives and data selection

The analytical work presented here attempts to identify the impact of the residential housing environment on (a) the satisfaction with the residential environment and (b) the health status of residents, using the WHO LARES data set.

The survey was carried out in eight cities of Europe in 2002–2003 and constitutes a database of 3373 households and 8519 individuals (further information at www.euro. who.int/housing).

The analysis was based on a variety of characteristics of housing environment such as greenery, playgrounds, security, and cleanliness/maintenance issues (taken from the inhabitant questionnaire and the inspection sheet). Data on the building and neighbourhood types, and their location, were taken from a housing inspection sheet, while the residential environment satisfaction was obtained through an inhabitant questionnaire. Data on noise exposure, mental health, sleep disturbance, and the prevalence of diagnosed diseases such as cardiovascular symptoms were collected from a health questionnaire. In addition, personal characteristics (age, gender, socioeconomic


status (SES), functional limitations) were obtained through the health questionnaire as well.

The database enables a variety of investigations linking the residential quality of the housing environment with a number of health outcomes while adjusting for individual characteristics of the residents. Although the results of this cross sectional study cannot be interpreted as causal evidence, they are capable of providing important indications on the existence and the strength of associations between the residential environment, and the health status of the residents.

4 Methods

The results presented are based on multivariate logistic regression models. These models use various residential environment characteristics as independent variables, which are selected on the basis of a literature review on residential environments and health, and the results of a correlation test using the Spearman rank correlation coefficient. The chosen independent variables are related to the aspects identified as some of the most relevant residential and urban stressors (see Table 1). For the analysis, all variables were transferred into a dichotomous format.

Table 1 Independent variables used for analysis

Residential environment category Independent variable Original variable format

Vegetation along streets Binary variable (Yes – No) Play areas for children 3 point scale (Yes – to some

extent – No)

Greenery, public spaces, and play areas

Place to sit and relax outside of dwelling

Binary variable (Yes – No)

Traffic noise 5 point scale (not at all – extreme) Noise exposure Surrounding area noise 5 point scale (not at all – extreme) Safety perception in residential area at night

3 point scale (Yes – to some extent – No)

Public safety

Feeling of safety in the dwelling 5 point scale (strongly agree – strongly disagree)

Graffiti on residential buildings 4 point scale (No – one or two – three to five – six and more)

Upkeep and maintenance

Litter and trash in residential environment

5 point scale (Very dirty/littered – not at all dirty/littered)

As dependent outcome variables, residential environment satisfaction and three specific health outcomes (sleep disturbance, depression, and cardiovascular symptoms) have been selected (see Table 2).

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Table 2 Dependent variables used for analysis

Outcome variables Dependent variable Original variable format

Satisfaction Residential environment satisfaction

5 point scale (very good – very bad living area assessment)

Sleep Sleep disturbance Binary variable (Yes – No) Mental health Depression screening tool

SALSA Four aggregated binary variables (Yes – No) based on sleep, motivation, self esteem and appetite)

Circulatory system Cardiovascular symptom index Three aggregated binary variables (Yes – No) based on diagnosed hypertension, heart attacks and strokes

For each outcome variable, an individual regression model was computed. All regression models included the variable ‘city’ in order to control for the variation among the eight cities included in the database. It was perceived as very important to make sure that identified differences could not be attributed to city differences, and would have only little relation to residential conditions.

To adjust for the confounding influences of known health determinants related to individual characteristics, the four variables ‘age group’ (child – adult – senior), ‘gender’ (male – female), ‘SES score’ (high – low) and ‘functional limitations’ (yes – no) were selected as potential confounding variables to be tested for.

Each regression model was run in two steps. With the first step, the direct impact of the selected residential environment characteristics on the four selected outcome variables was tested. The second step included also, the four confounder variables, testing the stability of the associations found during the first step.

5 Results

Table 3 shows the results of four independent regression models using diverse outcomes as the criterion variable. Each regression was done in two steps, with the first step covering the residential environment characteristics only, and the second step covering both the residential environment characteristics and the personal characteristics of the residents.

5.1 Low residential environment satisfaction

The results for the model ‘Low residential environment satisfaction (low RES)’ (Step 1) indicate that all nine selected residential environment variables provide a significant influence on the occurrence of low RES. The highest impact is identified for ‘surrounding area noise’ and ‘play areas for children’, while ‘feeling of safety in the dwelling’ shows the lowest association.


Table 3 Logistic regression results: examining the impact of the residential environment on residential environment satisfaction and health outcomes

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Table 3 Logistic regression results: examining the impact of the residential environment on residential environment satisfaction and health outcomes (continued)


Table 3 Logistic regression results: examining the impact of the residential environment on residential environment satisfaction and health outcomes (continued)

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The integration of individual characteristics (Step 2) does not lead to any relevant change, as all residential environment variables remain significant. Furthermore, the odds ratio (OR) values of low RES, change only very little for the individual residential environment variables, and in seven out of nine cases it even slightly strengthens the odds of low RES. The low impact of individual characteristics on RES is also visible in the OR values for the individual characteristics, out of which only ‘children’ provide a significant association.

After inclusion of the individual characteristics, noise exposure is identified as the largest cause of low RES: ‘surrounding area noise’ shows, by far, the highest impact of all variables (OR of 2.89), while ‘traffic noise’ doubles the odds of a bad assessment of the residential environment. The impact of ‘play areas for children’, however, remains the second strongest influencing factor with an OR of 2.13. ‘Litter and trash in residential environment’, providing an OR of 1.87, show that the aesthetic perception of environmental pollution also has a significant impact on RES.

5.2 Sleep disturbance

Model 2, analysing the impact of the residential environment on sleep disturbance, is naturally dominated by the impact of the residential environment – especially the two noise variables (‘traffic noise’, ‘surrounding area noise’). In Step 1, all residential environment variables except ‘vegetation along streets’ and ‘graffiti on residential buildings’ show a significant association with reported sleep disturbance, but for three variables only, there are strongly increased odds ratios (‘traffic noise’ and ‘surrounding area noise’ with ORs over 6; and ‘feeling of safety in the dwelling’ with an OR of 2.53).

The full model, including personal characteristics (Step 2), shows a very modest impact of the individual characteristics of the residents, and consequently does not provide any relevant change to the impact of the residential environment on sleep disturbance. All seven significant variables remain significant, and there are only extremely small changes for the OR and CI values. This is especially valid for ‘traffic noise’, which was identified as the most relevant predictor of sleep disturbance in Step 1, and only shows an OR decrease from 6.52 to 6.42 after the inclusion of the confounders. Similarly, ‘surrounding area noise’ and ‘feeling of safety in the dwelling’ remain much more relevant than any of the other significant residential characteristics.

Compared to the strong influence of the environmental variables, there is almost no significant impact of any personal characteristics except for ‘age group’, in which adults are slightly more likely to suffer from sleep disturbance than the elderly (OR 1.23). The results therefore clearly show that for reported sleep disturbance, the contextual dimension is much more relevant than compositional factors.

5.3 Depression

The third model, assessing the impact of the residential environment on depression as an indicator for mental health status, tries to focus in more detail on the mental health dimension that was already uncovered by the analysis for sleep disturbance. Step 1 identifies four residential environment variables that are significantly associated with depression: ‘traffic noise’, ‘surrounding area noise’, ‘safety perception in residential area at night’ and ‘feeling of safety in the dwelling’, with ‘surrounding area noise’ being more


relevant than ‘traffic noise’ and ‘feeling of safety in the dwelling’ being more relevant than ‘safety perception in residential areas at night’.

Again, the integration of the confounding factors in Step 2 does not provide any significant change in the interactions between the residential environment and depression, and only slightly alters the OR and CI values of the residential environment variables. ‘Surrounding area noise’ (OR reduction from 2.15 to 2.13) and ‘feeling of safety in the dwelling’ (OR reduction from 2.3 to 2.11) therefore, remain the most relevant environmental predictors for depression, while ‘traffic noise’ (OR reduction from 1.56 to 1.41) and ‘safety perception in residential areas at night’ (OR reduction from 1.42 to 1.3) also remain significant. All other residential environment variables are not significant, although recreational factors (‘play areas for children’ and ‘place to sit and relax outside of dwelling’) and ‘litter and trash in residential environment’ show increased ORs for depression that do not remain significant.

However, different from the model for sleep disturbance, all individual characteristics provide a significant influence on depression – especially ‘age group’ (OR 0.34 for children compared to elderly) and ‘functional limitations’ (OR 2.6 for handicapped persons). SES (OR 1.42) and gender (OR 0.59) show OR values that are weaker than those of ‘surrounding area noise’ or ‘feeling of safety in dwelling’, showing that the impact of the residential environment conditions matches or even exceeds the impact of SES on depression.

5.4 Cardiovascular symptoms

Finally, the last model for cardiovascular symptoms tried to assess the effect of the residential environment on the circulatory system, based on research findings (Sundquist et al., 2004; Diez Roux, 2001) that have shown the impact of neighbourhood conditions on cardiovascular health. Step 1 identifies three significant residential environment variables (‘vegetation along streets’, ‘traffic noise’, and ‘safety perception in residential area at night’), but the OR values are rather low (ranging from 1.24 to 1.34).

The integration of the individual characteristics in Step 2 changes the results to a large extent. ‘Safety perception in residential area at night’ (OR 1.22) is the only contextual variable that remains significant in Step 2. ‘Traffic noise’, representing the residential environment variable with the highest OR (1.24), is not significant (CI 0.98–1.57). All other environmental variables show very little influence, as their ORs range closely around 1 (0.95–1.09).

Vice versa, all individual characteristics provide a significant influence on cardiovascular symptoms, although ‘age group’ shows, by far, the largest OR variation. ‘Socioeconomic status’ (OR 1.48 for low SES) and ‘gender’ (OR 0.85 for males) provide relatively little influence on the prevalence of cardiovascular symptoms.

6 Discussion

The results of the first model, identifying the relevance of neighbourhood characteristics for the satisfaction with the local environment, clearly showed that the general assessment of the residential environment quality is strongly shaped by the individual residential environment characteristics. While all residential environment’ variables provided significantly increased OR for low RES, there was only one individual factor

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‘children’ (under age group) that also showed significant influence. It is likely that this could be an artefact, as children did not make a vote themselves – the assessment of the household head who answered the inhabitant questionnaire was used as a proxy. It is therefore possible that families with children may have a slightly more negative assessment of their residential environments, which then leads to a biased result for children. Such an assumption is easy to justify as one can easily imagine that households with children may have specific expectations and requirements for their residential environment and therefore more easily report negative assessments. A potential indication for this is the OR of 2.13 for the lack of ‘play areas for children’, which is the second highest OR after ‘surrounding area noise’.

Although noise is identified as a major cause for low RES, the variation between ‘traffic noise’ and ‘surrounding area noise’ shows that neighbourhood noise source may even have a stronger impact on residential well-being than traffic induced noise – possibly due to the fact that noise from the surrounding area may have some social and emotional dimensions attached to it, which may alter the subjective annoyance caused by it. As well, traffic noise usually decreases in the evening and on weekends, while surrounding area noise may have its exposure peaks exactly in these hours.

Finally, a strong distinction is visible for the two safety variables: while a lack of ‘safety perception in residential area at night’ increases the chance of low RES at a highly significant level (OR 2.03, CI 1.72-2.39), the perception of ‘feeling of safety in the dwelling’ (OR 1,29, CI 1.03-1.61) provides the lowest impact on the satisfaction rating. This contrast shows the difference of the two concepts of safety in the home and safety in the neighbourhood, and suggests that the residents are able to distinguish between these concepts: if one variable out of the nine would have been expected to be not significant, ‘feeling of safety in the dwelling’ would have probably been the first candidate. In parallel, one would expect that ‘safety perception in residential area at night’ should have quite some impact on the assessment of residential environment satisfaction.

In summary, the results show that the residential environment characteristics selected for this study do have a large impact on the overall perception of and satisfaction with the residential environment. This is especially true when looking at the relative impact of the residential environment variables in relation to the individual characteristics, which have almost no influence at all. This suggests that satisfaction with the residential environment, representing a most important basis for social cohesion and social security in residential neighbourhoods, can be significantly influenced by environmental conditions and urban planning policies. In this context, the results presented are in line with previous research in relation to noise, maintenance and safety issues which have been already defined as powerful indicators for residential quality.

The results of the impact of the residential environment on sleep disturbance represent a clear proof of how contextual parameters can be the most dominant factors for the expression of health effects. The more a specific health outcome is related to the environmental conditions, the less relevant is the impact of the individual characteristics. For sleep disturbance, only age has some measurable but limited impact, while gender, functional limitations and especially SES – known as one of the most influential parameters – provide no significant change of the results. The fact that those residents who are exposed to noise are more than six times as likely to report disturbed sleep shows that – unsurprisingly – noise exposure must be seen as one of the major public health problems in urban settings. The significant associations with ‘play areas for children’,


‘place to sit and relax outside of dwelling’ and ‘litter and trash in residential environment’ show that noise exposure is most likely partially linked to the general quality of the urban setting. Not enough space for recreational activities seems to be a typical characteristic of noise exposed neighbourhoods – a fact that, by itself, provides an additional burden on the soundscape of a neighbourhood, especially in the case of a lack of children’s playgrounds. However, these results also point out that urban planning can considerably improve the degree of noise exposure, if noise is actively considered and mitigated, and more space for noisy leisure activities is given in locations where no resident is exposed.

On the other hand, the residential environment variables show that sleep disturbance is not only an outcome of the direct noise exposure, but is also a psychosocial phenomenon. The perception of safety, and especially the feeling of being protected from the outside influences while at home, is strongly associated with sleep disturbance – independent of gender, age or SES. This finding provides further evidence on the relevance of the mental status for health and well-being, and fits well with other studies, showing that mental unrest is associated with insecurity of tenure or rent levels. Any insecurity or doubt about being in control of the own home – be it financially or emotionally motivated – does have an impact on the mental well-being of the residents. Sleep disturbance may be one of the consequences of such compromised psychosocial benefits of the home (Kearns et al., 2000).

The results of the model for depression further support the argument that bad housing and inadequate residential environments can strongly affect the mental well-being of residents. The fact that – before and after the integration of the confounders – the two noise variables as well as the two safety variables are significantly associated with depression shows that these factors have an independent and stable influence that, compared to some of the individual characteristics, is rather strong. This shows that noise and safety constraints are relevant aspects for depression, and provide another piece of evidence for a psychosocial link between housing and health.

In this context, it is interesting to look in detail at the difference between the two noise variables. ‘Traffic noise’, which in comparison to ‘surrounding area noise’ provided the higher OR for the prevalence of sleep disturbance, is much less relevant for depression: it shows an increased OR of 1.41 only, while ‘surrounding area noise’ increases the likelihood for depression more than twofold (OR 2.13). The data therefore suggests that noise sources may have a varying impact on different health outcomes, and possibly the perception and the characteristics of the noise exposure explain the reaction. Traffic noise may be easier to accept, as most likely, every resident has a personal interest in functional traffic systems. Traffic noise may therefore be a major source for sleeping problems, but could be less relevant for mental health effects as they are evaluated as an unavoidable characteristic of urban living, and therefore subjectively accepted and justified. Neighbourhood noise from other sources, such as playgrounds and other outside areas, may have a more devastating effect on mental health, as the perception of such ‘unacceptable’ noise has a more emotional dimension and may also contain some social characteristics. Such noise is often the reason for social conflicts, even leading to legal cases. Due to this psychosocial dimension, it seems natural that the effects of residential noise exposure are more strongly expressed in the mental health domain.

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A different pattern, however, is visible for the safety parameters, for which ‘feeling of safety in the dwelling’ shows the higher relevance for both sleep disturbance and depression. Here, the perception of safety in the residential area at night is less important. A possible explanation would be that a resident has a variety of alternatives to avoid or reduce the feeling of being exposed to threats in the residential area by his or her behavioural choices (not going out at night, not going alone, taking a taxi, etc.). Such a choice does not exist when being in a home that does not provide the feeling of total security and protection from outside threats.

However, any interpretation of the results must keep in mind that the LARES data is cross sectional. Therefore, it cannot be excluded that those residents suffering from depression have a more negative assessment of their residential environment (or tend to live in less healthy neighbourhoods due to some socioeconomic selection mechanisms), which will inevitably result in statistical associations between the residential environmental variables and depression.

Looking at the cardiovascular symptoms, the data clearly showed that they are mostly independent from the residential environment parameters selected for the regression models (only one of the contextual variables – ‘safety perception in residential area at night’- is significantly associated). As expected, age turned out to be the dominant parameter that remains unchallenged by any other factor.

However, looking at the discussion on noise exposure and the increased risk for heart attacks and infarcts, the OR of 1.24 (CI 0.98–1.57) for ‘traffic noise’ is remarkable as it corresponds to the findings provided by various other studies. Babisch (2001) and Stansfeld and Matheson (2003) suggested that with noise exposure, there is a 20% increased likelihood for heart attacks and myocardial infarction. Despite the fact that the regression model did not provide a significant result in the LARES study, there is some relevance in the fact that ‘traffic noise’ shows the highest OR increase of all residential environment variables. Based on the more detailed results not shown in this paper, there is substantial reason for assuming that a larger sample size could have provided a similar but significant result.

For cardiovascular symptoms, ‘safety perception in residential area at night’ was the only significant residential environment variable. For sleep disturbance and depression, the ‘feeling of safety in the dwelling’ was more influential. It has been argued above, that the perception of security in the home may be more strongly associated with mental or psychosocial health, while the perception of not being safe in the residential area – based on the results of the LARES survey – could be more linked with physical health effects such as cardiovascular symptoms.

7 Conclusion

Looking at the results of the analysis of the impact of the residential environment characteristics on residential environment satisfaction and the selected health outcomes, the multivariate analyses identified two key problems that showed the highest and most consistent impact on the likelihood to be less satisfied with the residential environment, or to suffer from the three analysed health outcomes. These two problem areas were (a) noise exposure and (b) perceived safety. Both were significantly and strongly associated with low residential environment satisfaction, sleep disturbance, and depression. For cardiovascular symptoms, only perceived safety aspects were significant,


but the traffic noise variable provided an even higher OR just below the significance level.

Less expressed but still relevant, a lack of ‘play areas for children’ was strongly associated with low residential environment satisfaction while in general, recreational characteristics (‘play areas for children’, ‘place to sit and relax outside of dwelling’) were associated with sleep disturbance and provided slightly increased, but not significant, OR values for depression (1.15 and 1.13 respectively). The last relevant residential environment characteristic was ‘litter and trash in residential environment’, which was significantly associated with low residential environment satisfaction and sleep disturbance and provided a not significant OR of 1.22 for depression.

In concrete terms, the results support the argument that the quality of the residential environment is significantly associated with the residential satisfaction as well as with a variety of health outcomes and therefore has a direct impact on the well-being, health and the health related quality of life of the residents. This finding, based on data from eight European cities, corresponds to the results of other research on the health impact of neighbourhood conditions (Howden-Chapman, 2004; Drukker et al., 2003; Dunn, 2002). The study thereby suggests that the relationship between the selected contextual residential environment factors on one side, and residential environment satisfaction and the selected health outcomes on the other is valid for all cities. Based on the results presented, it can thus be concluded that – at least – ‘neighbourhoods have a small but consistent impact on health status’ (Howden-Chapman, 2004, p.165), and it can be added that a low rating of residential environment satisfaction is most likely a telltale sign for health-related neighbourhood problems.

Although urban planning and neighbourhood development projects are often based on a variety of health relevant objectives (WHO Regional Office for Europe, 1999), they have yet to develop an improved understanding of how their practice relates to human well-being and satisfaction (Dannenmaier, 1995). Physical features of neighbourhoods (traffic system, density, recreational facilities, greenery, physical incivilities such as trash), together with the social components (residential behaviours, social incivilities, safety perception), can translate into physical as well as nonphysical effects that shape residential satisfaction and health related quality of life of a residential area (WHO Regional Office for Europe, 1999). In this context, it remains:

... one of the major challenges … to bridge the divide between the environmental quality/wellbeing/quality of life specialists and the players who make urban policy and who shape our physical and social environments – in other words the engineers, planners, architects, service delivery specialists, etc. (Brown, 2003, p.85).

Urban and neighbourhood planning has both the capacity and the mandate for developing health supportive settings, and for promoting and supporting the health status of individuals (WHO Regional Office for Europe, 1999, 2004). Well designed neighbourhoods and residential environments are able to reduce or compensate health relevant exposures to physical, social and mental challenges, with residential satisfaction being a valuable indicator of the overall residential conditions. If adequately considered and located, many neighbourhood features could improve the health related quality of life of the residents, and represent a strong and preventive contribution to population health.

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Acknowledgements

The author would like to acknowledge the work and support of Xavier Bonnefoy and Nathalie Röbbel at the WHO European Centre for Environment and Health, Bonn, WHO Regional Office for Europe, in the context of the LARES survey, as well as the comments and advice given by Philippa Howden-Chapman and Irene van Kamp during the development of this paper.

The LARES survey was supported by Grant No. 328-1720/60 from BMGS (German Federal Ministry of Health and Social Security). The support from the German Ministry of Environment, Ministries of Health of Portugal, France and Lithuania, from all participating municipalities, and from the Canton of Geneva is duly acknowledged.

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