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Division of Psychiatry, University of Bristol, UK
Universidad de Chile, Chile
Universidad de la Republica, Chile
Division of Psychiatry, University of Bristol, UK
Correspondence: Dr Ricardo Araya, Division of Psychiatry, University of Bristol, Cotham House, Cotham Hill, Bristol BS6 6JL, UK. Email: r.araya{at}bristol.ac.uk
* Freely available online through the British Journal of Psychiatry
open access option. 
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Aims To estimate the variation in the prevalence of common mental disorders attributable to individuals and the built environment of geographical sectors where they live.
Method A sample of 3870 adults (response rate 90%) clustered in 248 geographical sectors participated in a household cross-sectional survey in Santiago, Chile. Independently rated contextual measures of the built environment were obtained. The Clinical Interview Schedule was used to estimate the prevalence of common mental disorders.
Results There was a significant association between the quality of the built environment of small geographical sectors and the presence of common mental disorders among its residents. The better the quality of the built environment, the lower the scores for psychiatric symptoms; however, only a small proportion of the variation in common mental disorder existed at sector level, after adjusting for individual factors.
Conclusions Findings from our study, using a contextual assessment of the quality of the built environment and multilevel modelling in the analysis, suggest these associations may be more marked in non-Western settings with more homogeneous geographical sectors.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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We are unaware of any other Latin American study assessing the contextual effect of the built environment directly and using multilevel models to investigate its association with mental illness. We tested the hypothesis that contextual measures reflecting the quality of the built environment in Santiago, Chile would be associated with common mental disorders independent of individuals' characteristics.
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Sampling strategy
A cross-sectional survey was conducted between 1996 and 1998. The sampling
framework was the adult population, restricted to ages 16–64 years,
living in private households in the Greater Santiago area. The sampling
strategy involved a three-stage design, which included all the 35 boroughs of
Greater Santiago, 248 sectors and 4300 households randomly selected with a
probability proportional to the size of the sampling units. The number of
households within each sector varied from 26 to 5. One person per household
was chosen randomly for interview using Kish tables
(Kish, 1965). Individuals from
sectors with fewer than five observations were excluded from this analysis.
Responses were obtained from 3870 households (response rate 90%). Further
details of the sampling design can be found elsewhere
(Araya et al,
2001).
Mental health, social and demographic questionnaire
Psychiatric symptoms were assessed with the Revised Clinical Interview
Schedule (CIS–R; Lewis et
al, 1992), a structured and detailed psychiatric interview
used extensively in primary care and community studies in Chile and elsewhere.
This interview has 14 items assessing the severity of the most common
psychiatric symptoms. Each item is given a score, which can then be summed to
yield a total score. This continuous measure, reflecting psychiatric symptom
severity, was used as our main outcome. The mean weighted 
across all
16 sections of the CIS–R was 0.87 (s.d.=0.08). The validity and
reliability of the CIS–R are comparable with the Composite International
Diagnostic Interview (CIDI; Lewis et
al, 1992; Andrews &
Peters, 1998; Brugha et
al, 2005).
The gender and age of respondents were recorded. Individuals also answered questions regarding their socio-economic status including marital status, educational level and monthly per capita income. The latter was estimated as the sum of net monthly salaries and other income (pensions, dividends, interests or rents) contributed by each household member, divided by the number of residents regardless of age. Interviewers rated the quality of housing through visual inspection as luxurious, good, average, poor or very poor. In order to do this, interviewers used the same criteria and received the same training as for the Chilean national census. The presence of a self-reported physical disease was ascertained from the response to an open-ended question: `Do you suffer from any physical problem or disability at present?'. Two independent physicians assessed if the physical problem would require medical attention, in which case they classified it according to the bodily system involved. The self-reported number of friends or relatives who could provide emotional or practical support if needed was determined with a single, open-ended question. The number of alcohol units consumed daily was entered as a continuous variable. All violent crimes reported to the local police station in each one of the sectors were added and this figure was divided by the population of the sector to create an index of violence. The following borough variables were also included: education and health budget per capita, and number of social organisations divided per population size in the borough.
Built Environment Assessment Tool
The Built Environment Assessment Tool (BEAT) was developed to collect data
through visual inspection on a wide range of features of small geographical
sectors. The sectors included in the assessment were the clusters used in the
survey and are those that the Chilean Office of National Statistics (INE) had
chosen to maintain updated statistics of the population between censuses.
Although the sectors vary in size, they usually represent homogeneous areas of
approximately ten small contiguous streets whose maps were prepared by the
INE.
The main purpose was to try to create a score that reflected the desirability or attractiveness of a sector. Although many characteristics of a local sector are likely to affect satisfaction, the study focused on those that could be easily and reliably assessed by a walk through the area. Most items were derived from the Residential Environment Assessment Tool (REAT; Dunstan et al, 2005) used to measure area characteristics in Wales. Some additional items were included because they were thought to be important locally, such as the presence of stray dogs or bad odours. The final instrument (available from the authors) included items relating to the following characteristics, with the number of items in parentheses: public lighting (2), state of roads (6), sidewalks (4), public green areas (5), green elements on sidewalks and front gardens (4), dirtiness (1), traffic and noise (2), bad odours (1), general maintenance of properties (1), general use of the sector (4), empty sites (2), external beautification (1), presence of homeless people (1), presence of stray dogs (1), access to properties (2), balconies (1), street signs (1), public transport (4), security and safety devices (6), and a list of facilities including:
Some of the items required dichotomous ratings, for example the presence of lamp-posts; others had a range of possible values that could be ordered from high to low, such as the level of maintenance of front gardens. Ratings for all items were converted into a score between 0 and 1, with the value of 1 always representing either a more desirable feature (e.g. cleaner roads) or more of that particular item (e.g. more essential facilities). For example, the item for `beauty of front garden' was initially coded 1, 2 or 3 and subsequently recoded as 0, 0.5 or 1, with 1 representing `very beautiful' front gardens. The presence of any of the listed facilities in the sector was rated as `1' and individual scores were summed to generate three facility indices. These total scores for the facilities indices were subsequently recoded into three scales with scores ranging from 0 to 1.
Ratings were made for observations of the sector as a whole, for example the extent of street litter within the sector rather than in any particular street. Operational definitions were provided for each item, including a set of photographs to illustrate different degrees of items such as litter. The tool was initially piloted in a few neighbourhoods in Santiago of varying socio-economic status. Raters were university students of architecture and psychology trained over 2 days to ensure consistency in their understanding and ratings of items. During this training, instructions were given and photographs used to discriminate between ratings.
Two independent raters assessed each geographical sector. Each one scored all items, and after the assessment both raters met and agreed on a consensus rating, which is the one used in this study. There were ten pairs of raters in all. The walk-through assessment was undertaken over a period of 30 days in January 2002. All assessments were conducted in the mornings in order to avoid potential differences due to timing, for instance in ratings of noise. Raters were given a map of the sector with clear boundaries and instructions to follow a predetermined route, specified in the map, when walking through the area and to cover all streets in the sector. The assessment of each sector took approximately 60 min to complete. In certain areas it was necessary to arrange for someone to accompany the raters for reasons of safety, but the raters were instructed not to talk about the neighbourhood to anyone until the ratings had been completed.
Statistical analyses
Composite score for the BEAT scale
All variables with 95% or more of respondents in one category were
eliminated. Subsequently we performed factor analysis with varimax rotation of
all the remaining items to assess if and how these items loaded into common
factors. Cronbach's 
was estimated for all the items in the scale and
for the items within each one of the newly derived factors after the factor
analysis. All variables were entered, including the facilities indices, into
the factor analysis model and those with loadings lower than 0.4 after varimax
rotation or high uniqueness values were also excluded. The scores of each item
(0, 1) within a factor were added to generate a total composite score for the
factor. A higher score in the factor reflected a more attractive area. We
generated an alternative total score applying weights to the individual item
scores according to the loadings of that particular item in the factor
analysis. The total unweighted and weighted scores for each factor were
compared using correlation coefficients to explore for possible differences,
depending on the methods used to estimate total factor scores. Finally, for
all regression models, the four factor scores were rescaled to a minimum of 0
and a maximum of 10 to facilitate comparison of effect sizes between the
factors.
Testing associations across levels
Associations between sector (level 2) and borough (level 3) exposures and
individual CIS–R scores were investigated using multilevel regression
models (MLwiN version 2.02, Institute of Education, University of London, UK,
and Stata Release 9), before and after adjusting for individual (level 1)
predictor variables. All analyses excluded individuals from sectors with fewer
than five replies and those with incomplete data for any of the individual,
sector-level and borough-level variables to be included in the models. We did
this because we felt that using data from only a handful of individuals might
not offer a fair representation of the sector. Individuals included and
excluded from the analyses were compared in terms of age, gender and
socio-economic characteristics. The modelling strategy consisted of first
fitting a simple variance components null model to quantify the three
components of residual variation in CIS–R score as a continuous
variable: borough, sectors and individuals. Analyses involving CIS–R
score as a continuous outcome were based on a normally distributed multilevel
model using the observed and log-transformed CIS–R scores. Estimation in
all models was based on iterative generalised least squares. As the
associations between individual-level variables and mental health are already
well known, the primary aim of this study was to investigate the effects of
the local and wider neighbourhood on mental health using the CIS–R total
scores, after taking individual factors into account. Therefore the modelling
strategy we adopted was to investigate sector-level predictors of CIS–R
first, then to add individual and finally borough-level variables to the model
and to note changes in the components of variance and coefficients for the
sector-level factors. We investigated whether there were any differential
associations between factor 1 and CIS–R for categories of selected
individual variables by fitting appropriate interaction terms in the
regression models.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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These four factors all had eigenvalues over 1 and together explained 90% of
the total variance (Table 1).
The mean Cronbach's for the items in the scale was 0.87. There were
small differences between these values of and those generated using
only the items within each factor. Lower values for the items
contained in factors with lower eigenvalues are explained because the first
factor with the highest eigenvalue contains the most items, thus making the
greatest contribution to the overall variation in scores. Kappa coefficients
for items between pair of interviewers fluctuated from 0.69 to 0.92, with 78%
of the estimated coefficients above 0.85 and full agreement for 70% of
the items. Simply summing items to get a total factor score assumes equal
weighting of each item and that `non-loading' items are not important. For
this reason we compared weighted (according to eigenvalues) and unweighted
scores for each of the factors. We found high correlations between these two
different ways of scoring the factors (correlation coefficients for factors 1,
2, 3 and 4 were 0.99, 0.87, 0.92 and 0.88 respectively), and therefore we
decided to use the simple, unweighted scores in all analyses. Correlation
coefficients between the four sector-level factors are presented in
Table 2.
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Characteristics of the sample surveyed
A total of 3870 interviews were completed to give a response rate of 90%.
These 3870 individuals clustered into 248 sectors and 35 boroughs. A total of
488 individuals (12.6%) were excluded because of missing data or because they
lived in a sector with fewer than five respondents, leaving 3382 observations
from 210 sectors within 31 boroughs for analysis. Excluded individuals were no
different to those included in terms of age (P=0.56), gender
(P=0.17) or marital status (P=0.59), but had lower median
income (in Chilean pesos, CLP62 500 v. CLP100 000,
P<0.0001), were less likely to be educated to university level (20
v. 36%, P<0.001), were more likely to live in very poor
or poor quality housing (22 v. 15%, P<0.001), have fewer
supportive individuals (3.7 v. 4.2, P=0.01) and have lower
alcohol consumption (1.5 v. 1.8, P=0.005). Excluded
individuals also had higher mean CIS–R scores (8.5 v. 7.2,
P<0.001). There was no evidence that excluded sectors were any
different from those included in terms of the four factors generated from the
BEAT scores or violent incidents reported to police. The number of individuals
per sector in the final data-set ranged from 5 to 26 and the number of sectors
per borough from 2 to 26. Characteristics of the individuals, sectors and
boroughs are presented in Table
3.
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Variance components null model for common mental disorder
Mean CIS–R score for the total sample was 7.19 (s.d.=8.00, range
0–49). We estimated that approximately 5.6% (95% CI 1.8–9.4) of
the residual variation in total CIS–R score lies at the borough level,
3.8% (95% CI 1.8–5.7) at the sector level and 90.6% (95% CI
86.2–95.1) at the individual level
(Table 4). In view of the
non-normal distribution of CIS–R scores we also undertook all multilevel
modelling using log-transformed scores and the results in all these models
were almost identical to those presented here (further details available from
the authors).
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Effect of including individual, sector and borough characteristics in the model
Sector-, individual- and borough-level fixed effects were added to the null
model in a cumulative manner (Table
4). Inclusion of the sector-level exposures reduced the total
residual variation. The estimated percentage of residual variation at borough
level decreased to 0.13%, whereas the percentage residual variation at sector
(3.55%) and individual (96.32%) levels remained similar and increased
respectively. Additional inclusion of individual-level variables reduced the
overall variance further, with none of the residual variation now explained at
the borough level. Estimates remained unchanged after the addition of
borough-level variables. Estimated associations between sector-level factors
and mental health were therefore based on a simpler, two-level model that
included only sector (level 2) and individual (level 1) variables.
Effect of neighbourhood quality on common mental disorders
Table 5 shows the crude and
adjusted associations between each of the sector-level factors (rescaled so
that the possible range for each is 0–10) and CIS–R total score.
After adjusting for other sector-level and individual-level variables, factor
1 (overall quality of the built environment) was inversely associated with
total CIS–R score: that is, there was strong evidence that individuals
living in sectors with more desirable features such as better roads or more
green areas had better mental health, after taking into account individual
characteristics. There was also a significant association with factor 4 in the
adjusted model only, but this was in the opposite direction; higher factor
scores were associated with higher CIS–R scores.
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We tested interactions between factor 1 and the following individual
variables: gender, income and education. The only significant interaction was
for gender and factor 1 (–0.32, 95% CI –0.58 to – 0.05,
P=0.02) in which male respondents living in less desirable areas had
significantly lower CIS–R scores than female respondents. No significant
interaction was found for income (0.004, 95% CI –0.001 to 0.008,
P=0.10) or education (secondary, 0.23, 95% CI –0.26 to 0.72;
university, 0.16, 95% CI –0.35 to 0.68; overall 
2=0.91,
d.f.=2, P=0.63).
Although our primary interest was to investigate the association of these factors with mental health, which was best represented by the continuous distribution in CIS–R total scores, we also explored associations with the most common ICD–10 disorders (World Health Organization, 1992), anxiety and depressive disorders, using logistic regression models. There were 154 (4.6%) cases of depression and 309 (9.1%) of anxiety. There was no evidence of any association with depression for factors 1, 2, or 4 (factor 1, OR=1.00, 95% CI 0.87 to 1.13, P=0.92; factor 2, OR=0.91, 95% CI 0.71 to 1.17, P=0.46; factor 4, OR=1.05, 95% CI 0.99 to 1.13, P=0.16) but some evidence of an association with factor 3, representing public green areas (OR=0.94, 95% CI 0.90 to 0.99, P=0.01). For anxiety disorders there was some evidence of an association with factor 4 (empty sites, OR=1.05, 95% CI 1.00 to 1.11, P=0.04), but no association was detected for the other factors (factor 1, OR=0.94, 95% CI 0.86 to 1.03, P=0.20; factor 2, OR=1.01, 95% CI 0.85 to 1.21, P=0.87; factor 3, OR=0.99, 95% CI 0.95 to 1.02, P=0.38).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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The Built Environment Assessment Tool
We developed and tested a quick and reliable method to assess the built
environment using a walk-through method. The great majority of the items
reflected the built environment, but there were a few –such as the
presence of stray dogs –that represented the observable residential
environment rather than something built. We had previous experience with
developing a similar instrument for a study in South Wales
(Dunstan et al, 2005)
and we studied carefully other similar instruments
(Sampson et al, 1997;
Cohen et al, 2000;
Weich et al, 2002;
Hembree et al, 2005).
Our measure showed good interrater reliability and internal consistency; in
the absence of a gold standard, however, it is difficult to assess its
criterion validity. Overall, the psychometrics of this tool are comparable
with those found for the two similar tools developed in the UK
(Weich et al, 2001;
Dunstan et al,
2005).
Variance in mental disorders according to geographical aggregation
Studies in Western countries using multilevel models to estimate area-level
variation in common mental disorders have found little or no variation at
higher levels of aggregation, after accounting for individual differences
(Weich, 2005). The
contribution of smaller area effects to the total variance in common mental
disorders usually fluctuates between 0.5% and 4% before adjusting for
individuals' characteristics, and drops to levels between 0% and 1% after
adjustment (Weich, 2005). Our
findings are in keeping with the higher end of previous estimates; we found
that 3.8% of the variance in common mental disorders was explained at the
small sector level in the unadjusted models, reducing to nearly 1% in the
adjusted models.
As eloquently argued by Weich (2005), there may be a number of reasons to explain this lack of positive findings. For instance, sectors that are large or heterogeneous tend to yield negative results. However, in a previous UK study in South Wales using small geographical units (postcodes with approximately 150 people) we also found little variation at this level (further details available from the authors). It is possible that using geographical units identified on the basis of an arbitrary geographic classification, which might not reflect neighbourhood unity, may influence the results. In this respect, Reijneveld et al (2000) found that the clustering of common mental disorders was higher at neighbourhood level (sectors with similar types of building delineated by natural boundaries) than at postcode level using arbitrary geographical boundaries. We tried to deal with both of these possibilities, so we used small geographical sectors of approximately 300 people that were sufficiently homogeneous in terms of their neighbourhood. Our study did not measure the outcome (CIS–R) aggregated at household level and so it is possible that some of the variance found at higher (sector) or lower (individual) levels might reflect variance present at household level. In our previous study in Wales we found that 37% (95% CI 27–49) of variance existed at household level (further details available from the authors). Although the CIS–R variance at borough level appears greater than at sector level in Table 4, the confidence intervals of these estimates (5.6%, 95% CI 1.8–9.4% at the borough level and 3.8%, 95% CI 1.8–5.7 at the sector level) show that one cannot reach this conclusion. More importantly, once adjustment for other variables are introduced this borough variance comes close to nil but the sector variance remains only slightly attenuated. Yet the variance we found at borough level in adjusted models is considerable in comparison with other studies. The most likely explanation is that Santiago, like other cities in Latin America, is quite compartmentalised in terms of the quality and socio-economic status of the geographical areas, with little variation within but more variation between boroughs.
Previous studies have been criticised because they tend to rely entirely on brief psychiatric self-reported questionnaires to measure the outcome. Our study used a detailed structured psychiatric interview to overcome this limitation. So, as it stands, we have to conclude that there seems to be little variation in prevalence variation in prevalence of common mental of common mental disorder explained at area level and much of this variance resides at individual level. However, even if small sectors contribute little to this overall variance, is it still possible that some features of these areas may be associated with common mental disorders?
Quality of residential environment and common mental disorder
There have been only a handful of mental health studies that have used
truly contextual and independent measures of the built environment throughout
the world. In the UK there have been only two such studies. Weich et
al (2002) found a
significant association between the prevalence of depression and properties
with predominantly deck access (OR=1.28, 95% CI 1.03–1.58) and of recent
construction (OR=1.43, 95% CI 1.06–1.91). It is worth noting that this
was a cross-sectional study in which no multilevel modelling was used in the
analysis (Weich et al,
2002). Using a similar contextual assessment of the built
environment as in the study we report here and multilevel modelling in the
analysis we did not find any significant association between the total score
of an index depicting quality of the residential environment and the
prevalence of common mental disorders in our study in South Wales (further
details available from the authors). It must also be borne in mind that both
Weich et al (2002)
and our previous study used brief questionnaires to measure mental disorder
and studied smaller samples than in this study (Weich et al, 76
sectors, n=1887; our previous study, 51 sectors, n=1500). A
larger number of sectors could help to improve the accuracy of the estimates
and provide greater power to test smaller effects.
We found strong evidence of an association (P
0.05) between
two factors of our index of quality of the built environment (BEAT) and common
mental disorders, after adjusting for individual differences. These factors
represented almost two-thirds of the total variance in the quality of the
built environment and thus one can confidently conclude that they are good
indicators of the built environment in the city of Santiago. We used a similar
method as in the South Wales study (REAT;
Dunstan et al, 2005),
but there were some differences that might help explain the discrepant
results. The REAT assessed mainly the more private built environment such as
houses, gardens or housing density. The BEAT assessed extensively other
aspects of the built environment such as roads, pavements and public
facilities. The REAT provided a total score reflecting the quality of the
residential environment, whereas we used four factors with their corresponding
individual scores. Although it may seem intuitive that a better built
environment might help us feel better, the precise mechanism by which the
built environment influences our mental health is still a matter of
conjecture.
Why did only two factors show significant associations in our study? The first factor represented the largest proportion of the variance and it was the most comprehensive indicator of the quality of the neighbourhood and built environment. Although the relative contribution of this factor to change in CIS–R score is approximately ten times smaller than that associated with individual variables such as being female, it is a factor amenable to change and it is widely spread. Interestingly, the only significant interaction across levels showed that women were more affected (higher CIS–R scores) than men when living in less desirable areas. This would be in keeping with our hypotheses because the women –especially those who did not work outside the home –probably spent more time in the areas studied than men and were therefore more exposed.
We found, rather surprisingly, that factor 4 (empty sites) was associated in the opposite direction: fewer sites were associated with higher CIS–R scores. However, factor 4 was not a key indicator of the area environment, contributing only 8% of the variance in our factor analysis, and in the unadjusted model (see Table 5) this association was not significant at a 5% level. Our assumption was that fewer empty sites, especially if they were unoccupied, would be a good feature of the sector; however, it is possible that our assumptions were baseless and that empty sites in Santiago may not represent abandoned, derelict places where rubbish accumulates, as in other settings. We expected that factor 2, representing 20% of the total variance, would be significantly associated with CIS–R scores. However, this factor was a rare combination of essential and leisure facilities and noise and traffic in the area. Our assumption here was that an increased number of facilities would represent an asset for the locality, but it may be that more facilities bring more noise and traffic to the area and that this is more important. Nevertheless, overall it is reassuring that the strongest and clearest association is for the best and most comprehensive indicator of the quality of the built environment. When we explored associations of these factors with ICD–10 categorical disorders the results were puzzling. We found that there was no association between these disorders and factor 1, representing the overall quality of the neighbourhood. Even more surprisingly, individuals who were depressed were more likely to live in areas with more public green areas, an association that we did not find when using CIS–R total scores. More in keeping with the other results, individuals living in areas with fewer empty sites were less likely to have an anxiety disorder, an association that we found for CIS–R total scores but in the opposite direction. It is difficult to find a reasonable explanation for these disparate findings, especially for those related to depressive disorders. However, our interest was to focus on population changes in mental health (symptom scores) rather than concentrate on specific subgroups, mainly because the former approach would be more informative for public health decision-makers (Rose, 1993).
Santiago is fairly well compartmentalised according to socio-economic
grouping. Wealthy people live in areas completely removed from the areas where
poorer people live, something not always found in UK cities with a much more
mixed socio-economic distribution within geographical sectors. This clear and
distinct geographical distribution might have helped reduce `contamination'
and accentuated the differences between the sectors selected in our clustered
sampling strategy. We selected the sectors in our sample to represent an
adequate spread of neighbourhood deprivation, so we expected this would ensure
an adequate spread of residential quality. We think that a drop of one point
in the total CIS–R score attributable to living in the sectors with
better built environment quality is a meaningful change, bearing in mind the
large proportion of people who might potentially benefit from interventions to
reduce this difference. When a common threshold of common mental disorder
caseness with the CIS–R (
12) is used, those living in areas with
better built environment are approximately 20% less likely to meet caseness
criteria than those living in areas with poorer built environments. Leventhal
& Brooks-Gunn (2003) found
that families who moved from a very poor neighbourhood to a non-poor
neighbourhood showed better mental health than control families who did not
move. A similar issue related to mobility is whether or not individuals with
poorer mental health may selectively move to more deteriorated areas rather
than poorer areas making individuals unhappier (causation v.
selection). Unfortuantely the design of our study does not allow adequate
testing of this theory, and the stability of residence was not recorded.
Strengths and limitations
Our study benefited from using a truly contextual and independent
assessment of the built environment rather than measures derived from
aggregating individual data. The small size of our surveyed areas ensured
reasonable homogeneity within sectors. We used multilevel modelling to account
for the hierarchical structure of the data. The study was large but its unique
setting means its results are not necessarily generalisable to other cities
throughout the world. Our independent measures at the highest level
concentrated on the physical aspects of the environment, mostly because we
thought that these could be measured reliably. Of course, the quality of the
built environment also reflects something of the psychosocial environment, but
we did not include these aspects in this study. This study should be taken as
an invitation to explore this field further.
The assessment of the geographical sectors was undertaken almost 4 years after we finished the survey of the individuals. Although it is possible that the conditions in those neighbourhoods could have changed in the interim period, we did not find evidence that sectors had experienced major structural changes during the interval according to a survey of local government authorities (Secretaría Regional Ministerial de Planificación y Coordinación, n, 2005). A few sectors with a larger proportion of socially disadvantaged individuals were excluded from the analysis. The main reason for sector exclusion was the small number of people in the sector or the lack of data. Common mental disorders are more prevalent among socially deprived individuals; thus our estimates may be an underrepresentation of the true association. Finally, this is a cross-sectional study and as such we cannot infer the direction of causality. Equally, this kind of design cannot account for factors related to selective migration or population instability.
In conclusion, measuring the impact of the quality of neighbourhoods on mental health and understanding the complex interrelationships between individuals' characteristics and their local environment are challenges that should be confronted, so that appropriate and effective interventions can be implemented to improve the mental health of the population.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Received for publication March 21, 2006. Revision received October 24, 2006. Accepted for publication November 14, 2006.
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