Department of Psychiatry, University of Bristol, UK and University of Ioannina School of Medicine, Greece
Department of Psychiatry, University of Bristol
Department of Psychiatry, University of Bristol
School of Geographical Sciences, University of Bristol
School of Social Sciences, University of Cardiff, UK
Correspondence: Petros Skapinakis, Department of Psychiatry, University of Bristol, Cotham House, Cotham Hill, Bristol BS6 6JL, UK. E-mail: p.skapinakis{at}bristol.ac.uk
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ABSTRACT |
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Aims To investigate whether regional mental health differences in Wales would persist after having taken into account the characteristics of individuals and regional social deprivation.
Method Data from the 1998 Welsh Health Survey were used. Common mental disorders were assessed with the mental health index included in the Short-Form 36 health survey (SF36).The data were analysed using a multi-level linear regression model.
Results Of the total variance in the mental health index, 1.47% occurred at regional level (95% CI 0.562.38). Adjustment for individual characteristics did not explain the between-region variation. A higher area deprivation score was associated with a higher score on the mental health index.
Conclusions Mental health differences in Wales are partly explained by the level of regional social deprivation.
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INTRODUCTION |
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METHOD |
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Information on Wales
Wales occupies a broad peninsula on the western side of Great Britain, with
a total area of 20 760 square kilometres and a population of approximately 2.9
million in 2001. The average population density is lower than that in England
but in the industrialised south is comparable to other highly populated areas
in Britain. The median population (mid-year estimates in 2001) in the 22
regional unitary authorities was 122 850, with a range between 55 900 in
Merthyr and 305 200 in Cardiff.
Measures
Assessment of common mental disorders
The main outcome used in the present study was psychiatric morbidity, as
measured by the mental health index included in the SF36. The
SF36 (Ware & Sherbourne,
1992) is an instrument widely used to assess the health status of
patients and it has also been used in community studies. The psychometric
properties of the SF36 were tested in a study in the UK general
population and the mental health index showed good internal consistency
(Cronbachs =0.83; Jenkinson
et al, 1993). In addition, a study carried out in Wales
compared the mental health index with the 12-item General Health Questionnaire
(GHQ12), an instrument commonly used to assess common mental disorders
in the community, and found it to be comparable
(Winston & Smith,
2000).
The mental health index is a set of five questions asking about the presence of negative (three questions) or positive (two questions) feelings during the past 4 weeks. The five questions used for the index are:
Each of the questions has six response categories ranging from all of the time to none of the time. For the purposes of the present paper we reversed the order of scoring for the three negative questions and therefore a higher score on the index represents greater psychiatric morbidity. We then transformed the raw scores (ranging from 5 to 30) on a scale from 0 (no morbidity) to 100 (high morbidity). In our analysis we used the transformed scores as a continuous variable. It should be noted that this simple instrument assumes that common mental disorders represent a single dimension. Several epidemiological studies have confirmed the usefulness of this assumption for the common mental disorders of depression or anxiety (Goldberg & Huxley, 1992).
Individual characteristics
Information on the following individual-level variables was available: age,
gender, marital status (coded in four categories: single; divorced; widowed;
married/living as couple), employment status (coded in four categories:
employed full-time or part-time; unemployed or unable to work because of
long-term disability; retired; economically inactive), the Registrar
Generals social class based on the participants present or most
recent occupation (coded in five categories: I/II; III, non-manual; III,
manual; IV/V; missing values), and housing tenure status (either owner or
tenant).
Deprivation at the authority level
Levels of deprivation across regions were estimated with the Welsh Index of
Multiple Deprivation (National Assembly
for Wales, 2004). This is a composite measure developed by the
local government data unit with the aim of modelling levels of deprivation in
Wales and supporting policy development and targeting of resources. The data
used in the derivation of the index are based on direct measures of
deprivation at the small-area level (the electoral division level). Data from
the following domains were included: income, employment, health, education,
housing and geographical access to services. Detailed information for the
derivation of this index is given elsewhere
(National Assembly for Wales,
2004). For the purposes of the present paper we used the average
electoral division rank. This was a number between 104 for the most deprived
area and 708 for the least deprived area. For easier interpretation of the
index we subtracted the actual score from 1000 and therefore the new score has
a median of 558 and a range of 292896, with a higher score meaning a
higher level of deprivation. The 22 regions were categorised further into
three groups according to their deprivation score, as follows: low level of
deprivation (scores of 292490), middle level of deprivation
(491651) and high level of deprivation (652896). The cut-offs
chosen were the 25th (490) and 75th (651) percentiles of the transformed score
on the deprivation index.
Statistical analysis
Analyses were carried out with MLWin software
(Rasbash et al,
2001). The score on the mental health index was used as the
continuous dependent variable in a hierarchical linear regression model. The
estimation procedure applied was the restricted iterative generalised
least-squares method (Goldstein,
1995), which leads to unbiased estimates of the random parameters.
The P values were based on Walds test (two-sided). Our
strategy for the analysis consisted first of fitting a simple variance
component model (null model) to identify the two components of variation: that
between regions (level 2 variance) and that between individuals within a
region (level 1 variance). The next step was to include all level 1 variables
in the model. The level 1 variables were entered as fixed effects, which
assumes that they are related to the mental health index in the same way
across level 2 units. The degree to which the estimated level 2 variance
decreased after entering the explanatory level 1 variables indicated how well
the model explained the between-region variance. To examine whether
deprivation at the regional level was associated independently with the mental
health of the individuals, we first entered the deprivation variable into the
null model (to obtain crude estimates) and then adjusted for all level 1
variables. The deprivation index was entered as a categorical variable, using
dummy variables for the categories of middle and high level of deprivation. We
also explored graphically whether differences in mental health between regions
persisted after taking into account the individual characteristics and
regional deprivation, by plotting the 22 residuals in the null model after
adjustment for individual variables and after adjustment for both individual
variables and regional deprivation.
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RESULTS |
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Table 1 shows the
hierarchical structure of the data and the crude regional averages of the
mental health index. Regions have been arranged in rank order of their social
deprivation indices. There are differences between regions, with a high
average of 32.7 (s.d.=20.5) in Blaenau Gwent and a low of 25.1 (s.d.=17.6) in
Monmouthshire. There is a strong relationship between the rank order of
regional deprivation and the rank order of the mental health index, with
Spearmans =0.60 (P=0.003).
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Null model
The intercept-only model is presented in
Table 2. The constant value of
27.85 (s.e.=0.51) represents the average mental health score across regions.
This value does not remain constant across regions and the random effect
variances are presented. Most variance occurs at level 1 (individuals) and
only 1.47% of the total (unexplained) variance occurs at level 2 (95% CI
0.562.38). Although small, this amount of variation at the regional
level is statistically significant (P=0.002).
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Inclusion of individual characteristics and regional deprivation
Model 1 in Table 2 shows the
degree to which the two variances are decreased after entering the individual
characteristics into the model. It can be seen that the total unexplained
variance at level 2 is reduced by 32.6% but is still significant. Further
adjustment for regional deprivation (model 2) led to a 50% reduction in the
total unexplained variance at level 2, but this remained significant
(P=0.005).
Association between regional deprivation and common mental disorders
Table 3 shows the
association between level 2 deprivation and scores on the mental health index
before and after adjustment for individual-level socio-demographic
characteristics. It can be seen that the regional deprivation level is
associated with common mental disorders, even after adjustment for the
characteristics of individuals (likelihood ratio test=13.6 on 2 d.f.;
P=0.001).
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Regional residuals
Figure 1 presents the
specific residuals for the 22 regions in the null model, after adjustment for
individual characteristics (model 1) and after adjustment for both individual
characteristics and regional deprivation (model 2). The residuals represent
departures of each region from the average score on the mental health index,
predicted by the fixed part of the multi-level model. A positive residual
means that this particular region is associated with a higher morbidity. In
the null model, 13 out of the 22 residuals were significantly different from
zero. Adjustment for individual variables did not have a significant impact on
regional mental health differences because 12 regions still had significant
residuals. After adjustment for regional deprivation, regional differences
were reduced substantially and only seven regions had residuals significantly
different from zero. This effect was more evident in regions where the crude
association between deprivation and common mental disorders was high, such as
Rhondda Cynon Taff, Caerphilly, Blaenau Gwent and Merthyr.
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DISCUSSION |
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Limitations of the study
Certain limitations should be considered before interpreting these results.
First, it has been pointed out by others that there is a lack of theory on the
mechanisms that link area of residence and health in general, or mental health
in particular (Macintyre et al,
2002; OCampo,
2003). The study of the effects of area of residence on mental
health is so limited that our study is mainly exploratory in nature. Although
we hypothesised that a measure of deprivation at the regional level would be
associated with common mental disorders, our finding should be interpreted
more as a preliminary effort that can help the generation of new hypotheses,
rather than as an indication that this specific factor might
explain mental health differences across regions. Second, we
used large administrative areas as our higher level of aggregation, and our
analysis included only two levels. We did not have data on other intermediate
levels, such as the electoral ward. However, for the specific hypothesis of
our study this design is adequate. Third, the cross-sectional design is
certainly limited and issues related to reverse causality and duration of
exposure can be dealt with only by longitudinal designs. Fourth, we assessed
common mental disorders in a crude way, using a simple five-item self-reported
measure. Although this measure has been found to be comparable with other
similar instruments, such as the GHQ12, a degree of random
misclassification will be inevitable and may have biased our results in either
direction. Finally, this was a postal survey with an average response rate of
60%. The most likely effect of this relatively low response is type II errors,
but if there were an association between regional deprivation, common mental
disorders and probability of response to the survey, the results could be
biased in either direction.
Area effects
Previous research that aimed to investigate the effect of area of residence
on mental health has observed generally that, once individual characteristics
have been taken into account, the amount of variation attributed to the higher
levels is very small and not significant
(Duncan et al, 1995;
Weich et al,
2003a; further details available from G.L. on request).
Our own estimates are somewhat higher and statistically significant. A number
of reasons may explain this discrepancy: the choice of instruments to measure
psychiatric morbidity (other studies mainly have used the General Health
Questionnaire) may have contributed to this diffference; and the power of
previous studies to find a statistically significant difference may have been
compromised by the choice of the higher level. With regard to the latter, it
has been argued (Snijders & Bosker,
1999; Diez Roux,
2004) that the power to detect the higher level variance component
is influenced by the number of individual observations in each group. A
greater number of higher level groups with relatively few individual
observations per group will yield large standard errors and may have
insufficient power to detect a significant variance component at this level
(although it will maximise the power to detect an association between a higher
level risk factor and the individual outcome). It is interesting to note that
most previous studies have used either the postcode or the electoral ward as
the higher level and this resulted in a small number of observations per group
in the range of 1423 persons. In contrast, our own study had a mean of
1214 individual observations per region. The study by Duncan et al
(1995) used the regions in
Britain as the higher level but this study failed to find a significant result
even in the null model.
Our study consisted of two levels of analysis whereas previous studies included a third intermediate level, most commonly the household level, and this may be a further reason for our significant results (or the non-significant results of previous studies) on the variation attributed to the higher levels. It should be noted that previous studies had selected more than one individual per household and this made necessary the inclusion of the household level to take account of the clustering of observations. In our own study only one individual per household was selected. Failure to include the household level in multi-level studies of mental health has been criticised in the past (Weich et al, 2003b). However, inclusion of an intermediate level will also increase the chances of overadjustment, which is considered an important problem in multi-level research (Diez Roux, 2004). For example, if the effects of area of residence on individual mental health are mediated through unknown household factors, then inclusion of this level will reduce the reported associations at the higher level (Davey Smith et al, 1998).
All studies that have investigated the effect of area of residence on several health outcomes generally have found small size effects, in the range of 15% of the total (unexplained) variance (Boyle & Willms, 1999). Our own result of 0.9% confirms these findings. Do these small figures have any public health importance? To answer this question one should take into account the possible ways in which a higher level context may affect an individual outcome. Blakely & Woodward (2000) have discussed this issue in detail. Contextual factors may have a direct effect on individual mental health or they may influence other intervening variables that mediate their effect. It has been argued that a direct effect is not possible because it will require a final reduction to an individual process. However, as Blakely & Woodward (2000) rightly point out, such reductionism is not helpful in public health terms because knowledge of one component of a causal chain may be sufficient for public health interventions. In addition, modifications of higher level risk factors are more efficient from a public health perspective compared with interventions that target individuals. Certainly, further research is needed to understand better what constitutes an adequate amount of explained variation (Boyle & Willms, 1999).
Association of deprivation with mental health
Our hypothesis that an index of regional deprivation would be associated
with common mental disorders was confirmed in this data-set. The few previous
studies that have investigated the same issue generally have found negative
results, after taking into account the individual characteristics
(Reijneveld & Schene,
1998; McCulloch,
2001; Weich et al,
2003b). As mentioned before, the choice of levels of
analysis and the problems of overadjustment may have contributed to this
discrepancy. In addition, selection bias could be an alternative explanation.
Individuals select the places they live and the (unidentified) individual
factors that influence this selection may be responsible for the reported
association. The resulting bias, however, could be in either direction
(Duncan et al,
2004).
Regional residuals
Analysis of the 22 regional residuals
(Fig. 1) may shed more light on
the reported association between regional deprivation and common mental
disorders. The residuals reflect the unexplained variability between regions
and from Fig. 1 several points
can be made. First, adjustment for the individual variables generally had
little effect on reducing the differences between regions. In contrast,
further adjustment for regional deprivation had a significant effect and only
7 out of 22 regions had residuals significantly different from zero. Second,
for most regions, adjustment for deprivation reduced the value of the
residual. This effect was more evident in regions where there was a strong
association between deprivation and common mental disorders. It can be seen
from the figure that for Merthyr, Blaenau Gwent, Caerphilly and Rhondda Cynon
Taff regional deprivation explained most of the variation. Third, for some
regions (e.g. Cardiff and Newport) adjustment for regional deprivation had the
opposite effect and the value of the residual was increased, indicating that
other higher level variables, possibly related to the urban environment
(Weich et al,
2003b), may be more relevant. Fourth, Pembrokeshire,
Gwynedd and the Isle of Anglesey differed in that they had significant
negative residuals even though they had more than the average regional
deprivation. Whether this discrepancy is owing to the rural/urban difference
in rates of common mental disorders is not known, but certainly requires
further research.
Interpretation of the association between regional deprivation and common mental disorders is not easy. Regional deprivation is most probably a proxy for other unmeasured regional attributes and the pathways involved are likely to be complex and include feedback loops (Diez Roux, 2004). Longitudinal studies may be of particular importance. However, clarification of these pathways will certainly require a combination of methods, both qualitative and quantitative.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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Received for publication March 3, 2004. Revision received September 14, 2004. Accepted for publication September 30, 2004.
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