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Division of Health in the Community, Warwick Medical School, Coventry
Institute for the Geography of Health, Department of Geography, University of Portsmouth, Portsmouth
Division of Psychiatry, University of Bristol, Bristol, UK
Correspondence: Dr Scott Weich, Division of Health in the Community, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK. E-mail: s.weich{at}warwick.ac.uk
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Aims To investigate rural/non-rural differences in the onset and maintenance of episodes of common mental disorders, after adjusting for the characteristics of respondents and their households.
Method A12-month cohort study of 7659 adults aged 16-74 years living in 4338 private households, nested within 626 electoral wards in England, Wales and Scotland. Common mental disorders were assessed using the General Health Questionnaire (GHQ).Electoral wards were characterised by Office for National Statistics classification and by population density. Data were analysed using multilevel statistical modelling.
Results Rural residents had slightly better mental health than non-rural counterparts. The effects of geographical location on the mental health of participants were neither significantly confounded nor modified by socio-economic status, employment status or household income.
Conclusions There are small but statistically significant differences in rates of common mental disorders between urban and rural residents. Quantifying between-place differences using population density alone risks missing important contextual effects on mental health.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Assessment of the onset and maintenance of episodes of common mental disorders
Common mental disorders were assessed using the self-administered 12-item
GHQ (Goldberg & Williams,
1988). Designed as a case-finding measure in community settings,
where sensitivity and specificity are about 80%, the GHQ has been validated
against standardised clinical interviews. We followed evidence that the common
mental disorders are validly represented as a single dimension encompassing
comorbid symptoms of anxiety and depression
(Krueger, 1999;
Vollebergh et al,
2001; Kendell & Jablensky,
2003). The GHQ has been widely used for epidemiological research
in general population samples and is robust to retest effects
(Pevalin, 2000).
Each GHQ item has four response categories. For example, responses to the question, ‘Have you recently been feeling unhappy and depressed?’ are ‘not at all’, ‘no more than usual’, ‘rather more than usual’ and ‘much more than usual’. Items are scored in two ways, by the ‘GHQ method’ as present or absent (1 point for either of the latter two of the four potential responses and 0 otherwise) or by the Likert method (responses code in order as 0, 1, 2 or 3). This score represents the probability of being identified as having non-psychotic psychiatric morbidity if interviewed with a standardised clinical interview (Goldberg & Williams, 1988). We took a score of 3 or more (out of 12) by the GHQ method to determine caseness (Goldberg & Williams, 1988; Weich & Lewis, 1998), i.e. the presence of a common mental disorder. Likert scores (range 0-36) more closely approximate a normal distribution and were used when the GHQ score was treated as a continuous outcome.
When analysing GHQ score as a dichotomous outcome, data were stratified according to case status at wave 1. ‘Episode onset’ refers to those who did not meet case criteria at wave 1 but who did meet them at wave 2. ‘Episode maintenance’ describes individuals who met case criteria at both waves 1 and 2. In each instance, individuals meeting these outcome criteria were compared with those of similar case status at wave 1.
Individual- and household-level risk factors
In keeping with previous studies (Weich
& Lewis, 1998; Lorant
et al, 2003), age, gender, marital status, ethnicity,
education, employment status, financial strain and the number of current
physical health problems were all included as potential individual-level
confounders of associations between area-level exposures and rates of common
mental disorders.
Recent studies have reported significant variation in rates of common mental disorders between households even after taking into account individual-level confounders (Weich et al, 2003a). Some exposures can only be assigned to the household level, such as overcrowding, household type, housing tenure and structural housing problems. This is not so for others, particularly income, for which data are most commonly aggregated at the household level (Weich et al, 2001). Another example is occupational social class, where stronger associations with rates of common mental disorders have been found for the social class of the head of the household than for individual social class, particularly among women (Weich & Lewis, 1998). Household characteristics were assessed at wave 1 and included structural housing problems, household income, car access, housing tenure, social class (by head of household), overcrowding (more than two household members per bedroom) and household type (based on household composition). Structural housing problems were defined as any major problem or two or more minor problems from a list comprising damp, condensation, leaking roof and/or rotting wood. The BHPS data-set includes net income data, which have been validated against official UK income distribution figures (Jarvis & Jenkins, 1995). Low income was defined as household income below half the median income for the sample.
Spatial scale
There were three potential ‘area’ levels above households
within this data-set: electoral ward, postcode sector (the primary sampling
unit for the BHPS) and region. Electoral wards (2400 addresses on average with
a mean population of 5222 (s.d.=3899)) are currently the smallest geographical
area at which BHPS data are available. Sensitivity analyses were undertaken by
substituting each of the other two geographical levels for wards. The BHPS
investigators and authors therefore agreed a method for matching respondents
and characteristics of electoral wards, without disclosure of information that
might permit identification of respondents.
Area-level characteristics
Electoral wards were characterised in two ways: using the UK Office for
National Statistics (ONS) classification of wards
(Wallace & Denham, 1996)
and population density, defined as the number of 25- to 64-year-olds per
km2. Both measures were derived from the 1991 census; the density
measure was based on reworked 1991 census data which attempted to adjust for
the census undercount (Simpson &
Dorling, 1994).
The ONS classification of wards (Wallace & Denham, 1996) comprises 14 principal groups and 43 clusters, based primarily on demographic and socio-economic composition (Table 1). More than 30 census variables were used to generate this classification, including age, ethnicity, household composition, education, housing tenure, employment status and the proportion of residents working in different occupations (including agriculture, forestry and fishing). Although no direct measures of the physical environment were used, proportions of respondents living in terraced and purpose-built housing were included. Groups and clusters were derived using two-stage cluster analysis, followed by a k-means procedure with iteration to ensure that wards were assigned to the cluster with the smallest dissimilarity between it and the cluster centroid (Wallace & Denham, 1996; Bailey et al, 1999). The final classification was designed to ensure that clusters were homogeneous and sufficiently populous to permit the study of geographical patterns. Groups and clusters were given names by the originators of the classification ‘for ease of reference, based on the general characteristics of cluster members... combined with [their] geographic attributes’ (Bailey et al, 1999). These names are shorthand rather than precise descriptions. A full list of groups and clusters, and portraits of each, are available elsewhere (Wallace & Denham, 1996; Bailey et al, 1999).
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Using the ONS classification of wards, three rural groups were identified a priori on the basis of their geographical distribution and the identities of their clusters (Wallace & Denham, 1996). These three groups (‘rural fringe’, ‘rural area’ and ‘prosperous area’) were aggregated to produce a single dummy variable representing ‘ONS rural grouping’. It was not possible to identify specific ‘urban’ areas in this way. As Table 1 shows, the three rural groups were those with the lowest population densities. The mean population density in this ‘ONS rural grouping’ was significantly lower than that in the remaining 11 ONS groups (difference between means 1242.5, 95% CI 1179.1-1305.9, P<0.001).
Statistical analysis
Multilevel models were developed using MLwiN software (Centre for
Multilevel Modelling, University of Bristol, Bristol, UK; see
http://www.mlwin.com/index.html).
We analysed onset of episodes of common mental disorders separately from
episode maintenance. In each instance a null, random-effects model was derived
for persons nested in households, with households nested within wards
(Snijders & Bosker, 1999).
Individual-, household- and ward-level exposures were added to the models in
subsequent analyses.
General Health Questionnaire scores were analysed first as a dichotomous
outcome (cases v. non-cases) using multilevel logistic regression.
These analyses were undertaken using a logit link function and assumed
non-constant, between-individual variance based on a Bernoulli distribution
(Goldstein, 1995). However, the
properties of binomial distributions (including Bernoulli) differ from those
of continuous normally distributed outcomes. In particular, the variance
associated with the intercept term is neither constant across groups nor
independent of the mean value within the groups. Therefore it is not possible
to ascertain the true variance of the intercept term at higher levels or
(hence) to directly quantify total variance associated with models of this
nature. We addressed these difficulties by means of a logit model based on the
notion of a continuous latent variable, in which a threshold defines the
binary outcome (see Snijders & Bosker,
1999: p. 223). We therefore assumed an underlying standard
logistic distribution for the binary outcome (onset or not, maintenance or not
across the two waves) at the individual level (level 1). Level 1 variance on
this latent variable was always standardised to the standardised logistic
variance of 
2/3=3.29. When unexplained random variance at level
2 was indicated as r02, the proportion of the
total unexplained variance occurring at this level was estimated (from a
two-level null random intercept model) as
r02/(r02+3.29).
In each of the logistic models, the constant term is the logit
(loge of the odds) of a person in the base (reference) category
being an individual experiencing either the ‘onset’ or
‘maintenance’ of a common mental disorder. The proportion of each
onset or maintenance group was therefore estimated from the constant term in
the null model, which is equal to ln(P/1+P).
In the logistic models, parameters were estimated using second-order Taylor
expansion with predictive quasi-likelihood. This estimation procedure is
considered superior to first- or second-order marginal quasi-likelihood when
clusters, such as households, are small
(Goldstein, 1995). Markov chain
Monte-Carlo methods may further improve the accuracy of such estimates but the
method involves intensive computation and was only used here in the discussion
of higher-level variation. Statistical significance of individual fixed
estimates was tested using a Wald test against a 
2
distribution. Since difficulties may be encountered due to the distribution of
parameter estimates when the variances are close to zero (negative variances
cannot exist), 95% interval estimates (the ‘credible interval’)
derived from Markov chain Monte-Carlo procedures are also reported for random
model parameters.
General Health Questionnaire scores at wave 2 were also analysed as a continuous outcome, using hierarchical linear regression, controlling for GHQ score at wave 1. Intraclass correlation was used to assess stability of GHQ scores across waves and to indicate the scale of unobserved symptom fluctuation. We also considered the possibility that any rural/non-rural difference in common mental disorders might result from inherently greater between-ward variability in GHQ scores in rural areas. We ran separate null, random-effects linear regression models using Markov chain Monte-Carlo methods for ONS-defined rural and non-rural wards on cross-sectional data from wave 1 with GHQ score as a continuous outcome.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Among individuals living in ‘rural’ wards (using the ONS group classification), 72% were living in wards with population densities in the bottom quartile for the study sample, 22% in the third population density quartile, 4% in the second and 2% in the most densely populated quartile (Table 2). Most indices of ward-level deprivation are higher in the 3rd and top quartile, with the exception of the percentage of low-income households (Table 3). The proportion of residents from Black and minority ethnic groups increased sharply with population density and was eight times greater (4.0%) in non-rural compared with rural wards (0.5%).
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Onset and maintenance of episodes of common mental disorders
Population density was significantly associated with the maintenance of
episodes of common mental disorders but not their onset
(Table 4). In neither case,
however, was there any evidence that the association was linear. Adjusting for
individual and household characteristics had little effect on these
associations. Table 4 shows
that rates of both episode onset and maintenance were lower in rural than
non-rural wards. Although the size of the non-rural/rural gradient was similar
for both episode onset and maintenance, only the former reached statistical
significance, before adjusting for individual and household
characteristics.
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Score on GHQ as a continuous outcome
The intraclass correlation coefficient for GHQ score at waves 1 and 2 was
+0.44. Although there were no statistically significant differences in the
change in mean GHQ score between waves across population-density groups, the
increase in GHQ scores in non-rural wards was significantly greater than in
rural wards (Table 5). This
difference remained after adjusting for individual and household
characteristics.
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The effects of ward population density or ONS rural/non-rural location (Table 5) did not vary with either baseline employment status or household income in their associations with change in GHQ score between assessments. Using cross-sectional data from wave 1, ward-level variances in GHQ score were 0.17 (credible interval 0.001-0.74, P=0.43) in ONS-defined rural areas and 0.18 (CI 0.002-0.48, P=0.18) in non-rural areas.
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DISCUSSION |
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With the exception of episode maintenance, the clearest gradients in rates of common mental disorders and in change in GHQ score between waves were found when rurality was defined using the ONS classification of wards rather than population density. However, our findings also indicate that there was a high rate of episode remission among participants with common mental disorders at baseline living in wards in the bottom quartile of population density. In contrast to our cross-sectional findings (Weich et al, 2003b), we found no evidence that the effects of geographical location on change in GHQ score between assessments varied with employment status or household income. These results highlight the complexity of comparing outcomes in ‘urban’ and ‘rural’ environments, in part because there is little agreement about how these should be defined (MacIntyre et al, 2002; Weich et al, 2002; Middleton et al, 2003; van Os, 2004).
These findings are consistent with cross-sectional research showing little geographical patterning in the prevalence of common mental disorders. Although it might be argued that our results lack clinical significance, even very small differences in risk are cumulatively important in public health terms when multiplied by the numbers exposed (Rose, 1992).
Classifying ‘rural’ and ‘urban’ areas
Urban and rural areas differ in ways that encompass both the physical and
social environments, ranging from factors such as access to education,
employment, transport, healthcare and leisure facilities to noise, crowding,
rates of crime and fear of crime
(Wandersman & Nation,
1998). Although rurality is often defined on the basis of
population density (e.g. Sundquist et
al, 2004; Wang,
2004), our findings indicate that this may result in
misclassification. More than one-fifth of participants classified as living in
a ‘rural’ area on the basis of ward socio-demographic composition
(including percentage employed in agriculture) and geographical location fell
outside of the bottom quartile for population density. Participants living in
‘non-rural’ areas were distributed fairly uniformly across all
population density quartiles, with only one-third living in the most densely
populated wards. Levels of population density are not evenly distributed
across the country and there are small pockets of high density in otherwise
remote areas (Middleton et al,
2003).
As in a study that contrasted population density with a measure of remoteness from population concentrations (Middleton et al, 2003), our findings would have differed substantially had we defined rurality according to population density alone. Although some researchers have developed alternative quantitative measures of rurality (such as geographical remoteness), others have resorted to using interviewers' impressions of rurality to overcome the perceived limitations associated with ward-level population density (Meltzer et al, 1995; Paykel et al, 2000).
The complexities of comparing ‘rural’ and ‘urban’ areas
Area-level studies based on aggregate measures of socio-economic
deprivation consistently portray rural areas as less deprived and healthier
than urban areas. Recent evidence indicates that this may be a statistical
artefact resulting from the smaller size of rural wards and their greater
internal (i.e. between-individual) variability with respect to deprivation.
Although rural wards are more internally heterogeneous, even over areas
smaller than wards, there is less variation in deprivation between rural areas
than their urban counterparts (Haynes
& Gale, 2000). In other words, affluent and deprived
individuals are more likely to live in close proximity in rural than urban
areas. Previous research found that associations between area-level
socio-economic deprivation and worse health emerged for rural areas when wards
were aggregated to approximate the greater size of urban wards
(Haynes & Gale, 2000).
This was not the case when areas smaller than wards were studied or when
different indices of deprivation were employed. These findings support our
decisions to study ‘rural areas’ as a single group and to control
for socio-economic status at both individual and household levels. In this
study, ward-level variance in GHQ score at wave 1 was almost identical in
rural and non-rural areas. This argues against the possibility that the main
study finding of a small rural/non-rural difference in common mental disorders
was a result of a small number of affluent, healthy, rural wards.
In the present study, the only evidence of an adverse effect of population
density on mental health was a statistically significant but non-linear
association with episode maintenance. This contrasts with a substantial excess
in hospital admissions for depression among those living in the most densely
populated parts of Sweden (Sundquist
et al, 2004). Notwithstanding the different outcomes in
these studies, the discrepant findings might partly result from the far
steeper gradient in population density in Sweden. The ratio of mean population
densities for the top and bottom quintiles in the study by Sundquist et
al (2004) was 120,
compared with less than 10 in the present study. Likewise, the
‘urban’ density criterion of 
people per km2 used
in a Canadian study (Wang,
2004) suggests lower population densities compared with the UK,
although the author admitted that this cut-off may have been too low. The
relative lack of variability in population density in Britain may preclude the
emergence of associations with mental health outcomes and/or the detection of
statistically significant effects. More importantly, definitions of rurality
in Britain that rely exclusively on population density might fail to detect
important differences in physical and social contexts.
Cross-national comparisons are particularly problematic, given historic, socio-economic and ethnic differences in rural and urban populations in different countries (Costello et al, 2001). Studies in New Zealand (Romans-Clarkson et al, 1990), the USA (Blazer et al, 1985), Scandinavia (Lehtinen et al, 2003) and Canada (Wang, 2004) found no evidence of statistically significant rural/non-rural differences in the prevalence of common mental disorders, although a modest difference emerged after adjusting for residents' characteristics in one study (Wang, 2004). Interpreting findings based on treated incidence is also inherently difficult given differences in service provision and pathways to care in urban and rural areas (Sundquist et al, 2004).
Strengths and limitations of the study
Cross-sectional studies may conceal associations between risk factors and
either the onset or outcome of episodes of disorder. Previous findings suggest
that social and economic risk factors may have a greater impact on the
duration of episodes of common mental disorders than on their onset
(Weich & Lewis, 1998;
Lorant et al, 2003).
This is one of the first prospective studies of rural/non-rural differences in
rates of common mental disorders in Britain. The multilevel structure of the
data-set allowed us to include household as a distinct level between place
(ward) and the individual, which many studies overlook
(McCulloch, 2001;
Wainwright & Surtees,
2003). Our estimates of standard errors for associations between
area-level exposures and individual-level outcomes were less prone to bias
than those arising from studies in which individual- and household-level
exposures were conflated (McCulloch,
2001; Wainwright &
Surtees, 2003). The BHPS is arguably the largest most
comprehensive and representative survey ever of individuals and households in
the UK.
Choice of spatial scale
A particular challenge facing studies of this nature is defining the
appropriate spatial scale over which contextual characteristics are supposed
to affect mental health. ‘Neighbourhoods’ are difficult to define
(Burrows & Bradshaw, 2001;
O'Campo, 2003) and it may be
argued that wards are far too large to detect contextual influences. This view
is consistent with evidence of statistically significant associations between
rates of common mental disorders and specific features of the built
environment assessed across small areas, after adjustment for characteristics
of individual residents (Halpern,
1995; Weich et al,
2002). We had no alternative to the use of wards in this study and
although residents may not equate wards with ‘neighbourhoods’,
they are more than arbitrary administrative boundaries. In Britain wards are
used for electoral purposes, with voters in each ward electing local
government representatives.
Measuring the common mental disorders
The study was limited by use of the GHQ rather than a standardised clinical
interview. However, traditional objections to findings not based on clinical
diagnostic categories are reduced by evidence that the common mental disorders
are validly represented as a single dimension encompassing the comorbid
conditions of anxiety and depression
(Krueger, 1999;
Vollebergh et al,
2001; Kendell & Jablensky,
2003). Furthermore, it may be argued that even if our findings are
not readily translated into absolute incidence and maintenance rates for
specific categorical disorders, they are indicative of rates of ‘at-risk
mental states’ which are intimately related to, and highly correlated
with, these disorders (van Os,
2004). Nevertheless, associations between poverty and the common
mental disorders are generally larger in studies using standardised clinical
interviews (Meltzer et al,
1995).
Since the GHQ is sensitive to recent change in psychological functioning, false positives might have included individuals with mild or transient psychological disturbance. By contrast, individuals with chronic symptoms of anxiety and depression may be given non-case status (false negatives). This misclassification should have biased associations towards the null. Although physical ill health also leads to false positives, study findings were adjusted for the number of current physical health problems. Those in lower occupational grades (Stansfeld et al, 1995) may underreport psychiatric symptoms on the GHQ compared with responses to a standardised clinical interview. Although this may have led to an underestimate of the extent of confounding by individual socio-economic status, it was unlikely to have altered our main findings. We are unaware of response bias to the GHQ between urban and rural residents.
Defining episodes of disorder
The study was limited by the absence of data on the duration of episodes of
anxiety and depression. ‘Episode onset’ was defined as the
presence of common mental disorder at wave 2 (T2) among participants who did
not meet criteria for caseness at wave 1 (T1) on the GHQ. Many (if not most)
of these were likely to have been relapses rather than first inceptions.
‘Episode maintenance’ was defined as the proportion who met
criteria for caseness at T1 that also met criteria for caseness at T2. We
recognise that this may be viewed as implying continuous morbidity throughout
the year and the term ‘maintenance’ was only used in the absence
of any widely recognised alternative. Without interval data, it is possible
that some individuals in the case group at T1 remitted and then relapsed
between assessments, and that a proportion of people in the ‘episode
onset’ group experienced multiple episodes between assessments. Episodes
that began and then remitted between waves may have been missed among those
identified as not meeting case criteria at both waves. However, the high
intraclass (within individual) correlation in GHQ scores at T1 and T2
(r=+0.44) suggests only limited intraparticipant fluctuation in case
status between waves.
Likewise, participants' exposure status was classified using information collected at wave 1. Some participants may have moved between urban and rural locations, between areas of differing population density, or in or out of employment between assessments. The present analyses therefore take no account of the duration of exposure to these risk factors. Were this type of mobility random, our results would have been biased towards the null. Although the modest numbers who moved into employment were likely to have been healthier than those who remained out of work (and vice versa), little is known about the effects of geographical mobility on patterns of psychiatric morbidity. This type of misclassification was unlikely to have a profound effect on our findings and is common to all cohort studies of this nature.
Understanding place and mental health
In general, the effects of place on rates of the common mental disorders
appear modest (Weich et al,
2003a,b,
2005;
Wainwright & Surtees,
2004). The present findings confirm this counter-intuitive
phenomenon and fail to support the view that the effects of place vary with
individual and household characteristics
(Amato & Zuo, 1992;
MacIntyre et al,
2002; Weich et al,
2003b; van Os,
2004). Nevertheless we found statistically significant
longitudinal differences in rates of the common mental disorders in rural and
non-rural areas. Although we adjusted for household composition (and therefore
living alone), we were not able to control for other factors that might
differentially affect mental health in urban and rural areas, including social
support and social networks, access to transport and healthcare, and stigma
associated with mental health problems. Further research is needed to better
understand these differences, and how these might affect individuals' mental
health.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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