Section of Psychological Medicine, University of Glasgow
Section of Public Health and Health Policy, University of Glasgow
Information and Statistics Division Scotland, Edinburgh
Department of Clinical Research, Crichton Royal Hospital, Dumfries, UK
Correspondence: Dr Judith Allardyce, Section of Psychological Medicine, Academic Centre, Gartnavel Royal Hospital, 1055 Great Western Road, Glasgow G12 0XH, UK. E-mail: j.allardyce{at}clinmed.gla.ac.uk
Declaration of interest. None.
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ABSTRACT |
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Aims To investigate the association between first-admission rates for psychosis and area-based measures of social fragmentation, deprivation and urban/rural index.
Method We used indirect standardisation methods and logistic regression models to examine associations of social fragmentation, deprivation and urban/rural categories with first admissions for psychoses in Scotland for the 5-year period 19891993.
Results Areas characterised by high social fragmentation had higher first-ever admission rates for psychosis independent of deprivation and urban/rural status. There was a doseresponse relationship between social fragmentation category and first-ever admission rates for psychosis. There was no statistically significant interaction between social fragmentation, deprivation and urban/rural index.
Conclusions First-admission rates are strongly associated with measures of social fragmentation, independent of material deprivation and urban/rural category.
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INTRODUCTION |
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METHOD |
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Case identification
The Scottish Office Information and Statistics Division collates in-patient
activities in Scottish hospitals. All psychiatric facilities return a form
(SMR04) for each patient after an in-patient stay; this form provides both
demographic and diagnostic information. During the period 19891993 the
main diagnoses were coded according to ICD9
(World Health Organization,
1978). The record linkage section of the Information and
Statistics Division identified for this period all cases with a discharge
diagnosis of schizophrenia (code 295), schizoaffective disorder (295.6),
delusional disorder (297), mania (296.0, 296.2, 296.4), acute, transient or
unspecified psychotic disorder (298) or drug-induced disorder (292.1), in
which the patient had had no previous admission (in Scotland) for any of the
above psychotic diagnoses. We excluded patients over the age of 64 years (as
there is likely to be significant diagnostic difficulty with elderly people)
and those without a permanent address in Scotland.
Population at risk
The population of Scotland was determined at around 5 million (all age
groups) throughout the study period. At the 1991 census only 2.8% of the
population had been born outside of the UK. The General Register Office of
Scotland provided detailed population data for the 5-year period stratified by
age, gender and postcode sector (an area with an average population of 5000
considered of sufficient size to provide fairly reliable rates for health
events; Carstairs & Morris,
1991).
Area-based measures
Area-based measures were calculated for every postcode.
Social fragmentation
We calculated social fragmentation using information from the 1991 census
on mobility in the previous year, number of privately rented households,
single-person households and number of unmarried persons
(Congdon, 1996). The social
fragmentation index for each postcode sector was calculated by adding the
z scores (the number of standard deviations above or below the
population mean when the underlying distribution is normal) for each of the
four characteristics. The scores ranged from -4.8 to 33.79. For the purpose of
the analysis presented here we collapsed the index into categories, created by
quartiles. However, because there was a strong positive skew in the
distribution of the social fragmentation scores, the upper quartile was
divided at the 90th percentile, creating five categories category 1
being the most socially cohesive area and 5 the most socially fragmented.
Material deprivation
Material deprivation was measured using Carstairs scores, the indicators
routinely used in Scotland (Carstairs &
Morris, 1991). Carstairs scores correlate highly with other
commonly used indices of deprivation (Townsend 0.96, Jarman 0.83). The scores
were calculated using the 1991 census data for overcrowding, male
unemployment, low social class and no car. The postcode sector scores range
from -8.5 (most affluent) to 12.8 (most deprived). The deprivation scores for
each postcode sector are transformed routinely into categories, using
pre-defined cut-off scores, which range from category 1 (most affluent) to 7
(most deprived) (McLoone,
1995).
Urban/rural index
We measured the urban/rural index using data from the 1991 census and the
official Scottish classification (Carstairs
& Morris, 1991). The degree of urbanicity is calculated for
each postcode sector by adding to the population total the population of each
directly adjacent neighbourhood: category 1 is most urban, and category 5 and
6 are the most rural.
Analysis
Using the indirect standardisation method we calculated standardised
(first) admission ratios by category of social fragmentation, deprivation and
urban/rural classification. For each (10-year) age and gender band we used
both the national first-admission rates and the rates for stratum 1 of each
social characteristic as the reference. Next, we calculated the
age/gender-adjusted admission rates for first-ever psychosis for each postcode
sector in order to model their dependence on social fragmentation, adjusting
for deprivation category and urban/rural index. As there was evidence of
overdispersion in the admission rates they could not be adequately modelled.
To overcome this the admission rate distribution was dichotomised into the
high-rate quintile (rate lies within the top 20% of admission rates) and the
remainder. Logistic regression analysis was used to determine whether social
fragmentation, deprivation and urban/rural index were independently associated
with first admission rates classified as high. A sensitivity
analysis was carried out using the 75th percentile and the 85th percentile to
check whether the actual cut-off point used to define high rates was critical
in the interpretation of the results.
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RESULTS |
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Univariate analysis showed a very significant association for social fragmentation category, deprivation category and urban/rural index with high rate postcode sectors. In the adjusted model this very significant association remained for social fragmentation only. There was no significant interaction between social fragmentation, deprivation and the urban/rural index in the model.
The model is a good fit to the data (HosmerLemeshow test, P = 0.78) and the results are essentially the same using the 75th and 85th percentiles as the cut-off for defining high admission rate.
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DISCUSSION |
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We are unaware of any recently published work exploring rates of psychosis with composite measures of social fragmentation. Faris & Dunham (1939) recognised urban areas with high rates of psychiatric morbidity to be characterised by social disintegration, excessive residential mobility, ethnic conflict, communication breakdown and lack of consensus. Social isolation leading to mental health inequality was suggested by Hare (1956), who found an ecological correlation with single-person households in Bristol and rates of first admission for schizophrenia. A more recent study has shown area-based measures of single and divorced residents to be associated with higher first-contact rates for psychosis, independent of a number of neighbourhood social and demographic characteristics and individual measures of age, gender and marital status (van Os et al, 2000). Thornicroft et al (1993) demonstrated that in urban areas the proportion of unmarried people and the proportion of people living alone were strongly correlated with admissions for psychosis.
Material deprivation
Our results are consistent with previous work demonstrating an association
between admissions for psychosis and population-based measures of material
deprivation (Harrison et al,
1995; Boardman et al,
1997; Koppel & McGuffin,
1999). We show this association to hold for first-admission data
also. Adjustment for social fragmentation and urbanicity in our logistic model
weakened the association. Although Thornicroft et al
(1993) found an association
between deprivation and service utilisation rates for psychosis in south
Verona (an urban area), there was no relationship in the same study with
deprivation in the rural area of Portogruaro. However, we found no interaction
of deprivation and urban/rural terms in our model, i.e. the effect of
deprivation does not vary across urban/rural categories. We studied all
postcodes in Scotland, allowing examination of rural areas heterogeneous for
material and social deprivation. The 11 rural districts in the Italian study
might have been too similar to detect any association in this relatively small
area.
Urban/rural variation
The urban/rural differences in admission rates for psychoses demonstrated
in this study have been well documented in previous studies
(Marcelis et al,
1998; Mortensen et
al, 1999; Allardyce et
al, 2001). However, we have not shown a statistically
significant variation in admission rates with urbanicity after adjustment for
social fragmentation and deprivation. It is therefore possible that
deprivation and social fragmentation are important explanatory factors in the
urban effect seen in previous studies.
Methodological considerations
Data-set and admission rate calculations
The SMR04 data-set provides national, comprehensive (100% coverage)
information for in-patient care over three decades in Scotland. The usefulness
of such a data-set depends on the accuracy of its information, and despite
earlier criticism (Kendrick & Clarke,
1993) the quality of the SMR04 data is now considered good
(Harley & Jones, 1996).
The quantity of the data should reduce the effect of variation in local coding
practices, but some variation due to regional differences may remain; we have
used the broad diagnostic category psychosis to calculate
first-ever admission rates as it is likely to have the greatest diagnostic
consistency (Allardyce et al,
2001).
We examined admission rates from the fine-grain level of postcode sectors and have offset the possible disadvantage of low numbers by taking admissions over a 5-year period and using a dichotomised outcome measure. Admission rates reflect only the met demand for in-patient care, and it is possible that areas with better community facilities and day hospital provision will use fewer beds; however, this assumption is not supported for psychosis (Jarman et al, 1992; Flannigan et al, 1994). In Scotland during the period of study, there was no specific day care alternative to admission for people with severe mental illness.
Measures of area-based exposures
The area-based measures of deprivation and social fragmentation were
generated from aggregation of census-based variables. The census is the only
source of objective and uniform data for the entire population and therefore
any proxy measure is constrained by the data available from it. As a sound
conceptual base for either social fragmentation (social cohesion) or
deprivation is lacking, there is no absolute underlying theory in the
selection of variables used in the aggregate scores
(Carr-Hill, 1988). The
demographic factors we used to measure fragmentation namely
non-married adults, one-person households, population turnover and private
renting may not in themselves be valid indicators of social
fragmentation, or may adequately measure social fragmentation in some areas
but not in others. For example, in urban areas the combination of young,
single people living in non-family households may not measure disorganised
communities but rather communities with young professionals or students
(Congdon, 1996). Similarly, the
individual census variables chosen may reflect deprivation in some areas
better than others. For example, overcrowding is an almost exclusively urban
phenomenon and is likely to be irrelevant in identifying deprivation in rural
areas. Lack of a car may be an indicator of deprivation in an urban context,
but possession of a car in rural areas may be almost a necessity. In rural
areas car ownership is highly correlated with remoteness rather than
socio-economic group (Midwinter et
al, 1988).
As a population census is only performed every 10 years in the UK, it is more than possible that an areas characteristics may change during this time with obviously no concomitant change in the census aggregated score. However, as we have analysed data from the period 19891993, area-based measures and admission data are chronologically matched.
There is no universally accepted definition of rural. Characteristics may include open spaces, green scenery, agricultural activities, remoteness and lack of people. Most published work has used quantitative definitions of urban/rural, but as there is no point on the continuum from large agglomerations to small clusters or scattered dwellings where urban disappears and rural begins, the division between urban and rural population will always be arbitrary. Despite these methodological limitations, area-based measures are increasingly used in public health research and practice (Smith & Hart, 1999).
It is unlikely that any area, however small, will be totally homogeneous for exposure levels of deprivation, social fragmentation and urban/rural characteristics. This is especially so if the geographical categorisation is logistical, as in postcode sectors. However, in Scotland postcode sectors are more socioculturally homogeneous than elsewhere in the UK (Reijneveld et al, 2000). Although area-based measures may not apply equally to all individuals within an area, all these individuals are exposed to living in a neighbourhood with (for example) low social fragmentation or high deprivation levels. Social characteristics such as social fragmentation in our society are likely to have profound effects on health and yet are incompletely captured and described by individual approaches to measurement.
Ecological study design
This is an area-based study comparing groups rather than individuals,
allowing the ecological effects of constructs conceptualised at the group/area
level, such as social fragmentation, deprivation and urban/rural categories,
to be demonstrated. A study with both individual and ecological information
would allow us to look at the personenvironment interaction, which
would be very informative; however, data at the individual level were not
available.
Finally, standardised (first) admission ratios may not be the best way of comparing morbidity in different geographical areas, because each subgroup is adjusted to a different standard. These ratios are, however, fairly robust with respect to the violation of the assumption of proportionality (Court & Cheng, 1995). This method has the advantage over direct standardisation in that it has a smaller variance. The wide confidence intervals that would be generated by other methods of standardisation would cause difficulty in interpreting the results and might be misleading. We have complemented the standardised admission ratios analysis with a logistic regression model.
Clinical implications
Association does not imply causality. Living in a socially fragmented
deprived area may precipitate a first episode of psychosis (social causation
hypothesis); alternatively, individuals predisposed to psychosis may drift
into or out of or be left in areas (social selection). It is also possible
that some other variable might be confounding the effect and the deprivation
and social fragmentation indices are simply proxy measures. Our study
suggests, whatever the underlying mechanism, that both material deprivation
and social fragmentation are likely to influence first-admission rates for
psychosis at area level. However, we found social fragmentation to have the
greatest effect. The observed health inequalities appear to be mediated by
both material deprivation and social fragmentation. Deprivation scores such as
Carstairs indices are often used to measure health inequalities between areas,
but our results suggest that this would not fully describe the ecological
relationship and that other measures of societal influences should be explored
if we wish to clarify and tackle this inequality.
We are unable to determine whether the area-based measures operate at the individual (compositional) or at the macro-environmental (contextual level). Further studies with individual and area-based measures of social fragmentation and deprivation and the onset of psychosis would clarify the relative importance of the personal and area characteristics.
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Clinical Implications and Limitations |
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LIMITATIONS
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Received for publication February 25, 2004. Accepted for publication December 9, 2004.
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