Community Based Clinical Sciences Research Division, Faculty of Medicine, Health and Biological Sciences, University of Southampton
Correspondence: C. Thompson, Department of Psychiatry, Royal South Hants Hospital, Brinton's Terrace, Southampton SO14 0YG
Declaration of interest The study was funded by the Medical Research Council. No conflict of interest known.
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ABSTRACT |
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Aims To examine the influence of Jarman under-privileged area (UPA) scores on the prevalence and outcome of depressive symptoms in general practice patients.
Method 18 414 patients attending 55 representative practices completed the Hospital Anxiety and Depression Scale and a questionnaire for employment status. Outcome of those screening positive was assessed at 6 weeks and 6 months.
Results The UPA score accounted for 48.3% of the variance between practices in prevalence of depressive symptoms. Attending a high UPA score practice predicted persistence of depressive symptoms to 6 months.
Conclusions The socio-economic deprivation of a practice locality is a powerful predictor of the prevalence and persistence of depressive symptoms.
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INTRODUCTION |
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METHOD |
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A total of 20 832 valid screening questionnaires were collected over the four screening phases. Duplicates were avoided by including only the first attendance of patients with an identical gender, date of birth and practice, and excluding forms where this was unavailable, leaving 18 414 patients (85.4% of the whole sample).
Measures of depression and employment
Two questionnaires were administered to each patient containing: (a) a
brief explanation of the study, a request for demographic information and
employment status in eight categories; (b) the Hospital Anxiety and Depression
(HAD) scale (Zigmond & Snaith,
1983), which yields separate scores for symptoms of anxiety (A)
and depression (D). Each scale has seven items, scores 0-3. There are no items
related to biological symptoms of anxiety or depression, thus allowing
exclusion of artificially high scores due to comorbidity with physical
illness. The scale has been validated as a screening tool in general practice
(Wilkinson & Barczak,
1988) and has been shown to be sensitive to change
(Herrman, 1997).
There are two levels of case definition for the D scale: possible
depression at 8 and probable depression at
11.
Both thresholds were used in this study. All patients with possible depression
were sent another HAD by post 6 weeks and 6 months later. Patients who did not
respond were sent another 2 weeks later. Questionnaires returned more than 12
weeks after the census date were excluded. At both of these times
remission was defined as a D score less than 8 and
improvement was defined as a score 50% or less than at the index
consultation. For practice prevalence estimates, the higher threshold of
11 was used (probable depression), as it has a greater specificity against
clinically diagnosed depression (Upadhyaya
& Stanley, 1993).
At each consultation, practitioners rated depression on a 4-point scale. Ratings of 2: clinically significant depressive illness, mild, and 3: clinically significant depressive illness, moderate or severe indicated recognition of the depression.
Measures of deprivation
The UPA scores for each electoral ward, taken from the 1991 census, were
used as the index of deprivation. Patients were allocated the deprivation
scores for the practice (or branch surgery) where the index consultation took
place, irrespective of their own address. The non-UPA statistics from the 1991
census (Office of Population Censuses and
Surveys, 1993) for car ownership and housing tenure have also been
included as they appear in other measures of deprivation
(Morris & Carstairs,
1991).
Data analysis
For each practice, indirect standardisation was used to adjust for age (in
10-year age bands) and gender differences in practice samples. Standardised
practice prevalence ratios of probable depression were calculated by dividing
the number of observed by expected cases. The relation between crude and
standardised practice case prevalence and UPA score was modelled using linear
regression.
At the patient level, two analyses were carried out, one using probable cases examined prevalence and the other using possible cases (including all probable cases) examined outcome. Multiple logistic regression was used to estimate odds ratios (ORs) for risk of probable depression. A series of models were constructed with probable depression as the dependent variable. Individual covariates of gender, age and employment status were added to the model. Odds ratios for the UPA are presented for an increase of 10 points. The final model included all possible two-way interactions with backward stepwise selection to remove non-significant terms at P < 0.05. Multiple logistic regression was used in the possible cases to identify variables predicting remission and improvement. The following covariates were entered: age, gender and employment status, recognition of depression by the practitioner, depression and anxiety scores at index consultation, practice UPA score. The analyses were controlled for fund-holding and mental health resources. Interaction terms were not explored. Backward stepwise selection was used to remove non-significant variables at P < 0.05. Statistical procedures were performed in SPSS 6.1.2 for Windows.
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RESULTS |
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Of the patients, 12 168 (66.1%) were female and 6246 (33.9%) were male. The
youngest patient was 16 and there were 194 aged between 85 and 94 years. The
number of possible cases was 3668 (19.9%) patients and of these 2299 (62.7%)
returned a HAD at 6 weeks and 1956 (53.3%) at 6 months. Respondents at 6 weeks
did not differ significantly from non-responders in depression or anxiety
scores at the index consultation (Mann-Whitney U-test,
Z=-1.81, P=0.07 and Z=-0.84, P=0.40) or
UPA score (Mann-Whitney U-test, Z=-0.298, P=0.77).
Neither did they differ in the eventual HAD scores at 6 months' follow-up
(Mann-Whitney U-test, Z=-0.475, P=0.64 and
Z=-0.841, P=0.40). However, women were more likely to return
questionnaires at 6 week follow-up (2=8.62, d.f.=1,
P=0.003), and the response rate was positively associated with age at
both assessments. At 6 months, patients from high UPA practice areas were less
likely to return the questionnaire (Mann-Whitney U-test,
Z=-0.287, P=0.004), possibly confounded by greater
geographical mobility.
Prevalence of depressive symptoms and deprivation scores of the
practice location
The mean practice prevalence of probable depression was 7.2% (s.d. 2.6%),
ranging from 2.4% to 13.7% (2=169.8, d.f.=57,
P<0.0001). The regression of crude practice prevalence rates
(n=58 surgery addresses) against UPA score gives ß=0.10 (95% CI
0.07-0.13) and r2=45.3%. The regression of standardised
probable case ratios against UPA gives ß=1.43 (%) (95% CI 1.03-1.82),
r2=48.3% (Fig.
1). Analysis excluding the two practices with extreme UPA scores
reduced the attributable variance only slightly to
r2=45.2%.
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Each of the eight variables of the UPA score together with car ownership and housing tenancy was examined separately for its correlation with the standardised probable case ratios (Table 1). Geographical mobility (change of address in the past year) was the only variable that did not correlate significantly. The percentage of households with two or more cars was the most highly correlated single variable (rs=-0.70) with unemployment close behind (0.69).
Figure 2 shows frequency curves for practices falling above and below the median UPA score. The correlation of probable cases with deprivation appears to be due to an increase across the range of mild to moderate severity (D score 6-17) rather than to a small excess of severe cases or to a right shift in the whole population (Anderson et al, 1993).
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Associations between individual characteristics and depression
All age groups from 25 years to 54 years had significantly raised ORs for
depression prevalence compared to the age group 16-24 years, and the ORs fell
below 1.0 (but not significantly) for all groups over 65 years. Exactly 7.1%
of both females and males were cases and there was therefore no association
with gender. The unemployed, temporarily away from work or permanently unable
to work, and those looking after home and family, were significantly more
likely to be depressed than those in employment
(Table 2). The retired and
student groups were at lower risk than employees.
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The relationship between the two main variables was further analysed to examine the effect of practice UPA score on the odds of an individual patient being a case. Here, the UPA score of the patient's practice becomes a variable belonging to the patient. Tables 3 and 4 show the effect of UPA score in these models. The important result is that individual patient variables account for some, but not all of the variance and the UPA score remained significant after adjusting for these effects. The odds of being a case increase by 1.10 (1.06 to 1.13) for every 10 UPA points, or 1.37 (1.24 to 1.52) for 34 UPA points (2 standard deviations).
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Unemployed women were less likely to be depressed than unemployed men. The self-employed, those permanently unable to work or looking after home and family were less likely to be depressed if they were older. There were no significant interaction terms with UPA score and employment status, suggesting that the effects of unemployment were as important in residents of socio-economically deprived areas as in wealthier parts.
Predictors of outcome in possible depression
The variables that significantly predicted outcome in the logistic
regression models are shown in Table
5. Higher UPA scores most strongly predicted poor outcome at both
6 weeks and 6 months, for both remission and improvement. Remission was
predicted by a lower initial severity of depression and anxiety and being a
student. Improvement was independent of initial severity of depression but
adversely influenced by anxiety.
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Recognition of depression by the GP predicted remission (but not improvement) at 6 weeks but not at 6 months. No differential effect was found for gender. Older patients were less likely to have improved at 6 weeks and the retired group at 6 months. Thus the most consistent predictors of outcome were the socio-economic variables of area deprivation and individual employment status.
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DISCUSSION |
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The general practices were representative of Hampshire, being larger than those in England as a whole and generally more affluent than inner-city or northern areas. However, they were situated in areas with a wide range of deprivation scores. The study was carried out within 4-5 years of the 1991 census from which UPA scores were derived. The definition of depression was not dependent on the accuracy of general practitioners' diagnoses, thus avoiding potential bias.
Limitations of the study
It was logistically impossible to carry out a diagnostic interview of more
than 18 000 patients. We have therefore studied the prevalence of
self-reported depressive symptoms rather than of an operationally defined
diagnosis. Nevertheless, in primary care studies screening for depressive
symptoms rather than syndromes is an appropriate strategy, since sub-syndromal
major depression has been shown to contribute significantly to the public
health burden (Hays et al,
1995; Olfson et al,
1996). For this purpose, the choice of the HAD appears to have
been appropriate despite its modest sensitivity and specificity for DSM-III
(American Psychiatric Association,
1980) mood disorders.
The sample of practices was broadly representative but it was not randomly selected since it depended upon at least one practitioner in each practice agreeing to take part. No inferences can be made from this study about depressive symptoms in the community at large nor about patients seen at home. However, it is known that home visits account for only 10% of doctor-patient contacts (McCormick et al, 1995) apart from elderly patients, where they are more frequent (35% at 75-84 years and 65% over 85 years), so the study results may not hold true for the elderly infirm.
The rate of follow-up was lower than we had expected and was affected by age, gender and UPA score, but not HAD scores, on at least one time point. It seems unlikely that the association we found between HAD and UPA scores was critically influenced by non-response in the absence of an association between response rate and HAD score. However, we cannot rule out the possibility that this association was affected by non-responders.
The measure of area deprivation was not exact because some patients might have lived in an adjacent electoral ward, not in that of the practice. This would have introduced a conservative bias into the associations. The UPA score has been criticised on a number of counts (Morris & Carstairs, 1991) and some have suggested that complex indices are unnecessary because unemployment rates alone are an adequate measure of deprivation (Payne et al, 1993). We found positive correlations between depression and most component variables of the index but the total score showed a higher correlation than any single variable, with only unemployment and car owner-ship approaching it. The precise choice of index is probably irrelevant since they are all highly correlated (Morris & Carstairs, 1991).
We did not measure causes of poverty other than unemployment at the individual level so we have insufficient data fully to separate out the effects of area and individual deprivation, but correcting for individual unemployment only slightly reduced the effect of UPA score. It seems, therefore, that there may be an effect of living in a deprived area which may have an influence over and above that of individual deprivation.
Interpretation of the findings
Given the strength of these correlations it would not be unreasonable to
assume that they are a reflection of those in the general population, even
though they were measured in primary care attenders. If so, the associations
we have demonstrated may come about through two main routes (Stansfield et
al, 1998) and our results do not allow us to distinguish between them.
First (the social cause hypothesis), living in a deprived area may cause
depression perhaps with material insecurity, loss of social cohesion or a
non-economic predisposing factor, such as certain types of early life
experience acting as intervening variables
(Power & Hertzman, 1997).
That the environment may play a part is suggested by studies that have
reported reductions in mental illness after improvement in the urban
environment (Halpern, 1995).
Second, there may be social selection with depressed individuals moving into
deprived areas - as occurs with some suffering from schizophrenia
(Sloggett & Joshi, 1994). The relative influence of social cause and social selection may vary with the
economic climate (Hammarstrom &
Janlert, 1997).
This is the first time that area deprivation has been examined as a predictor of the persistence of depression in consulting patients. Support for these findings can, however, be found in studies of individual risk factors where chronic social difficulties, low household income and perceived financial strain have been associated with the maintenance of depression (Brown & Moran, 1997; Ronalds et al, 1997; Weich et al, 1997).
The difference in the distribution of D scores in practices in the top and bottom quartile of the deprivation score is the kind described by Anderson et al (1993) as showing the presence of "a subset of individuals [who are] particularly exposed or susceptible [to a postulated cause acting at an individual level]". This distinguishes it from a cause acting on a population, all of whom are equally susceptible, which would produce a right shift in the entire distribution.
Implications of the results
In practical terms, our findings have two implications for health policy.
First, the emphasis on enhancing provision to deprived areas is correct, as
anticipated, but there is no threshold above which the effects of deprivation
begin to become apparent. Thus, any model of deprivation payments that has a
built-in threshold will disadvantage those areas just below it. Since
deprivation accounts for such a large proportion of the depression workload
this may have a measurable effect on the equity of primary mental health care
provision. Second, strategies to reduce the public health burden of depression
might usefully combine population-based and high-risk approaches: for example,
strong national environmental and employment policies in combination with
health care that acknowledges the adverse influence of living in a deprived
area. While individuals may benefit from specific treatments for depression
there is little evidence that even their most effective use, without other
measures, could significantly reduce the public health burden of the
condition.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication November 19, 1999. Revision received April 5, 2000. Accepted for publication June 19, 2000.