British Journal of Psychiatry (1990), 156, 704—713
The Influence of Social Factors on Common Mental Disorders Destabilisationand Restitution DAVID GOLDBERG, KEITH BRIDGES, DIANE COOK, BARBARA EVANS and DAVID GRAYSON
Thisstudydistinguishesbetweenprocessesthat causeindividualsto experiencesymptoms—¿ destabilisation—¿ andthosethat are associatedwith lossof symptomsovertime —¿ restitution. It is shown that different clinical,social,and personalityvariablesare associatedwith each of these processes.Where destabilisationis concerned,it is shown that different variables
were associated with the development of symptoms of anxiety and those of depression. Differentvariableswereassociatedwith restitution,andthey didnotshowthe samerelationship with the symptom dimensionsof anxiety and depressionas those which were associated with destabilisation.
Surveys carried out in community settings invariably reveal
a substantial
proportion
of
patients
with
psychological disorders which fall short of criteria required for a formal psychiatric diagnosis. These patients are described as ‘¿false positives' on screening questionnaires and, if they are labelled at all, their psychological distress is referred to by terms such as ‘¿transient situational disturbances' or ‘¿adjustment disorders' (Goldberg & Huxley, 1980). When such patients are followed up systematically, the majority will be found to have remitted
spontaneously (Goldberg & Blackwell, 1970; Brodaty, 1983; Grayson et al, 1987, 1990). Indeed, it can be argued that in order to satisfy criteria for ‘¿caseness', which
require
symptoms
to have
lasted
for at least two weeks (American Psychiatric Association, 1987), it is necessary for two conditions to be satisfied: the individual must have begun to experience symptoms, and processes of normal homeostasis must have failed. We call the former process destabilisation, while the latter is referred
to as a failure of restitution.
Individuals with
transient disorders can be thought of as those who have destabilised but are able to restitute sponta neously. It seems likely that different social and environmental variables are associated with each of these processes. The aim of the present study is to investigate the social and personality characteristics which co-vary with destabilisation and restitution. The former can be accomplished by studying those environmental
variables that are related to possession of symptoms at the inception of an episode of illness, including in the study all those seeking medical care whether
or not they are psychologically distressed. We therefore document those environmental variables that are related to symptom experience when help is first
sought.
The latter
can be accomplished
by
704
discovering which variables are related to symptom loss over time, and confining the analyses to those patients receiving research diagnoses of mental illness. Our earlier research (Goldberg et al, 1987) has indicated that the symptoms that are commonly encountered in primary-care settings can be con ceptualised in two-dimensional space, representing the dimensions of anxiety-related and depression-related symptoms. Any individual can be given a location in this two-dimensional space, depending upon the particular combination of symptoms reported at psychiatric interview. Although conventional clinical wisdom separates anxiety states from depressive disorders, it is certain that the two symptom dimensions are highly correlated in community settings, and it seems of interest to investigate whether they can be shown to have different determinants. A secondary aim of the study is therefore to discover whether the social variables studied exert their effects along a single, general dimension of
mixed anxiety/depression, or whether it can be shown that different social variables exert their effects on
the separate symptom dimensions. In analysing such a complex data set (bivariate dependent variables of anxiety
and
depression,
and
multiple
independent
variables), one must be aware of the possibility of spurious associations, or of ‘¿proxying' by one variable of another. For instance, it may be that sex influences anxiety symptom counts. Yet, if substantial correlation exists between anxiety and depression, there will also be an association between sex and depression. If this latter association is fully accounted for by the anxiety-depression correlation, it may be spurious to say sex also affects depression. Similarly, one variable can act as a proxy for another as a potential cause. For example, if sex affects
705
SOCIAL FACTORS AND COMMON MENTAL DISORDERS anxiety and if sex is correlated
with age, then age
will be ‘¿associated' with anxiety spuriously. In both these examples, univariate analyses are incapable of teasing apart such relationships and of protecting against the emergence of indirect or spurious
associations. Our intention is to explore the potential causes of destabilisation and restitution by first segregating the possible causes into subgroups homogeneous
The sample of 191patients was not random, since those presumed
psychologically
well (on the basis of the GP's
ratingand a lowscoreon the GeneralHealthQuestionnaire (GHQ)) wereundersampledrelativeto those presumedill (high GHQ score or thought
psychologically
ill by GP) in
the ratio of 1 : 2.7. For the studies dealing with the determinants of destabilisation, the patients who had been
undersampledwere‘¿weighted back' to representa notional 264 patients with consecutive new illnesses for whom social
in their
assessments were available. However, in order that tests
effects in bivariate illness space, and then performing
of significance would be based on an appropriate number of degrees of freedom, this weighted data set was then reduced
multiple-regression analyses of illness within each subgroup to tease apart genuine effects from
spurious ones. The exploratory
nature of this
research should be emphasised for two reasons. First,
the putative causes were measured by retrospective self-report at initial consultation simultaneously with the symptom counts, so that causal inferences concerning destabilisation are based only on cor
relational data. Second, we analyse simultaneously many variables on 191 subjects. We have therefore been conservative in reducing statistically indicated.
the
data
where
The present analyses are a secondary analysis on a data set which was collected in order to investigate the factors
that distinguish
somatic
forms
of
psychiatric disorder from entirely psychological forms of illness. As Hyman (1972) has pointed out, such secondary analyses can have several limitations. Those that apply to the present study are that the illnesses seen are not a random sample of illnesses in primary care, for reasons explained below. The independent variables used in this study were those thought to be of interest for the original aim of the study, and therefore do not include several otherwise interesting
variables
which might well have improved
the results reported below had they been included. Method The sample consistedof 191patients complainingof new episodes
of illness in 15 general practices
back to a total of 191 patients, so that the number of
‘¿notional patients' (whonowdo representa randomsample of inceptions of illness) was now equal to the number of actual patients seen on the survey. The dependent variables are depression and anxiety
symptom counts derived from the PAS. Each subject is assigned a score out of 10 for anxiety, and a score out of 14 for depression, using scales derived from our earlier latent-trait analysis (Goldberg et a!, 1987: see Table 2 of
that paper for the prevalence of each symptom in this setting, and Table 4 for the symptoms loading on the latent traits for anxiety and depression). The scores of subjects on these two scales correlate with one another + 0.63. The potential causes of destabilisationand restitutionare explored by first segregating the possible causes into subgroups homogeneous in their effects in bivariateillness space, and second performing multiple-regression analyses for each of these subgroups separately, in order to tease apartgenuine effects from spurious ones. The independent variables derived from the two social assessments(SIS and
SSSI) were reduced to 0/1 dichotomies wherever possible by
usingmediansplits,and are shownin TableI, togetherwith the values for level 1 of each dichotomy. Thus, for example, ‘¿occupational stress: stressed' means that this item of the
SSSI has been scored in such a way that the half of the subjects with the most stress at work have been assigned a score of unity, and the remainderassigned a score of zero. Had variables been left continuous it would have raised issues
about the appropriateness of scaling in building quantitative regressionslater on. By dichotomisingwe are simplyopposing subjects unambiguously low and high on the construct in question. This is more sensible in an exploratory analysis.
in the Greater
Manchester area. The sample is less than that reported in companion papers (Goldberg et a!, 1987; Goldberg &
Strategy for the destabilisatlon analysis
Bridges, 1987), since in order to be included it was necessary
The first stage of analysis consisted of taking each of the
that a full social assessment was available by an independent researcher(BE) using the Social Interview Schedule (SIS; Clare & Cairns,
1978). The social interview was carried out
with an independent informant in the patient's home within two weeks of the initial consultation. The patients were interviewed by one of us (KB) using the Psychiatric Assessment Schedule (PAS; Dean et a!, 1983) with the addition of questions dealing with the nature of the patient's present
complaint
and
its likely
aetiology,
as well as the
Social Stress and Support Interview (SSSI; Jenkins et a!, 1981). In addition, we had available ratings made by the general practitioner (GP) concerning the patient's complaint and its presumed aetiology.
variables in Table I and performing a multivariate t-test
using initial anxiety and depression symptom counts as the dependent variables. Such an analysis can be thought of as a t-test which first asks: is there a significant difference
between the two groups of subjects (0,1 on the Table I variable) in bivariate symptom space? It also produces the ‘¿direction' in bivariate space along which the two groups most differ. This direction
is given as a univariate
linear
combination of the depression and anxiety score. For instance, we might find that sex (I = female, 0= male) has
a maximum effect in the direction: y= 0.36 (anxiety score)—0.04 (depression
score)
706
GOLDBERG ET AL
The ‘¿loadings' + 0.36 on anxiety and —¿ 0.04 on depression indicate that the effect of female sex is essentially directly
Strategy for the restitution analysis
on anxiety (there will, however be a male/female depression difference, but only of the magnitude expected by the
The 130 patients who were found to be psychiatrically unwell by the researchpsychiatristat the inception of their
correlation between anxiety and depression of + 0.63).
illness were followed
Having performed such analyses on each putative cause, we attended further only to those with a P value 0.05) partial-regression
coefficients.
A second regression was
therefore performed with the remaining four putative causes.
SOCIAL FACTORS AND COMMON MENTAL DISORDERS
707
A test wasperformedcomparinginitialand secondmodels,
by current symptom state were omitted from consideration.
and showed that the omitted eight variables removed no variance in depression beyond error. The second regression was final, as each variable had a significant partial
the most salient feature is the emergence of variables relating to stress and unemployment that previously were
In comparing these results with those already reported,
analyses below (including the restitution phase), and only the final models are reported. The fi coefficientsin Table II indicate,for example,where
‘¿overshadowed' by the variables which have now been removed. The model which accounts for 31¾of the variance in anxiety is therefore more conservative, and probably more appropriate. Such ‘¿overshadowing' is
regression
coefficient.
This procedure
was adopted
in all
‘¿trait anxiety' is concerned, the difference between the two
precisely what would be expected
groups (e.g. high or low on trait anxiety) in units of the
variables restatements of symptom state, since genuine
dependent variable (i.e. number of symptoms), with all other variables in the model held constant. Thus, subjects with a high score on trait anxiety have 1.16 more depression
causes could account for little additional symptom variance
symptoms than those with low scores, when they have the same amount of support from family and confidant and
model. We have thereforeremovedthe same variablesfrom the analyses to be reported later in our section dealing with
when they are equallysatisfied with their interaction with
restitution.
relatives. Similarly, subjects who are simultaneously high on trait anxiety, dissatisfied with relatives' interaction, and
report little support from family or confidant have (1.16+1.49+ 0.75+ 0.67)4.07 more depressionsymptoms than those simultaneously
at the other levels of these four
variables. It can be seen that lack of close support increases depression,
and the amount
of missing
support
seems
related (those with low support from both family and
confidant have more depression than either alone). All statistical tests in all regressions were performed at the P=0.05 level. The R2 value of 0.30 for depression indicates that 30¾ of the variance in depression was explained by the model reported in Table II. Anxiety seems related to an assessment by the psychiatrist
that the aetiology of the patient's problem is entirely psychological,
or that there is a mixture of psychological
and organic factors at work. It can also be seen that higher levels of anxiety symptoms occur with those who have had
were these removed
while other (proxy) measures of symptoms had been unknowingly
included
For depression,
as independent
variables
in the
however, the omission of trait anxiety
did not allow other variables to emerge, so it is probably sound to stay with the original model, and to regard trait anxiety as a safe indicator
of predisposition
to depressive
illness. Similar arguments apply to the (A + D) model. Briefly then, lack of support from a ‘¿close other' and trait disposition are associated with the onset of depressive symptoms, whereas stress particularly related to work and income are associated with anxiety, even when sex is controlled
for (as in a multiple-regression
model).
In
addition, there is a sex effect on anxiety (females more anxious) above and beyond that induced by sex differences in work and income status. With these views we have
accounted for about 30% of variance in each of the dependent variables. Note that we could account for about a further 10—15% of the variance in depression if we
problems for longer periods of time before their initial
included the anxiety variables in the depression model, by relying on the correlation between anxiety and depression of 0.63. If, for example, being morbidly anxious led to being
consultation.
depressed, then it would be appropriate to include such
The influence of a high vocabulary score and having
causes as indirect causes of depression.
However,
in the
symptoms other than autonomic or pain appears to protect against a general psychiatric malaise, measured by (A + D) in this model: however, it can be seen that the magnitude of this effect (reflected in the value of R2) is relatively
present work we have made no such assumptions.
slight compared with factors that affect anxiety or depression separately. The (A— D) variableof opportunitiesfor interactionwith relatives is omitted from Table II. When such opportunities
This part of the research examines the determinants of symptom loss, whether or not such determinants are clearly
werereportedas restricted,subjects'A—D scoreswere0.83 less than when not so restricted (P=0.0l, R2= 0.03). That is, such restrictionslead to a relativeincreasein depression and drop in anxiety symptoms. The R2 of 0.81 for the A regression seems very high. One possible reason why it may be spuriously high is that the putative causal factors are confounded by symptom
Restitution
causal. For example, being an ‘¿owner occupier' seems unlikely to have of itself a causal influence on restitution, but it may act as a proxy for some causal process, perhaps
the possession of a wide range of social advantages.
We thought it wise to omit the duration of illness variables in order to avoid circularity (long illnesses last a long time, and vice versa), and we included two new
variables: ‘¿mode of presentation' assessedby the psychiatrist
are obtained simultaneously. Similarly, the variables dealing
(somatisers, psychologisers, and a mixed group), and ‘¿type of complaint' as assessed by the GP (entirely physical, entirely psychiatric, and a mixed group). The first stage of
with ‘¿duration of illness' include as ‘¿duration 0' all those
the analysis is shown as Table III. The most salient feature
not sufferinga physical(or psychiatric)illnessat all. Thus, such variables are not independent of current symptom
A—D direction, while only four appeared to lead to
state. The same may be said of trait anxiety (‘Do you often
recovery
state, since both aetiological
assessments
and symptom
feel tense?' etc.). Table II therefore also presents additional
scores
is that 7 of the 13 predictors exerted their influence in the in either
anxiety
or depression
alone.
Table IV showsthe final regressionmodels. The factors regression
analyses where any variables which might be confounded
predicting loss of depression symptoms (and of anxiety, but
no more than that related to the correlation of 0.43 in
708
GOLDBERGET AL TABLE I Inception of illness: effects of individual variables in bivariate symptom spacefor 191 subjects
Dichotomous
variable
P value AnxietyDepressionScale High forindicatesvalueloadingloadingregression valueEigen
Sociodemographic
Age
older
0.01
0.29
female with spouse
0.07 0.02
0.00 0.20
0.36
—¿0.04
A
0.15
0.00
0.36
—¿0.03
A
present present present
0.01 0.09 0.06
0.47 0.00 0.00
0.27 0.20
0.10 0.19
A A+ D
Psychiatric aetiology
present
0.49
0.00
0.42
—¿0.04
A
Organic aetiology
present
1.15
0.00
0.46
0.03
A
present
0.23
0.00
0.34
0.03
A
Sex Marital status Employment
status
Aspects of presentation Pain symptoms Autonomic symptoms Other symptoms
Psychiatric/organic
aetiology
Duration (physical)
long
1.36
0.00
0.47
0.04
A
Duration (psychiatric)
long
3.85
0.00
0.61
0.15
A
Personality variables Educational level Vocabulary score
high high
0.02 0.03
0.21 0.06
0.13
0.25
A+D
Social desirability
high
0.02
0.18
Defensiveness Whitely index
high high
0.01 0.06
0.57 0.01
0.40
—¿0.15
Locus of control Trait anxiety
high high
0.00 0.14
0.83 0.00
0.09
0.31
D
Occupation: stress Occupation: support
stressed supported
0.04 0.01
0.03 0.54
0.29
0.07
A
Money: stress Money: support
stressed supported
0.01 0.01
0.25 0.40
—¿0.02
0.37
D
A
Social Stress and Support Inventory (from patient)
Housing: stress
stressed
0.04
0.02
Housing: support
supported
0.02
0.21
Family: stress Family: support
stressed supported
0.00 0.03
0.88 0.09
0.03
0.34
D
Confidant: stress Confidant: support
stressed supported
0.08 0.12
0.00 0.00
0.12 0.13
0.27 0.27
D D
0.07
0.31
D
0.28
0.09
A
SocialInterviewSchedulevariables(from independentinformation)
Accommodation House,
?owned
Residentialstability
poor
0.00
0.84
not owned
0.00
0.73
unstable
0.00
0.69
Household care Satisfied with house Household management Household composition Income: amount Income: management Income: satisfaction Leisure: opportunity Leisure: extent
poor dissatisfied difficulties inadequate inadequate difficulties dissatisfied restricted inadequate
0.01 0.03 0.01 0.00 0.10 0.00 0.00 0.01 0.02
0.39 0.10 0.25 0.88 0.00 0.77 0.83 0.33 0.15
Leisure: satisfaction
dissatisfied
0.09
0.00
—¿0.03
0.39
D
Social contacts: extent Social contacts: satisfaction
inadequate dissatisfied
0.06 0.10
0.01 0.00
—¿0.19 0.01
0.46 0.37
D D (Continued)
SOCIAL FACTORS AND COMMON MENTAL DISORDERS (Continued)Dichotomous
709
TABLE I
variableHigh regressionInteractions
value indicatesEigen valueP
valueAnxiety
for
loadingDepression loadingScale
neighboursopportunitiesrestricted0.010.41qualitydifficulties0.020.10satisfactiondissatisfied0.110.000.020.36DInteractions with
relativesopportunitiesrestricted0.040.04—0.410.50A with Dqualitydifficulties0.050.01—0.150.50Dsatisfactiondissatisfied0.150.000.040.38D
—¿
1. Six types of employment status were examined, which could not be dichotomised on a priori grounds. Following the multivariate I-tests,
these
were
collapsed
into
three
groups
represented
by the dichotomies
shown
in Table
II: full time
or retired
(1) versus
other
four (0); and unemployed or student (1) versus other four (0). The loadings in columns 5 and 6 show only the direction of each effect with respect to anxiety and depression, not the size or nature of the effect. TABLE II
Inceptionof illness:multiple-regression modelsindicatingrelativecontributionsof individualvariablesto thecausation of anxietyand depressionrespectively,and to ‘¿general severity'of dysphoria Beta Anxiety (no additional
Sign@cance
effect on depression)
Multiple-regressionanalysis long duration
psychological
symptoms
entirely psychological aetiology both organic and psychological aetiology*
long duration physical symptoms
4.07
0.00
0.78 0.72
0.03 0.05
—¿ 1.32 R2=0.8l
0.10 P
The Influence of Social Factors on Common Mental Disorders Destabilisationand Restitution DAVID GOLDBERG, KEITH BRIDGES, DIANE COOK, BARBARA EVANS and DAVID GRAYSON
Thisstudydistinguishesbetweenprocessesthat causeindividualsto experiencesymptoms—¿ destabilisation—¿ andthosethat are associatedwith lossof symptomsovertime —¿ restitution. It is shown that different clinical,social,and personalityvariablesare associatedwith each of these processes.Where destabilisationis concerned,it is shown that different variables
were associated with the development of symptoms of anxiety and those of depression. Differentvariableswereassociatedwith restitution,andthey didnotshowthe samerelationship with the symptom dimensionsof anxiety and depressionas those which were associated with destabilisation.
Surveys carried out in community settings invariably reveal
a substantial
proportion
of
patients
with
psychological disorders which fall short of criteria required for a formal psychiatric diagnosis. These patients are described as ‘¿false positives' on screening questionnaires and, if they are labelled at all, their psychological distress is referred to by terms such as ‘¿transient situational disturbances' or ‘¿adjustment disorders' (Goldberg & Huxley, 1980). When such patients are followed up systematically, the majority will be found to have remitted
spontaneously (Goldberg & Blackwell, 1970; Brodaty, 1983; Grayson et al, 1987, 1990). Indeed, it can be argued that in order to satisfy criteria for ‘¿caseness', which
require
symptoms
to have
lasted
for at least two weeks (American Psychiatric Association, 1987), it is necessary for two conditions to be satisfied: the individual must have begun to experience symptoms, and processes of normal homeostasis must have failed. We call the former process destabilisation, while the latter is referred
to as a failure of restitution.
Individuals with
transient disorders can be thought of as those who have destabilised but are able to restitute sponta neously. It seems likely that different social and environmental variables are associated with each of these processes. The aim of the present study is to investigate the social and personality characteristics which co-vary with destabilisation and restitution. The former can be accomplished by studying those environmental
variables that are related to possession of symptoms at the inception of an episode of illness, including in the study all those seeking medical care whether
or not they are psychologically distressed. We therefore document those environmental variables that are related to symptom experience when help is first
sought.
The latter
can be accomplished
by
704
discovering which variables are related to symptom loss over time, and confining the analyses to those patients receiving research diagnoses of mental illness. Our earlier research (Goldberg et al, 1987) has indicated that the symptoms that are commonly encountered in primary-care settings can be con ceptualised in two-dimensional space, representing the dimensions of anxiety-related and depression-related symptoms. Any individual can be given a location in this two-dimensional space, depending upon the particular combination of symptoms reported at psychiatric interview. Although conventional clinical wisdom separates anxiety states from depressive disorders, it is certain that the two symptom dimensions are highly correlated in community settings, and it seems of interest to investigate whether they can be shown to have different determinants. A secondary aim of the study is therefore to discover whether the social variables studied exert their effects along a single, general dimension of
mixed anxiety/depression, or whether it can be shown that different social variables exert their effects on
the separate symptom dimensions. In analysing such a complex data set (bivariate dependent variables of anxiety
and
depression,
and
multiple
independent
variables), one must be aware of the possibility of spurious associations, or of ‘¿proxying' by one variable of another. For instance, it may be that sex influences anxiety symptom counts. Yet, if substantial correlation exists between anxiety and depression, there will also be an association between sex and depression. If this latter association is fully accounted for by the anxiety-depression correlation, it may be spurious to say sex also affects depression. Similarly, one variable can act as a proxy for another as a potential cause. For example, if sex affects
705
SOCIAL FACTORS AND COMMON MENTAL DISORDERS anxiety and if sex is correlated
with age, then age
will be ‘¿associated' with anxiety spuriously. In both these examples, univariate analyses are incapable of teasing apart such relationships and of protecting against the emergence of indirect or spurious
associations. Our intention is to explore the potential causes of destabilisation and restitution by first segregating the possible causes into subgroups homogeneous
The sample of 191patients was not random, since those presumed
psychologically
well (on the basis of the GP's
ratingand a lowscoreon the GeneralHealthQuestionnaire (GHQ)) wereundersampledrelativeto those presumedill (high GHQ score or thought
psychologically
ill by GP) in
the ratio of 1 : 2.7. For the studies dealing with the determinants of destabilisation, the patients who had been
undersampledwere‘¿weighted back' to representa notional 264 patients with consecutive new illnesses for whom social
in their
assessments were available. However, in order that tests
effects in bivariate illness space, and then performing
of significance would be based on an appropriate number of degrees of freedom, this weighted data set was then reduced
multiple-regression analyses of illness within each subgroup to tease apart genuine effects from
spurious ones. The exploratory
nature of this
research should be emphasised for two reasons. First,
the putative causes were measured by retrospective self-report at initial consultation simultaneously with the symptom counts, so that causal inferences concerning destabilisation are based only on cor
relational data. Second, we analyse simultaneously many variables on 191 subjects. We have therefore been conservative in reducing statistically indicated.
the
data
where
The present analyses are a secondary analysis on a data set which was collected in order to investigate the factors
that distinguish
somatic
forms
of
psychiatric disorder from entirely psychological forms of illness. As Hyman (1972) has pointed out, such secondary analyses can have several limitations. Those that apply to the present study are that the illnesses seen are not a random sample of illnesses in primary care, for reasons explained below. The independent variables used in this study were those thought to be of interest for the original aim of the study, and therefore do not include several otherwise interesting
variables
which might well have improved
the results reported below had they been included. Method The sample consistedof 191patients complainingof new episodes
of illness in 15 general practices
back to a total of 191 patients, so that the number of
‘¿notional patients' (whonowdo representa randomsample of inceptions of illness) was now equal to the number of actual patients seen on the survey. The dependent variables are depression and anxiety
symptom counts derived from the PAS. Each subject is assigned a score out of 10 for anxiety, and a score out of 14 for depression, using scales derived from our earlier latent-trait analysis (Goldberg et a!, 1987: see Table 2 of
that paper for the prevalence of each symptom in this setting, and Table 4 for the symptoms loading on the latent traits for anxiety and depression). The scores of subjects on these two scales correlate with one another + 0.63. The potential causes of destabilisationand restitutionare explored by first segregating the possible causes into subgroups homogeneous in their effects in bivariateillness space, and second performing multiple-regression analyses for each of these subgroups separately, in order to tease apartgenuine effects from spurious ones. The independent variables derived from the two social assessments(SIS and
SSSI) were reduced to 0/1 dichotomies wherever possible by
usingmediansplits,and are shownin TableI, togetherwith the values for level 1 of each dichotomy. Thus, for example, ‘¿occupational stress: stressed' means that this item of the
SSSI has been scored in such a way that the half of the subjects with the most stress at work have been assigned a score of unity, and the remainderassigned a score of zero. Had variables been left continuous it would have raised issues
about the appropriateness of scaling in building quantitative regressionslater on. By dichotomisingwe are simplyopposing subjects unambiguously low and high on the construct in question. This is more sensible in an exploratory analysis.
in the Greater
Manchester area. The sample is less than that reported in companion papers (Goldberg et a!, 1987; Goldberg &
Strategy for the destabilisatlon analysis
Bridges, 1987), since in order to be included it was necessary
The first stage of analysis consisted of taking each of the
that a full social assessment was available by an independent researcher(BE) using the Social Interview Schedule (SIS; Clare & Cairns,
1978). The social interview was carried out
with an independent informant in the patient's home within two weeks of the initial consultation. The patients were interviewed by one of us (KB) using the Psychiatric Assessment Schedule (PAS; Dean et a!, 1983) with the addition of questions dealing with the nature of the patient's present
complaint
and
its likely
aetiology,
as well as the
Social Stress and Support Interview (SSSI; Jenkins et a!, 1981). In addition, we had available ratings made by the general practitioner (GP) concerning the patient's complaint and its presumed aetiology.
variables in Table I and performing a multivariate t-test
using initial anxiety and depression symptom counts as the dependent variables. Such an analysis can be thought of as a t-test which first asks: is there a significant difference
between the two groups of subjects (0,1 on the Table I variable) in bivariate symptom space? It also produces the ‘¿direction' in bivariate space along which the two groups most differ. This direction
is given as a univariate
linear
combination of the depression and anxiety score. For instance, we might find that sex (I = female, 0= male) has
a maximum effect in the direction: y= 0.36 (anxiety score)—0.04 (depression
score)
706
GOLDBERG ET AL
The ‘¿loadings' + 0.36 on anxiety and —¿ 0.04 on depression indicate that the effect of female sex is essentially directly
Strategy for the restitution analysis
on anxiety (there will, however be a male/female depression difference, but only of the magnitude expected by the
The 130 patients who were found to be psychiatrically unwell by the researchpsychiatristat the inception of their
correlation between anxiety and depression of + 0.63).
illness were followed
Having performed such analyses on each putative cause, we attended further only to those with a P value 0.05) partial-regression
coefficients.
A second regression was
therefore performed with the remaining four putative causes.
SOCIAL FACTORS AND COMMON MENTAL DISORDERS
707
A test wasperformedcomparinginitialand secondmodels,
by current symptom state were omitted from consideration.
and showed that the omitted eight variables removed no variance in depression beyond error. The second regression was final, as each variable had a significant partial
the most salient feature is the emergence of variables relating to stress and unemployment that previously were
In comparing these results with those already reported,
analyses below (including the restitution phase), and only the final models are reported. The fi coefficientsin Table II indicate,for example,where
‘¿overshadowed' by the variables which have now been removed. The model which accounts for 31¾of the variance in anxiety is therefore more conservative, and probably more appropriate. Such ‘¿overshadowing' is
regression
coefficient.
This procedure
was adopted
in all
‘¿trait anxiety' is concerned, the difference between the two
precisely what would be expected
groups (e.g. high or low on trait anxiety) in units of the
variables restatements of symptom state, since genuine
dependent variable (i.e. number of symptoms), with all other variables in the model held constant. Thus, subjects with a high score on trait anxiety have 1.16 more depression
causes could account for little additional symptom variance
symptoms than those with low scores, when they have the same amount of support from family and confidant and
model. We have thereforeremovedthe same variablesfrom the analyses to be reported later in our section dealing with
when they are equallysatisfied with their interaction with
restitution.
relatives. Similarly, subjects who are simultaneously high on trait anxiety, dissatisfied with relatives' interaction, and
report little support from family or confidant have (1.16+1.49+ 0.75+ 0.67)4.07 more depressionsymptoms than those simultaneously
at the other levels of these four
variables. It can be seen that lack of close support increases depression,
and the amount
of missing
support
seems
related (those with low support from both family and
confidant have more depression than either alone). All statistical tests in all regressions were performed at the P=0.05 level. The R2 value of 0.30 for depression indicates that 30¾ of the variance in depression was explained by the model reported in Table II. Anxiety seems related to an assessment by the psychiatrist
that the aetiology of the patient's problem is entirely psychological,
or that there is a mixture of psychological
and organic factors at work. It can also be seen that higher levels of anxiety symptoms occur with those who have had
were these removed
while other (proxy) measures of symptoms had been unknowingly
included
For depression,
as independent
variables
in the
however, the omission of trait anxiety
did not allow other variables to emerge, so it is probably sound to stay with the original model, and to regard trait anxiety as a safe indicator
of predisposition
to depressive
illness. Similar arguments apply to the (A + D) model. Briefly then, lack of support from a ‘¿close other' and trait disposition are associated with the onset of depressive symptoms, whereas stress particularly related to work and income are associated with anxiety, even when sex is controlled
for (as in a multiple-regression
model).
In
addition, there is a sex effect on anxiety (females more anxious) above and beyond that induced by sex differences in work and income status. With these views we have
accounted for about 30% of variance in each of the dependent variables. Note that we could account for about a further 10—15% of the variance in depression if we
problems for longer periods of time before their initial
included the anxiety variables in the depression model, by relying on the correlation between anxiety and depression of 0.63. If, for example, being morbidly anxious led to being
consultation.
depressed, then it would be appropriate to include such
The influence of a high vocabulary score and having
causes as indirect causes of depression.
However,
in the
symptoms other than autonomic or pain appears to protect against a general psychiatric malaise, measured by (A + D) in this model: however, it can be seen that the magnitude of this effect (reflected in the value of R2) is relatively
present work we have made no such assumptions.
slight compared with factors that affect anxiety or depression separately. The (A— D) variableof opportunitiesfor interactionwith relatives is omitted from Table II. When such opportunities
This part of the research examines the determinants of symptom loss, whether or not such determinants are clearly
werereportedas restricted,subjects'A—D scoreswere0.83 less than when not so restricted (P=0.0l, R2= 0.03). That is, such restrictionslead to a relativeincreasein depression and drop in anxiety symptoms. The R2 of 0.81 for the A regression seems very high. One possible reason why it may be spuriously high is that the putative causal factors are confounded by symptom
Restitution
causal. For example, being an ‘¿owner occupier' seems unlikely to have of itself a causal influence on restitution, but it may act as a proxy for some causal process, perhaps
the possession of a wide range of social advantages.
We thought it wise to omit the duration of illness variables in order to avoid circularity (long illnesses last a long time, and vice versa), and we included two new
variables: ‘¿mode of presentation' assessedby the psychiatrist
are obtained simultaneously. Similarly, the variables dealing
(somatisers, psychologisers, and a mixed group), and ‘¿type of complaint' as assessed by the GP (entirely physical, entirely psychiatric, and a mixed group). The first stage of
with ‘¿duration of illness' include as ‘¿duration 0' all those
the analysis is shown as Table III. The most salient feature
not sufferinga physical(or psychiatric)illnessat all. Thus, such variables are not independent of current symptom
A—D direction, while only four appeared to lead to
state. The same may be said of trait anxiety (‘Do you often
recovery
state, since both aetiological
assessments
and symptom
feel tense?' etc.). Table II therefore also presents additional
scores
is that 7 of the 13 predictors exerted their influence in the in either
anxiety
or depression
alone.
Table IV showsthe final regressionmodels. The factors regression
analyses where any variables which might be confounded
predicting loss of depression symptoms (and of anxiety, but
no more than that related to the correlation of 0.43 in
708
GOLDBERGET AL TABLE I Inception of illness: effects of individual variables in bivariate symptom spacefor 191 subjects
Dichotomous
variable
P value AnxietyDepressionScale High forindicatesvalueloadingloadingregression valueEigen
Sociodemographic
Age
older
0.01
0.29
female with spouse
0.07 0.02
0.00 0.20
0.36
—¿0.04
A
0.15
0.00
0.36
—¿0.03
A
present present present
0.01 0.09 0.06
0.47 0.00 0.00
0.27 0.20
0.10 0.19
A A+ D
Psychiatric aetiology
present
0.49
0.00
0.42
—¿0.04
A
Organic aetiology
present
1.15
0.00
0.46
0.03
A
present
0.23
0.00
0.34
0.03
A
Sex Marital status Employment
status
Aspects of presentation Pain symptoms Autonomic symptoms Other symptoms
Psychiatric/organic
aetiology
Duration (physical)
long
1.36
0.00
0.47
0.04
A
Duration (psychiatric)
long
3.85
0.00
0.61
0.15
A
Personality variables Educational level Vocabulary score
high high
0.02 0.03
0.21 0.06
0.13
0.25
A+D
Social desirability
high
0.02
0.18
Defensiveness Whitely index
high high
0.01 0.06
0.57 0.01
0.40
—¿0.15
Locus of control Trait anxiety
high high
0.00 0.14
0.83 0.00
0.09
0.31
D
Occupation: stress Occupation: support
stressed supported
0.04 0.01
0.03 0.54
0.29
0.07
A
Money: stress Money: support
stressed supported
0.01 0.01
0.25 0.40
—¿0.02
0.37
D
A
Social Stress and Support Inventory (from patient)
Housing: stress
stressed
0.04
0.02
Housing: support
supported
0.02
0.21
Family: stress Family: support
stressed supported
0.00 0.03
0.88 0.09
0.03
0.34
D
Confidant: stress Confidant: support
stressed supported
0.08 0.12
0.00 0.00
0.12 0.13
0.27 0.27
D D
0.07
0.31
D
0.28
0.09
A
SocialInterviewSchedulevariables(from independentinformation)
Accommodation House,
?owned
Residentialstability
poor
0.00
0.84
not owned
0.00
0.73
unstable
0.00
0.69
Household care Satisfied with house Household management Household composition Income: amount Income: management Income: satisfaction Leisure: opportunity Leisure: extent
poor dissatisfied difficulties inadequate inadequate difficulties dissatisfied restricted inadequate
0.01 0.03 0.01 0.00 0.10 0.00 0.00 0.01 0.02
0.39 0.10 0.25 0.88 0.00 0.77 0.83 0.33 0.15
Leisure: satisfaction
dissatisfied
0.09
0.00
—¿0.03
0.39
D
Social contacts: extent Social contacts: satisfaction
inadequate dissatisfied
0.06 0.10
0.01 0.00
—¿0.19 0.01
0.46 0.37
D D (Continued)
SOCIAL FACTORS AND COMMON MENTAL DISORDERS (Continued)Dichotomous
709
TABLE I
variableHigh regressionInteractions
value indicatesEigen valueP
valueAnxiety
for
loadingDepression loadingScale
neighboursopportunitiesrestricted0.010.41qualitydifficulties0.020.10satisfactiondissatisfied0.110.000.020.36DInteractions with
relativesopportunitiesrestricted0.040.04—0.410.50A with Dqualitydifficulties0.050.01—0.150.50Dsatisfactiondissatisfied0.150.000.040.38D
—¿
1. Six types of employment status were examined, which could not be dichotomised on a priori grounds. Following the multivariate I-tests,
these
were
collapsed
into
three
groups
represented
by the dichotomies
shown
in Table
II: full time
or retired
(1) versus
other
four (0); and unemployed or student (1) versus other four (0). The loadings in columns 5 and 6 show only the direction of each effect with respect to anxiety and depression, not the size or nature of the effect. TABLE II
Inceptionof illness:multiple-regression modelsindicatingrelativecontributionsof individualvariablesto thecausation of anxietyand depressionrespectively,and to ‘¿general severity'of dysphoria Beta Anxiety (no additional
Sign@cance
effect on depression)
Multiple-regressionanalysis long duration
psychological
symptoms
entirely psychological aetiology both organic and psychological aetiology*
long duration physical symptoms
4.07
0.00
0.78 0.72
0.03 0.05
—¿ 1.32 R2=0.8l
0.10 P
