Division of Psychiatry, University of Bristol, Bristol, UK
Institute of Psychiatry, Rawalpindi Medical College, Pakistan
Correspondence: David B. Mumford, Division of Psychiatry, University of Bristol, 41 St Michael's Hill, Bristol BS2 8DZ, UK. Tel: 0117 928 7773; fax: 0117 925 9709
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ABSTRACT |
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Aims To investigate emotional distress and common mental disorders in a poor urban district using the same survey method.
Method First-stage screening of a slum district of Rawalpindi used the Bradford Somatic Inventory. Psychiatric interviews were conducted with stratified samples using the ICD10 research diagnostic criteria.
Results On a conservative estimate, 25% of women and 10% of men suffered from anxiety and depressive disorders. Levels of emotional distress increased with age in both men and women. Women living in joint households reported more distress than those living in unitary families. Higher levels of education were associated with lower risk of common mental disorders, especially in younger women. Emotional distress was negatively correlated with socio-economic variables among women.
Conclusions This study found levels of emotional distress and psychiatric morbidity in a poor district of Rawalpindi to be less than half those in a nearby rural village in the Punjab, although rates in women were still double those in men. Possible explanations are that more healthy people migrate to the cities or that urban living is more conducive to good mental health in Pakistan.
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INTRODUCTION |
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METHOD |
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Before starting the study, the local councillor and other notables were approached to secure their cooperation and support. The study population was defined as all persons aged 18 years and over living in Mohalla Sultanpura: 1210 persons, according to Municipal Corporation statistics. A sketch map of the area was drawn, streets and plots were identified and each house was assigned a unique study number.
The Bradford Somatic Inventory
In a south Asian context, if a screening instrument is based primarily on
psychological symptoms, many cases of neurosis are likely to be missed. Most
people with common mental disorders spontaneously express their experience not
in psychological symptoms but in somatic complaints. The Bradford Somatic
Inventory (BSI; Mumford et al,
1991) was developed from symptom reports by psychiatric patients
in Pakistan and Britain with clinical diagnoses of anxiety or depression. The
BSI enquires about a wide range of somatic symptoms during the previous month
and, if the subject has experienced a particular symptom, whether the symptom
has occurred on more or less than 15 days during the month (scoring 2 or 1,
respectively). In two recent epidemiological surveys in Pakistan (Mumford
et al, 1996,
1997) the BSI proved to be an
effective first-stage screening instrument, as judged by subsequent selective
psychiatric interviews using ICD10 research diagnostic criteria
(World Health Organization,
1993). Testretest reliability of the BSI administered after
an interval of a week was good, with an overall reliability coefficient of
0.86 and a median value of 0.63 in a British primary care population
(Mumford, 1992).
First stage
The epidemiological survey was conducted in two stages: screening
questionnaire and psychiatric interviews. Four survey workers for the first
stage were recruited from among the paramedical staff at the Department of
Psychiatry, Rawalpindi General Hospital. They were trained in the
administration of the BSI and a household pro forma in a 3-day training
programme. Each worker achieved satisfactory interrater reliability standards:
the overall BSI item agreement was 95%.
At each house, a list was compiled of all the adults and children, recording their name, gender, age, marital status, relationship to the head of the household and years of education. After obtaining consent, the BSI44 was administered orally in Urdu to each person aged 18 years and over. Male subjects were interviewed by male raters and female subjects by female raters; some degree of privacy was attempted.
Raters revisited many houses to complete the administration of questionnaires for all the adult members of the household, in order to minimise bias in recruitment. Subjects suffering from acute or chronic psychotic disorders or showing evidence of cognitive impairment were identified and excluded.
Second stage
Psychiatric assessments were undertaken on subjects scoring above the
cut-off point on the BSI44 and on a random sample of subjects scoring
below this threshold. The choice of cut-off point was determined in a pilot
study of psychiatric out-patients at Rawalpindi General Hospital with
diagnoses of anxiety or depressive disorders. The psychiatric interviews were
conducted without knowledge of the BSI scores obtained in the first stage.
The interview consisted of two elements: the identification and diagnosis of any psychiatric disorder according to ICD10 research diagnostic criteria, using a psychiatric assessment schedule administered in Urdu/Punjabi, and the identification of any concurrent physical illness (whenever this seemed a possibility, a physical examination was carried out).
The psychiatric assessment schedule utilised selected (double asterisked) questions from the Present State Examination (PSE; Wing et al, 1974) as screening items, followed by the appropriate criteria from the ICD10 research diagnostic criteria. The psychiatric assessment systematically enquired about the following disorders (in hierarchical order): depressive episode, dysthymia, obsessive-compulsive disorder, panic disorder, agoraphobia, social phobia, specific phobias, generalised anxiety disorder and somatisation disorder. This diagnostic hierarchy reflects the ICD-10 research diagnostic criteria and thereby avoids dual diagnoses.
The interviews were conducted by I.A. and S.A., both trainee psychiatrists, who received a 4-day training programme in the use of the psychiatric assessment schedule, achieving an item concordance rate of 90%. During the study, psychiatric diagnoses were made after discussion between the interviewer and F.A.M.
After administering the psychiatric assessment schedule, the interviewer used the Socio-Economic Scales for Pakistan (SESP; Mumford & Mirza, details available from D.B.M. upon request) to code household income, sanitation and plumbing, electrical appliances and family wealth in land and livestock. Other households were visited separately to complete the SESP. The interval between the first and second stages of the survey was kept to less than 7 days, particularly to avoid losing subjects who might leave the area.
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RESULTS |
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There was a great diversity of family structure in Sultanpura, which was difficult to categorise simply. The 240 households were grouped broadly into unitary (parents and children only, or a married couple without children), extended (three generations), joint (with married sons or daughters, their spouses and children), polygamous and male-only households: 78% of the households were unitary, 4% were extended, 13% were joint households, 2% were polygamous and 3% were male-only shared apartments. The mean household size was 3.2 adults and 2.6 children under age 18 years. The socio-economic profile of households is given in Table 1.
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First stage
The BSI44 was administered in Urdu to 760 adults, representing 98%
of the total adult population in the study frame. Of the fourteen subjects not
included, five were excluded on medical grounds (dementia, deafness, serious
physical illness), four refused to participate and five subjects were
unavailable every time the raters called at their houses.
Figure 1 shows the mean scores
on the BSI by gender and age band: women had significantly higher BSI scores
than men in every age band; 28% of women and 11% of men had BSI scores above
the 20/21 threshold.
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Second stage
It was decided to interview subjects scoring 21 or above on the BSI and
also a 1 in 10 sample of subjects scoring 20 or below, chosen using random
number tables. On this basis, 220 subjects (138 women and 82 men) were
selected: interviews were conducted with 215 (98%). Five subjects could not be
traced at the time of the interview; no one declined to be interviewed. The
numbers of men and women receiving psychiatric diagnoses at interview are
given in Table 2.
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Estimated prevalence of psychiatric disorders
The point prevalence of non-psychotic psychiatric disorders, based only on
subjects with BSI scores of 21 and above, was 25% for women and 10% for men.
The estimate of prevalence, including subjects scoring in the sub-threshold
range of BSI scores (weighted to allow for the 1:10 sampling ratio), rises to
36% for the women and 16% for men. In addition, 1% of subjects received other
psychiatric diagnoses (three cases of dementia, one of learning disability and
one of acute psychotic illness, cannabis-induced).
Using the 20/21 threshold, the sensitivity of the BSI44 in this study was 65.5%, specificity was 98.6%, the positive predictive value (PPV) was 94.2% and the negative predictive value (NPV) was 88.8%, after weighting for the actual sampling ratio of sub-threshold subjects. The sensitivity, specificity, PPV and NPV of the BSI were similar between men and women.
Bradford Somatic Inventory scores and socio-demographic factors
Using the BSI as a general measure of emotional distress, the relationships
between BSI scores and demographic and socio-economic variables were explored,
including age, gender, marital status, educational status, household income
and family wealth.
Demographic variables
Age. Figure
1 shows that mean BSI scores increase with age in both men and
women, although the pattern is different. In women, there is a steady rise
from age 18-20 years onwards, reaching a plateau from the age of 50 years.
With men, there is a plateau from age 18 to 40 years and a steady rise
thereafter. Analysis of variance of BSI scores confirmed the effects for both
gender (F=83.2, P<0.001) and age band (F=8.3,
P<0.001), but the two-way interaction was not significant. Because
of this strong effect of gender and age on BSI scores, all subsequent analyses
using BSI scores are controlled for gender and age.
Marital status. Mean BSI scores were almost identical between single and married women aged 20-30 years, and the same applied in men of this age. Widows aged 30-50 years had higher mean BSI scores than married women of the same age (23.6 v. 15.2), but this difference did not reach statistical significance because there were only eight widows.
Household type. For men, an analysis of variance of BSI scores on household type showed no significant difference overall, nor when heads of household, sons or brothers were analysed separately. However, for women there was a significant overall variance in BSI scores by household type (n=363, F=3.437, P<0.001), also found separately with wives of the heads of household (n=216, F=4.478, P<0.0001), their daughters (n=77, F=3.406, P=0.008) and mothers (n=19, F=7.082, P=0.002), although not with daughters-in-law. These effects remained significant after age had been included as a covariate.
Among wives of the heads of household, the highest mean BSI scores were found in joint households (20.9, n=28), polygamous households (19.1, n=8) and mature unitary families where the majority of offspring were adults, all unmarried (18.3, n=28); the lowest mean BSI scores were found with married couples without children (4.2, n=11). Among their daughters, much higher mean BSI scores were found in joint households (23.4, n=16) than in unitary families (8.6, n=60). Among mothers of the heads of household, mean BSI scores were much higher in joint households than in unitary or extended households. Women living in standard nuclear households generally had lower than average BSI scores.
Years of education. Subjects were categorised according to the number of years of formal education received (nil, 1-5 years, 6-9 years and 10+ years). Lower levels of education were associated with higher BSI scores. In an analysis of variance of BSI scores, educational category was a highly significant factor (F=4.35, P=0.005) alongside age (F=4.03, P=0.001) and gender (F=78.7, P<0.001). The beneficial effect of education was most marked among younger women aged 18-40 years (F=2.75, P<0.05). The same trend was found in both younger and older men. Among older women (aged 41 years and over) there was no relationship between BSI scores and educational category, but 83% of women in this age group had had no formal education.
Socio-economic factors
There were significant negative correlations between individual BSI scores
and several socio-economic variables (total household income, education of the
head of the household, ownership of a motor vehicle). The magnitude of the
correlation coefficients differed between men and women. Among men, BSI scores
correlated most strongly with household income (-0.11); among women, the
strongest correlations were with household income (-0.20), number of
electrical appliances (-0.14) and education of the head of the household
(-0.12) (in every case, P<0.05).
An exploratory factor analysis was used to condense these socio-economic variables into one or more simpler summary factors. Cattell's screen test (Cattell, 1966) suggested the extraction of only one factor, which was obtained using a principal components analysis. This general wealth factor had high loadings for total household income (0.81), number of electrical appliances (0.75), plumbing facilities (0.68), ownership of motor vehicles (0.60), number of rooms in the house (0.51) and type of education given to the children (0.46). The total variance explained was 38%. The general wealth factor correlated -0.08 with BSI scores overall (P=0.02): correlations were much higher in women (-0.12) than in men (-0.03, NS).
Socio-demographic factors in combination
A stepwise multiple linear regression analysis was conducted with BSI
scores as the dependent variable and age, years of education and the general
wealth factor as predictor variables. With both men and women, age and years
of education (but not general wealth) independently contributed to the
variance in BSI scores: the total variance explained was 6-7%.
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DISCUSSION |
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The most striking finding from the present study is the much lower rate of psychiatric morbidity in this urban slum area than in the rural areas surveyed previously (Table 3). This was unexpected. This finding applies to both men and women, although women have much higher rates of morbidity than men in all three studies.
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Urban versus rural populations
In Western societies, poor urban populations have generally been found to
exhibit higher rates of morbidity than rural communities
(Harpham, 1992). Brown &
Harris (1978) reported the
1-month prevalence of all psychiatric illness as 17% among women in
Camberwell, London, compared with a 1-year prevalence of 12% among
women in North Uist, Outer Hebrides (Brown
& Prudo, 1981). The Epidemiologic Catchment Area (ECA) study
(Regier & Kaelber, 1995)
found the 1-year prevalence of major depression in an urban area in North
Carolina to be more than twice that of a remote and isolated rural area,
although in St Louis the prevalence of depression was lower in the urban
centre than in an adjacent rural area connected to it by suburban sprawl.
In urban areas in the developing world, poor social conditions in the rapidly growing cities may be responsible for high rates of stress and psychiatric morbidity (Mari, 1987; Desjarlais et al, 1995). Almedia-Filho (1987) reviewed many epidemiological studies conducted in Latin America that have sought to assess the impact of urban stress and the effects of economic and social marginalisation on the mental health of rural migrants to the cities.
However, there have been few paired studies of rural and urban populations in the same geographical region to allow direct comparisons. An epidemiological study in Puerto Rico found higher prevalence rates of most DSM-III-defined (American Psychiatric Association, 1980) psychiatric disorders among urban than rural residents (Canino et al, 1987). In South Korea, Lee et al (1990) found no difference in rates of major psychiatric disorders between urban and rural Seoul. In Taiwan, small town samples showed significantly higher rates for most non-psychotic disorders than rural villages, but both were higher than in modern metropolitan Taipei (Hwu et al, 1989). However, in another study of urbanisation in Taiwan, Cheng (1989) found higher rates of depression among young women in rural villages than in urban or suburban areas, associated with chronic stressors, mostly around difficulties in family relationships.
Other explanations for observed differences between urban and rural populations must be considered. Guarnaccia et al (1990) have warned of the effects of an acquiescent response style on self-report symptom measures, resulting in inflated prevalence estimates. Urban populations may be more sophisticated in their response style than rural people. However, in all three studies in Pakistan, the BSI questionnaires were followed by selective psychiatric interviews using ICD-10 research diagnostic criteria (on which the prevalence estimates were based), which seem to be less susceptible to this kind of bias. The sensitivity and specificity of the BSI with respect to ICD-10 diagnoses were similar in all three studies.
Socio-demographic factors and stress
The pattern of BSI scores with respect to age and gender
(Fig. 1) was identical to that
in the rural Punjab and very similar to that in Chitral. Women have much
higher BSI scores than men in every age group. In all countries of the world,
women experience higher levels of psychiatric morbidity than men, but the gap
appears greatest in poorer countries (Lee
et al, 1990;
Desjarlais et al,
1995). Life may be particularly stressful for most women in
Pakistan because of their lack of control over their lives.
One notable finding in Sultanpura was the great variety of domestic family patterns, making the classification of household types not at all straightforward. Whereas in the rural studies households fell quite neatly into three categories (joint, extended and unitary), in the urban setting almost any permutation and variation was found. This reflects the splitting up of families into different living groups, often with part of the joint/extended family still living back in the ancestral village. These fragmented and shifting family living arrangements seem to be the direct social consequence of rural-urban migration.
This study found that women are much more affected than men by the type of household and their place within the family. Women from joint households and mature unitary families showed especially high mean BSI scores. Polygamy seems bad for women's mental health but good for men's - like marriage in general, only more so!
All three of these studies used the same set of socio-economic scales for Pakistan, which included a wide range of potential indicators of socio-economic status relevant to both rural and urban communities. Higher levels of emotional distress (as measured by BSI scores) were significantly associated with poorer socio-economic status in Chitral, in rural Punjab and in urban Rawalpindi, although the most significant indicators of socio-economic status were different. In rural societies, wealth is measured by ownership of land and livestock, whereas in the cities household income is the most important factor. The educational level achieved and the number of electrical appliances were useful indicators of socio-economic status in both urban and rural settings. In Sultanpura, as in rural Punjab and in Chitral, socio-economic status has more impact on the mental health of women than of men.
Migration and mental health
The present study has confounded our expectations and found much lower
rates of common psychiatric disorders in the urban slum area of Sultanpura
than in the rural populations of Punjab and Chitral. What light might this
shed on the social processes and psychiatric consequences of the worldwide
phenomenon of migration from rural areas of developing countries into the
cities?
Sultanpura is a comparatively recent urban settlement and is typical of a certain kind of peripheral urban development, but it is not representative of the whole city. Most existing householders were the primary migrants and had built their own houses there. Most had migrated from the surrounding rural areas of the Punjab. We enquired of a sample of these householders why they had chosen to move to the city. Most gave one of three reasons: poverty and lack of work in the village; to obtain better education for their children; or because of family quarrels, particularly in a joint family system.
Since Odegaard's pioneering study of Norwegians migrating to Minnesota (1932), the debate has generally focused on selection factors versus environmental factors to explain higher rates of severe mental illness among migrants. In the present study we are seeking to explain lower rates among the migrants across a broad spectrum of common psychiatric disorders. The two principal explanations for our findings are that: the more healthy migrate to the cities; and urban living is more conducive to good mental health than the rural environment in Pakistan.
Individuals and families who migrate to the cities may be psychologically more resourceful and resilient than those they leave behind. However, in the Pakistani context, the decision to migrate is made principally by the senior male head of the family in the village or sometimes by the head of the migrating household (usually one of his sons). The women of the family often have little influence on the decision to migrate, yet they also enjoy much better mental health than women back in the village.
The new urban environment may contribute to an improvement in mental health. Life in the city is much less constrained, both socially and economically, than in the village. There is more choice with regard to work, friends, schools and social activities. Earnings are much higher, offering greater material prosperity, wider consumer choice and the opportunity to fulfil personal ambitions. Physical health may also be better in cities than in villages, partly as a result of access to better medical facilities.
Two of the authors of the present study (I.A. and S.A.) have direct experience of this migration process within their own families in Pakistan. Their fathers migrated from their villages into towns in order to obtain better education for their immediate families, which opened up for them the possibility of a university education and a professional career in medicine. Not all the family group has migrated, however, and they still have uncles and cousins who remain in the ancestral village. Some of the observed rural-urban discrepancy in common psychiatric disorders may perhaps reflect frustration and discontent among those of the joint/extended family who remain behind and are denied the opportunities offered in the city.
However, these explanations remain speculative. We need to conduct more detailed family studies of internal migration and its effects. There is often a considerable degree of mobility between branches of the same joint/extended family resident in the city and in the village. This will facilitate case-control studies of migrants with varying lengths of exposure to the urban environment, and also cohort studies of migrant branches of families using members of the same families left behind in the ancestral village for comparison. Greater understanding of the impact on mental health of migration into the cities of Pakistan may help to shed light on the factors behind the poor mental health status of people who live in rural areas.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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Received for publication December 10, 1999. Revision received June 7, 2000. Accepted for publication June 9, 2000.