Centre for the Economics of Mental Health, Institute of Psychiatry, King's College London
Centre for Development Studies, Swansea (formerly Institute for Health Sector Development, London
National Institute of Mental Health and Neurosciences, Bangalore, India
Institute of Psychiatry, Rawalpindi, Pakistan
Correspondence: Daniel Chisholm, Senior Lecturer, Centre for the Economics of Mental Health, Institute of Psychiatry, 7 Windsor Walk, Denmark Hill, London SE5 8BB
Declaration of interest The study was funded by the Department for International Development, UK (HP-ACORD Small Project Grant RD 463).
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ABSTRACT |
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Aims To demonstrate cost-outcome methods in the evaluation of mental health care programmes in low-income countries.
Method Four rural populations were screened for psychiatric morbidity. Individuals with a diagnosed common mental disorder were invited to seek treatment, and assessed prospectively on symptoms, disability, quality of life and resource use.
Results Between 12% and 39% of the four screened populations had a diagnosable common mental disorder. In three of the four localities there were improvements over time in symptoms, disability and quality of life, while total economic costs were reduced.
Conclusion Economic analysis of mental health care in low-income countries is feasible and practicable. Our assessment of the cost-effectiveness of integrating mental health into primary care was confounded by the naturalistic study design and the low proportion of subjects using government primary health care services.
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INTRODUCTION |
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METHOD |
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Study design
The chosen design of the study was to recruit and follow up patients
meeting ICD-10 diagnostic criteria for affective and neurotic disorders
(World Health Organization,
1992a) from two rural catchment areas, one reflecting the standard
primary health care system, and one in which mental health care had been
incorporated into primary health care practice. The standard primary care
centres were Jigani (Bangalore) and Lehtrar (Rawalpindi), and the centres in
which mental health training and support had been given were Sakalwara
(Bangalore) and Taxila (Rawalpindi). Following previous studies and methods
(Mumford et al, 1997),
a community survey design was adopted, involving the mapping of whole local
communities and randomly selecting members of individual households. This
approach enabled the health-seeking behaviour of the whole local population
(rather than primary care attenders only) to be observed, thereby affording an
opportunity of assessing the extent of unmet mental health need.
Recruitment of study participants
A two-stage process was used to recruit subjects: a) initial screening for
mental disorder by trained research field-workers via the Self Report
Questionnaire (SRQ) (Harding et
al, 1980); b) for all those scoring above the SRQ threshold
for caseness (in India, a score of 5 or more indicates a probability of
psychiatric morbidity; in Pakistan, 6 or more), a diagnostic assessment was
made by a psychiatrist (the Pakistan Assessment Schedule:
Mumford et al, 1997;
in India, the Schedule for Clinical Assessment in Neuropsychiatry:
World Health Organization,
1992b). Both instruments provide ICD-10 diagnoses. Only new
episodes of affective and neurotic disorder were included (ICD-10 categories
F32-48), defined as the presence of a set of symptoms for which no mental
health treatment had been sought in the past month. Other inclusion criteria
included the age of patients (18-60 years) and local residence.
Description of study intervention
Subjects meeting diagnostic criteria were informed of their health status
by the psychiatrist and were provided with information about possible
treatment options, how and where to seek local treatment for their condition,
and advice about psychological problems, such as alcohol or drug dependency in
the spouse.
Tests administered to subjects
A range of clinical/social instruments were administered via face-to-face
interviews at entry into the study and again 3 months later, in order to
observe any changes in outcomes. Data were obtained on symptoms (Hamilton
Depression Rating Scale) (Hamilton,
1960), disability (Brief Disability Questionnaire)
(Von Korff et al,
1996) and quality of life (WHOQOL Brev)
(World Health Organization,
1998). Further data were collected from subjects via the
completion of a socio-demographic form (age, gender, education, employment,
income) and a service utilisation form (contact in the previous 3 months with
primary health care providers, traditional healers and hospital services;
medication use; support and help from family and friends), based on the Client
Sociodemographic and Service Receipt Inventory
(Chisholm et al, 2000)
and adapted for the purpose and context of the present study.
Cost measures
The economic analysis was from the view-point of society: not only were the
costs of the health sector considered, but also the time costs and
out-of-pocket expenses of users and their (informal) carers. Unit costs were
derived for a range of primary health care contacts (keyworkers, such as a
doctor, nurse or pharmacist; other workers, such as female health visitors;
and psychiatrist), on the basis of facility-specific data on staffing levels
and salaries, plus other revenue and capital costs relating to the premises at
which the professionals worked. Total annual costs of professionals were
divided by working days per year and hours worked per day, and the resulting
values were subsequently weighted by the ratio of time spent in contact with
patients to time spent not in contact with patients. A series of other unit
costs was also estimated for out-patient and in-patient hospital contacts,
based on available hospital finance data.
Privately purchased health care and medications were costed as the fees that patients or their families actually paid to local providers (recorded in the service receipt schedule). Where patients or families contributed to the cost of publicly provided hospital care, this was separately quantified and subtracted from the total cost that would have applied if fully financed by government. Finally, estimates were calculated for the opportunity costs associated with informal care-giving (hours per week multiplied by the hourly wage rate for a house-servant or maid: Indian Rupees 6; Pakistani Rupees 12), and also for time spent travelling to, or waiting for, care providers, and lost opportunities for work (derived from the patient's estimated wage, based on gender-adjusted average earnings for labourers or skilled workers).
Analysis
The purpose, design and scale of this demonstration project precluded the
conduct of a full-scale cost consequences analysis (which would require a
controlled experimental study design, a larger sample size and a longer
follow-up period). Moreover, the observational design of the study, together
with differences in the structure of local health services, means that
relative changes in costs and outcomes between catchment areas are
not necessarily causally related, so that it would not be safe to draw
inferences based on such comparisons (adjustment for differences in
characteristics of subjects and sites, using treatment-effect regression
coefficients, would not alter this fact). Accordingly, the focus of analysis
was not on comparisons between catchment areas or sites, but on changes over
time in the principal cost and outcome domains for each of the localities
(using a two-sided paired-sample t-test statistic).
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RESULTS |
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Psychiatric epidemiology of catchment areas
A total of 795 adults was screened with the SRQ in the two catchment areas
of the Bangalore site. The prevalence of diagnosable common mental disorder
using samples from these localities was 18.9% in Sakalwara and 12.5% in
Jigani. In the Rawalpindi site, 948 SRQs were administered, and the proportion
of confirmed cases as a percentage of the screened population was 28% in
Lehtrar and 39% in Taxila. Comparison of the mean SRQ scores for the cases
selected for full psychiatric assessment in the two sites revealed a
statistically significant difference, with subjects in Rawalpindi (mean=11.17,
s.d. 3.48) scoring on average 1.33 points higher than subjects in Bangalore
(mean=9.84, s.d. 3.96).
The various (primary) diagnoses reached for the populations sampled are given in Table 1. Most cases (72% in Bangalore, 92% in Rawalpindi) fall under the broad diagnostic category of mood (affective) disorders (ICD-10 codes F32-39), the remainder being made up of neurotic and somatisation disorders. The most common (primary) diagnoses in the Bangalore site were dysthymia (68% of the sample), and phobic and other anxiety disorders (19%). In contrast, only 8% of cases in Rawalpindi were diagnosed as having phobic and other anxiety disorders, and there were no diagnosed cases of dysthymia. Rather, the majority of cases were diagnosed as having depressive episodes (22% mild, 32% moderate and 35% severe, of which over a third had psychotic symptoms).
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Socio-demographic characteristics of the populations sampled
The socio-demographic characteristics of the populations sampled in each of
the four catchment areas of the study are given in
Table 2. There are a number of
broad similarities between the catchment areas, including the preponderance of
women (71-87% of the samples), the proportion of married people (62-80%), and
the small minority who are currently employed (2-13%). There are also clear
differences between and within sites, however; such as the age distribution
(for example, 50% of subjects in Lehtrar were aged 45 years or more, compared
with only 13% in Taxila), the range of monthly incomes and the extent of
educational achievement (although much of the information was missing for the
Rawalpindi area).
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Rates of contact with services
Rates of contact with primary care, hospital care and private services are
reported in Table 3. In the
standard model locality in India (Jigani), 17% of the population had contact
with a government primary care provider at baseline, 33% had contact with a
private community-based provider, 10% had attended as out-patients and 5% had
been in-patients during the previous month. Rates of service uptake in the
integrated care locality of Sakalwara were 37% for a government primary health
care (PHC) worker, 25% for private community-based practitioners, 7% for
out-patient services and 3% for in-patient services. These rates do not alter
appreciably over the follow-up period.
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Rates of contact were generally higher in the Rawalpindi site. For example, the proportion seeking care from a government PHC provider in the non-intervention locality (Lehtrar) is 67% at baseline, considerably higher than the rate observed in Taxila (27%). Over the follow-up period, PHC rates of contact increased to 88% in Lehtrar and 52% in Taxila, a positive trend. Rates of contact with out-patient services are higher for the Taxila locality, which is in part because the access to such care is relatively easier here than in Lehtrar.
Health care costs
While there were considerable variations in cost between sampled
individuals within particular localities, a number of general trends emerge
from the cost analysis (Table
4). The mean cost of contacts with government PHC workers, which
would be expected to rise if individuals seek appropriate treatment for their
diagnosed mental health condition, does in fact increase in the localities
where mental health care training and support has been introduced (Sakalwara
and Taxila), whereas there is very little change in the standard care
localities. By contrast, the costs of contacts with community-based private
health care providers (general practitioners, traditional healers) dropped in
all localities, significantly so except in the Sakalwara locality. Privately
incurred expenditures on medication, on the other hand, remained relatively
constant over the 3-month period. Aggregated health care costs are increased,
but not statistically significantly so, in both integrated care localities,
resulting mainly from increased contact with secondary care services.
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Total costs to society
The opportunity costs associated with lost work, time and travel to obtain
treatment, and informal care-giving by family members were also estimated
(Table 4). The costs of lost
working days decreased significantly in all localities, particularly in the
Bangalore site, where costs fell by 80-90%. Opportunity costs associated with
informal care-giving (help from relatives in or outside the home, with e.g.
child care, cooking and shopping) are less in the Bangalore site also, but
considerably more in the Rawalpindi site, notably in Taxila.
When all costs are combined (health care and patient/family costs), the magnitude of the economic impact of depression and anxiety becomes evident: in the Bangalore site, the cost at baseline is Indian Rupees 700 per month, and in the Rawalpindi site the baseline cost is more than Pakistani Rupees 3000 per month. To put this in context, this is equivalent to between 7 and 14 days of an agricultural worker's wages in India, and approximately 20 days in Pakistan. These total costs, however, had fallen appreciably by the follow-up assessment point in all localities except Taxila, where there was a significant increase.
Outcome assessments
Baseline depression scores were markedly higher in the Rawalpindi site,
indicating greater psychiatric morbidity. However, since the interviewers were
not trained simultaneously or by the same trainer, it is conceivable that
rating methods differed in the two sites. Results are therefore couched in
terms of changes in scores over time in the two separate sites
(Table 5). In overall terms,
there has been an improvement in the positive outcome domain of quality of
life, and significant reductions in symptomatology and disability. More
specifically, in three of the four localities, there is a substantial
reduction in levels of depression symptoms (between 5.1 and 8.6 points lower,
each statistically significant at P<0.01). The exception is
Taxila, which showed only a very modest, and not significant, reduction (0.5
points). Results for the Brief Disability Questionnaire (BDQ) closely reflect
those for depression scores, in the sense that in all localities except Taxila
there is a significant reduction by 6.2 to 7.9 points in overall disability
score. There is a slight, though not statistically significant, increase in
BDQ score for Taxila (0.3 points higher). There were statistically significant
improvements in quality of life scores in Sakalwara and Lehtrar. There were
only modest improvements in the Jigani catchment area population, and in
Taxila there was no clear change either way.
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Summary of changes in scores for key cost and outcome domains
A summary of observed changes over time in the principal cost and outcome
domains is given in Table 6,
including a comparison of change scores between those who had been in contact
with government primary and secondary health care and those who had only
consulted local practitioners or not accessed care at all. For both localities
in the Bangalore site, there are higher service costs but greater improvements
in depression score (significantly so in Jigani, but not in Sakalwara, the
integrated care locality). Government health care users also show greater
improvements in disability change scores. There are no statistically
significant differences in change scores in the Rawalpindi site, but again
service costs are higher among government health care users. Depression and
disability change scores are actually better among the (very small number of)
non-users in the Lehtrar locality, and in Taxila users of government health
care services showed a marginally better mean change score for depression but
a slightly worse score for disability. In the two integrated care localities,
therefore, there are no clear advantages in clinical or economic outcomes for
users of government primary and secondary care services.
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DISCUSSION |
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Economic burden of common mental disorders
The health care and other opportunity costs incurred by individuals (and
their families) in the sample with a diagnosed common mental disorder were
considerable. It is important to note that the preponderance of these costs
were privately incurred expenditures, and that a significant category of
health care cost was consultations with local general or traditional
practitioners (neither of whom are trained or qualified to detect or treat
psychiatric morbidity). Thus, while individuals (and households) are seeking
help, and spending significant amounts of money in the process, they are not
in the main receiving appropriate care for their mental health condition. The
imputed costs associated with reported levels of informal care-giving,
travelling time and expenses, and lost days of work are also very
considerable, and in fact exceed formal health care costs by as much as three
times (a finding echoed in other studies of the cost of illness, for
depression and other affective disorders, carried out in industrialised
countries) (Greenberg, 1993;
Kind & Sorensen, 1993). Although self-reported estimates of specific care-giving activities such as
help around the home may not be totally reliable (typically,
over-estimation of opportunity cost), use of clearly specified activities and
minimal wage rates for a house servant nevertheless help to demonstrate the
economic impacts of common psychiatric disorders on the productive opportunity
of individuals and families alike.
Strengths and limitations of the study
An important feature of the chosen study design was that it enabled the
prevalence of common mental disorders in the sampled catchment areas to be
estimated. This study reinforces the findings of earlier epidemiological
studies in each site, that common mental disorders are indeed common,
particularly among women (an estimated 12-18% of the adult populations of the
Bangalore catchment areas, and 28-39% in the Rawalpindi site). Although there
are notable differences in the diagnostic profiles of the two sites (high
rates of moderate and severe depressive episodes in the Rawalpindi site, and a
high prevalence of dysthymia in the Bangalore site), which could be because we
used different schedules, the focus of this study was on analysis of health
care-seeking patterns within rather than between sites, and our results in
fact closely reflect those reported locally in other studies
(Ustün &
Sartorius, 1995; Mumford et
al, 1997). The observational study design also enabled the
health-seeking behaviour of whole catchment populations to be assessed, which
demonstrated the economic consequences associated with unmet need at the level
of individuals, families and local health services. For example, it was found
that only just over half of the sampled populations in the Bangalore site had
contacted services at all, and an even smaller proportion were in contact with
government primary health care workers.
The high proportion of subjects who did not access government primary health care services in the two localities where mental health care had been integrated confounded the assessment of the relative cost-effectiveness of the programme at the catchment area level (only about half the subjects encountered PHC-based intervention). To sort out this question requires an experimental study design involving the comparison of attenders only at primary care centres, with and without the integrated care model. Comparison between those who did and did not access government primary and secondary services in the integrated care localities, however, showed no statistically significant advantages in clinical or economic outcomes for the former subgroup. In particular, the recent introduction of mental health training and support in the main PHC centre in Taxila does not yet appear to have benefited those of the mentally ill population of that area who were in the sample (a plausible reason for this is that there was a strong preference for, and consequent reliance on, private care providers in this population).
A striking finding of the study is the significant improvement in the outcome domains of depression, disability and quality of life for both standard care localities. These results may represent a regression to the mean, or be partly explained by spontaneous remission or improvement (particularly in the Rawalpindi site, where there was a significant proportion of cases with acute depression), but also suggest that interviewing individuals about their mental health state, and advising them to seek care locally, may itself act as an intervention.
Future policy priorities and research needs
Since governments of low-income countries are fundamentally constrained by
lack of resources, constructive ways of harnessing existing local resources
must be given consideration, not only in terms of integrating mental health
care into the primary care system but also in terms of engaging other
professionals and leaders. In many cases the first port of call
for an individual with mental disorder (or a member of their household) is the
traditional or general practitioner. Simple mental health training for these
local private providers might represent an effective means of improving the
detection, referral and management of common mental disorders. Of particular
relevance in this respect is the currently widespread prescription by local
private practitioners of medications for these disorders, the cost of which is
invariably met by the patient or family. Training in the detection and
treatability of common mental disorders needs to be accompanied by the
availability of suitable drugs (and simple psychosocial interventions).
Although the high acquisition cost of the newer anti-depressants is an obvious
constraint, conventional tricyclics are very cheap and equally effective (if
not quite as well tolerated). And yet many of the pharmacies visited in the
present study do not stock or cannot get such medication. The establishment
and implementation of an essential drug list for mental disorders is likely to
represent a further policy consideration in many low-income countries.
There remains a chronic shortage of economic data to support discussions on mental health policy or resource allocation at a national or international level. There is consequently a need to undertake further studies that not only address the relative cost-effectiveness of alternative interventions of strategies (using a prospective, experimental study design where possible), but also broaden our understanding of the interrelationship between psychiatric morbidity and disability on the one hand, and access to, and uptake of, services on the other. Indeed, interventions for common mental disorders need to be carefully planned in accordance with the prevailing types of health-seeking behaviour of the local population(s), as well as other demographic, cultural and socio-economic factors, since these factors are likely to contribute significantly to their overall effectiveness and cost-effectiveness.
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Clinical Implications and Limitations |
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STUDY LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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Received for publication May 24, 1999. Revision received October 22, 1999. Accepted for publication October 26, 1999.