Health Services Research Department, King's College of Medicine and Institute of Psychiatry, London, UK
University of Washington, Seattle, Washington, USA
Health Services Research Department, King's College of Medicine and Institute of Psychiatry, London, UK
University of Washington, Seattle, Washington, USA
Pfizer, Groton, Connecticut (formerly Eli Lilly and Company, Indianapolis, Indiana),USA
Center for Health Studies, Group Health Cooperative of Puget Sound, Seattle, Washington, USA
the LIDO Group
Correspondence: Dr Dan Chisholm, Global Programme on Evidence for Health Policy, World Health Organization, Avenue Appia, 1211 Geneva 27, Switzerland. E-mail: chisholmd{at}who.int
Declaration of interest This research was funded by research contracts from Eli Lilly and Company to Health Research Associates, Inc.
See editorial, pp. 9294, this issue.
This report does not necessarily represent the decisions or stated policy
of the World Health Organization.
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ABSTRACT |
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Aims To explore the relationship between depression status (with and without medical comorbidity), work loss and health care costs, using cross-sectional data from a multi-national study of depression in primary care.
Method Primary care attendees were screened for depression. Those meeting eligibility criteria were categorised according to DSMIV criteria for major depressive disorder and comorbid status. Unit costs were attached to self-reported days absent from work and uptake of health care services.
Results Medical comorbidity was associated with a 1746% increase in health care costs in five of the six sites, but a clear positive association between costs and clinical depression status was identified in only one site.
Conclusions The economic consequences of depression are influenced to a greater (and considerable) extent by the presence of medical comorbidity than by symptom severity alone.
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INTRODUCTION |
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METHOD |
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Sampling strategy
Patients attending primary care clinics in six participating sites (Be'er
Sheva, Israel; Barcelona, Spain; Porto Alegre, Brazil; Melbourne, Australia;
St Petersburg, Russia; Seattle, WA, USA) were approached systematically in
person by the primary care physician, clinic or research staff and invited to
complete a screening assessment package, which was scored for initial
eligibility (a score of 16 or greater on the Center for Epidemiologic Studies
Depression rating scale, CESD;
Radloff, 1977). In order to
undertake subgroup analyses of gender differences, booster sampling of men was
carried out in each site (a target quota of one-third of recruited subjects).
Written informed consent was obtained from participating subjects following a
description of the study. For patients meeting the initial eligibility
criteria, a baseline assessment was conducted that included administration of
a depression diagnostic instrument (Composite International Diagnostic
Interview, version 2.1, CIDI; Weiller
et al, 1994) and other measures of socio-demographic
status and service contact. Patients with a chronic medical or psychiatric
comorbid condition were eligible, but those with a known organic or major
psychiatric disorder (dementia, psychosis, bipolar disorder) were excluded. A
concurrent conditions checklist was used to identify subjects with one or more
out of 12 major chronic medical conditions
(Wells et al, 1991),
comorbid anxiety was assessed via the Hopkins Symptom Checklist (SCL90,
with a cut-off score of 1.7; Derogatis
et al, 1976) and high alcohol use was defined as at least
21 units/week for men, 14 units/week for women or at least six drinks on a
single occasion in the previous month. Functional status was assessed using
the 12-item Short Form Health Survey (SF12) physical component score
(Ware et al, 1995).
Patients receiving treatment for depression currently or in the previous 3
months were excluded from the study, so that the reference population for the
analysis is that of currently untreated cases of depression seen in primary
care.
Principles and processes of service costing
Measurement of resource use was carried out via the administration of a
service receipt schedule adapted specifically for use in this project from the
Client Service Receipt Inventory (CSRI;
Chisholm et al,
2001b). A range of primary care, psychiatric, social and
general medical services was identified that gave a comprehensive profile of
potential service receipt for the patient population in the six sites
(Chisholm et al,
2001a). The three main categories of service contact
were: primary care and out-patient services, which covered the frequency and
average duration of contacts with primary care or mental health care
professionals; day care services, provided to several patients at a time and
usually offering a combination of treatment for problems related to mental
illness; and in-patient hospital services services, incorporating both
psychiatric and general medical admissions.
A set of unit-cost templates was developed for computing the cost of services provided by both individual professionals and facilities. Site-specific unit costs for each service are listed in Table 1. Site-specific service costs were then converted into a common currency via purchasing-power parities (World Bank, 2000), which enables direct comparison of costs using the same metric (international dollars). In this paper, we primarily report cost results in national currency units because the focus is more on site-specific rather than pooled relationships.
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Lost opportunities for employment were assessed via self-reported days absent from work. The costs of lost employment were estimated by multiplying days absent from work by the local wage rate for the occupational category of the patient. Other indirect costs, such as reduced productivity while at work or informal care support, were not collected in this study because of expected measurement difficulties at the international level.
Analysis
The sampled population in each site was split into four groups: (A)
subclinical depression (CESD score > 16) but no medical comorbidity;
(B) subclinical depression (CESD score > 16) with medical
comorbidity; (C) clinical depression (CIDI positive) but no medical
comorbidity; (D) clinical depression (CIDI positive) with medical comorbidity.
This enabled us to test four hypotheses: that individuals with clinical
depression consume more resources and have greater absence from work than
those with subclinical depression, either discrete/non-comorbid (C > A) or
comorbid (D > B); and that medical comorbidity has a cost-raising influence
on health care use or work loss, for both subclinical depression (B > A)
and clinical depression (D >C).
Total health care costs were made up of three categories: mental health out-patient costs (contact with a psychiatrist, psychologist or other mental health worker, and attendance at a day care programme); general medical out-patient visits (primary care doctor, non-mental health specialist physician or other health care worker such as a nurse practitioner, plus day hospital attendance for physical health problems); and general medical in-patient care (psychiatric admission in the 3 months prior to baseline would have excluded the subject from the study). Costs of out-patient services were adjusted for the average duration of visits.
Analyses of variance (with the Scheffé test for pairwise comparisons) and chi-squared test statistics were used for testing bivariate mean and proportional differences between the four analytical groups AD, respectively. Owing to the skewed distribution of cost data, confidence intervals for means were derived using bootstrapping, a non-parametric approach that avoids strong distributional assumptions by employing large numbers of re-sampling computations (Efron & Tibshirani, 1993). In order to adjust for key socio-demographic and clinical characteristics, total costs of health care were subsequently entered into a linear regression analysis in each site (using age, gender, marital status, education and employment as covariates alongside CIDI depression status, and dummy variables for comorbid anxiety and high alcohol use as well as chronic medical illness). A variety of different model specifications were fitted, including ordinary least squares (OLS), with both an untransformed and log-transformed dependent variable, and also generalised linear modelling with a gamma error distribution and a log-link function. Our chosen model specification was an OLS regression with the log of total service cost (+1, to avoid zero values for cost), which satisfied distributional assumptions (as well as homoscedasticity and independence), provided slightly improved explanatory power and allowed simplified inter-site comparison in terms of proportionate effects of specified variables on service costs (Diehr et al, 1999; Knapp et al, 2002).
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RESULTS |
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Socio-demographic characteristics of the sampled population
Comparison of the socio-demographic characteristics of the sampled
populations who met the eligibility criteria for the study in each site is
given in Table 2. The mean age
of subjects was close to 40 years in each site except for St Petersburg, where
the mean was 47 years (s.d.=16.2). Subjects with comorbid depression were
appreciably older than those with non-comorbid depression, as were those with
subclinical as opposed to clinical depression. The average number of years of
schooling for the total sample in each site ranged from 9.3 (s.d.=3.4) in
Porto Alegre to 13.7 (s.d.=2.9) in St Petersburg; in all six sites, subjects
with comorbid clinical depression had fewer years of schooling. The striking
similarity with respect to the gender of the sampled populations in
each of the six sites, women constituted two-thirds to three-quarters of the
sample is an artefact of the booster sampling of male attenders. The
proportion of subjects who were married ranged from one-quarter in Melbourne
to two-thirds in Be'er Sheva, but in all but the latter site, the subjects
with clinical depression were more likely to be unmarried. The proportion of
study subjects in employment ranged from approximately 50% in Porto Alegre and
St Petersburg to 67% in Seattle, with rates consistently lower among the
comorbid groups.
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Rates and costs of resource utilisation
Table 3 provides a breakdown
of the (unadjusted) rates of contact and costs of resource use across the six
sites. Rates of contact between sites were 514% for mental health
out-patient visits, 94100% for general medical/primary care visits and
518% for in-patient admissions. Across the six sites, mean utilisation
for the six sampled populations in the 3 months preceding baseline was
0.20.7 for mental health out-patient visits, 1.58.0 for medical
out-patient or primary care attendances and 0.10.2 for in-patient days
(not tabulated). Both the rate (%) and amount of contact were typically
highest among subjects with clinical depression who had a medical and/or
psychiatric comorbidity. The mean costs of this resource utilisation (for the
sampled populations as a whole, not just service users) are reported in
Table 3 (costs are expressed in
national currencies, but can be converted into US dollars or other monetary
units using the set of purchasing-power-parity conversion factors provided in
Table 1). Focusing on total
health care costs similar findings are obtained for the three
subcategories of resource cost we find the following.
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An overview of the cost differences for each hypothesis and site is given in Fig. 1.
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Rates and costs of work disability
Incidence of self-reported days absent from work ranged from 20% of cases
in Porto Alegre to 55% in Seattle, whereas the average number of days taken
off work in the previous 3 months ranged from 1.4 days (s.d.=5.6) in Porto
Alegre to 7.6 days (s.d.=15.3) in St Petersburg
(Table 4). By attaching site-
and occupation-specific daily wage rates to work absences, an estimate (in
human capital) of the costs of lost productivity can be obtained. This
approach reveals that the monetary value accorded to these lost work days
constitutes an appreciable element of the overall economic costs of
depression. In five of the six study sites the cost of lost work days was
some-what less than the total cost of health care, but nevertheless
represented 1540% of the total combined costs of health care and work
loss. In the sixth site (Barcelona), lost work day costs were 75% greater than
total health care costs.
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With respect to the four subgroups, we found weak support for the hypothesised excess costs associated with clinical depression status: costs in the non-comorbid groups (Hypothesis 1: C > A) were similar in three of the sites but at least doubled in Be'er Sheva and Melbourne (difference not significant at the 5% level) and St Petersburg, whereas the costs in the comorbid groups (Hypothesis 2: D > B) were higher by a factor of 3 in Barcelona (Scheffé test: P < 0.05), Melbourne and St Petersburg but actually lower in Be'er Sheva and Seattle. The hypothesised increase in the number and cost of lost work days among those with comorbid depression was not supported by these data (Hypotheses 3 and 4: B > A; D > C). In Be'er Sheva and Melbourne, costs were in fact significantly lower in the comorbid groups (Scheffé test: P < 0.05).
Multivariate analysis
Six site-specific regression models were developed in order to assess the
contribution of depression status and medical comorbidity towards excess costs
of health care (Table 5). Using
the natural logarithm of total health service cost as the dependent variable
and controlling for key socio-demographic and clinical variables, we found a
significant proportionate increase in cost attributable to being clinically
depressed (as ascertained by the CIDI) in Porto Alegre (52%), a modest
increase in Barcelona, Seattle and St Petersburg (418%) and a decrease
in Be'er Sheva and Melbourne (416% less). With respect to medical
comorbidity, there was a significant effect in Barcelona, Be'er Sheva,
Melbourne and Seattle (costs increase by 2446%) and a lesser effect in
Porto Alegre (17% increase). In St Petersburg, costs were 15% lower in
medically comorbid cases.
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A range of other factors had an impact on costs but the only consistent finding was for the physical component score of the SF12, which showed a statistically significant negative relationship in all sites, reflecting a lowering of costs as the score decreases towards no physical illness. Psychiatric comorbidity had a discernible effect in Seattle anxiety increased costs by nearly 50%, whereas high alcohol use reduced costs by 35% but elsewhere had no significant or consistent influence. Overall, the multivariate models had quite low explanatory power (adjusted R2 values were 818%) and provided no consistent cost relationships across all sites other than the medical comorbidity and physical illness score.
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DISCUSSION |
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Our findings are consistent with earlier studies that showed a strong association between depression and medical or physical comorbidity. For example, an earlier analysis of health care costs among primary care patients with recognised depression in one of the study sites (Seattle) found that on a multiplicative (logarithmic) scale depression was associated with a 5075% increase in health service costs at all levels of medical comorbidity (Simon et al, 1995). A number of studies have also demonstrated the influence of psychiatric comorbidity on the service utilisation rates of people with depression, including an analysis of the US National Co-morbidity Survey, which showed that having a comorbid (alcohol or non-alcohol) disorder was associated with an increased likelihood of service utilisation (Wu et al, 1999). In the sampled populations that made up the LIDO study, however, we did not find a consistent trend in terms of the impact of psychiatric comorbidity on costs. This may be attributable in part to the limited measurement of these comorbidities in the present study.
Economic burden of untreated depression in primary care
An important outcome of this research has been the generation of detailed
resource utilisation and costs data in a number of culturally diverse primary
care settings, based on a common methodology and accompanying protocol. Such
data are not only valuable within the national contexts of participating study
sites, but are also potentially informative at an international level of
comparison. Using purchasing-power parities to convert total health care
consumption per subject into US dollars, for example, reveals that the
economic burden of currently untreated depression in primary care either
approaches or exceeds average per capita health care expenditures
(World Health Organization,
2001) in four of the six study sites. This economic burden is
substantially increased if the cost of lost work days is also included; 3.7
work days on average (inter-site range: 1.58.0) were lost for the total
baseline sample in the 3-month period prior to baseline assessment, at a
converted cost of $225 per subject. In addition to these whole days of lost
work, but not measured here, so-called cut-back days are a
further important source of lost productivity in the working population
(Kessler & Frank, 1997).
These estimates may diverge from that estimated for a population of treated
primary care attenders; however, the follow-up of these subjects at 9 months
suggests that costs remain quite similar overall (additional
depression-specific treatment costs are offset by reduced work days lost and
health care consultations), in part because only a modest proportion of
subjects received treatment (Simon et
al, 2002).
Health system disparities and the challenges of cross-cultural health
services research
In spite of the consistent methodology used, we see a marked disparity in
terms of resource costs associated with health care utilisation and lost work
days, most notably in St Petersburg, where a forbidding combination of
societal stigma, health system reform, low health professional salaries and
financial barriers to access at the user level means that our estimated health
care costs are not just relatively but also absolutely low. Such fundamental
differences in health system characteristics present a major challenge to
multicultural studies that seek to measure the costs or cost-effectiveness of
mental health care. In the LIDO study, a deliberate attempt was made to
collect data relating to modes of health care financing and provision as well
as perceived barriers to access (Chisholm
et al, 2001a). However, the resulting site-level
disparity required us to focus more on site-specific rather than pooled
analyses, in order to determine whether there were similar cost trends
such as a proportionate increase associated with medical comorbidity
across the six diverse primary care settings. One drawback of such a
site-specific analytical strategy is the loss of analytical power that would
be available for pooled analyses. Even with samples of more than 300 subjects
per site, and despite the magnitude of certain cost differences between the
four subgroups, results did not generally reach statistical significance at
the 10% level. Such non-significant findings are in part attributable to the
fact that all subjects at baseline assessment had depressive symptoms
(CESD > 16), but are also determined by the skewed distribution of
resource utilisation rates and costs, which is a common feature of these types
of data (Sturm et al,
1999).
Cross-sectional analyses such as these have inherent restrictions, notably the absence of follow-up assessment that allow the examination of longitudinal relationships among resource costs, work absences and depression outcomes. Examination of these prospective associations was a further objective of the LIDO study and the results are reported elsewhere (Simon et al, 2002). Our hope is that the economic investigations undertaken as part of this observational study collectively lead to improved understanding, over time and across cultures, of the complex interaction among depression symptoms (alone and in combination with other morbidity), economic costs and treatment outcomes. Such insights into the current, largely untreated burden of depression will, we hope, stimulate greater efforts to develop cost-effective, primary-care-based interventions for depressive disorders and their associated comorbidities.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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The research team: Donald Patrick (University of Washington, Seattle, WA, USA); Don Buesching, Carol Andrejasich & Michael Treglia (Eli Lilly and Company, Indianapolis, IN, USA); Mona Martin & Don Bushnell (HRA Inc., Seattle, WA, USA); Diane Jones-Palm (HRA, European Office, Frankfurt, Germany); Stephen McKenna (Galen Research, Manchester, UK); and John Orley & Rex Billington (World Health Organization, Mental Health Division, Geneva, Switzerland).
Study advisors: Greg Simon (Group Health Cooperative of Puget Sound, Seattle, WA, USA); Daniel Chisholm & Martin Knapp (Institute of Psychiatry, London, UK); Diane Whalley (Galen Research, Manchester, UK); Paula Diehr (University of Washington, Seattle, WA, USA).
Site investigators: Helen Herrman (University of Melbourne, Australia); Marcelo Fleck (Federal University of the State of Rio Grande do Sul, Brazil); Marianne Amir (Ben-Gurion University, Be'er Sheva, Israel); Ramona Lucas (Barcelona, Spain); Aleksandr Lomachenkov (Bekhterev Psychoneurological Research Institute, St Petersburg, Russia); and Donald Patrick (University of Washington, Seattle, WA, USA).
We thank the following local site economists, who provided valuable assistance in the collation of health system and cost data: Ismail Budhiarso (Seattle), Dan Greenberg (Be'er Sheva), Igor Krasilnikov (St Petersburg), Julia Monserrat (Barcelona), Jeff Richardson (Melbourne) and Roger dos Santos Rosa (Porto Alegre). We also thank Ismail Budhiarso and Don Bushnell (HRA, Seattle) for data management and analysis support.
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Received for publication April 23, 2002. Revision received October 11, 2002. Accepted for publication October 31, 2002.
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