Department of Health System Financing, Expenditure and Resource Allocation,WHO and Department of Mental Health and Substance Abuse, WHO
School of Public Health, Queensland University of Technology, Australia
Department of Psychiatry, Hospital Universitario de la Princesa,Universidad Autonoma de Madrid, Spain
Department of Mental Health and Substance Abuse,WHO,Geneva
Correspondence: Dan Chisholm,CEP Team (Room 3169), Department of Health System Financing, Expenditure and Resource Allocation, Evidence and Information for Policy,World Health Organization, 1211 Geneva, Switzerland; e-mail: ChisholmD{at}who.int
See pp.
386392 and
editorial, pp.
379380, this
issue.
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ABSTRACT |
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Aims To estimate the population-level cost-effectiveness of evidence-based depression interventions and their contribution towards reducing current burden.
Method Primary-care-based depression interventions were modelled at the level of whole populations in 14 epidemiological subregions of the world. Total population-level costs (in international dollars or I$) and effectiveness (disability adjusted life years (DALYs) averted) were combined to form average and incremental cost-effectiveness ratios.
Results Evaluated interventions have the potential to reduce the current burden of depression by 1030%. Pharmacotherapy with older antidepressant drugs, with or without proactive collaborative care, are currently more cost-effective strategies than those using newer antidepressants, particularly in lower-income subregions.
Conclusions Even in resource-poor regions, each DALYaverted by efficient depression treatments in primary care costs less than 1 year of average per capita income, making such interventions a cost-effective use of health resources. However, current levels of burden can only be reduced significantlyif there is a substantialincrease substantial increase intreatment coverage.
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INTRODUCTION |
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METHOD |
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Setting
The 192 member states of the WHO were divided into five mortality strata on
the basis of their levels of child and adult mortality
(World Health Organization,
2002). When these mortality strata were applied to the six regions
of the WHO, they gave rise to 14 epidemiologically defined subregions
(Table 1). Costs and effects of
key depression interventions were modelled at the level of the total
population in these subregions, and are provided in a way that allows for
contextualised analyses by country-level analysts.
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Population model for depression
Intervention effectiveness was determined through a state transition
population model (PopMod; Lauer et
al, 2003), which traces the development of a subregional
population taking into account births, deaths and the disease in question.
Susceptibles (i.e. persons not currently depressed) become cases at an
instantaneous transition rate i (incidence, including recurrence);
persons with a depressive episode go back to being susceptible at remission
rate r; cases are subject to the instantaneous case-fatality rate
f; and both susceptibles and cases are subject to a general mortality
rate m. For all hazard rates, units are the number of events per year
at risk. The model distinguishes male and female populations, each segmented
into 1-year age groups. In addition, a disability weight or health state
valuation is specified (on a 01 scale, where 1 equals full health) for
time spent in the diseased state and also for time spent susceptible but not
depressed.
The population model was run for two scenarios over a lifetime analytic horizon (100 years, by which time a steady state or equilibrium has been reached), to give the total number of healthy years lived by the population. The first scenario was an epidemiological situation representing the natural history of depression (no depression interventions in place), and the second was an epidemiological situation reflecting the population-level impact of each specified intervention (such as reduced illness duration resulting from use of an antidepressant drug), implemented for a period of 10 years (thereafter, epidemiological rates and health state valuations move back to natural history values). The difference between these two simulations represents the population-level health gain (the DALYs averted) resulting from the implementation of the intervention over a 10-year period, relative to the situation of doing nothing. In line with the GBD 2000 study, DALYs averted per year were discounted (at 3%) and age-weighted in the base case analysis.
Natural history of ICD10 depressive episode
Depression was modelled as an episodic disorder (ICD10 code F32, 33;
World Health Organization,
1992) with a high rate of remission (recovery) and subsequent
recurrence, and with excess mortality from unnatural causes (suicide). Cases
of dysthymia were excluded. Comorbidity was incorporated into the
epidemiological estimates underlying the population model by adopting the
strategy employed in the GBD 2000 study
(Üstün et al,
2004, this issue); namely, only counting the case in the condition
with the more severe disability (depressive episode) and subtracting that case
from the prevalence figure of the other conditions (most notably, anxiety
disorders and substance misuse). Using GBD 2000 disability weights for
different severity levels, the composite health state valuation (HSV) for an
untreated depressive episode was calculated as a weighted average of 0.62,
where 1 equates to full health, giving 23% severe, HSV=0.24; 47% moderate,
HSV=0.65; and 30% mild, HSV=0.86.
Point prevalence and duration for depressive episodes in different subregions were drawn from GBD 2000, based on an extensive international review of epidemiological studies (Üstün et al, 2004, this issue; see also Table 1). Incidence and remission rates were derived with reference to prevalence and duration as follows:
Case fatality rates were based on a lifetime suicide risk for affective disorders of 6% among adults aged over 15 years (Inskip et al, 1998), with incident proportions subsequently converted into instantaneous rates. Because of a higher risk of mortality at younger ages, this rate was adjusted up to 9% for age groups between 15 and 45 years and reduced to 3% for age groups over 45 years. Consistent with a meta-analysis by Harris & Barraclough (1998), no excess risk of mortality from natural causes was attributed. Detailed tabulation of data sources and model inputs can be found for each subregion on the WHO-CHOICE website (http://www.who.int/evidence/cea).
Effectiveness of interventions
The expected population-level impacts of seven (self-standing or combined)
primary-care-based interventions capable of being implemented in different
regions of the world were assessed:
Episodic treatment regimens for antidepressant pharmacotherapy and brief psychotherapy (interventions ae) followed guideline-level therapeutic dosages or number of sessions over the average duration of an untreated episode. Maintenance treatment for recurrent depression was incorporated into a proactive collaborative care strategy (interventions f and g), which pursues a multifaceted disease management protocol that seeks to increase conformity with evidence-based guidelines through patient education and enhanced primary care clinician support (Katon et al, 2001; Simon et al, 2001).
The main modelled impact of intervention targeted at episodic treatment of a new depressive episode was a reduction in the duration of time depressed, equivalent to an increase in the remission rate (Table 2). Remission rates under treatment, ranging from 2.42.5 for psychotherapy to 2.72.8 for collaborative care, were based on pragmatic trials that reported the proportion of study subjects recovered at time intervals, which could be used to calculate a duration and converted into an instantaneous remission rate (Solomon et al, 1997; Thase et al, 1997; Malt et al, 1999; Chilvers et al, 2001; Katon et al, 2001). Brief psychotherapy was modelled to have a slightly lower rate of remission than pharmacotherapy because the onset of effect is not as rapid for more severe depression (Thase et al, 1997). No difference was found for combined drug and psychosocial strategies using older v. newer antidepressants. Following recent studies that indicate larger treatment effect sizes for both single and combined interventions in developing countries (Araya et al, 2003; Bolton et al, 2003; Patel et al, 2003), a modest advantage in treated remission rates was ascribed to developing subregions. In addition, all interventions were attributed a moderate improvement in the disability level or health state valuation of an unremitted depressive episode (1318%), resulting from increased proportions of cases moving from more to less severe health states (Table 2). Temporal symptom severity profiles for unremitted episodes were informed by a commissioned analysis of the Pittsburgh 600 data-set assembled from six research projects conducted between 1982 and 1992 (M. A. Dew, personal communication, 2001).
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No intervention effect was attributed to the incidence of first episodes. However, for the estimated 56% of prevalent cases eligible for maintenance treatment (at least two lifetime episodes), the additional impact of efficacious maintenance treatment was incorporated into the proactive collaborative care strategies by reducing the incidence of recurrent episodes by 50% (i.e., an absolute risk reduction in recurrence of 0.50; Geddes et al, 2003). None of the selected depression interventions was credited with resulting in a reduction in case fatality, owing to the lack of robust clinical evidence that antidepressants or psychotherapy per se alter the relative risk of death by suicide (Storosum et al, 2001).
Estimates of efficacy obtained from clinical trials were adjusted to better reflect outcome in the real world, specifically with reference to treatment coverage, partial response and adherence (Table 2). Given the modest care-seeking and recognition rates observed in international studies of depression and other common mental disorders, a 50% target coverage rate was adopted for all subregions. Recent meta-analyses have reported adherence rates of 70% for TCAs and 73% for SSRIs (Barbui et al, 2002), and higher rates still for cognitive therapy (Gloaguen et al, 1998), but these can be viewed as upper limits given the controlled research environment within which source studies were conducted. Accordingly, these adherence rates were adjusted downwards by a further real world factor of 0.60.75 to give an overall level of adherence of between 45% and 55%.
Costs of interventions
Costs were considered at the patient level and the programme level.
Programme-level costs included central administration and training, with an
estimate of 23 days per trainee used for training primary care doctors
and case managers in the management of depression, whereas 10 days of initial
training (including role play) and 2 days of supervision per year were
allocated for psychosocial treatments
(Dowrick et al, 1998).
Patient-level resource use profiles per 6-month treatment period were
generated for each severity category of depressive episode, based on data from
prospective studies (Chisholm et
al, 2000; Katon et
al, 2001; Simon et
al, 2001; Patel et
al, 2003) and also informed by a dedicated multi-country
Delphi consensus study of resource use in developing countries
(Ferri et al, 2004).
Resource use components included, as applicable, drug dosage and frequency
(e.g. 20 mg fluoxetine daily); brief psychotherapy (68 sessions); case
management (46 contacts); primary care (36 visits); psychiatric
out-patient care (3366% of cases, 46 visits); and in-patient
stays (515% of moderatesevere cases, 12 weeks). The
severity-weighted estimate for each resource component was then multiplied by
the subregion-specific unit cost of the service, to give a mean cost per
treated episode.
Unit costs of primary and secondary care services were derived from an econometric analysis of a multinational data-set of hospital costs, using gross national income per capita (plus other explanatory variables) to predict unit costs in different WHO subregions (Adam et al, 2003). For the costs of antidepressant medication, supplier prices for generically produced drugs (e.g. $0.01 per 25 mg amitriptyline or imipramine tablet, equivalent to $0.030.05 per daily dose) were obtained from the International Drug Price Indicator Guide for the year 2000 (http://erc.msh.org/dmpguide), with deviations from the baseline price for SSRIs assessed by sensitivity analysis (e.g. unit prices of $0.10 and $1.00 per 20 mg fluoxetine were considered alongside a baseline value of $0.25, reflecting expected variations in both the extent of government bulk purchasing for primary care providers and also the availability of generic, rather than branded, products).
Mean costs per episode were multiplied by the number of treated episodes in the subregional population (at a coverage rate of 50%), to give a total cost of care for 1 year of implementation. Fully worked resource profiles and cost templates for all interventions in each subregion can be found on the WHO-CHOICE website (http://www.who.int/evidence/cea/region/region). All baseline analysis costs for the 10-year implementation period were discounted at 3% and expressed in international dollars (I$), which adjust for differences in the relative price and purchasing power of countries and thereby facilitate interregional analysis. That is, I$1 buys the same quantity of health care resources in China or India as it does in the USA.
Uncertainty analyses
First, a series of one-way sensitivity analyses that assessed the impact on
final cost-effectiveness analysis results of discounting and age-weighting
were performed. Second, best- and worst-case scenarios incorporating upper and
lower values for key drivers of cost (unit price of SSRI drugs and health care
services, the proportion of individuals using secondary services) and
treatment effectiveness (efficacy and adherence) were generated. Third,
baseline data (with pessimistic and optimistic scenarios as lower and upper
ranges) were entered into an analytical software package (MCLeague;
Tan Torres et al,
2003), which performs a probabilistic uncertainty analysis using
Monte Carlo simulation (2000 runs were made, using a truncated normal
distribution).
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RESULTS |
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Costs and cost-effectiveness of interventions
Patient-level costs per treated episode are shown in
Table 4. As expected, there is
considerable variation in the average cost of a treated episode, both between
subregions and also between interventions. The lowest patient-level costs per
treated episode relate to older antidepressants, ranging from I$5080 in
high-mortality developing subregions (AfrD, AfrE, AmrD, EmrD, SearD) to
approximately I$400 in the most economically developed subregions (AmrA, EurA,
WprA). At the other end of the cost spectrum, the average cost per treated
episode for proactive collaborative care with newer generic antidepressants
(intervention g) ranges from I$130150 in high-mortality developing
regions to I$700750 in developed subregions. Programme-level costs
accounted for only 110% of total costs, with the highest proportion
applicable to brief psychotherapy because of more intensive training needs.
Total intervention costs per year for each subregion are reported in
Table 4, which illustrates
further the wide variation in costs, as a function of both differential price
levels and population size. The differential cost of care between
pharmacological interventions with TCAs v. SSRIs is greater in
lower-income regions than in industrialised subregions, a consequence of the
relatively high price payable in low-income subregions for newer
antidepressant drugs (to illustrate, one newer antidepressant tablet bought at
a price of $1 would be equivalent to 25% of the cost of an out-patient visit
in African subregions, compared with 2% in North America).
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Relative to the natural history of depression, and before allowance for model uncertainty, the most cost-effective stand-alone intervention in all subregions was pharmacotherapy with older antidepressants (cost per DALY averted: I$7001000 in high-mortality developing subregions AfrD, AfrE, AmrD, EmrD, SearD; I$11001800 in low-mortality developing regions AmrB, EmrB, SearB, WprB; and I$16001700 in developed subregions AmrA, EurA, EurB, EurC, WprA). Across the 14 subregions, newer antidepressants had an average cost-effectiveness ratio (CER) in the range I$10007000, which resulted in an incremental CER of I$75009000 for moving from older to newer antidepressants (Table 4). The incremental CER for psychotherapy alone v. older antidepressants was more variable (reflecting differences in psychotherapists salaries), ranging from I$60007000 in high-mortality developing subregions to nearly I$50 000 in developed subregions with very low rates of child and adult mortality. The most cost-effective combination strategy was proactive collaborative care with older antidepressants (incremental CER: I$16501850 in high-mortality developing subregions; I$20003000 in low-mortality developing subregions; I$230014 000 in developed subregions). In all subregions, the incremental CER of pharmacotherapy with older antidepressants and with the exception of the lowest-income subregions, proactive collaborative care with older antidepressants too was considerably less than average yearly income per capita, which is an international threshold value recently proposed for accepting an intervention as very cost-effective (Commission on Macroeconomics and Health, 2001).
Uncertainty analysis
Summary findings of a series of one- and multi-way sensitivity analyses are
presented in Table 5.
Substitution of the baseline discount rate of 3% with values of 0% and 6%
altered total costs and average CERs for all interventions by 14% and
11%, respectively. The removal of age-weighting had a more significant
impact on results, reducing total health gain estimates by 1625% across
subregions (resulting in a corresponding increase of 1934% in average
CERs). Under the best-case scenario (see
Table 5 for details), total
costs were 3050% lower and total effects 2430% higher than base
case results, thereby lowering the average cost per DALY averted by
5060%. Results for the worst-case scenario were more extreme, with
respective increases of 4590% and 110220% in the average cost
and cost-effectiveness of interventions using older antidepressant drugs, and
even larger changes for interventions with newer antidepressants because of
the significantly higher drug price. To illustrate, the average CER for
pharmacotherapy with SSRIs in the Western Pacific subregion WprB baseline
value: I$1560) ranged from I$600 to I$7000. Under the best-case scenario, the
rank order of cost-effectiveness was unchanged in all but the three
high-income subregions (AmrA, EurA and WprA, where generic SSRIs become the
most cost-effective strategy); under the worst-case scenario, the combined
approach of TCAs plus proactive care became the single most cost-effective
strategy in Eastern Europe (subregions EurB and EurC), but elsewhere the rank
order was preserved.
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Finally, by entering costs and effectiveness data into a stochastic uncertainty framework, it is possible to assess the likelihood of each intervention being considered cost-effective at different levels of resource availability. Figure 1 provides a graphical display of these competing probabilities in the South-East Asian subregion SearD. In this subregion, pharmacotherapy with older antidepressants is the cost-effective choice when resources are very restricted (other interventions exceed the available budget), but at higher resource levels the probability is reduced as other single or combined interventions, including psychotherapy and proactive collaborative care, become candidates for inclusion (for example, proactive collaborative care with older antidepressants becomes most likely to be cost-effective once the level of resource availability is quadrupled). Similar in concept to cost-effectiveness accept ability curves, this approach to uncertainty analysis provides decision-makers with information on the most economically feasible strategies for reducing the current burden of depression over the short- and longer-term, while acknowledging the inherent imprecision underlying baseline results.
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DISCUSSION |
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Depression care and health sector efficiency
Using the criteria of the Commission for Macroeconomics and Health
(2001), the results of this
study indicate that implementation of efficient depression interventions in
primary care settings would be very cost-effective (each DALY averted costs
less than 1 year of average per capita income). These findings therefore
provide relevant new information to health policy makers regarding the
relatively good value of investing in depression treatment, and in so doing
could help to remove one of many remaining barriers to a more appropriate
public health response to the burden of common mental disorders.
Comparison of results with other cost-effectiveness analysis studies of depression that have employed population-level approaches to health measurement is limited, although one study of a quality improvement programme from North America (Schoenbaum et al, 2001) reported an incremental cost per quality-adjusted life year of $950036 000 for medication and psychotherapy regimens over usual primary care, which is in the same range as that given here for subregion AmrA. Estimated depression-free days (1828 additional days per 6-month treatment period compared with no treatment) were also in line with other studies that have used this metric. Simon et al (2001), for example, report an incremental gain of 12.616.7 depression-free days for collaborative care over usual primary care. When considered alongside the cost-effectiveness of other interventions evaluated by WHO-CHOICE to date, interventions for depression are in the same range as treatment strategies for reducing hypertension or cholesterol levels (World Health Organization, 2002), suggesting that evidence-based interventions for depression could have just as much a claim on scarce health resources as those for other chronic, non-communicable conditions that impose a significant burden on societies.
Reducing the global burden of depression
In terms of global effectiveness, evaluated interventions can potentially
avert between 7 and 14 (out of 65) million DALYs, yet by expressing these
health gains as a proportion of the current (and very largely untreated)
burden of depression ranging from 10% to 30% it is evident
that these technologies have a limited impact at the population level, even at
a target coverage rate in excess of that prevailing now in most regions. A
similar finding was reached in a recent analysis for Australia, where an
estimated 22% of the burden of depression is currently being averted by
specific treatment, and only 45% of the current burden would be avoided even
at a 100% effective coverage rate (Andrews
et al, 2000). Over and above health system challenges
such as increased access or coverage, there is therefore an evident need to
increase the capability or efficacy of pharmacological and psychosocial
treatments to resolve depressive symptoms promptly, as well as to avert their
occurrence or recurrence through the development of effective community-based
prevention and promotion strategies.
Interregional variation in the cost-effectiveness of interventions
A central debate in the health economics of depression concerns whether the
higher acquisition costs of newer antidepressants are offset by greater
compliance and reductions in use of health care and other services
(Barbui et al, 2002).
The results from this analysis, in which a small advantage for SSRIs in terms
of adherence and disability improvement was modelled, suggest that a
cost-offset cost-offset hypothesis currently has limited pertinence in
low-income subregions, since the higher acquisition price of generic SSRIs
increases total costs of care substantially (if branded newer antidepressants
were used, the costs would be far higher still). Consequently, the baseline
incremental CER of I$75009000 for moving from older to newer
antidepressants constitutes a relatively cost-ineffective use of resources in
resource-poor subregions, whereas in the most industrialised subregions such a
ratio could easily be justified on efficiency grounds. However, and as
examined in the best-case scenario analysis, this situation can be expected to
change as the price of generic SSRIs falls, as it has already done in
countries such as India (the incremental CER for all subregions falls below
I$2000).
By contrast, and not withstanding the severe current shortage of training, lower salaries make the use of brief evidence-based psychotherapy a potentially more attractive treatment alternative to older antidepressants in developing regions compared with high-income regions. Finally, there appear to be good grounds for thinking that proactive care strategies incorporating maintenance treatment offer a cost-effective option in all regions, as a significant reduction in the incidence of recurrent episodes (plus increased adherence) is achieved at a moderate additional cost (follow-up by a case manager).
Limitations of the population-based modelling approach
This analysis is constrained in a number of important respects. First, the
use of epidemiological subregions as the unit of analysis is a compromise
between a global level of aggregation and country-by-country assessment.
Because policies are implemented by individual countries, there is a clear
requirement to contextualise subregional estimates down to this level, in
particular adjusting results for local variations in epidemiology, clinical
effectiveness, service use patterns and unit costs. Such a process is now well
under way in a number of countries as part of the WHO-CHOICE programme,
results from which will provide an important test of the validity of the
models used here. Second, the analysis did not confront the complex issue of
comorbidity in depression, other than by including individuals for whom the
co-occurring illness had a lower level of disability than the depression (e.g.
anxiety disorders). Because treatment response might be slower in comorbid
cases, as well as more costly to obtain, this is a potential source of
overestimation of effectiveness and cost-effectiveness, which future revisions
might be able to correct for as more knowledge on the costs and effects of
treatments for comorbid depression becomes available.
Third, the population and costing models rest upon a series of best estimates, including the average duration of depressive episodes (plus related GBD 2000 parameters), expected patterns of resource use and, perhaps most importantly, estimates of intervention efficacy. Efficacy estimates were drawn mainly from trials undertaken in industrialised countries, although recent controlled trials from India, Uganda and Chile found treatment effects at least as large as studies carried out in the USA and UK for antidepressant therapy, group psychotherapy and proactive care respectively (Araya et al, 2003; Bolton et al, 2003; Patel et al, 2003). Uncertainty analyses can help in assessing the sensitivity and robustness of baseline estimates, and showed that whereas absolute values could deviate from base case findings by as much as half or more than double, the prevailing pattern or rank order of intervention cost-effectiveness was preserved in all low- and middle-income subregions. However, there clearly remain important questions around, for example, the transferability and cultural sensitivity of structured psychotherapies to regions as diverse as Africa, Asia and Latin America.
Finally, and despite the pursuit of a societal perspective, considerable challenges in the international measurement of productivity gains and of patient and informal carer time spent seeking or providing care have precluded their valuation in the present analysis (Tan Torres et al, 2003). Incorporation of these wider costs and consequences, however, would be expected only to enhance the cost-effectiveness of depression interventions (Chisholm et al, 2000; Patel et al, 2003).
From economic evidence to mental health service development
Evidence for the comparative cost-effectiveness of interventions for
depression provides only one input into the decision-making process. Mental
health policy makers also need to address a series of issues beyond which
interventions to choose purely from an efficiency perspective, most notably
how to increase access to services of sufficient quality to ensure both the
continuity of and adherence to these effective treatments. Moving from
todays very modest level of effective treatment coverage to one that
can make a significant impression on the existing burden of depression will
require political commitment, public awareness campaigns and investment in
health professionals working in primary and mental health care.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication July 18, 2003. Revision received December 2, 2003. Accepted for publication December 15, 2003.
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