Department of Health System Financing, and Department of Mental Health and Substance Abuse
Department of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland
Department of Psychiatry, Hospital Universitario de la Princesa, Universidad Autonoma de Madrid, Spain
Department of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland
Correspondence: Dr Dan Chisholm, Department of Health System Financing, Evidence and Information for Policy (EIP), World Health Organization, 1211 Geneva, Switzerland. E-mail: ChisholmD{at}who.int
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Aims To estimate the cost-effectiveness of interventions for reducing the global burden of bipolar disorder.
Method Hospital- and community-based delivery of two generic mood stabilisers (lithium and valproic acid), alone and in combination with psychosocial treatment, were modelled for14 global sub-regions. A population model was employed to estimate the impact of different strategies, relative to no intervention. Total costs (in international dollars (I$)) and effectiveness (disability-adjusted life years (DALYs) averted) were combined to form cost-effectiveness ratios.
Results Baseline results showed lithium to be no more costly yet more effective than valproic acid, assuming an anti-suicidal effect for lithium but not for valproic acid. Community-based treatment with lithium and psychosocial care was mostcost-effective (cost per DALY averted: I$2165-6475 in developing sub-regions; I$5487-21123 in developed sub-regions).
Conclusions Community-based interventions for bipolar disorder were estimated to be more efficient than hospital-based services, each DALY averted costing between one and three times average gross national income.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
METHOD |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Setting
The 192 member states of the WHO were divided into five mortality strata
based on child and adult mortality rates
(World Health Organization,
2001). When these strata were applied to the six WHO regions, they
gave rise to 14 epidemiologically defined sub-regions
(Table 1). Intervention costs
and effects were modelled at the total population level in each sub-region and
have been derived in a way that allows for contextualised analyses at the
country level.
|
Population model for bipolar disorder
Intervention effectiveness was determined via a state transition population
model (PopMod; Tan Torres et al,
2003). Key transition rates include the incidence of bipolar
disorder in the population, case fatality and remission (defined as full
recovery of a case). In addition, a disability weight is specified (on a
01 scale, where 0 equals no disability) for time spent in different
mood states.
People with bipolar disorder are modelled to live in one of three health states: (a) manic episodes, (b) depressive episodes, or (c) relatively euthymic health states during which persons are nonsymptomatic or symptomatic below the threshold of a manic or depressive episode. In our model, treatment has two possible effects: (a) a change in the distribution of time spent in each state (treated cases spend more time in the intermittent health state and thus experience less disability) and (b) a change in the case fatality rate (reduced suicide). Interventions have no effect on rates of incidence (i.e. onset of bipolar disorder is not prevented) or remission (i.e. the average duration of a case is not reduced).
Using a lifetime analytical horizon, but a 10-year treatment implementation period, population-level effects were derived by comparing number of healthy years lived by the population with and without intervention. The difference between these two simulations represents the population-level health gain (disability-adjusted life years (DALYs) averted) resulting from intervention, relative to the situation of doing nothing. In the base case analysis, nonuniform age weights (which give less weight to years lived at young and older ages) and a 3% discount rate were used, with the impact of these social preferences evaluated via sensitivity analysis.
Natural history of ICD10 bipolar disorder
The Global Burden of Disease Study (GBD 2000) has generated age- and
gender-specific data on the prevalence, incidence and case fatality of persons
with bipolar disorder for different regions
(http://www.who.int/evidence/bod;
Ayuso-Mateos, 2001). Prevalence
rates for bipolar disorder from the GBD 2000 are shown in
Table 1. Since the onset of
bipolar disorder is not preventable by health intervention, current incidence
coincides with natural (untreated) history. Remission was calculated based on
data from Angst & Preisig
(1995), who reported a 16%
remission rate defined as being episode-free for 5 years after
an average follow-up period of 21 years, equivalent to a yearly rate of less
than 1%. Case fatality rates were calculated based on a standardised mortality
ratio of 2.5, a weighted average from four natural history studies for the
pre-lithium treatment era (e.g. Helgason,
1964; others listed under Table 13a in
Harris & Barraclough,
1998).
For the disability weight of untreated bipolar disorder, similar assumptions to those employed by GBD 2000 were used, namely applying the same Dutch disability weight for a manic episode as for psychosis (0.72, where 0 equals no disability), and likewise for a (severe) depressive episode (0.76). A valuation of 0.14 for the intermittent state of euthymia was taken to be equivalent to mild depression (Ayuso-Mateos, 2001). Baldessarini & Tondo (2000) found that 360 people with bipolar disorder spent almost 50% of their time manic or depressed before receiving treatment, and Judd et al (2002) provided data on the amount of time that people with the disorder spent in depressive v. manic episodes (a ratio of 3:1). The composite disability weight of untreated bipolar disorder was therefore calculated to be 0.445 (Table 2).
|
Estimation of intervention effectiveness
Analyses were limited to first-line interventions. In strict terms, only
the conventional mood-stabilising drug lithium meets the criteria for proven
efficacy in the acute and prophylactic treatment of both manic and depressive
episodes (Bauer & Mitchner,
2004), but these strict criteria were relaxed at least to include
a comparator drug for which evidence exists for a prophylactic effect on both
manic and depressive episodes (valproic acid;
Bowden et al, 2000).
In addition, the literature indicates that psychosocial approaches enhance
adherence to medication (Huxley et
al, 2000; Gonzalez-Pinto
et al, 2004) and potentially affect longer-term
improvements in functioning (e.g. Colom
et al, 2003). Owing to the restricted level of evidence
for these longer-term outcomes (intensive treatment regimens tested within
specialist study settings in high-income countries), effects of psychosocial
treatment were confined to improved adherence.
Table 2 documents the reduced duration of time spent in a manic or depressed state due to acute and prophylactic treatment (resulting in lower disability), first under optimal conditions (efficacy), then adjusting for adherence and treatment coverage to derive an estimate of population-level effectiveness. An acute treatment effect was calculated as the product of response rate and reduced episode duration. Similar to Goodwin & Jamison (1990), a slightly higher weighted response rate was found for patients in manic episodes treated with valproic acid than lithium (58.1 v. 55.0%) but a much lower response rate for patients in depressed episodes (38.2 v. 66.7%) (23 source references available from authors on request). The average length of untreated episodes of mania and depression is estimated to be 4 and 5 months, respectively (Angst & Sellaro, 2000). Therefore an initial response (within 1 month) will reduce by 75% the time spent in mania and by 80% the time spent depressed. A prophylactic treatment effect was also ascribed: a longitudinal study of 360 people with bipolar disorder adherent to lithium treatment for at least 1 year observed a larger reduction of time spent in mania than depression (61 v. 53%; Tondo et al, 2001a), and Bowden et al (2000) found a trend favouring a longer time before relapse for valproic acid (median=275 days) compared with lithium (median=189 days). Given a ceiling effect of 1-year follow-up, small sample sizes and exclusion of severe cases, the implied 45% difference in time to relapse is potentially overstated, and accordingly a 10% increased efficacy of valproic acid over lithium in lengthening time to relapse was modelled (half and double this amount were assessed via sensitivity analysis).
A secondary effect of treatment reduction of the case fatality rate was also ascribed to lithium (though not to other treatments in the base-case analysis, owing to a current absence of evidence; Goodwin et al, 2003). An optimistic estimate comes from a multicentre study by Wolf et al (1996), who derived a standardised mortality ratio of 1.1 (natural and unnatural causes of death) for 827 patients treated in lithium clinics over an average period of 7 years. A less optimistic estimate because it includes studies from the prelithium era comes from the meta-analysis by Harris & Barraclough (1998), who found a standardised mortality ratio of 2.0 for both natural and unnatural causes (1.5 for natural causes only; 9.2 for unnatural causes only), which is consistent with a review of 22 studies by Tondo et al (2001b). A standardised mortality ratio of 1.5 was used in our base-case analysis for people with bipolar disorder treated with lithium, corresponding to a 65% reduction in the instantaneous rate of case fatality. Variations from these estimates were examined via sensitivity analysis.
Changes in disability and case fatality require adjustment for intervention coverage, as well as rates of adherence to intervention. Based on the significant underrecognition and consequent treatment gap for bipolar disorder, a target coverage rate of 50% was set for all sub-regions (Kohn et al, 2004). The review of efficacy studies by Goodwin & Jamison (1990) found adherence rates for lithium to fall in the range 4772%. We considered the upper end of this range to reflect rates found in controlled trial settings and the lower end to be a closer estimate of realworld effectiveness. Since our estimates of efficacy already incorporate an element of non-adherence non-adherence (about 30%, see above), we apply a real-world adjustment factor (of two-thirds) to obtain a more representative estimate of real-world adherence to lithium (e.g. 70%x66%=47%). The literature suggests that adherence to valproic acid is better than for lithium (e.g. Emilien et al, 1996; Bowden et al, 2000); we therefore set the rate for valproic acid 10% higher than for lithium. Following the reviews of Huxley et al (2000) and Gonzalez-Pinto et al (2004), we also applied a modest improvement of 10% in adherence for combined interventions incorporating a psychosocial component.
Estimation of intervention costs
Two service models were evaluated, a hospital-based in-patient model and a
community-based out-patient model. Patient-level resource inputs for an
average patient with bipolar disorder were weighted according to
time spent in manic, depressed or intermittent states, based on earlier
empirical or modelling studies (developed countries;
Frye et al, 1996;
Keck et al, 1996) and
on a multinational Delphi consensus panel (developing countries;
Ferri et al, 2004).
Annual expected resource requirements which did not vary between
regions because the same level of effective coverage is being modelled
included daily drug supply (e.g. 1200 mg lithium carbonate), blood monitoring
and other tests (monthly for lithium treatment, every 2 months for valproic
acid), psychosocial support (eight sessions per year, where applicable),
monthly out-patient attendances and primary care attendances (2030% of
cases, with an average of six to eight visits). In-patient hospital and
residential care differed according to the service model: for the
hospital-based service model, 40% of depressive episodes and 4550% of
manic episodes were estimated to lead to an acute psychiatric admission,
average length of stay 2128 days (1020% of patients were
estimated to reside in longer-term psychiatric facilities); for the
community-based model, admission rates to acute psychiatric wards for
depression (15%) and mania (25%) were estimated to be lower, as were the
numbers of patients expected to require residential care support in
community-based housing (510%). Finally, a 10% reduction in the
expected need for admission for acute in-patient care for mania was estimated
for combination treatment v. pharmacology alone, and a 10% reduced
length of stay was modelled for valproic acid v. lithium.
Resource items were multiplied by respective sub-regional unit costs (Tan Torres et al, 2003; see WHOCHOICE website at http://www.who.int/evidence/cea) to give an annual cost per treated case, which was then applied to the 50% of cases in the population that are modelled to be exposed to the intervention strategies. Programme-level costs of central administration (planning, implementation, monitoring) and training (adaptation of guidelines, printing of materials) were also derived for each sub-region. All baseline analysis costs for the 10-year implementation period were discounted at 3% and expressed in international dollars (I$), which adjusts for differences in the purchasing power of countries and thereby facilitates comparison within and across sub-regions (i.e. I$1.00 buys the same quantity of healthcare resources in China or India as it does in the USA).
Uncertainty analysis
One-way sensitivity analyses were performed, first on the impact on final
cost-effectiveness analysis of analytical social preferences such as
discounting and age-weighting, and second on key drivers of cost (unit price
of healthcare services, proportion of patients using secondary services) and
treatment effectiveness (changes in mortality, disability and adherence).
Best- and worst-case scenarios were also generated; these incorporated the
combined impact of upper and lower values.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
Differences in the effectiveness of the four interventions in the base-case analysis are modest, but strategies using lithium generate marginally greater population-level health gain than those with valproic acid, on account of the additional impact of lithium on case fatality rates. Adjuvant psychosocial treatment provided in tandem with mood stabiliser drugs also improves outcomes by approximately 10%, reflecting the improved adherence modelled.
Intervention costs
Intervention costs, both per million total population and per treated case,
are presented in Table 4.
Hospital-based service models incur notably higher costs than community-based
service models (3550% in very low-income regions to as much as 70% in
high-income regions) as a result of differences in the expected use of acute
in-patient and longer-term residential facilities. The total programme- and
patient-level cost for interventions implemented via a community-based
out-patient model, in millions of international dollars per million population
(therefore equivalent to cost per capita), ranged from 0.85 to 1.78 in
high-mortality, developing sub-regions, 1.773.23 in low-mortality,
developing sub-regions and 2.7410.57 in developed sub-regions.
Corresponding baseline results per treated case were I$538998,
I$9251524 and I$11684187 per year, respectively.
|
Within sub-regions and service models, variations in intervention costs were very modest, which is attributable to the fact that additional costs of valproic acid over lithium (I$168 per year) and adjuvant psychosocial treatment were expected to slightly reduce the need for in-patient care. This modelled cost-offset effect is more pronounced in high-income sub-regions where the unit cost of an in-patient day is high, resulting in valproic acid becoming a marginally less expensive intervention strategy than lithium. In low-income sub-regions, lithium is estimated to be the cheaper option.
Intervention cost-effectiveness
When total population-level costs and effects are merged to produce average
cost-effectiveness ratios (Table
5), it becomes apparent that a community-based approach represents
a more efficient strategy than a hospital-based approach for addressing the
current burden of bipolar disorder (cost-effectiveness ratios are estimated to
be 2540% lower). Differences in cost-effectiveness ratio between
interventions are modest, but in all sub-regions the single most
cost-effective strategy for the base-case analysis is lithium with
psychosocial care, delivered within a community-based service framework, each
averted DALY costing I$21653830 in high-mortality, developing
sub-regions, I$39536475 in low-mortality, developing sub-regions and
I$548721123 in developed sub-regions. This is equivalent to averting
47182 DALYs per I$1 million expenditure in developed sub-regions,
154253 DALYs in low-mortality, developing sub-regions and 261462
DALYs in high-mortality, developing sub-regions.
|
Uncertainty analysis
Substitution of the baseline discount rate of 3% with values of 0% and 6%
altered total costs and average cost-effectiveness ratios for all
interventions by +14% and 711%, respectively. The removal of age-weighting had
a larger impact on results, reducing health gain estimates by 1123%
across sub-regions (resulting in an increase of 1330% in average
cost-effectiveness ratios).
One-way sensitivity analysis showed that cutting the impact of lithium on case fatality rates by half (from 65% to 32.5%, equivalent to a revised standardised mortality ratio of 2.0 compared with 1.5 for the base case) reduced DALYs averted by approximately 8%; attribution of a small anti-suicide effect for valproic acid (a reduction of 16%, standardised mortality ratio 2.25) increased total health gain by 4%. Either change is enough to remove the small baseline effectiveness advantage of lithium over valproic acid. Use of alternative disability weights for mania, depression and euthymia had no bearing on the relative effectiveness of different interventions (and only a small impact on absolute levels of health gain). Higher and lower values for the assumed prophylactic advantage of valproic acid over lithium likewise had a negligible impact. A more sensitive variable is adherence, where plausible variations in both the level of adherence to lithium (10%) as well as the expected size of adherence differentials between mood stabilisers and between monotherapy v. combination treatments (a lower value of 5% and an upper value of 15%, compared with a baseline difference of 10%) changed baseline effectiveness results by 1020%.
Best- and worst-case scenarios were derived for cost-effectiveness by according lower and upper 95% CIs to the unit costs of health services, the proportion of cases using secondary care hospital services (relative changes of 2050%, for example an admission rate of 30% rather than 20%) and number of psychosocial treatment sessions, in addition to the upper and lower values reported above for treatment response and adherence. Under the best-case scenario, total costs were 3147% lower for the hospital service model and 2037% lower for the community service model, total effects were 1839% higher (including a potential impact of valproic acid on suicide rates), thereby lowering the overall cost per DALY averted by approximately half. Results for the worst-case scenario were in the same range; in this case increases of close to 4575% in costs and 2330% less health gain led to average cost-effectiveness ratios 120150% higher than their baseline values. To illustrate, the expected cost per DALY for community-based lithium treatment in the Western Pacific sub-region WprB (baseline value I$4989) ranged from I$2771 to I$10 952. The principal finding from these multiway sensitivity analyses was that lithium-based treatments remain the cost-effective choice in high-mortality developing sub-regions, whereas in the three high-income sub-regions of America, Europe and the Western Pacific valproic acid alone or in combination with psychosocial treatment now produces the lowest cost per DALY averted (full details in spreadsheet format available from the authors on request).
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Comparative cost-effectiveness of interventions for bipolar disorder
The treatments analysed in this sectoral cost-effectiveness analysis of
interventions for bipolar disorder enabled three key comparisons: older
v. newer mood-stabilising drugs (lithium v. valproic acid);
combined pharmacotherapy and psychosocial care v. pharmacotherapy
alone; and hospital-based v. community-based service models. Valproic
acid was modelled to have a marginally greater prophylactic treatment effect
and better adherence than lithium, but a lower acute treatment effect for
depressive episodes and no effect on case fatality. It is the expected health
gain associated with a demonstrated impact on suicide rates
(Tondo et al,
2001b; Goodwin et
al, 2003) that suggests lithium is a more beneficial and no
more costly population-level treatment compared with valproic acid. Adjuvant
psychosocial treatment alongside use of mood-stabilising drugs is expected to
improve the cost-effectiveness of treatment for bipolar disorder, as a result
of improved adherence (which in effect reduces the proportion of time spent in
a manic or depressed state), and because the additional costs of psychosocial
treatment are largely offset by a reduced probability of admission to
hospital. Finally, and again because of expected reductions in the use of
expensive hospital in-patient facilities, treatments provided within a
community-based service model offer a more efficient (and because of improved
accessibility, more equitable) use of resources than hospital-based
services.
At a broader, sectoral level of comparison, interventions for bipolar disorder are not substantially different from each other. Expressed in relation to gross national income, the cost-effectiveness ratios for community-based interventions fall between one and three times the gross national income per capita, a range considered by the Commission on Macroeconomics and Health (2001; p. 103) to be cost-effective (as opposed to very cost-effective, below average gross national income per capita; or not cost-effective, more than three times gross national income per capita). Comparisons with other studies are limited owing to the aggregate level of analysis employed here; however, our results for the Western Pacific sub-region WprA (which includes Australia) can be compared with the recent study by Sanderson et al (2003); our estimated cost of I$20 00022 000 for each DALY averted by community-based treatment is slightly higher than their estimated cost for current and optimal treatment (Aus$24 000, or I$18 200), which can be attributed to our inclusion of programme-level costs, a higher expected rate of in-patient admission for mania and also greater downward adjustment of efficacy estimates for real-world effectiveness at the population level.
Compared with many other strategies analysed to date under the WHOCHOICE project including population-based interventions to reduce heavy alcohol use, smoking and cardiovascular disease bipolar disorder interventions have a relatively high ratio of cost to health outcome, and exceed the average cost per averted DALY for efficient primary-care-based depression interventions by a factor of 46 (Chisholm et al, 2004). Such a finding is hardly surprising in terms of costs, given the multiple health service needs of persons with a diagnosis of bipolar disorder, but does serve to highlight the rather modest impact of these interventions on the natural course of this disorder. Indeed, even at a population treatment coverage rate of 50%, modelled interventions are only able to avert between 10 and 33% of the burden, which points to a clear need to further develop culturally appropriate psychosocial approaches capable of delivering improved long-term functioning over and above shorter-term considerations such as medication adherence (e.g. Colom et al, 2003).
Limits and limitations of economic modelling
In common with other modelling studies, this analysis is a highly
restricted representation of reality. Bipolar disorder has a heterogeneous
course, with patients experiencing marked differences in rates of cycling
between different mood states, which is not well captured here. Separate
sub-analyses for rapid cyclers would be expected to reveal
higher costs and worse outcomes, for example, as might an analysis that would
take into account the full range of possible comorbidities. In modelling the
average patient with bipolar I disorder, we also make use of
best available evidence on sub-regional epidemiology and the expected impact
of interventions, which for many developing regions has necessitated
extrapolation from neighbouring regions or from the international literature.
Although comprehensive literature reviews for the period 19902002 were
performed for key model parameters, including remission, mortality,
functioning, acute/prophylactic treatment effects and resource use patterns
(Goodwin & Jamison (1990)
was relied on for pre-1990 data inputs), there are evident limitations
inherent to such an approach, which techniques like sensitivity analysis can
only partially address. Until such time that there exists robust evidence at a
genuinely global level, however, we see this as a valuable way of providing
evidence-based guidance to policy makers on broad strategies to reduce leading
contributors to current disease burden.
Use of a population-level measure of health gain such as the DALY has advantages in terms of comparability with other diseases, for instance but does not encompass the full range of consequences that may follow from intervention. For bipolar disorder, important additional benefits of treatment include reduction of family burden (including informal caregiving time) and reduced absenteeism and unemployment (productivity). A recent cost-of-illness study of bipolar disorder in the UK estimated that no less than 86% of total societal costs were attributable to these indirect costs, mainly due to excess unemployment (Das Gupta & Guest, 2002). Despite the pursuit of a societal perspective in WHOCHOICE (Tan Torres et al, 2003), considerable challenges in the measurement of productivity gains, as well as patient and informal carer time spent seeking or providing care, have precluded their valuation in the present analysis.
![]() |
Clinical Implications and Limitations |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
LIMITATIONS
![]() |
ACKNOWLEDGMENTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Angst, J. & Sellaro, R. (2000) Historical perspectives and natural history of bipolar disorder. Biological Psychiatry, 48, 445 -457.[CrossRef][Medline]
Ayuso-Mateos, J. L. (2001) Global Burden of Bipolar Disorder in the Year 2000: Version 1 Estimates. http://www.who.int/evidence/bod
Baldessarini, R. J. & Tondo, L. (2000) Does
lithium treatment still work? Evidence of stable responses over three decades.
Archives of General Psychiatry,
57, 187
-190.
Bauer, M. & Mitchner, L. (2004) What is a
mood stabiliser? An evidence-based response. American
Journal of Psychiatry, 161, 3
-18.
Bowden, C. L., Calabrese, J. R., McElory, S. L., et al
(2000) A randomized, placebo-controlled 12-month trial of
divalproex and lithium in treatment of outpatients with bipolar I disorder.
Divalproex Maintenance Study Group. Archives of General
Psychiatry, 57, 481
-489.
Chisholm, D., Sanderson, K., Ayuso-Mateos, J. L., et al
(2004) Reducing the global burden of depression.
Population-level analysis of intervention cost-effectiveness in 14 world
regions. British Journal of Psychiatry,
184, 393
-403.
Colom, F., Vieta, E., Martínez-Aran, A., et al
(2003) A randomized trial on the efficacy of group
psycho-education in the prophylaxis of recurrences in bipolar patients whose
disease is in remission. Archives of General
Psychiatry, 60, 402
-407.
Commission on Macroeconomics and Health (2001) Macroeconomics and Health: Investing in Health for Economic Development. Geneva: World Health Organization.
Das Gupta, R. & Guest, J. F. (2002) Annual
cost of bipolar disorder to UK society. British Journal of
Psychiatry, 180, 227
-233.
Emilien, G., Maloteaux, J. M., Seghers, A., et al (1996) Lithium compared to valproic acid and carbamazepine in the treatment of mania: a statistical meta-analysis. European Neuropsychopharmacology, 6, 245 -252.[CrossRef][Medline]
Ferri, C., Chisholm, D., Van Ommeren, M., et al (2004) Resource utilisation for neuropsychiatric disorders in developing countries: a multinational Delphi consensus study. Social Psychiatry and Psychiatric Epidemiology, 39, 218 -227.[CrossRef][Medline]
Frye, M. A., Altshuler, L. L., Szuba, M. P., et al (1996) The relationship between anti-manic agent for treatment of classic or dysphoric mania and length of hospital stay. Journal of Clinical Psychiatry, 57, 17-21.
Gonzalez-Pinto, A., Gonzalez, C., Enjuto, S., et al (2004) Psycho-education and cognitivebehavioural therapy in bipolar disorder: an update. Acta Psychiatrica Scandinavica, 109, 83 -90.[CrossRef][Medline]
Goodwin, F. K. & Jamison, K. R. (1990) Manicdepressive Illness. New York: Oxford University Press.
Goodwin, F. K., Fireman, B., Simon, G. E., et al
(2003) Suicide risk in bipolar disorder during treatment with
lithium and divalproex. JAMA,
290, 1467
-1473.
Harris, E. C. & Barraclough, B. (1998) Excess mortality of mental disorder. British Journal of Psychiatry, 173, 11 -53.[Abstract]
Helgason, T. (1964) Epidemiology of mental disorders in Iceland: a psychiatric and demographic investigation of 5395 Icelanders. Acta Psychiatrica Scandinavica, 40 (suppl. 173), 1 -180.
Huxley, N. A., Parikh, S.V. & Baldessarini, R. J. (2000) Effectiveness of psychosocial treatments in bipolar disorder: state of the evidence. Harvard Review of Psychiatry, 8, 126 -140.[CrossRef][Medline]
Judd, L. L., Akiskal, H. S., Schettler, P. J., et al
(2002) The long-term natural history of the weekly
symptomatic status of bipolar I disorder. Archives of General
Psychiatry, 59, 530
-537.
Keck, P. E. Jr, Nabulsi, A. A., Taylor, J. L. (1996) A pharmacoeconomic model of divalproex vs. lithium in the acute and prophylactic treatment of bipolar I disorder. Journal of Clinical Psychiatry, 7, 213 -222.
Kohn, R., Saxena, S., Levav, I., et al (2004) The treatment gap in mental health care. Bulletin of the World Health Organization, 82, 858 -866.[Medline]
Sanderson, K., Andrews, G., Corry, J., et al (2003) Reducing the burden of affective disorders: is evidence-based health care affordable? Journal of Affective Disorders, 77, 109 -125.[CrossRef][Medline]
Tan Torres, T., Baltussen, R. M., Adam, T., et al (2003) Making Choices in Health: WHO Guide to Cost-effectiveness Analysis. Geneva: World Health Organization.
Tondo, L., Baldessarini, R. J. & Floris, G.
(2001a) Long-term clinical effectiveness of lithium
maintenance treatment in types I and II bipolar disorders. British
Journal of Psychiatry, 178 (suppl.
41), s184-s190.
Tondo, L., Hennen, J. & Baldessarini, R. J. (2001b) Lower suicide risk with long-term lithium treatment in major affective illness: a meta-analysis. Acta Psychiatrica Scandinavica, 104, 163 -172.[CrossRef][Medline]
Weissman, M. M., Bland, R. C., Canino, G. J., et al (1996) Cross-national epidemiology of major depression and bipolar disorder. JAMA, 276, 293 -299.[Abstract]
Wolf, T., Muller-Oerlinghausen, B., Ahrens, B., et al (1996) How to interpret findings on mortality of long-term lithium treated manic-depressive manic-depressive patients? Critique of different methodological approaches. Journal of Affective Disorders, 39, 127 -132.[CrossRef][Medline]
World Health Organization (1992) The ICD10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. Geneva: World Health Organization.
World Health Organization (2001) The World Health Report 2001 Mental Health: New Understanding, New Hope. Geneva: World Health Organization.
Wyatt, R. J. & Henter, I. (1995) An economic evaluation of manic-depressive illness 1991. Social Psychiatry and Psychiatric Epidemiology, 30, 213 -219.[Medline]
Received for publication July 7, 2004. Revision received December 16, 2004. Accepted for publication January 5, 2005.
Related articles in BJP:
HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |