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Although genetic epidemiological studies have confirmed increased rates of major depressive disorder among the relatives of people with bipolar affective disorder, no report has compared the clinical characteristics of depression between these two groups.
To compare clinical features of depressive episodes across participants with major depressive disorder and bipolar disorder from within bipolar disorder pedigrees, and assess the utility of a recently proposed probabilistic approach to distinguishing bipolar from unipolar depression. A secondary aim was to identify subgroups within the relatives with major depression potentially indicative of ‘genetic’ and ‘sporadic’ subgroups.
Patients with bipolar disorder types 1 and 2 (n = 246) and patients with major depressive disorder from bipolar pedigrees (n = 120) were assessed using the Diagnostic Interview for Genetic Studies. Logistic regression was used to identify distinguishing clinical features and assess the utility of the probabilistic approach. Hierarchical cluster analysis was used to identify subgroups within the major depressive disorder sample.
Bipolar depression was characterised by significantly higher rates of psychomotor retardation, difficulty thinking, early morning awakening, morning worsening and psychotic features. Depending on the threshold employed, the probabilistic approach yielded a positive predictive value ranging from 74% to 82%. Two clusters within the major depressive disorder sample were found, one of which demonstrated features characteristic of bipolar depression, suggesting a possible ‘genetic’ subgroup.
A number of previously identified clinical differences between unipolar and bipolar depression were confirmed among participants from within bipolar disorder pedigrees. Preliminary validation of the probabilistic approach in differentiating between unipolar and bipolar depression is consistent with dimensional distinctions between the two disorders and offers clinical utility in identifying patients who may warrant further assessment for bipolarity. The major depressive disorder clusters potentially reflect genetic and sporadic subgroups which, if replicated independently, might enable an improved phenotypic definition of underlying bipolarity in genetic analyses.
Mental health survey data are now being used proactively to decide how the burden of disease might best be reduced.
To study the cost-effectiveness of current and optimal treatments for mental disorders and the proportion of burden avertable by each.
Data for three affective, four anxiety and two alcohol use disorders and for schizophrenia were compared in terms of cost, burden averted and efficiency of current and optimal treatment. We then calculated the burden unavertable given current knowledge. The unit of health gain was a reduction in the years lived with disability (YLDs).
Summing across all disorders, current treatment averted 13% of the burden, at an average cost of AUS$30 000 per YLD gained. Optimal treatment at current coverage could avert 20% of the burden, at an average cost of AUS$18 000 per YLD gained. Optimal treatment at optimal coverage could avert 28% of the burden, at AUS$16 000 per YLD gained. Sixty per cent of the burden of mental disorders was deemed to be unavertable.
The efficiency of treatment varied more than tenfold across disorders. Although coverage of some of the more efficient treatments should be extended, other factors justify continued use of less-efficient treatments for some disorders.
This paper is part of a project to identify the proportion of the burden of each mental disorder averted by current and optimal interventions, and the cost-effectiveness of both.
To use epidemiological data on schizophrenia to model the cost-effectiveness of current and optimal treatment.
Calculate the burden of schizophrenia in the years lived with disability (YLD) component of disability-adjusted life-years lost, the proportion averted by current interventions, the proportion that could be averted by optimal treatment and the cost-effectiveness of both.
Current interventions avert some 13% of the burden, whereas 22% could be averted by optimal treatment. Current interventions cost about AUS$200 000 per YLD averted, whereas optimal treatment at a similar cost could increase the number of YLDs averted by two-thirds. Even so, the majority of the burden of schizophrenia remains unavertable.
Optimal treatment is affordable within the present budget and should be implemented.
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