BACKGROUND: Major depression is the largest single cause of nonfatal disease burden in Australia. Effective drug and psychological treatments exist, yet are underused. OBJECTIVE: To quantify the burden of disease currently averted in people seeking care for major depression and the amount of disease burden that could be averted in these people under optimal episodic and maintenance treatment strategies. DESIGN: Modeling impact of current and optimal treatment strategies based on secondary analysis of mental health survey data, studies of the natural history of major depression, and meta-analyses of effectiveness data. Monte Carlo simulation of uncertainty in the model. SETTING: The cohort of Australian adults experiencing an episode of major depression in 2000 are modeled through "what if" scenarios of no treatment, current treatment, and optimal treatment strategies with cognitive behavioral therapy or antidepressant drug treatment. MAIN OUTCOME MEASURE: Disability-Adjusted Life Year. RESULTS: Current episodic treatment averts 9% (95% uncertainty interval, 6%-12%) of the disease burden of major depression in Australian adults. Optimal episodic treatment with cognitive behavioral therapy could avert 28% (95% uncertainty interval, 19%-39%) of this disease burden, and with drugs 24% (95% uncertainty interval, 19%-30%) could be averted. During the 5 years after an episode of major depression, current episodic treatment patterns would avert 13% (95% uncertainty interval, 10%-17%) of Disability-Adjusted Life Years, whereas maintenance drug treatment could avert 50% (95% uncertainty interval, 40%-60%) and maintenance cognitive behavioral therapy could avert 52% (95% uncertainty interval, 42%-64%), even if adherence of around 60% is taken into account. CONCLUSIONS: Longer-term maintenance drug or psychological treatment strategies are required to make significant inroads into the large disease burden associated with major depression in the Australian population.
BACKGROUND: Major depression is the largest single cause of nonfatal disease burden in Australia. Effective drug and psychological treatments exist, yet are underused. OBJECTIVE: To quantify the burden of disease currently averted in people seeking care for major depression and the amount of disease burden that could be averted in these people under optimal episodic and maintenance treatment strategies. DESIGN: Modeling impact of current and optimal treatment strategies based on secondary analysis of mental health survey data, studies of the natural history of major depression, and meta-analyses of effectiveness data. Monte Carlo simulation of uncertainty in the model. SETTING: The cohort of Australian adults experiencing an episode of major depression in 2000 are modeled through "what if" scenarios of no treatment, current treatment, and optimal treatment strategies with cognitive behavioral therapy or antidepressant drug treatment. MAIN OUTCOME MEASURE: Disability-Adjusted Life Year. RESULTS: Current episodic treatment averts 9% (95% uncertainty interval, 6%-12%) of the disease burden of major depression in Australian adults. Optimal episodic treatment with cognitive behavioral therapy could avert 28% (95% uncertainty interval, 19%-39%) of this disease burden, and with drugs 24% (95% uncertainty interval, 19%-30%) could be averted. During the 5 years after an episode of major depression, current episodic treatment patterns would avert 13% (95% uncertainty interval, 10%-17%) of Disability-Adjusted Life Years, whereas maintenance drug treatment could avert 50% (95% uncertainty interval, 40%-60%) and maintenance cognitive behavioral therapy could avert 52% (95% uncertainty interval, 42%-64%), even if adherence of around 60% is taken into account. CONCLUSIONS: Longer-term maintenance drug or psychological treatment strategies are required to make significant inroads into the large disease burden associated with major depression in the Australian population.
Authors: B A A Bus; M L Molendijk; I Tendolkar; B W J H Penninx; J Prickaerts; B M Elzinga; R C O Voshaar Journal: Mol Psychiatry Date: 2014-08-26 Impact factor: 15.992
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Authors: Y Y Lee; J J Barendregt; E A Stockings; A J Ferrari; H A Whiteford; G A Patton; C Mihalopoulos Journal: Epidemiol Psychiatr Sci Date: 2016-08-11 Impact factor: 6.892