The Cost Consequences of Treatment-Resistant Depression
Texas A&M University - Galveston, Galveston, Texas, United States The Journal of Clinical Psychiatry
(Impact Factor: 5.5).
04/2004; 65(3):341-7. DOI: 10.4088/JCP.v65n0309
Treatment-resistant depression is a significant public health problem with profound effects on general medical and mental health-related health care costs.
To describe health care costs of patients with treatment-resistant depression as their illness progresses, in terms of pharmaceutical and medical expenditures, and to identify factors associated with increasing degrees of treatment resistance.
The MEDSTAT MarketScan Private Pay Fee for Service (FFS) Database, a medical and prescription claims database covering over 3.5 million enrollees, from 1995-2000. DESIGN AND STUDY SUBJECTS: 7737 patients with depression (ICD-9) who had 2 or more unsuccessful trials of antidepressant medication at an adequate dose for at least 4 weeks from 1995-2000 were defined as treatment-resistant in this study. Demographic and clinical characteristics were assessed for these patients with treatment-resistant depression. The number of changes in depression medication treatment regimens was used as a proxy for increasing degrees of treatment resistance and its severity. MAJOR OUTCOME MEASURE: Differences in health care expenditures associated with increasing degrees of treatment-resistant depression.
Total depression-related and general medical health care expenditures increased significantly as treatment-resistant depression increased in severity. Multivariate analyses of patient demographic characteristics were not associated with ongoing treatment resistance. Disease severity, type of antidepressant at index, comorbid mental health disorders, and membership in a managed health care plan were associated with increasing degrees of treatment resistance.
Depression and general medical health care expenditures increase with the degree of treatment-resistant depression. Disease management interventions for treatment-resistant depression that result in sustained remission early in the course of illness are most likely to be cost effective.
Available from: sciencedirect.com
- "For example, Kenny and Williams (2007) defined treatment resistance in relation to depression as having had three or more previous episodes, or one chronic episode lasting 1 year or more. Such clients consume a disproportionate amount of clinical resources (Amsterdam, Hornig, & Nierenberg, 2001; Crown et al., 2002; Russell et al., 2004) but, ironically, they are sometimes excluded from clinical trials to reduce variability and thus increase internal validity (Persons & Silberschatz, 1998; Westen, Novotny, & Thompson-Brenner, 2004; Zarin, Young, & West, 2005). Fortunately, there is some evidence that " third wave " (Hayes, 2004) forms of behavior therapy incorporating principles of mindfulness can successfully treat complex and intransigent clinical problems such as chronic or recurrent depression and personality disorder (e.g., Lynch, Trost, Salsman, & Linehan, 2007; Ma & Teasdale, 2004; Segal, Williams & Teasdale, 2002). "
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ABSTRACT: Acceptance and Commitment Therapy (ACT) is a theoretically coherent approach addressing common processes across a range of disorders. The aim of this study was to investigate the effectiveness of a group-based ACT intervention for ‘treatment-resistant’ participants with various diagnoses, who had already completed at least one psychosocial intervention. Of 61 individuals randomized into a service-based trial comparing ACT and Treatment as Usual based on Cognitive Behavior Therapy (TAU-CBT), 45 provided data (ACT n=26; TAU-CBT n=19). Primary outcomes were measures of psychological symptoms. All participants showed reduced symptoms immediately after intervention but improvements were more completely sustained in the ACT group at 6-month follow-up. More elaborate and more fully controlled evaluations are required to confirm the findings, improve understanding of ACT processes and assess health economic benefits.
Available from: Donald F Smith
- "Another challenging issue for PET neuroimaging of depressive disorders concerns financial support of research. Compared with the costs of brain diseases in the US and Europe (Sobocki et al. 2006;Greenberg et al. 2003;Russell et al. 2004), national funding of molecular brain imaging is miniscule. In Europe, for example, the total annual cost of depression in 2004 was 120 billion Euro, for a population of 466 million with at least 21 million affected residents (Sobocki et al. 2006), making depression the most costly brain disorder. "
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ABSTRACT: We thank everybody at the Center for Psychiatric Research and the PET Center of Aarhus University for providing a positive atmosphere in which to work. DFS thanks the Danish Medical Research Council for research funding, and PWM is grateful to the EPSRC for the award of a Life Sciences Interface fellowship (EP/E039278/1).
Available from: Andrew F Leuchter
- "In addition to health concerns, medical expenditures also increase with increasing numbers of ineffective treatment trials. A study of 7737 depressed subjects found higher inpatient, outpatient, and pharmaceutical health care costs with increasing numbers of changes in antidepressant treatment regimen . The introduction of reliable predictors of response to treatment thus could potentially shorten the course of treatment and improve long-term treatment outcomes in depression. "
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ABSTRACT: Recent studies have shown overall accuracy rates of 72% and 88% using baseline and/or 1-week change in QEEG biomarkers to predict clinical outcome to treatment with various antidepressant medications. In some cases, findings have been replicated across academic institutions and have been studied in the context of randomized, placebo-controlled trials. Recent EEG findings are corroborated by studies that use techniques with greater spatial resolution (eg, PET, MEG) in localizing brain regions pertinent to clinical response. As such, EEG measurements increasingly are validated by other physiologic measurements that have the ability to assess deeper brain structures. Continued progress along these lines may lead to the realized promise of QEEG biomarkers as predictors of antidepressant treatment outcome in routine clinical practice. In the larger context, use of QEEG technology to predict antidepressant response in major depression may mean that more patients will achieve response and remission with less of the trial-and-error approach that currently accompanies antidepressant treatment.
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