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Cognitive Behavioral Case Management for Depressed Low-income Older Adults

  • Patricia A. Areán
  • George Alexopoulos
  • Joyce P. Chu

The purpose of this chapter is to introduce the reader to cognitive behavioral case management (CB case management) for low-income elderly patients. This intervention was developed at San Francisco General Hospital and has been researched by the Over-60 Team at UCSF. CB case management arose out of the recognition that older adults living in financial distress often have difficulty utilizing the important therapeutic strategies that are present in CBT (Areán & Miranda, 1996). It also arose out of the fact that income status appears to be an important factor in treatment outcomes for late-life depression; older adults living in poverty appear to have poor and unstable response to antidepressant medication and other psychotherapies, such as interpersonal therapy (Cohen et al., 2006). In addition, our clients told us that while they valued the utility of depression interventions such as CBT, the interventions themselves did not address complex and urgent social problems that the clients were at a loss to manage themselves; in essence, they needed another person’s help to negotiate the complex social service system to have their needs met. Our team at UCSF piloted the combination of CBT and clinical case management in treating major depression in low-income elderly, and found that the combination of these two interventions was more powerful in alleviating depression than the two interventions on their own. These data were recently published in Psychology and Aging. This chapter discusses the prevalence of poverty in late life, the consequences of poverty in an aging population, and the rationale and process by which CB case management alleviates depression.

Keywords

Depressive Symptom Mental Health Service Ethnic Minority Case Management Severe Mental Illness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Patricia A. Areán
    • 1
  • George Alexopoulos
    • 2
  • Joyce P. Chu
    • 1
  1. 1.University of CaliforniaSan FranciscoUSA
  2. 2.Cornell UniversityItachaUSA

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