Food for thought: understanding the value, variety and usage of management algorithms for major depressive disorder

Martin A Katzman; Leena Anand; Melissa Furtado; Pratap Chokka

doi:10.1016/S0165-1781(14)70002-2

Food for thought: understanding the value, variety and usage of management algorithms for major depressive disorder

Psychiatry Res. 2014 Dec:220 Suppl 1:S3-14. doi: 10.1016/S0165-1781(14)70002-2.

Authors

Martin A Katzman¹, Leena Anand², Melissa Furtado², Pratap Chokka³

Affiliations

¹ START Clinic for Mood and Anxiety Disorders, Toronto, ON, Canada; Northern Ontario School of Medicine, Thunder Bay, ON, Canada; Department of Psychology, Lakehead University, Thunder Bay, ON, Canada; Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Adler Graduate Professional School, Toronto, ON, Canada. Electronic address: mkatzman@startclinic.ca.
² START Clinic for Mood and Anxiety Disorders, Toronto, ON, Canada.
³ University of Alberta, Edmonton, AB, Canada; Chokka Center for Integrative Health, Edmonton, AB, Canada.

PMID: 25539872
DOI: 10.1016/S0165-1781(14)70002-2

Abstract

By 2020, depression is projected to be among the most important contributors to the global burden of disease. A plethora of data confirms that despite the availability of effective therapies, major depressive disorder continues to exact an enormous toll; this, in part, is due to difficulties reaching complete remission, as well as the specific associated costs of both the disorder's morbidity and mortality. The negative effects of depression include those on patients' occupational functioning, including absenteeism, presenteeism, and reduced opportunities for educational and work success. The use of management algorithms has been shown to improve treatment outcomes in major depressive disorder and may be less costly than "usual care" practices. Nevertheless, many patients with depression remain untreated. As well, even those who are treated often continue to experience suboptimal quality of life. As such, the treatment algorithms in this article may improve outcomes for patients suffering with depression. This paper introduces some of the principal reasons underlying these treatment gaps and examines measures or recommendations that might be changed or strengthened in future practice guidelines to bridge them.

Keywords: Algorithms; Depression; Guidelines; Management.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Algorithms*
Depression / psychology
Depressive Disorder, Major / diagnosis
Depressive Disorder, Major / psychology
Depressive Disorder, Major / therapy*
Disease Management*
Humans
Quality of Life / psychology*
Remission Induction
Treatment Outcome