Brief Treatment and Crisis Intervention 4:123-136 (2004)
© Oxford University Press 2004; all rights reserved.
Evidence-Based Practice With Comorbid Substance Abuse, Mental Illness, and Suicidality: Can the Evidence Be Found?
From Faculty of Arts and Sciences, Rutgers University (Roberts); Department of Psychiatry, University of Toronto, and Kunin-Lunenfeld Applied Research Unit, Baycrest Centre for Geriatric Care (Streiner); and the Department of Psychiatry and Outpatient Clinic, Ohio State University Medical Center (Yeager).
Contact author: Dr. Albert Roberts, Professor of Criminal Justice and Social Work, Director of Faculty Development, Interdisciplinary Program in Criminal Justice, Rutgers University, Livingston College Campus, Lucy Stone Hall, B Wing, Piscataway, NJ 08854.
Evidence-based practice (EBP) comprises a number of "tools" in its toolbox. These include (1) being able to phrase a question in such a way that you can carry out a useful literature search; (2) knowing how to do the search; and (3) having an understanding of the basic terminology, such as absolute risk reduction (ARR) and number needed to treat (NNT). This paper introduces the concepts of ARR and NNT and explains why they are necessary to supplement the more widely used statistic, the relative benefit ratio. Using an example of a realistic clinical question, we show how to conduct a literature search to find the best available evidence and how to translate the findings into a meaningful decision about the effectiveness of a therapeutic intervention. Next, we show how a secondary analysis of an administrative database of 2,525 psychiatric inpatients can be used to identify self-harm risk factors in a group of suicidal substance-abusing patients. Finally, we examine significant findings among the 740 former patients who were readmitted within 31 days of discharge, owing to evidence of clear and present risk of harm to self or others.
KEY WORDS: evidence-based practice, major depression, substance abuse, suicide ideation, self-harm, rehospitalization, absolute risk reduction, computer-based searching
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