Literature DB >> 29282797

Decision curve analysis as a framework to estimate the potential value of screening or other decision-making aids.

Michael S Martin1, George A Wells1, Anne G Crocker2, Beth K Potter1, Ian Colman1.   

Abstract

OBJECTIVES: There is an increasing debate about the impact of mental health screening. We illustrate the use of a decision making framework that can be applied when there is no sufficient data to support a traditional cost-benefit analysis.
METHODS: We conducted secondary analyses of data from 459 male prisoners who were screened upon intake. We compared the potential benefit of different approaches (screening, history taking, and universal interventions) to allocating treatment resources using decision curve analysis.
RESULTS: Screening prisoners for distress at typical levels of sensitivity (75%) and specificity (71%) were estimated to provide the greatest net benefit if between 2 and 5 false positives per detected illness are tolerable. History taking and self-harm screening provide the largest net benefit when only 1 or 2 false positives per detected illness would be tolerable. The benefits of screening were less among those without a recent psychiatric history, ethnic minorities, and those with fewer psychosocial needs.
CONCLUSIONS: Although screening has potential to increase detection of treatment, important subgroup differences exist. Greater consideration of responses to positive screens or alternatives to screening are needed to maximize the impact of efforts to improve detection and treatment of mental illness.
Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  decision analysis; mental health; prisons; screening

Mesh:

Year:  2017        PMID: 29282797      PMCID: PMC6877186          DOI: 10.1002/mpr.1601

Source DB:  PubMed          Journal:  Int J Methods Psychiatr Res        ISSN: 1049-8931            Impact factor:   4.035


  41 in total

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Review 5.  Patients' expectations of the benefits and harms of treatments, screening, and tests: a systematic review.

Authors:  Tammy C Hoffmann; Chris Del Mar
Journal:  JAMA Intern Med       Date:  2015-02       Impact factor: 21.873

6.  Decision curve analysis: a novel method for evaluating prediction models.

Authors:  Andrew J Vickers; Elena B Elkin
Journal:  Med Decis Making       Date:  2006 Nov-Dec       Impact factor: 2.583

7.  Pathways to psychiatric care in European prison systems.

Authors:  Harald Dressing; Hans-Joachim Salize
Journal:  Behav Sci Law       Date:  2009 Sep-Oct

Review 8.  Risk of bias from inclusion of patients who already have diagnosis of or are undergoing treatment for depression in diagnostic accuracy studies of screening tools for depression: systematic review.

Authors:  Brett D Thombs; Erin Arthurs; Ghassan El-Baalbaki; Anna Meijer; Roy C Ziegelstein; Russell J Steele
Journal:  BMJ       Date:  2011-08-18

9.  Assessing needs for psychiatric treatment in prisoners: 2. Met and unmet need.

Authors:  Sharon Jakobowitz; Paul Bebbington; Nigel McKenzie; Rachel Iveson; Gary Duffield; Mark Kerr; Helen Killaspy
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2016-11-22       Impact factor: 4.328

Review 10.  Prognosis Research Strategy (PROGRESS) 3: prognostic model research.

Authors:  Ewout W Steyerberg; Karel G M Moons; Danielle A van der Windt; Jill A Hayden; Pablo Perel; Sara Schroter; Richard D Riley; Harry Hemingway; Douglas G Altman
Journal:  PLoS Med       Date:  2013-02-05       Impact factor: 11.069

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  1 in total

1.  Mental Health Screening and Differences in Access to Care among Prisoners.

Authors:  Michael S Martin; Anne G Crocker; Beth K Potter; George A Wells; Rebecca M Grace; Ian Colman
Journal:  Can J Psychiatry       Date:  2018-02-28       Impact factor: 4.356

  1 in total

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