OBJECTIVE: To identify how decisions about treatment are being made in secondary services for anxiety disorders and depression and, specifically, whether it was possible to predict the decisions to refer for evidence-based treatments. METHOD: Post hoc classification tree analysis was performed using a sample from an audit on implementation of the National Institute for Health and Clinical Excellence Guidelines for Depression and Anxiety Disorders. The audit was of 5 teams offering secondary care services; they included psychiatrists, psychologists, community psychiatric nurses, social workers, dual-diagnosis workers, and vocational workers. The patient sample included all of those with a primary problem of depression (n = 56) or an anxiety disorder (n = 16) who were offered treatment from February 16 to April 3, 2009. The outcome variable was whether or not evidence-based treatments were offered, and the predictor variables were presenting problem, risk, comorbid problem, social problems, and previous psychiatric history. RESULTS: Treatment decisions could be more accurately predicted for anxiety disorders (93% correct) than for depression (55%). For anxiety disorders, the presence or absence of social problems was a good predictor for whether evidence-based or non-evidence-based treatments were offered; 44% (4/9) of those with social problems vs 100% (6/6) of those without social problems were offered evidence-based treatments. For depression, patients' risk rating had the largest impact on treatment decisions, although no one variable could be identified as individually predictive of all treatment decisions. CONCLUSIONS: Treatment decisions were generally consistent for anxiety disorders but more idiosyncratic for depression, making the development of a decision-making model very difficult for depression. The lack of clarity of some terms in the clinical guidelines and the more complex nature of depression could be factors contributing to this difficulty. Further research is needed to understand the complex nature of decision making with depressed patients.
OBJECTIVE: To identify how decisions about treatment are being made in secondary services for anxiety disorders and depression and, specifically, whether it was possible to predict the decisions to refer for evidence-based treatments. METHOD: Post hoc classification tree analysis was performed using a sample from an audit on implementation of the National Institute for Health and Clinical Excellence Guidelines for Depression and Anxiety Disorders. The audit was of 5 teams offering secondary care services; they included psychiatrists, psychologists, community psychiatric nurses, social workers, dual-diagnosis workers, and vocational workers. The patient sample included all of those with a primary problem of depression (n = 56) or an anxiety disorder (n = 16) who were offered treatment from February 16 to April 3, 2009. The outcome variable was whether or not evidence-based treatments were offered, and the predictor variables were presenting problem, risk, comorbid problem, social problems, and previous psychiatric history. RESULTS: Treatment decisions could be more accurately predicted for anxiety disorders (93% correct) than for depression (55%). For anxiety disorders, the presence or absence of social problems was a good predictor for whether evidence-based or non-evidence-based treatments were offered; 44% (4/9) of those with social problems vs 100% (6/6) of those without social problems were offered evidence-based treatments. For depression, patients' risk rating had the largest impact on treatment decisions, although no one variable could be identified as individually predictive of all treatment decisions. CONCLUSIONS: Treatment decisions were generally consistent for anxiety disorders but more idiosyncratic for depression, making the development of a decision-making model very difficult for depression. The lack of clarity of some terms in the clinical guidelines and the more complex nature of depression could be factors contributing to this difficulty. Further research is needed to understand the complex nature of decision making with depressedpatients.
Authors: Peter Roy-Byrne; Michelle G Craske; Greer Sullivan; Raphael D Rose; Mark J Edlund; Ariel J Lang; Alexander Bystritsky; Stacy Shaw Welch; Denise A Chavira; Daniela Golinelli; Laura Campbell-Sills; Cathy D Sherbourne; Murray B Stein Journal: JAMA Date: 2010-05-19 Impact factor: 56.272
Authors: Madhukar H Trivedi; A John Rush; M Lynn Crismon; T Michael Kashner; Marcia G Toprac; Thomas J Carmody; Tracie Key; Melanie M Biggs; Kathy Shores-Wilson; Bradley Witte; Trisha Suppes; Alexander L Miller; Kenneth Z Altshuler; Steven P Shon Journal: Arch Gen Psychiatry Date: 2004-07
Authors: J John Mann; Steven P Ellis; Christine M Waternaux; Xinhua Liu; Maria A Oquendo; Kevin M Malone; Beth S Brodsky; Gretchen L Haas; Dianne Currier Journal: J Clin Psychiatry Date: 2008-01 Impact factor: 4.384