title: A model of the economic impact of a bipolar disorder screening program in primary care.
abstract: OBJECTIVE
  Unrecognized bipolar disorder in patients presenting with a major depressive episode may lead to delayed diagnosis, inappropriate treatment, and excessive costs. This study models the cost effectiveness of screening for bipolar disorder among adults presenting for the first time with symptoms of major depressive disorder.
  
  
  METHODS
  A decision-analysis model was used to evaluate the outcomes and cost over 5 years of screening versus not screening for bipolar disorder. Screening was defined as a 1-time administration of the Mood Disorder Questionnaire at the initial visit followed by referral to a psychiatrist for patients screening positive for bipolar disorder. Health states included correctly diagnosed bipolar disorder, unrecognized bipolar disorder, and correctly diagnosed major depressive episodes. Model outcomes included rates of correct diagnosis of bipolar disorder and discounted costs (2006 US dollars) of screening and treating major depressive episodes. Literature was the primary source of data and was collected from September 2007 through March 2009.
  
  
  RESULTS
  According to the model, 1,000 adults in a health plan with 1 million adult members annually present with symptoms of major depressive disorder. An additional 38 patients were correctly diagnosed with depression (unipolar or a major depressive episode) or bipolar disorder (440 with screening vs 402 without screening) through a 1-time screening for bipolar disorder. Estimated 5-year discounted costs per patient were $36,044 without screening and $34,107 with screening (savings of $1,937). Accordingly, total 5-year budgetary savings were estimated at $1.94 million. Results were most sensitive to difference in treatment costs for patients with recognized versus unrecognized bipolar disorder.
  
  
  CONCLUSIONS
  A 1-time screening program for bipolar disorder, when patients first present with a major depressive episode, can reduce health care costs to managed-care plans.
authors.affiliation: Boston Health Economics, Inc., 20 Fox Rd., Waltham, MA 02451, USA. jmenzin@bhei.com
authors.forename: Joseph
authors.initials: J
authors.lastname: Menzin
authors.forename: Matthew
authors.initials: M
authors.lastname: Sussman
authors.forename: Eskinder
authors.initials: E
authors.lastname: Tafesse
authors.forename: Christina
authors.initials: C
authors.lastname: Duczakowski
authors.forename: Peter
authors.initials: P
authors.lastname: Neumann
authors.forename: Mark
authors.initials: M
authors.lastname: Friedman
mesh_terms: D000293:Adolescent; D000328:Adult; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology; D017046:Cost Savings / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology; D003951:Diagnostic Errors; D042241:Early Diagnosis; D005260:Female; D017048:Health Care Costs; D006801:Humans; D008297:Male; D008329:Managed Care Programs / Q000191:economics; D008403:Mass Screening / Q000191:economics / Q000379:methods; D018803:Models, Economic; D009010:Monte Carlo Method; D011320:Primary Health Care / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data; D011795:Surveys and Questionnaires