title: The economic impact of bipolar disorder in an employed population from an employer perspective.
abstract: OBJECTIVE
  To determine the economic impact of bipolar disorder on health benefit costs and health-related work absences from an employer perspective.
  
  
  METHODS
  Data on health benefit costs and health-related absences during 2001 and 2002 were retrieved from a database and retrospectively examined. Regression modeling measured the cost differences while controlling for potentially confounding factors. The study population consisted of employees at multiple large employers who were widely dispersed throughout the United States. These employees were grouped into 2 cohorts: (1) employees with a bipolar disorder diagnosis (primary, secondary, or tertiary ICD-9 code of 296.0x, 296.1x, 296.4x, 296.5x, 296.6x, 296.7x, or 296.8x) in 2001 and (2) employees with no bipolar disorder diagnosis during 2001 or 2002 (comparison cohort). Specific outcome measures included annual health benefit claim costs and salary-replacement payments for the following employee health benefits: health care insurance, prescription drug, sick leave, short- and long-term disability, and workers' compensation. Additional outcome measures included annual absence days due to workers' compensation, short- and long-term disability, and sick leave (separately).
  
  
  RESULTS
  The analysis identified 761 employees (0.3%) with bipolar disorder and 229,145 eligible employees without bipolar disorder. Employees with bipolar disorder annually cost $6836 more than employees without bipolar disorder (p < .05) and were more costly in every health benefit cost category. Employees with bipolar disorder missed an average of 18.9 workdays annually, while employees without bipolar disorder missed 7.4 days annually (p < .05).
  
  
  CONCLUSIONS
  The impact of bipolar disorder can be costly in the workplace, leading to increased health benefit costs and increased absenteeism.
authors.affiliation: Human Capital Management Services, Cheyenne, WY 82001, USA.
authors.forename: Harold H
authors.initials: HH
authors.lastname: Gardner
authors.forename: Nathan L
authors.initials: NL
authors.lastname: Kleinman
authors.forename: Richard A
authors.initials: RA
authors.lastname: Brook
authors.forename: Krithika
authors.initials: K
authors.lastname: Rajagopalan
authors.forename: Truman J
authors.initials: TJ
authors.lastname: Brizee
authors.forename: James E
authors.initials: JE
authors.lastname: Smeeding
mesh_terms: D000041:Absenteeism; D000328:Adult; D001714:Bipolar Disorder / Q000175:diagnosis / Q000191:economics*; D015331:Cohort Studies; D017281:Cost of Illness; D003365:Costs and Cost Analysis; D017049:Employer Health Costs / Q000706:statistics & numerical data; D004651:Employment / Q000191:economics* / Q000706:statistics & numerical data; D005260:Female; D006264:Health Benefit Plans, Employee / Q000191:economics / Q000706:statistics & numerical data; D006801:Humans; D007342:Insurance Benefits / Q000191:economics; D018868:Insurance, Disability / Q000191:economics; D038801:International Classification of Diseases; D008297:Male; D012044:Regression Analysis; D012189:Retrospective Studies; D018582:Sick Leave / Q000191:economics / Q000706:statistics & numerical data; D014481:United States; D014942:Workers' Compensation / Q000191:economics / Q000706:statistics & numerical data; D017132:Workplace / Q000191:economics* / Q000706:statistics & numerical data