title: Modeling the clinical and economic implications of obesity using microsimulation.
abstract: OBJECTIVE
  The obesity epidemic has raised considerable public health concerns, but there are few validated longitudinal simulation models examining the human and economic cost of obesity. This paper describes a microsimulation model as a comprehensive tool to understand the relationship between body weight, health, and economic outcomes.
  
  
  METHODS
  Patient health and economic outcomes were simulated annually over 10 years using a Markov-based microsimulation model. The obese population examined is nationally representative of obese adults in the US from the 2005-2012 National Health and Nutrition Examination Surveys, while a matched normal weight population was constructed to have similar demographics as the obese population during the same period. Prediction equations for onset of obesity-related comorbidities, medical expenditures, economic outcomes, mortality, and quality-of-life came from published trials and studies supplemented with original research. Model validation followed International Society for Pharmacoeconomics and Outcomes Research practice guidelines.
  
  
  RESULTS
  Among surviving adults, relative to a matched normal weight population, obese adults averaged $3900 higher medical expenditures in the initial year, growing to $4600 higher expenditures in year 10. Obese adults had higher initial prevalence and higher simulated onset of comorbidities as they aged. Over 10 years, excess medical expenditures attributed to obesity averaged $4280 annually-ranging from $2820 for obese category I to $5100 for obese category II, and $8710 for obese category III. Each excess kilogram of weight contributed to $140 higher annual costs, on average, ranging from $136 (obese I) to $152 (obese III). Poor health associated with obesity increased work absenteeism and mortality, and lowered employment probability, personal income, and quality-of-life.
  
  
  CONCLUSIONS
  This validated model helps illustrate why obese adults have higher medical and indirect costs relative to normal weight adults, and shows that medical costs for obese adults rise more rapidly with aging relative to normal weight adults.
authors.affiliation: a a IHS Life Sciences , Washington , DC , USA.
authors.forename: W
authors.initials: W
authors.lastname: Su
authors.affiliation: b b Novo Nordisk, Inc. , Plainsboro , NJ , USA.
authors.forename: J
authors.initials: J
authors.lastname: Huang
authors.affiliation: a a IHS Life Sciences , Washington , DC , USA.
authors.forename: F
authors.initials: F
authors.lastname: Chen
authors.affiliation: a a IHS Life Sciences , Washington , DC , USA.
authors.forename: W
authors.initials: W
authors.lastname: Iacobucci
authors.affiliation: b b Novo Nordisk, Inc. , Plainsboro , NJ , USA.
authors.forename: M
authors.initials: M
authors.lastname: Mocarski
authors.affiliation: a a IHS Life Sciences , Washington , DC , USA.
authors.forename: T M
authors.initials: TM
authors.lastname: Dall
authors.affiliation: c c University of Colorado Anschutz Medical Campus , Aurora , CO , USA.
authors.forename: L
authors.initials: L
authors.lastname: Perreault
mesh_terms: D000041:Absenteeism; D000328:Adult; D000368:Aged; D000375:Aging; D015992:Body Mass Index; D001835:Body Weight; D015897:Comorbidity; D017281:Cost of Illness; D003365:Costs and Cost Analysis; D003865:Major Depressive Disorder / Q000191:economics / Q000209:etiology; D004651:Employment / Q000706:statistics & numerical data; D005260:Female; D006296:Health Services / Q000191:economics / Q000706:statistics & numerical data; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009765:Obesity / Q000150:complications / Q000191:economics* / Q000401:mortality; D011788:Quality of Life; D012891:Sleep Apnea Syndromes / Q000191:economics / Q000209:etiology; D012959:Socioeconomic Factors
keywords: Burden of illness; Economic analysis; Microsimulation; Obesity