title: Identifying patients at risk for high medical costs and good candidates for obesity intervention.
abstract: OBJECTIVE
  To develop a risk-scoring tool to identify in a base year patients likely to have high medical spending in the subsequent year and to understand the role obesity and obesity reduction may play in mitigating this risk.
  
  
  METHODS
  Cross-sectional analysis, using commercial claims and health risk assessment data.
  
  
  METHODS
  United States, 2004-2009.
  
  
  METHODS
  Panel of 192,750 person-year observations from 116,868 unique working-age employees of large companies.
  
  
  METHODS
  Probability of high medical expenses (80th percentile or above) in the following year; adjusted body mass index (BMI).
  
  
  METHODS
  Generate risk scores by modeling the likelihood of high next-year expenses as a function of base-year age, sex, medical utilization, comorbidities, and BMI. Estimate the effect of simulated bariatric intervention on patient risk scores.
  
  
  RESULTS
  Individuals with higher BMI were more likely to be categorized in the very high risk group, in which the average annual medical expense was $8621. A weight-loss intervention transitioning a patient to the next lower obesity class was predicted to reduce this risk by 1.5% to 27.4%-comparable to hypothetically curing a patient of depression or type 2 diabetes.
  
  
  CONCLUSIONS
  A logistic model was used to capture the effect of BMI on the risk of high future medical spending. Weight-loss interventions for obese patients may generate significant savings by reducing this risk.
authors.forename: Julia Thornton
authors.initials: JT
authors.lastname: Snider
authors.forename: Katalin
authors.initials: K
authors.lastname: Bognar
authors.forename: Denise
authors.initials: D
authors.lastname: Globe
authors.forename: Daisy
authors.initials: D
authors.lastname: Ng-Mak
authors.forename: Jeff
authors.initials: J
authors.lastname: Sullivan
authors.forename: Nicholas
authors.initials: N
authors.lastname: Summers
authors.forename: Dana
authors.initials: D
authors.lastname: Goldman
mesh_terms: D000293:Adolescent; D000328:Adult; D015897:Comorbidity; D003430:Cross-Sectional Studies; D005260:Female; D017048:Health Care Costs; D006293:Health Promotion; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000191:economics / Q000517:prevention & control*; D018579:Patient Selection; D018570:Risk Assessment / Q000379:methods*; D014481:United States; D055815:Young Adult