Identifying patients at risk for high medical costs and good candidates for obesity intervention

Am J Health Promot. Mar-Apr 2014;28(4):218-27. doi: 10.4278/ajhp.121116-QUAN-561. Epub 2013 Dec 20.


Purpose: 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.

Design: Cross-sectional analysis, using commercial claims and health risk assessment data.

Setting: United States, 2004-2009.

Subjects: Panel of 192,750 person-year observations from 116,868 unique working-age employees of large companies.

Measures: Probability of high medical expenses (80th percentile or above) in the following year; adjusted body mass index (BMI).

Analysis: 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.

Conclusion: 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.

MeSH terms

  • Adolescent
  • Adult
  • Comorbidity
  • Cross-Sectional Studies
  • Female
  • Health Care Costs*
  • Health Promotion*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Obesity / economics
  • Obesity / prevention & control*
  • Patient Selection*
  • Risk Assessment / methods*
  • United States
  • Young Adult