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.
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 obesepatients may generate significant savings by reducing this risk.
Authors: Joost Johan Godert Wammes; Philip J van der Wees; Marit A C Tanke; Gert P Westert; Patrick P T Jeurissen Journal: BMJ Open Date: 2018-09-08 Impact factor: 2.692