Abstract
Background: This paper assesses obesity- and smoking-related incremental healthcare costs for the employees and dependents of a large U.S. employer. Objectives: Unlike previous studies, this study evaluates the distributional effects of obesity and smoking on healthcare cost distribution using a recently developed econometric framework: the unconditional quantile regression (UQR). Methods: Results were compared with the traditional conditional quantile regression (CQR), and the generalized linear modeling (GLM) framework that is commonly used for modeling healthcare cost. Results: The study found strong evidence of association of healthcare costs with obesity and smoking. More importantly, the study found that these effects are substantially higher in the upper quantiles of the healthcare cost distribution than in the lower quantiles. The insights on the heterogeneity of impacts of obesity and smoking on healthcare costs would not have been captured by traditional mean-based approaches. The study also found that UQR impact estimates were substantially different from CQR impact estimates in the upper quantiles of the cost distribution. Conclusions: These results suggest the potential role that smoking cessation and weight management programs can play in arresting the growth in healthcare costs. Specifically, given the finding that obesity and smoking have markedly higher impacts on high-cost patients, such programs appear to have significant cost saving potential if targeted toward high-cost patients.
Highlights
A significant body of literature has shown that obesity and smoking, two of the most common modifiable risks for many chronic diseases, have substantial impact on overall healthcare costs.[1,2,3,4] The prevalence of obesity has been rising rapidly for a number of years and has more than doubled in the past 30 years in the United States.[5,6] This rapid rise in obesity has been associated with a substantial impact on obesity-related medical treatments and expenditures
Conculsions: These results suggest the potential role that smoking cessation and weight management programs can play in arresting the growth in healthcare costs
Note that we modeled obesity categories and smoking as independent predictors in the unconditional quantile regression (UQR), conditional quantile regression (CQR), and generalized linear modeling (GLM) frameworks
Summary
A significant body of literature has shown that obesity and smoking, two of the most common modifiable risks for many chronic diseases, have substantial impact on overall healthcare costs.[1,2,3,4] The prevalence of obesity has been rising rapidly for a number of years and has more than doubled in the past 30 years in the United States.[5,6] This rapid rise in obesity has been associated with a substantial impact on obesity-related medical treatments and expenditures. Quesenberry et al.[8] estimated that individuals who are moderately obese (30≤body mass index [BMI]≤34.9) and severely obese (BMI≥35) have 14% and 25% more physician visits, and 34% and 74% more inpatient days, respectively. Thompson et al.[9] found that obese adults (BMI≥30) have 38% more visits to primary care physicians and 48% more inpatient days per year. They found that obese individuals had higher use of prescription drugs in general, and had much higher use of prescription drugs for diabetes and cardiovascular disease. This paper assesses obesity- and smoking-related incremental healthcare costs for the employees and dependents of a large U.S employer
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