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Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression.
Bijan Borah1, James Naessens1, Kerry Olsen2
1Mayo Clinic, Health Care Policy and Research Division, College of Medicine, Rochester, MN, USA.
Journal of Health Economics and Outcomes Research
|September 4, 2023
Summary
Obesity and smoking significantly increase healthcare costs, especially for high-cost patients. Targeted weight management and smoking cessation programs show potential for substantial cost savings.
Area of Science:
- Health Economics
- Econometrics
- Public Health
Background:
- Assesses obesity- and smoking-related incremental healthcare costs for employees and dependents.
- Focuses on a large U.S. employer to analyze cost distributions.
Purpose of the Study:
- Evaluate the distributional effects of obesity and smoking on healthcare costs.
- Utilize the unconditional quantile regression (UQR) econometric framework.
- Compare UQR with traditional conditional quantile regression (CQR) and generalized linear modeling (GLM).
Main Methods:
- Employed unconditional quantile regression (UQR) for cost distribution analysis.
- Compared UQR results with conditional quantile regression (CQR) and generalized linear modeling (GLM).
Main Results:
- Confirmed strong associations between obesity, smoking, and healthcare costs.
- Demonstrated that obesity and smoking impacts are significantly higher in upper healthcare cost quantiles.
- Highlighted that traditional mean-based approaches miss these heterogeneous impacts.
Conclusions:
- Obesity and smoking disproportionately affect high-cost patients.
- Smoking cessation and weight management programs offer significant potential for healthcare cost containment.
- Targeting interventions towards high-cost individuals may maximize cost-saving benefits.
Keywords:
conditional quantile regressiongeneralized linear modelinghealthcare costsobesitysmokingunconditional quantile regressionMore Related Videos
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