Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression
**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 us...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Columbia Data Analytics, LLC
2016-11-01
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| Series: | Journal of Health Economics and Outcomes Research |
| Online Access: | https://doi.org/10.36469/9849 |
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| _version_ | 1823860391438974976 |
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| author | Bijan Borah James Naessens Kerry Olsen Nilay Shah |
| author_facet | Bijan Borah James Naessens Kerry Olsen Nilay Shah |
| author_sort | Bijan Borah |
| collection | DOAJ |
| description | **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. |
| format | Article |
| id | doaj-art-f4789687760f469c85692d56f66a6668 |
| institution | Kabale University |
| issn | 2327-2236 |
| language | English |
| publishDate | 2016-11-01 |
| publisher | Columbia Data Analytics, LLC |
| record_format | Article |
| series | Journal of Health Economics and Outcomes Research |
| spelling | doaj-art-f4789687760f469c85692d56f66a66682025-02-10T16:12:22ZengColumbia Data Analytics, LLCJournal of Health Economics and Outcomes Research2327-22362016-11-0111Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile RegressionBijan BorahJames NaessensKerry OlsenNilay Shah**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.https://doi.org/10.36469/9849 |
| spellingShingle | Bijan Borah James Naessens Kerry Olsen Nilay Shah Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression Journal of Health Economics and Outcomes Research |
| title | Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression |
| title_full | Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression |
| title_fullStr | Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression |
| title_full_unstemmed | Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression |
| title_short | Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression |
| title_sort | explaining obesity and smoking related healthcare costs through unconditional quantile regression |
| url | https://doi.org/10.36469/9849 |
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