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Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis

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  • Thomas Woods

    (Department of Statistics, Miami University, Oxford, OH 45056, USA)

  • Tatjana Miljkovic

    (Department of Statistics, Miami University, Oxford, OH 45056, USA)

Abstract

We propose a new approach for estimating the state-level direct and indirect economic cost of obesity in the United States for the time period 1996 to 2018. Our unique top-down methodology integrates a prevalence-based method with various medical-level costs, economic, demographic, and socio-economic factors. Using this approach, we investigate the relationship between the estimates of the total obesity-related costs and the health insurance premium by state in order to evaluate the state burden of obesity. Our estimate of the total national economic cost attributed to obesity is approximately $422 billion in 2018, representing about 2% of the national GDP for the same year. Using exponential smoothing models, we forecast that the total cost would reach $475 billion in 2021 without accounting for the impact of COVID-19 on obesity. The top states driving the cost estimates are California, Texas, New York, and Florida. A bootstrapping technique is employed to the state-level estimated cost in order to determine the average cost per person. We hope that our study will promote interest in this topic and open discussion for further research in this area.

Suggested Citation

  • Thomas Woods & Tatjana Miljkovic, 2022. "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis," Risks, MDPI, vol. 10(10), pages 1-28, October.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:10:p:197-:d:944097
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    References listed on IDEAS

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    3. Tatjana Miljkovic & Saleem Shaik & Dragan Miljkovic, 2017. "Redefining standards for body mass index of the US population based on BRFSS data using mixtures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 197-211, January.
    4. Palma Daawin & Seonjin Kim & Tatjana Miljkovic, 2019. "Predictive Modeling of Obesity Prevalence for the U.S. Population," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(1), pages 64-81, January.
    5. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
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