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Predictive Modeling of Obesity Prevalence for the U.S. Population

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  • Palma Daawin
  • Seonjin Kim
  • Tatjana Miljkovic

Abstract

Modeling obesity prevalence is an important part of the evaluation of mortality risk. A large volume of literature exists in the area of modeling mortality rates, but very few models have been developed for modeling obesity prevalence. In this study we propose a new stochastic approach for modeling obesity prevalence that accounts for both period and cohort effects as well as the curvilinear effects of age. Our model has good predictive power as we utilize multivariate ARIMA models for forecasting future obesity rates. The proposed methodology is illustrated on the U.S. population, aged 23–90, during the period 1988–2012. Forecasts are validated on actual data for the period 2013–2015 and it is suggested that the proposed model performs better than existing models.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:uaajxx:v:23:y:2019:i:1:p:64-81
    DOI: 10.1080/10920277.2018.1506348
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    Cited by:

    1. 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.

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