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Is the BMI a Relic of the Past?

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  • Lee, Wang-Sheng

    (Monash University)

Abstract

The most widely used measure of adiposity is to express weight adjusted for height using the body mass index (BMI). However, its limitations such as its inability to distinguish muscle weight from fat weight are well known, leading public health authorities in the UK and US to recommend measuring waist circumference as a complementary diagnostic tool for obesity. Recent attention placed on the syndrome referred to as 'normal weight obesity' – individuals with normal BMI but high body fat content – emphasizes the need for a more comprehensive diagnostic tool for obesity. Based on the NHANES III data, we utilize a semi-parametric spline approach to depict graphically the relationship between BMI, waist circumference and percent body fat. In this note, we propose that percent body fat charts that incorporate information from three anthropometric dimensions supersede the one-size-fits-all obesity diagnostic approach based on power-type indices such as the BMI.

Suggested Citation

  • Lee, Wang-Sheng, 2014. "Is the BMI a Relic of the Past?," IZA Discussion Papers 8637, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp8637
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    References listed on IDEAS

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    1. Simon N. Wood, 2006. "Low-Rank Scale-Invariant Tensor Product Smooths for Generalized Additive Mixed Models," Biometrics, The International Biometric Society, vol. 62(4), pages 1025-1036, December.
    2. Peter Hall & J. D. Opsomer, 2005. "Theory for penalised spline regression," Biometrika, Biometrika Trust, vol. 92(1), pages 105-118, March.
    3. Burkhauser, Richard V. & Cawley, John & Schmeiser, Maximilian D., 2009. "The timing of the rise in U.S. obesity varies with measure of fatness," Economics & Human Biology, Elsevier, vol. 7(3), pages 307-318, December.
    4. Dolan, C.M. & Kraemer, H. & Browner, W. & Ensrud, K. & Kelsey, J.L., 2007. "Associations between body composition, anthropometry, and mortality in women aged 65 years and older," American Journal of Public Health, American Public Health Association, vol. 97(5), pages 913-918.
    5. Clark, Andrew E. & Etilé, Fabrice, 2011. "Happy house: Spousal weight and individual well-being," Journal of Health Economics, Elsevier, vol. 30(5), pages 1124-1136.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    7. Yingxing Li & David Ruppert, 2008. "On the asymptotics of penalized splines," Biometrika, Biometrika Trust, vol. 95(2), pages 415-436.
    8. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    9. Burkhauser, Richard V. & Cawley, John, 2008. "Beyond BMI: The value of more accurate measures of fatness and obesity in social science research," Journal of Health Economics, Elsevier, vol. 27(2), pages 519-529, March.
    10. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    11. M. P. Wand, 2003. "Smoothing and mixed models," Computational Statistics, Springer, vol. 18(2), pages 223-249, July.
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    More about this item

    Keywords

    BMI; body fat; P-spline; waist circumference; semi-parametric;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General

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