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The relative health burden of selected social and behavioral risk factors in the united states: Implications for policy

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  • Muennig, P.
  • Fiscella, K.
  • Tancredl, D.
  • Franks, P.

Abstract

Objectives. We sought to quantify the potential health impact of selected medical and nonmedical policy changes within the United States. Methods. Using data from the 1997-2000 National Health Interview Surveys (linked to mortality data through 2002) and the 1996-2002 Medical Expenditure Panel Surveys, we calculated age-specific health-related quality-of-life scores and mortality probabilities for 8 social and behavioral risk factors. We then used Markov models to estimate the quality-adjusted life years lost. Results. Ranked quality-adjusted life years lost were income less than 200% of the poverty line versus 200% or greater (464 million; 95% confidence interval [Cl] = 368, 564); current-smoker versus never-smoker (329 million; 95% Cl = 226, 382); body mass index 30 or higher versus 20 to less than 25 (205 million; 95% Cl = 159, 269); non-Hispanic Black versus non-Hispanic White (120 million; 95% Cl =83, 163); and less than 12 years of school relative to 12 or more (74 million; 95% Cl = 52, 101). Binge drinking, overweight, and health insurance have relatively less influence on population health. Conclusions. Poverty, smoking, and high-school dropouts impose the greatest burden of disease in the United States.

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  • Muennig, P. & Fiscella, K. & Tancredl, D. & Franks, P., 2010. "The relative health burden of selected social and behavioral risk factors in the united states: Implications for policy," American Journal of Public Health, American Public Health Association, vol. 100(9), pages 1758-1764.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2009.165019_2
    DOI: 10.2105/AJPH.2009.165019
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    Cited by:

    1. Eduardo Simoes & Sergio Mariotti & Alessandra Rossi & Alicia Heim & Felipe Lobello & Ali Mokdad & Emanuele Scafato, 2012. "The Italian health surveillance (SiVeAS) prioritization approach to reduce chronic disease risk factors," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(4), pages 719-733, August.
    2. Henry S Kahn & Kai McKeever Bullard & Lawrence E Barker & Giuseppina Imperatore, 2012. "Differences between Adiposity Indicators for Predicting All-Cause Mortality in a Representative Sample of United States Non-Elderly Adults," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-12, November.
    3. Muennig, Peter, 2015. "Can universal pre-kindergarten programs improve population health and longevity? Mechanisms, evidence, and policy implications," Social Science & Medicine, Elsevier, vol. 127(C), pages 116-123.
    4. Jinjing Wu & Shelby Deaton & Boshen Jiao & Zohn Rosen & Peter A Muennig, 2018. "The cost-effectiveness analysis of the New Rural Cooperative Medical Scheme in China," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-13, December.
    5. Hatzenbuehler, Mark L. & Bellatorre, Anna & Lee, Yeonjin & Finch, Brian K. & Muennig, Peter & Fiscella, Kevin, 2014. "Structural stigma and all-cause mortality in sexual minority populations," Social Science & Medicine, Elsevier, vol. 103(C), pages 33-41.
    6. Peter Muennig & Meghan Kuebler & Jaeseung Kim & Dusan Todorovic & Zohn Rosen, 2013. "Gender Differences in Material, Psychological, and Social Domains of the Income Gradient in Mortality: Implications for Policy," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-8, March.

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