Savings in Medical Expenditures Associated with Reductions in Body Mass Index Among US Adults with Obesity, by Diabetes Status

John Cawley, Chad Meyerhoefer, Adam Biener, Mette Hammer, Neil Wintfeld, John Cawley, Chad Meyerhoefer, Adam Biener, Mette Hammer, Neil Wintfeld

Abstract

Background: The prevalence of obesity has more than doubled in the USA in the past 30 years. Obesity is a significant risk factor for diabetes, cardiovascular disease, and other clinically significant co-morbidities. This paper estimates the medical care cost savings that can be achieved from a given amount of weight loss by people with different starting values of body mass index (BMI), for those with and without diabetes. This information is an important input into analyses of the cost effectiveness of obesity treatments and prevention programs.

Methods: Two-part models of instrumental variables were estimated using data from the Medical Expenditure Panel Survey (MEPS) for 2000-2010. Models were estimated for all adults as well as separately for those with and without diabetes. We calculated the causal impact of changes in BMI on medical care expenditures, cost savings for specific changes in BMI (5, 10, 15, and 20 %) from starting BMI levels ranging from 30 to 45 kg/m(2), as well as the total excess medical care expenditures caused by obesity.

Results: In the USA, adult obesity raised annual medical care costs by $US3,508 per obese individual, for a nationwide total of $US315.8 billion (year 2010 values). However, the relationship of medical care costs over BMI is J-shaped; costs rise exponentially in the range of class 2 and 3 obesity (BMI ≥35). The heavier the obese individual, the greater the reduction in medical care costs associated with a given percent reduction in BMI. Medical care expenditures are higher, and rise more with BMI, among individuals with diabetes than among those without diabetes.

Conclusions: The savings from a given percent reduction in BMI are greater the heavier the obese individual, and are greater for those with diabetes than for those without diabetes. The results provide health insurers, employers, government agencies, and health economists with accurate estimates of the change in medical care expenditures resulting from weight loss, which is important information for calculating the cost effectiveness of interventions to prevent and treat obesity.

Figures

Fig. 1
Fig. 1
Predicted total annual medical expenditures from the instrumental variables model. Data: Medical Expenditure Panel Survey (MEPS) 2000–2010 [18]. Expenditures are in $US, year 2010 values. Dashed lines represent 95 % confidence intervals, which have been adjusted for the complex design of the MEPS. Medical expenditures are denoted by the solid line and are measured on the left axis. The dotted line indicates the distribution of individuals in the population
Fig. 2
Fig. 2
Predicted total annual medical expenditures from the instrumental variables model for those without type 2 diabetes. Data: Medical Expenditure Panel Survey (MEPS) 2000–2010 [18]. Expenditures are in $US, year 2010 values. Dashed lines represent 95 % confidence intervals, which have been adjusted for the complex design of the MEPS. Medical expenditures are denoted by the solid line and are measured on the left axis. The dotted line indicates the distribution of individuals in the population
Fig. 3
Fig. 3
Predicted total annual medical expenditures from the instrumental variables model for those with type 2 Diabetes. Data: Medical Expenditure Panel Survey (MEPS) 2000–2010 [18]. Expenditures are in $US, year 2010 values. Dashed lines represent 95 % confidence intervals, which have been adjusted for the complex design of the MEPS. Medical expenditures are denoted by the solid line and are measured on the left axis. The dotted line indicates the distribution of individuals in the population
Fig. 4
Fig. 4
Predicted total annual medical expenditures from the instrumental variables model. Data: Medical Expenditure Panel Survey (MEPS) 2000–2010 [18]. Expenditures are in $US, year 2010 values. Dashed lines represent 95 % confidence intervals, which have been adjusted for the complex design of the MEPS. Medical expenditures are denoted by the solid line and are measured on the left axis. The dotted line indicates the distribution of individuals in the population

References

    1. Burkhauser RV, Cawley J, Schmeiser MD. The timing of the rise in U.S. obesity varies with measure of fatness. Econ Hum Biol. 2009;7(3):307–318. doi: 10.1016/j.ehb.2009.07.006.
    1. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311(8):806–814. doi: 10.1001/jama.2014.732.
    1. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health. 2009;9:88. doi: 10.1186/1471-2458-9-88.
    1. Dixon JB. The effect of obesity on health outcomes. Mol Cell Endocrinol. 2010;316(2):104–108. doi: 10.1016/j.mce.2009.07.008.
    1. Hu FB. Obesity epidemiology. New York: Oxford University Press; 2008.
    1. Winter Y, Sankowski R, Back T. Genetic determinants of obesity and related vascular diseases. Vitam Horm. 2013;91:29–48. doi: 10.1016/B978-0-12-407766-9.00002-X.
    1. Trayhurn P, Beattie JH. Physiological role of adipose tissue: white adipose tissue as an endocrine and secretory organ. Proc Nutr Soc. 2001;60(3):329–339. doi: 10.1079/PNS200194.
    1. Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer. 2004;4(8):579–591. doi: 10.1038/nrc1408.
    1. Malnick SDH, Knobler H. The medical complications of obesity. QJM. 2006;99(9):565–579. doi: 10.1093/qjmed/hcl085.
    1. McLaren L. Socioeconomic Status and Obesity. In: Cawley JHJ, editor. The Oxford handbook of the social science of obesity. New York: Oxford University Press; 2011. pp. xvi, 894.
    1. Cawley J, Burkhauser R. Beyond BMI: the value of more accurate measures of fatness and obesity in social science research. NBER Working Paper No. 12291. National Bureau of Economic Research. June 2006. Available from: . Accessed 14 Oct 2014.
    1. Bound J, Brown C, Mathiowetz N. Measurement error in survey data. In: Heckman JJ, Leamer E, editors. Handbook of econometrics. New York: Springer; 2002. pp. 3705–3843.
    1. Finkelstein EAE, Trogdon JGJ, Cohen JJW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood) 2009;28(5):w822–w831. doi: 10.1377/hlthaff.28.5.w822.
    1. Trasande L, Liu Y, Fryer G, Weitzman M. Effects of childhood obesity on hospital care and costs, 1999–2005. Health Aff (Millwood) 2009;28(4):w751–w760. doi: 10.1377/hlthaff.28.4.w751.
    1. Thorpe KE, Florence CS, Howard DH, Joski P. The impact of obesity on rising medical spending. Health Aff (Millwood). 2004;Suppl Web W4–480–6.
    1. Finkelstein EA, Fiebelkorn IC, Wang G. National medical spending attributable to overweight and obesity: how much, and who’s paying? Health Aff (Millwood). 2003;Suppl Web:W3–219–26.
    1. Cawley J, Meyerhoefer C. The medical care costs of obesity: an instrumental variables approach. J Health Econ. 2012;31(1):219–230. doi: 10.1016/j.jhealeco.2011.10.003.
    1. Agency for Healthcare Research and Quality. Download Data Files, Documentation, and Codebooks. In: Medical Expenditure Panel Survey. U.S. Department of Health & Human Services, Agency for Healthcare Research and Quality. 2010. Available from: . Accessed 14 Oct 2014.
    1. Jones A. Health econometrics. In: Culyer AJ, Newhouse JP, editors. Handbook of health economics, chap. 6, vol. 1A; 2000. pp. 265–336.
    1. Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–494. doi: 10.1016/S0167-6296(01)00086-8.
    1. Albu J, Pi-Sunyer FX. Obesity and Diabetes. In: Bray GA, Bouchard C, editors. Handbook of obesity: clinical applications. New York: Marcel Dekker; 2004. pp. x, 403.
    1. Carroll RJ, Ruppert D, Stefanski LA. Measurement error in nonlinear models. New York: Chapman and Hall; 1995.
    1. Hardin J, Schmiediche H, Carroll R. Instrumental variables, bootstrapping, and generalized linear models. Stat J. 2003;3(4):351–360.
    1. Cawley J. The impact of obesity on wages. J Hum Resour. 2004;39(2):451–474. doi: 10.2307/3559022.
    1. Kline B, Tobias JL. The wages of BMI: Bayesian analysis of a skewed treatment-response model with nonparametric endogeneity. J Appl Econ. 2008;23(6):767–793. doi: 10.1002/jae.1028.
    1. Smith DG, Sterne JAC, Fraser A, Tynelius P, Lawlor DA, Rasmussen F. The association between BMI and mortality using offspring BMI as an indicator of own BMI: large intergenerational mortality study. BMJ. 2009;339:b5043.
    1. Farooqi IS, O’Rahilly S. Genetic factors in human obesity. Obes Rev. 2007;8(Suppl 1):37–40. doi: 10.1111/j.1467-789X.2007.00315.x.
    1. Pietilainen KH. Genetics and epigenetics: myths or facts? In: Haslam DW, Sharma AM, Le Roux CW, editors. Controversies in obesity. London: Springer; 2014.
    1. Stock J, Wright J, Yogo M. A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat. 2002;20(4):518–529. doi: 10.1198/073500102288618658.
    1. Grilo CM, Pogue-Geile MF. The nature of environmental influences on weight and obesity: a behavior genetic analysis. Psychol Bull. 1991;110(3):520–537. doi: 10.1037/0033-2909.110.3.520.
    1. Wardle J, Carnell S, Haworth CM, Plomin R. Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am J Clin Nutr. 2008;87(2):398–404.
    1. Davis K. Sample design of the 2011 medical expenditure panel survey insurance component. Methodology Report #27. Rockville: Agency for Healthcare Research and Quality; 2013. Available from .
    1. Black D, Berger M, Scott F. Bounding parameter estimates with nonclassical measurement error. J Am Stat Assoc. 2000;95(451):739–748. doi: 10.1080/01621459.2000.10474262.
    1. Frazis H, Loewenstein MA. Estimating linear regressions with mismeasured, possibly endogenous, binary explanatory variables. J Econ. 2003;117(1):151–178. doi: 10.1016/S0304-4076(03)00121-0.
    1. US Bureau of Labor Statistics. Consumer Price Indexes. BLS Handbook of Methods. Washington, DC: Bureau of Labor Statistics; 2013.
    1. Krewski D, Rao JNK. Inference from stratified samples: properties of the linearization, jackknife and balanced repeated replication methods. Ann Stat. 1981;9(5):1010–1019. doi: 10.1214/aos/1176345580.
    1. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71–82. doi: 10.1001/jama.2012.113905.
    1. Mehta NK, Chang VW. Mortality attributable to obesity among middle-aged adults in the United States. Demography. 2009;46(4):851–872. doi: 10.1353/dem.0.0077.
    1. Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–50.
    1. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.
    1. United States Census Bureau. Population and housing unit estimates. 2014. Available from . Accessed 30 Oct 2014.
    1. Fontaine KR, Bartlett SJ. Access and use of medical care among obese persons. Obes Res. 2000;8(5):403–6.
    1. Small D, Rosenbaum PR. War and wages: the strength of instrumental variables and their sensitivity to unobserved biases. J Am Stat Assoc. 2008;103(483):924–33.
    1. Burkhauser RV, Cawley J. Beyond BMI: the value of more accurate measures of fatness and obesity in social science research. J Health Econ. 2008;27(2):519–29.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren