Skip to main content

Obesity and Health-Care Costs in Switzerland: Dealing with Endogeneity in Non-Linear Regression Models

Summary

We draw microdata from the Swiss Household Panel to estimate the causal effect of obesity on the number of physician visits, the amount of hospital days, and the respective costs incurred. We do so by simultaneously coping with three endogeneity issues, comprising reporting errors, omitted variables, and simultaneity. Using the conditional expectation approach, we first account for the reporting errors in weight and height. Second, we address endogeneity in the body mass index (BMI) by applying a control function approach. In contrast to the method of two-stage least squares, this technique is consistent in non-linear regression settings. Using the mean BMI of relatives as an instrument for the respondent’s BMI, we show that naïve regression methods considerably underestimate the impact of weight on the use of inpatient care, outpatient care, and costs. Accordingly, an additional unit of BMI raises annual health-care costs by CHF 253 or 11.5%, while the non-IV estimate amounts to only CHF 34 or 1.5%. Several robustness checks suggest the average marginal effect to be in the range of between CHF 220 and CHF 294. The model also predicts that if the overweight and obese people in the sample lost weight to the threshold of being of normal weight (BMI = 25), health-care costs could be reduced by about −4.7%. We conclude that the negative external effects caused by overweight and obesity are considerably larger than previously thought.

References

  • Armstrong, Benedict G., and David Oakes (1982), “Effects of Approximation in Exposure Assessments on Estimates of Exposure-Response Relationships”, Scandinavian Journal of Work, Environment and Health, 8(suppl. 1), pp. 20–23.

    Google Scholar 

  • Auld, M. Christopher (2011), “Effect of Large-Scale Social Interactions on Body Weight”, Journal of Health Economics, 30(2), pp. 303–316.

    Article  Google Scholar 

  • Bagust, Adrian, and Tom Walley (2000), “An Alternative to Body Mass Index for Standardizing Body Weight for Stature”, QJM, 93(9), pp. 589–596.

    Article  Google Scholar 

  • Battle, E. Katherine, and Kelly D. Brownell (1996), “Confronting a Rising Tide of Eating Disorders and Obesity: Treatment vs. Prevention and Policy”, Addictive Behaviors, 21(6), pp. 755–765.

    Article  Google Scholar 

  • Bigal, Marcelo E., Joshua N. Liberman, and Richard B. Lipton (2006), “Obesity and Migraine: A Population Study”, Neurology, 66(4), pp. 545–550.

    Article  Google Scholar 

  • Buntin, Melinda Beeuwkes, and Alan M. Zaslavsky (2004), “Too Much Ado about Two-Part Models and Transformation? Comparing Methods of Modeling Medicare Expenditures”, Journal of Health Economics, 23(3), pp. 525–542.

    Article  Google Scholar 

  • Burkhauser, Richard V., and John Cawley (2008), “Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research”, Journal of Health Economics, 27(2), pp. 519–529.

    Article  Google Scholar 

  • Cameron, A. Colin, and Pravin K. Trivedi (2005), Microeconometrics: Methods and Applications, Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Cameron, A. Colin, and Pravin K. Trivedi (2013), Regression Analysis of Count Data, 2nd edn., Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Cawley, John (2000), “An Instrumental Variables Approach to Measuring the Effect of Body Weight on Employment Disability”, Health Serv. Res., 35(5 Pt 2), pp. 1159–1179.

    Google Scholar 

  • Cawley, John, and Richard V. Burkhauser (2006), “Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research”, Working Paper 12291, National Bureau of Economic Research.

    Book  Google Scholar 

  • Cawley, John, and Chad Meyerhoefer (2012), “The Medical Care Costs of Obesity: An Instrumental Variables Approach”, Journal of Health Economics, 31(1), pp. 219–230.

    Article  Google Scholar 

  • Cawley, John, and Christopher J. Ruhm (2011), “The Economics of Risky Health Behaviors”, in Handbook of Health Economics, Mark V. Pauly, Thomas G. McGuire and Pedro P. Barros, eds., vol. 2, chap. 3, pp. 95–199, Philadelphia, PA: Elsevier.

    Article  Google Scholar 

  • Dalton, M., A.J. Cameron, P.Z. Zimmet, J.E. Shaw, D. Jolley, D.W. Dunstan, and T. A. Welborn (2003), “Waist Circumference, Waist-Hip Ratio and Body Mass Index and Their Correlation with Cardiovascular Disease Risk Factors in Australian Adults”, Journal of Internal Medicine, 254(6), pp. 555–563.

    Article  Google Scholar 

  • Deb, Partha, William T. Gallo, Padmaja Ayyagari, Jason M. Fletcher, and Jody L. Sindelar (2011), “The Effect of Job Loss on Overweight and Drinking”, Journal of Health Economics, 30(2), pp. 317–327.

    Article  Google Scholar 

  • Dooley, David, Ralph Catalano, and Richard Hough (1992), “Unemployment and Alcohol Disorder in 1910 and 1990: Drift Versus Social Causation”, Journal of Occupational and Organizational Psychology, 65(4), pp. 277–290.

    Article  Google Scholar 

  • Finkelstein, Eric A., Ian C. Fiebelkorn, and Guijing Wang (2003), “National Medical Spending Attributable to Overweight and Obesity: How Much, and Who’s Paying?”, Health Affairs, W3 (suppl.), pp. 219–226.

    Google Scholar 

  • Finkelstein, Eric A., Justin G. Trogdon, Joel W. Cohen, and William Dietz (2009), “Annual Medical Spending Attributable to Obesity: Payer- and Service-Specific Estimates”, Health Affairs, 28(5), pp. w822–w831.

    Article  Google Scholar 

  • Fletcher, Jason M., David Frisvold, and Nathan Tefft (2010), “Can Soft Drink Taxes Reduce Population Weight?”, Contemporary Economic Policy, 28(1), pp. 23–35.

    Article  Google Scholar 

  • Fox, Marc (2003), “Medical Student Indebtedness and the Propensity to Enter Academic Medicine”, Health Economics, 12(2), pp. 101–112.

    Article  Google Scholar 

  • Frederick, Shane, George Loewenstein, and Ted O’Donoghue (2002), “Time Discounting and Time Preference: A Critical Review”, Journal of Economic Literature, 40(2), pp. 351–401.

    Article  Google Scholar 

  • FSO (2012), “Schweizerische Gesundheitsbefragung (SGB): CD-Rom Standardtabellen SGB12 (Auszug)”, Available online http://www.portal-stat.admin.ch/sgb2012/accessed 03.03.2014.

    Google Scholar 

  • Gangwisch, J. E., D. Malaspina, B. Boden-albala, and S. B. Heymsfield (2005), “Inadequate Sleep as a Risk Factor for Obesity: Analyses of the NHANES I”, Sleep, 28(10), pp. 1289–1296.

    Article  Google Scholar 

  • Gila, Joan, and Toni Morab (2011), “The Determinants of Misreporting Weight and Height: The Role of Social Norms”, Economics and Human Biology, 9(1), pp. 78–91.

    Article  Google Scholar 

  • Haberstick, Brett C., Jeffery M. Lessem, Matthew B. Mcqueen, Jason D. Boardman, Christian J. Hopfer, Andrew Smolen, and John K. Hewitt (2010), “Stable Genes and Changing Environments: Body Mass Index across Adolescence and Young Adulthood”, Behavior Genetics, 40(4), pp. 495–504.

    Article  Google Scholar 

  • Halford, W. Kim, and E. Learner (1984), “Correlates of Coping with Unemployment in Young Australians”, Australian Psychologist, 19(3), pp. 333–344.

    Article  Google Scholar 

  • Hansen, Lars Peter (1982), “Large Sample Properties of Generalized Method of Moments Estimators”, Econometrica, 50(4), pp. 1029–1054.

    Article  Google Scholar 

  • Hausman, Jerry A (1978), “Specification Tests in Econometrics”, Econometrica, 46(6), 1251–1271.

    Article  Google Scholar 

  • Häussler, Jan (2014), “Effects of Obesity and Physical Activity on Health Care Utilization and Costs”, Working Paper Series 2014-07, University of Konstanz.

    Google Scholar 

  • Holmes, Ann M., and Partha Deb (1998), “Provider Choice and Use of Mental Health Care: Implications for Gatekeeper Models”, Health Serv. Res., 33(5 Pt 1), pp. 1263–1284.

    Google Scholar 

  • Lee, Amanda J., Iain K. Crombie, William C. S. Smith, and Hugh D. Tunstall-Pedoe (1991), “Cigarette Smoking and Employment Status”, Soc. Sci. Med, 33(11), pp. 1309–1312.

    Article  Google Scholar 

  • Lindrooth, Richard C. and Burton A. Weisbrod (2007), “Do Religious Nonprofit and For-Profit Organizations Respond Differently to Financial Incentives? The Hospice Industry”, Journal of Health Economics, 26(2), pp 342–357

    Article  Google Scholar 

  • Lyles, Robert H., and Lawrence L. Kupper (1997), “A Detailed Evaluation of Adjustment Methods for Multiplicative Measurement Error in Linear Regression with Applications in Occupational Epidemiology”, Biometrics, 53(3), pp. 1008–1025.

    Article  Google Scholar 

  • Maes, Hermine H. M., Michael C. Neale, and Lindon J. Eaves (1997), “Genetic and Environmental Factors in Relative Body Weight and Human Adiposity”, Behavior Genetics, 27(4), pp. 325–351.

    Article  Google Scholar 

  • Manning, Willard G., and John Mullahy (2001), “Estimating Log Models: To Transform or not to Transform?”, Journal of Health Economics, 20(4), pp 461–494

    Article  Google Scholar 

  • Meer, Jonathan, and Harvey S. Rosen (2004), “Insurance and the Utilization of Medical Services”, Soc. Sci. Med., 58(9), pp. 1623–1632.

    Article  Google Scholar 

  • O’Neill, Donal, and Olive Sweetman (2013), “The Consequences of Measurement Error when Estimating the Impact of Obesity on Income”, IZA Journal of Labor Economics, 2(3), pp. 1–20.

    Google Scholar 

  • Park, Rolla E. (1966), “Estimation with Heteroscedastic Error Terms”, Econometrica, 34, p 888

    Article  Google Scholar 

  • Parks, Joanna C., Julian M. Alston, and Abigail M. Okrent (2012), “The Marginal External Cost of Obesity in the United States”, RMICWE Working Paper 1201, Robert Mondavi Institute Center for Wine Economics.

    Google Scholar 

  • Peterli, Ralph, Robert E. Steinert, Bettina Woelnerhanssen, Thomas Peters, Caroline Christoffel-Courtin, Markus Gass, Beatrice Kern, Markus von Fluee, and Christoph Beglinger (2012), “Metabolic and Hormonal Changes after Laparoscopic Roux-en-Y Gastric Bypass and Sleeve Gastrectomy: A Randomized, Prospective Trial”, Obesity Surgery, 22(5), pp. 740–748.

    Article  Google Scholar 

  • Plankey, Michael W., June Stevens, Katherine M. Flegal, and Philip F. Rust (1997), “Prediction Equations Do not Eliminate Systematic Error in Self-Reported Body Mass Index”, Obesity Research, 5(4), pp. 308–314.

    Article  Google Scholar 

  • Royston, Patrick, and Paul C. Lambert (2011), Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model, College Station, TX: Stata Press.

    Google Scholar 

  • Schmid, Alexandra, Heinz Schneider, Alain Golay, and Ulrich Keller (2005), “Economic Burden of Obesity and its Comorbidities in Switzerland”, Sozial- und Praeventivmedizin, 50(2), pp. 87–94.

    Article  Google Scholar 

  • Schousboe, Karoline, Gonneke Willemsen, Kirsten O. Kyvik, Jakob Mortensen, Dorret I. Boomsma, Belinda K. Cornes, Chayna J. Davis, Corrado Fagnani, Jacob Hjelmborg, Jaakko Kaprio, Marlies de Lange, Michelle Luciano, Nicholas G. Martin, Nancy Pedersen, Kirsi H. Pietilainen, Aila Rissanen, Suoma Saarni, Thorkild I.A. Sørensen, G. Caroline M. van Baal, and Jennifer R. Harris (2003), “Sex Differences in Heritability of BMI: A Comparative Study of Results from Twin Studies in Eight Countries”, Twin Research and Human Genetics, 6, pp. 409–421.

    Article  Google Scholar 

  • Schroeter, Christiane, Jayson Lusk, and Wallace Tyner (2008), “Determining the Impact of Food Price and Income Changes on Body Weight”, Journal of Health Economics, 27(1), pp. 45–68.

    Article  Google Scholar 

  • Shea, Dennis G, Joseph V Terza, Bruce C Stuart, and Becky Briesacher (2007), “Estimating the Effects of Prescription Drug Coverage for Medicare Beneficiaries”, Health Services Research, 42(3p1), pp. 933–949.

    Article  Google Scholar 

  • Shin, Jaeun, and Sangho Moon (2007), “Do HMO Plans Reduce Health Care Expenditure in the Private Sector?”, Economic Inquiry, 45(1), pp. 82–99.

    Article  Google Scholar 

  • Shiri, Rahman, Jaro Karppinen, Paivi Leino-Arjas, Svetlana Solovieva, and Eira Viikari-Juntura (2009), “The Association between Obesity and Low Back Pain: A Meta-Analysis”, American Journal of Epidemiology, 171(2), pp. 135–154.

    Article  Google Scholar 

  • Staiger, Douglas, and James H. Stock (1997), “Instrumental Variables Regression with Weak Instruments”, Econometrica, 65(3), pp. 557–586.

    Article  Google Scholar 

  • Terza, Joseph V., Anirban Basu, and Paul J. Rathouz (2008), “Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling”, Journal of Health Economics, 27(3), pp. 531–543.

    Article  Google Scholar 

  • Terza, Joseph V., W. David Bradford, and Clara E. Dismuke (2008), “The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note”, Health Services Research, 43(3), pp. 1102–1120.

    Article  Google Scholar 

  • Villanueva, Elmer V. (2001), “The Validity of Self-Reported Weight in US Adults: A Population Based Cross-Sectional Study”, BMC Public Health, 1, p. 11.

    Article  Google Scholar 

  • Windmeijer, Frank A.G., and Joao M.C. Santos Silva (1997), “Endogeneity in Count Data Models: An Application to Demand for Health Care”, Journal of Applied Econometrics, 12(3), pp. 281–294.

    Article  Google Scholar 

  • Wooldridge, Jeffrey M. (2014), “Quasi-Maximum Likelihood Estimation and Testing for Nonlinear Models with Endogenous Explanatory Variables”, Journal of Econometrics, 182(1), pp. 226–234.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefan Meyer.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Meyer, S. Obesity and Health-Care Costs in Switzerland: Dealing with Endogeneity in Non-Linear Regression Models. Swiss J Economics Statistics 152, 243–286 (2016). https://doi.org/10.1007/BF03399428

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03399428

JEL-Classification

  • I11
  • I12
  • C26

Keyword

  • obesity
  • health expenditure
  • measurement errors
  • endogeneity
  • control functions