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Health expenditure estimation and functional form: applications of the generalized gamma and extended estimating equations models

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  • Steven C. Hill

    (Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, Rockville, MD, USA)

  • G. Edward Miller

    (Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, Rockville, MD, USA)

Abstract

Health-care expenditure regressions are used in a wide variety of economic analyses including risk adjustment and program and treatment evaluations. Recent articles demonstrated that generalized gamma models (GGMs) and extended estimating equations (EEE) models provide flexible approaches to deal with a variety of data problems encountered in expenditure estimation. To date there have been few empirical applications of these models to expenditures. We use data from the US Medical Expenditure Panel Survey to compare the bias, predictive accuracy, and marginal effects of GGM and EEE models with other commonly used regression models in a cross-validation study design. Health-care expenditure distributions vary in the degree of heteroskedasticity, skewness, and kurtosis by type of service and population. To examine the ability of estimators to address a range of data problems, we estimate models of total health expenditures and prescription drug expenditures for two populations, the elderly and privately insured adults. Our findings illustrate the need for researchers to examine their assumptions about link functions: the appropriate link function varies across our four distributions. The EEE model, which has a flexible link function, is a robust estimator that performs as well, or better, than the other models in each distribution. Published in 2009 by John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/hec.1498
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 19 (2010)
Issue (Month): 5 ()
Pages: 608-627

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Handle: RePEc:wly:hlthec:v:19:y:2010:i:5:p:608-627

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
  2. Willard G. Manning & Anirban Basu & John Mullahy, 2003. "Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data," NBER Technical Working Papers 0293, National Bureau of Economic Research, Inc.
  3. Willard G. Manning & John Mullahy, 1999. "Estimating Log Models: To Transform or Not to Transform?," NBER Technical Working Papers 0246, National Bureau of Economic Research, Inc.
  4. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
  5. Partha Deb & James F. Burgess, Jr., 2003. "A Quasi-experimental Comparison of Econometric Models for Health Care Expenditures," Economics Working Paper Archive at Hunter College 212, Hunter College Department of Economics.
  6. Anirban Basu & Bhakti V. Arondekar & Paul J. Rathouz, 2006. "Scale of interest versus scale of estimation: comparing alternative estimators for the incremental costs of a comorbidity," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1091-1107.
  7. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
  8. Jean-Paul Chaze, 2005. "Assessing household health expenditure with Box-Cox censoring models," Health Economics, John Wiley & Sons, Ltd., vol. 14(9), pages 893-907.
  9. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
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Cited by:
  1. Jones, A.M, 2010. "Models For Health Care," Health, Econometrics and Data Group (HEDG) Working Papers 10/01, HEDG, c/o Department of Economics, University of York.
  2. Toni Mora & Joan Gil & Antoni Sicras-Mainar, 2012. "The influence of BMI, obesity and overweight on medical costs: a panel data approach," Working Papers 2012/37, Institut d'Economia de Barcelona (IEB).
  3. Courbage, Christophe & Rey, BĂ©atrice, 2012. "Priority setting in health care and higher order degree change in risk," Journal of Health Economics, Elsevier, vol. 31(3), pages 484-489.
  4. Jones, A. & Lomas, J. & Rice, N., 2011. "Applying Beta-type Size Distributions to Healthcare Cost Regressions," Health, Econometrics and Data Group (HEDG) Working Papers 11/31, HEDG, c/o Department of Economics, University of York.

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