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Attributing medical spending to conditions: A comparison of methods

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  • Kaushik Ghosh
  • Irina Bondarenko
  • Kassandra L Messer
  • Susan T Stewart
  • Trivellore Raghunathan
  • Allison B Rosen
  • David M Cutler

Abstract

To understand the cost burden of medical care it is essential to partition medical spending into conditions. Two broad strategies have been used to measure disease-specific spending. The first attributes each medical claim to the condition that physicians list as its cause. The second decomposes total spending for a person over a year to their cumulative set of health conditions. Traditionally, this has been done through regression analysis. This paper has two contributions. First, we develop a new cost attribution method to attribute spending to conditions using a more flexible attribution approach, based on propensity score analysis. Second, we compare the propensity score approach to the claims-based approach and the regression approach in a common set of beneficiaries age 65 and older in the 2009 Medicare Current Beneficiary Survey. Our estimates show that the three methods have important differences in spending allocation and that the propensity score model likely offers the best theoretical and empirical combination.

Suggested Citation

  • Kaushik Ghosh & Irina Bondarenko & Kassandra L Messer & Susan T Stewart & Trivellore Raghunathan & Allison B Rosen & David M Cutler, 2020. "Attributing medical spending to conditions: A comparison of methods," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
  • Handle: RePEc:plo:pone00:0237082
    DOI: 10.1371/journal.pone.0237082
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    References listed on IDEAS

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    1. Anne E. Hall & Tina Highfill, 2013. "A Regression-Based Medical Care Expenditure Index for Medicare Beneficiaries," BEA Working Papers 0092, Bureau of Economic Analysis.
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    5. Anne E. Hall & Tina Highfill, 2016. "Calculating Disease-Based Medical Care Expenditure Indexes for Medicare Beneficiaries: A Comparison of Method and Data Choices," NBER Chapters, in: Measuring and Modeling Health Care Costs, pages 113-141, National Bureau of Economic Research, Inc.
    6. Abe Dunn & Bryn Whitmire & Andrea Batch & Lasanthi Fernando & Lindsey Rittmueller, 2018. "High Spending Growth Rates For Key Diseases In 2000-14 Were Driven By Technology And Demographic Factors," BEA Working Papers 0153, Bureau of Economic Analysis.
    7. David Cutler & Kaushik Ghosh & Irina Bondarenko & Kassandra Messer & Trivellore Raghunathan & Susan Stewart & Allison B. Rosen, 2018. "Attributing Medical Spending to Conditions: A Comparison of Methods," NBER Working Papers 25233, National Bureau of Economic Research, Inc.
    8. 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.
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    1. Guillem López-Casasnovas & José Luis Pinto Prades, 2022. "QALY Maximization and the Social Optimum," Hacienda Pública Española / Review of Public Economics, IEF, vol. 242(3), pages 111-127, September.

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