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Estimates of patient costs related with population morbidity: can indirect costs affect the results?

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  • M. Carreras
  • M. García-Goñi
  • P. Ibern
  • J. Coderch
  • L. Vall-Llosera
  • J. Inoriza

Abstract

A number of health economics works require patient cost estimates as a basic information input. However the accuracy of cost estimates remains in general unspecified. We propose to investigate how the allocation of indirect costs or overheads can affect the estimation of patient costs in order to allow for improvements in the analysis of patient costs estimates. Instead of focusing on the costing method, this paper proposes to highlight changes in variance explained observed when a methodology is chosen. We compare three overhead allocation methods for a specific Spanish population adjusted using the Clinical Risk Groups (CRG), and we obtain different series of full-cost group estimates. As a result, there are significant gains in the proportion of the variance explained, depending upon the methodology used. Furthermore, we find that the global amount of variation explained by risk adjustment models depends mainly on direct costs and is independent of the level of aggregation used in the classification system.
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Suggested Citation

  • M. Carreras & M. García-Goñi & P. Ibern & J. Coderch & L. Vall-Llosera & J. Inoriza, 2011. "Estimates of patient costs related with population morbidity: can indirect costs affect the results?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(4), pages 289-295, August.
    • Handle: RePEc:spr:eujhec:v:12:y:2011:i:4:p:289-295
    DOI: 10.1007/s10198-010-0227-5
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    References listed on IDEAS

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    1. Manuel García-Goñi & Pere Ibern & José María Inoriza, 2009. "Hybrid risk adjustment for pharmaceutical benefits," Working Papers, Research Center on Health and Economics 1139, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Nicholas Graves & Damian Walker & Rosalind Raine & Andrew Hutchings & Jennifer A. Roberts, 2002. "Cost data for individual patients included in clinical studies: no amount of statistical analysis can compensate for inadequate costing methods," Health Economics, John Wiley & Sons, Ltd., vol. 11(8), pages 735-739, December.
    3. Manuel García‐Goñi & Pere Ibern, 2008. "Predictability of drug expenditures: an application using morbidity data," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 119-126, January.
    4. Sarah Wordsworth & Anne Ludbrook & Fergus Caskey & Alison Macleod, 2005. "Collecting unit cost data in multicentre studies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(1), pages 38-44, March.
    5. Jan B. Oostenbrink & Maiwenn J. Al, 2005. "The analysis of incomplete cost data due to dropout," Health Economics, John Wiley & Sons, Ltd., vol. 14(8), pages 763-776, August.
    6. Manuel García-Goñi & Pere Ibern & José Inoriza, 2009. "Hybrid risk adjustment for pharmaceutical benefits," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 10(3), pages 299-308, July.
    7. Zsolt Mogyorosy & Peter Smith, 2005. "The main methodological issues in costing health care services: A literature review," Working Papers 007cherp, Centre for Health Economics, University of York.
    8. Andrew Briggs & Taane Clark & Jane Wolstenholme & Philip Clarke, 2003. "Missing.... presumed at random: cost‐analysis of incomplete data," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 377-392, May.
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    Cited by:

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    2. Beáta Gavurová & Viliam Kováč & Tatiana Vagašová, 2017. "Standardised mortality rate for cerebrovascular diseases in the Slovak Republic from 1996 to 2013 in the context of income inequalities and its international comparison," Health Economics Review, Springer, vol. 7(1), pages 1-12, December.
    3. Beáta Gavurová & Tatiana Vagašová, 2016. "Regional differences of standardised mortality rates for ischemic heart diseases in the Slovak Republic for the period 1996–2013 in the context of income inequality," Health Economics Review, Springer, vol. 6(1), pages 1-12, December.
    4. Malgorzata Cyganska & Piotr Cyganski & Chris Pyke, 2019. "Development of clinical value unit method for calculating patient costs," Health Economics, John Wiley & Sons, Ltd., vol. 28(8), pages 971-983, August.

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    More about this item

    Keywords

    Patient costs; Clinical risk groups; Variation explained; Overhead allocation; B41; D24; I11;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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