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Predictability of drug expenditures: an application using morbidity data

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Author Info

  • Manuel Garc�a-Go�i

    (Universidad Complutense de Madrid, Madrid, Spain)

  • Pere Ibern

    (Universitat Pompeu Fabra, Barcelona, Spain)

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    Abstract

    The growth of pharmaceutical expenditure and its prediction is a major concern for policymakers and healthcare managers. This paper explores different predictive models to estimate future drug expenses, using demographic and morbidity individual information from an integrated healthcare delivery organization in Catalonia for years 2002 and 2003. The morbidity information consists of codified health encounters grouped through the Clinical Risk Groups (CRGs). We estimate pharmaceutical costs using several model specifications, and CRGs as risk adjusters, providing an alternative way of obtaining high predictive power comparable to other estimations of drug expenditures in the literature. These results have clear implications for the use of risk adjustment and CRGs in setting the premiums for pharmaceutical benefits. Copyright © 2007 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/hec.1238
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    Bibliographic Info

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

    Volume (Year): 17 (2008)
    Issue (Month): 1 ()
    Pages: 119-126

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    Handle: RePEc:wly:hlthec:v:17:y:2008:i:1:p:119-126

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

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    References

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    1. Coulson, N. Edward & Stuart, Bruce, 1992. "Persistence in the use of pharmaceuticals by the elderly : Evidence from annual claims," Journal of Health Economics, Elsevier, vol. 11(3), pages 315-328, October.
    2. Arlene Ash & Randall P. Ellis & Gregory Pope & John Ayanian & David Bates & Helen Burstin & Lisa Iezzoni & Elizabeth McKay & Wei Yu, 2000. "Using Diagnoses to Describe Populations and Predict Costs," Papers 0099, Boston University - Industry Studies Programme.
    3. Lamers, Leida M. & van Vliet, Rene C. J. A., 2004. "The Pharmacy-based Cost Group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation," Health Policy, Elsevier, vol. 68(1), pages 113-121, April.
    4. Puig-Junoy, Jaume, 2004. "Incentives and pharmaceutical reimbursement reforms in Spain," Health Policy, Elsevier, vol. 67(2), pages 149-165, February.
    5. Van de ven, Wynand P.M.M. & Ellis, Randall P., 2000. "Risk adjustment in competitive health plan markets," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 14, pages 755-845 Elsevier.
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    Cited by:
    1. Vivas-Consuelo, David & Usó-Talamantes, Ruth & Trillo-Mata, José Luis & Caballer-Tarazona, Maria & Barrachina-Martínez, Isabel & Buigues-Pastor, Laia, 2014. "Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups," Health Policy, Elsevier, vol. 116(2), pages 188-195.
    2. 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, vol. 12(4), pages 289-295, August.
    3. Manuel García-Goñi & Pere Ibern & José Inoriza, 2009. "Hybrid risk adjustment for pharmaceutical benefits," The European Journal of Health Economics, Springer, vol. 10(3), pages 299-308, July.

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