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Predicting healthcare expenditure by multimorbidity groups

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  • Caballer-Tarazona, Vicent
  • Guadalajara-Olmeda, Natividad
  • Vivas-Consuelo, David

Abstract

This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets.

Suggested Citation

  • Caballer-Tarazona, Vicent & Guadalajara-Olmeda, Natividad & Vivas-Consuelo, David, 2019. "Predicting healthcare expenditure by multimorbidity groups," Health Policy, Elsevier, vol. 123(4), pages 427-434.
    • Handle: RePEc:eee:hepoli:v:123:y:2019:i:4:p:427-434
    DOI: 10.1016/j.healthpol.2019.02.002
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    References listed on IDEAS

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    5. 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.
    6. 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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    9. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
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    Cited by:

    1. Silvia González-de-Julián & Isabel Barrachina-Martínez & David Vivas-Consuelo & Álvaro Bonet-Pla & Ruth Usó-Talamantes, 2021. "Data Envelopment Analysis Applications on Primary Health Care Using Exogenous Variables and Health Outcomes," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    2. Maria Consuelo Company-Sancho & Víctor M. González-Chordá & María Isabel Orts-Cortés, 2022. "Variability in Healthcare Expenditure According to the Stratification of Adjusted Morbidity Groups in the Canary Islands (Spain)," IJERPH, MDPI, vol. 19(7), pages 1-13, April.
    3. Maynou, Laia & Street, Andrew & García−Altés, Anna, 2023. "Living longer in declining health: Factors driving healthcare costs among older people," Social Science & Medicine, Elsevier, vol. 327(C).
    4. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
    5. Ghazalbash, Somayeh & Zargoush, Manaf & Mowbray, Fabrice & Costa, Andrew, 2022. "Impact of multimorbidity and frailty on adverse outcomes among older delayed discharge patients: Implications for healthcare policy," Health Policy, Elsevier, vol. 126(3), pages 197-206.

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