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Budgetary policies and available actions: a generalisation of decision rules for allocation and research decisions

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  • Claire McKenna

    (Centre for Health Economics, University of York, UK)

  • Zaid Chalabi

    (Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK)

  • David Epstein

    (Centre for Health Economics, University of York, UK)

  • Karl Claxton

    (Centre for Health Economics, University of York, UK)

Abstract

The allocation problem in health care can be characterised as a mathematical programming problem but attempts to incorporate uncertainty in costs and effect have suffered from important limitations. A two stage stochastic mathematical programming formulation is developed and applied to a numerical example to explore and demonstrate the implications of this more general and comprehensive approach. The solution to the allocation problem for different budgets, budgetary policies, and available actions are then demonstrated. This analysis is used to evaluate different budgetary policies and examine the adequacy of standard decision rules in cost-effectiveness analysis. The research decision is then considered alongside the allocation problem. This more general formulation demonstrates that the value of further research depends on: i) the budgetary policy in place; ii) the realisations revealed during the budget period; iii) remedial actions that may be available; and iv) variability in parameters values.

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File URL: http://www.york.ac.uk/media/che/documents/papers/researchpapers/rp44_budgetary_policies_and_decision_rules_for_allocation_and_research_decisions.pdf
File Function: First version, 2008
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Bibliographic Info

Paper provided by Centre for Health Economics, University of York in its series Working Papers with number 044cherp.

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Length: 26 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:chy:respap:44cherp

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  1. Eugene M. Laska & Morris Meisner & Carole Siegel & Aaron A. Stinnett, 1999. "Ratio-based and net benefit-based approaches to health care resource allocation: proofs of optimality and equivalence," Health Economics, John Wiley & Sons, Ltd., vol. 8(2), pages 171-174.
  2. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347.
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  5. Karl Claxton, 2008. "Exploring Uncertainty in Cost-Effectiveness Analysis," PharmacoEconomics, Springer Healthcare | Adis, vol. 26(9), pages 781-798.
  6. Chalabi, Zaid & Epstein, David & McKenna, Claire & Claxton, Karl, 2008. "Uncertainty and value of information when allocating resources within and between healthcare programmes," European Journal of Operational Research, Elsevier, vol. 191(2), pages 530-539, December.
  7. Stephanie R. Earnshaw & Susan L. Dennett, 2003. "Integer/Linear Mathematical Programming Models: A Tool for Allocating Healthcare Resources," PharmacoEconomics, Springer Healthcare | Adis, vol. 21(12), pages 839-851.
  8. Stinnett, Aaron A. & Paltiel, A. David, 1996. "Mathematical programming for the efficient allocation of health care resources," Journal of Health Economics, Elsevier, vol. 15(5), pages 641-653, October.
  9. Azondekon, Sebastien H. & Martel, Jean-Marc, 1999. ""Value" of additional information in multicriterion analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 117(1), pages 45-62, August.
  10. Leung, Stephen C.H. & Tsang, Sally O.S. & Ng, W.L. & Wu, Yue, 2007. "A robust optimization model for multi-site production planning problem in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 181(1), pages 224-238, August.
  11. Karl P. Claxton & Mark J. Sculpher, 2006. "Using Value of Information Analysis to Prioritise Health Research: Some Lessons from Recent UK Experience," PharmacoEconomics, Springer Healthcare | Adis, vol. 24(11), pages 1055-1068.
  12. Elamin H. Elbasha & Mark L. Messonnier, 2004. "Cost-effectiveness analysis and health care resource allocation: decision rules under variable returns to scale," Health Economics, John Wiley & Sons, Ltd., vol. 13(1), pages 21-35.
  13. Johannesson, Magnus & Weinstein, Milton C., 1993. "On the decision rules of cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 12(4), pages 459-467, December.
  14. Birch, Stephen & Gafni, Amiram, 1993. "Changing the problem to fit the solution: Johannesson and Weinstein's (mis) application of economics to real world problems," Journal of Health Economics, Elsevier, vol. 12(4), pages 469-476, December.
  15. Gafni, Amiram & Birch, Stephen, 2006. "Incremental cost-effectiveness ratios (ICERs): The silence of the lambda," Social Science & Medicine, Elsevier, vol. 62(9), pages 2091-2100, May.
  16. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
  17. Brandeau, Margaret L. & Zaric, Gregory S. & Richter, Anke, 2003. "Resource allocation for control of infectious diseases in multiple independent populations: beyond cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 22(4), pages 575-598, July.
  18. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost-effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787.
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Cited by:
  1. Nadia Demarteau & Thomas Breuer & Baudouin Standaert, 2012. "Selecting a Mix of Prevention Strategies against Cervical Cancer for Maximum Efficiency with an Optimization Program," PharmacoEconomics, Springer, vol. 30(4), pages 337-353, April.

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