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

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  • McKenna, Claire
  • Chalabi, Zaid
  • Epstein, David
  • Claxton, Karl

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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Bibliographic Info

Article provided by Elsevier in its journal Journal of Health Economics.

Volume (Year): 29 (2010)
Issue (Month): 1 (January)
Pages: 170-181

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Handle: RePEc:eee:jhecon:v:29:y:2010:i:1:p:170-181

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Web page: http://www.elsevier.com/locate/inca/505560

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Keywords: Allocation decisions Cost-effectiveness analysis Research decisions Stochastic mathematical programming Decision analysis;

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References

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  1. Birch, Stephen & Gafni, Amiram, 1992. "Cost effectiveness/utility analyses : Do current decision rules lead us to where we want to be?," Journal of Health Economics, Elsevier, Elsevier, vol. 11(3), pages 279-296, October.
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  6. Sendi, Pedram & Al, Maiwenn J. & Gafni, Amiram & Birch, Stephen, 2003. "Optimizing a portfolio of health care programs in the presence of uncertainty and constrained resources," Social Science & Medicine, Elsevier, Elsevier, vol. 57(11), pages 2207-2215, December.
  7. 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, Elsevier, vol. 191(2), pages 530-539, December.
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  13. 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, Springer Healthcare | Adis, vol. 24(11), pages 1055-1068.
  14. 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, Elsevier, vol. 22(4), pages 575-598, July.
  15. Gafni, Amiram & Birch, Stephen, 2006. "Incremental cost-effectiveness ratios (ICERs): The silence of the lambda," Social Science & Medicine, Elsevier, Elsevier, vol. 62(9), pages 2091-2100, May.
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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, Springer, vol. 30(4), pages 337-353, April.

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