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Needs-Based Health Care Funding: Implications for Resource Distribution in Ontario

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Author Info

  • Kelly Bedard

    (UC - Santa Barbara)

  • John Dorland

    (Queen's University)

  • Allan W. Gregory

    (Queen's University)

  • Joanne Roberts

    (University of Toronto)

Abstract

Capitation models have been suggested as an alternative to funding methods baseed on historical utilization patterns. Capitation funding distributes resources to regions or programs according to their population, adjusted for the age and gender composition and relative need. The most commonly used relative needs measure is the Standardized Mortatility Ratio (SMR). This paper compares the distribution of resources in Ontario implied by a variety of capitation formula. Another aspect of this research is to design a mechanism that translates the SMR into a funding allocation index. We specify a non-linear model to capture the relationship between current expenditures and the SMR while controlling for historical utilization factors. In contrast to previous work which has assumed a linear relationship between expenditures and need, our estimates suggest that the relationship may actually be highly non-linear. This non-linearity increases transfers of relative need relative to a linear capitation program.

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Bibliographic Info

Paper provided by Claremont Colleges in its series Claremont Colleges Working Papers with number 1999-18.

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Handle: RePEc:clm:clmeco:1999-18

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Cited by:
  1. Marcelin Joanis & David Boisclair & Claude Montmarquette, 2004. "La santé au Québec : des options pour financer la croissance," CIRANO Project Reports 2004rp-04, CIRANO.
  2. Kelly Bedard & John Dorland & Allan W. Gregory & Mark Rosenberg, 1999. "Standardized Mortality Ratios and Canadian Health-Care Funding," Canadian Public Policy, University of Toronto Press, vol. 25(1), pages 47-64, March.
  3. Montero Granados, Roberto & Jimenez Aguilera, Juan de Dios & Martin Martin, Jose Jesus, 2007. "Estimation of an index of regional health needs in Spain using count regression models with filter," Health Policy, Elsevier, vol. 81(1), pages 4-16, April.

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