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Cadth 2015 b5 end of life premia cadth 130415

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End-of-life premiums in
reimbursement decision making
Christopher McCabe PhD
Capital Health Endowed Research Chair
Univers...

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Acknowledgements
Funded by Genome Canada, Canadian Institutes
for Health Research, Alberta Innovates Health
Solutions, Cap...

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Overview
• End-of-life premium a la NICE
• A model of CEA-based reimbursement
• Incorporating additional value criteria
• ...

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Cadth 2015 b5 end of life premia cadth 130415

  1. 1. End-of-life premiums in reimbursement decision making Christopher McCabe PhD Capital Health Endowed Research Chair University of Alberta
  2. 2. Acknowledgements Funded by Genome Canada, Canadian Institutes for Health Research, Alberta Innovates Health Solutions, Capital Health Research Chair Endowment, UK National Institute for Health Research. Co-authors: Mike Paulden, Anthony Culyer, Richard Edlin, James O’Mahoney, Tania Stafinski, Devidas Menon
  3. 3. Overview • End-of-life premium a la NICE • A model of CEA-based reimbursement • Incorporating additional value criteria • Value based reimbursement • Vertical and Horizontal Equity • Conclusion
  4. 4. End of Life premium a la NICE • The treatment is indicated for patients with a short life expectancy; normally less than 24 months. • There is sufficient evidence to indicate that the treatment offers an extension to life, normally of at least 3 months, compared to current NHS treatment; and • The treatment is licensed, or otherwise indicated, for small patient populations.
  5. 5. When these criteria are met, the appraisal committee is to consider: “the magnitude of additional weight that would be need to be assigned to QALY benefits in this patient group for the cost effectiveness of the therapy to fall within the current threshold range.” NICE Guide to the Methods of Health Technology Appraisal (2013)
  6. 6. 0 Health care expenditures Health benefit per $1,000 A model of CEA based reimbursement Current treatments covered by health care system Budget Treatments not covered by the health care system Worse Than Current Better value Worse value Willingness to Pay
  7. 7. 0 Budget Willingness to Pay New Willingness to Pay Health benefit per $1,000 Health care expenditures A model of CEA based reimbursement
  8. 8. 0 Health benefit per $1,000 Budget Willingness to Pay Net Benefit of changing how health care $$ are spent New Willingness to Pay Health care expenditures A model of CEA based reimbursement
  9. 9. Incorporating additional values
  10. 10. • Prevalence (rarity) of disease • Severity (seriousness) of disease • Identifiability • Is the disease life-threatening or chronically debilitating? • Evidence of treatment efficacy/effectiveness • Magnitude of treatment benefit • Safety profile of treatment • Is treatment innovative? • Societal impact of treatment • Impact of treatment upon the distribution of health • Commercial considerations • Legal considerations • End-of-life • Price (cost) of treatment • Budget impact of treatment • Cost-effectiveness of treatment • Availability of alternatives • Feasibility of diagnosis • Feasibility of providing treatment • Price (cost) of treatment • Budget impact of treatment • Cost-effectiveness of treatment • Availability of alternatives • Feasibility of diagnosis • Feasibility of providing treatment
  11. 11. Decision maker’s valuation of the orphan therapy and each relevant comparator Vi=hP1i,P2i,…,PJi,Q1i,Q2i,…,QKi where i represents the treatment Decision maker’s valuation of the opportunity cost of the orphan therapy and each relevant comparator Vi'=hP1i',P2i',…,PJi',Q1i',Q2i',…,QKi' where i' represents the opportunity cost of treatment i Potential decision-bearing factors Cost-effectiveness Feasibility of Dx / Tx When making a coverage decision, the decision maker compares its valuations of the orphan therapy, its relevant comparators, and the opportunity cost of each Value placed on any treatment by each stakeholder Pji=fjv1,v2,…,vn where i represents the treatment and j the stakeholder Potential value-bearing factors (v1,v2,…,vn) ‘Opportunity cost’-determining factors Cost of treatment Budget impact Patient prefs Societal prefs Value placed on any treatment by each value proposition Qki=gkv1,v2,…,vn where i represents the treatment and k the value proposition Rule of rescue Equity principle Disease-related Treatment-related Population-related Socio-economic-related Rights-based
  12. 12. Valued Effect per $1,000 0 Health care expenditures Health benefit per $1,000 Value based reimbursement Current treatments covered by health care system Budget Treatments not covered by the health care system Better value Worse value Willingness to Pay
  13. 13. Valued Effect per $1,000 0 Health care expenditures Value based reimbursement Current treatments covered by health care system Budget Treatments not covered by the health care system Better value Worse value Willingness to Pay
  14. 14. Valued Effect per $1,000 0 Health care expenditures Value based in reimbursement Current treatments covered by health care system Budget Treatments not covered by the health care system Better value Worse value Willingness to Pay
  15. 15. Valued Effect per $1,000 0 Health care expenditures Value based reimbursement Current treatments covered by health care system Budget Treatments not covered by the health care system Better value Worse value Willingness to Pay
  16. 16. Valued Effect per $1,000 0 Health care expenditures Value based reimbursement Current treatments covered by health care system Budget Treatments not covered by the health care system Better value Worse value Willingness to Pay Willingness to Pay
  17. 17. Equity • Vertical – Treating unequals Unequally • Horizontal – Treating equally Equally
  18. 18. Conclusion • Current approaches to end of life premia pursue vertical equity at the expense of horizontal equity • End-of-Life (and all other value premia): HANDLE WITH CARE

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