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Severity-Adjusted Probability of Being Cost Effective.

Matthijs M Versteegh1, Isaac Corro Ramos2, Nasuh C Buyukkaramikli2

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This study introduces a severity-adjusted probability of cost-effectiveness to address uncertainty in healthcare priority setting. This method improves decision-making for health technologies, especially for severe diseases.

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Area of Science:

  • Health Economics
  • Decision Analysis
  • Public Health Policy

Background:

  • Differential cost-effectiveness thresholds reflect higher societal willingness to pay for quality-adjusted life-year gains in the worse-off.
  • Uncertainty in severity estimates complicates cost-effectiveness analyses and healthcare priority setting.

Purpose of the Study:

  • To standardize severity assessment and integrate its uncertainty with cost-effectiveness results.
  • To provide decision-makers with a severity-adjusted probability of being cost-effective.

Main Methods:

  • Severity is quantified using proportional and absolute shortfall, life tables, and country-specific EQ-5D values.
  • Utilizes severity-based cost-effectiveness thresholds (€20,000, €50,000, €80,000 per QALY) from the Netherlands.
  • Integrates uncertainty in severity estimation with cost-effectiveness uncertainty using a hypothetical oncology treatment example.

Main Results:

  • The proposed methods identify the likelihood of an intervention being cost-effective under uncertainty regarding severity-based thresholds.
  • Accounting for uncertainty in both cost-effectiveness and severity estimation is crucial for accurate evaluations.

Conclusions:

  • Societal concerns for equitable resource distribution necessitate higher willingness-to-pay thresholds for severe diseases.
  • The severity-adjusted probability of cost-effectiveness accounts for uncertain severity estimates and patient heterogeneity.
  • Failure to adopt this method may lead to incorrect reimbursement decisions for new health technologies, as suggested by the Netherlands case study.