PharmacoEconomics · 2026
Beyond Traditional Cost-Effectiveness Analysis: Operationalizing the Equivalent Income Approach for Equity-Sensitive Health Technology Assessment.
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Chauvin Pauline, Thébaut Clémence
Аннотация
BACKGROUND: Distributional concerns have gained increasing attention in health technology assessment, complementing traditional cost-effectiveness methods. Among the proposed equity-sensitive frameworks, the equivalent income approach evaluates health interventions by jointly considering health, income, healthcare financing, and societal aversion to inequality. However, its implementation requires additional data and methodological developments.
OBJECTIVE: We aimed to provide a practical tutorial for implementing the equivalent income approach in health technology assessment and to compare its recommendations with those derived from conventional cost-effectiveness analysis.
METHODS: We developed a generic individual-level Markov model comparing two hypothetical treatment strategies over a 30-year horizon in a representative sample of the French population. For each individual and cycle, the model estimates health status, income, healthcare costs, willingness-to-pay for health improvements, and health insurance contributions. Individual equivalent incomes are aggregated using a social welfare function incorporating different levels of inequality aversion. In parallel, a conventional cost-effectiveness analysis based on discounted health outcomes and costs was performed.
RESULTS: Whereas cost-effectiveness analysis recommendations depended solely on treatment effectiveness and costs, the equivalent income approach additionally accounted for the distribution of disease burden, healthcare financing arrangements, and societal preferences regarding inequality. Reimbursement recommendations differed substantially between the two approaches, particularly when disadvantaged populations were disproportionately affected by the disease or exempted from healthcare contributions.
CONCLUSIONS: The equivalent income approach extends conventional cost-effectiveness analysis by explicitly incorporating distributive concerns into health technology assessment. Beyond comparing recommendations, this tutorial provides a transparent and reproducible framework for operationalizing equivalent income-based evaluations and supporting their future use in routine health technology assessment.
OBJECTIVE: We aimed to provide a practical tutorial for implementing the equivalent income approach in health technology assessment and to compare its recommendations with those derived from conventional cost-effectiveness analysis.
METHODS: We developed a generic individual-level Markov model comparing two hypothetical treatment strategies over a 30-year horizon in a representative sample of the French population. For each individual and cycle, the model estimates health status, income, healthcare costs, willingness-to-pay for health improvements, and health insurance contributions. Individual equivalent incomes are aggregated using a social welfare function incorporating different levels of inequality aversion. In parallel, a conventional cost-effectiveness analysis based on discounted health outcomes and costs was performed.
RESULTS: Whereas cost-effectiveness analysis recommendations depended solely on treatment effectiveness and costs, the equivalent income approach additionally accounted for the distribution of disease burden, healthcare financing arrangements, and societal preferences regarding inequality. Reimbursement recommendations differed substantially between the two approaches, particularly when disadvantaged populations were disproportionately affected by the disease or exempted from healthcare contributions.
CONCLUSIONS: The equivalent income approach extends conventional cost-effectiveness analysis by explicitly incorporating distributive concerns into health technology assessment. Beyond comparing recommendations, this tutorial provides a transparent and reproducible framework for operationalizing equivalent income-based evaluations and supporting their future use in routine health technology assessment.