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Supply-Side Cost-Effectiveness Thresholds: Questions for Evidence-Based Policy.

Christopher James SampsonBernarda ZamoraSam WatsonJohn CairnsKalipso ChalkidouPatricia Cubi-MollaNancy DevlinBorja García-LorenzoDyfrig A HughesAshley A LeechAdrian Towse
Published in: Applied health economics and health policy (2022)
There is growing interest in cost-effectiveness thresholds as a tool to inform resource allocation decisions in health care. Studies from several countries have sought to estimate health system opportunity costs, which supply-side cost-effectiveness thresholds are intended to represent. In this paper, we consider the role of empirical estimates of supply-side thresholds in policy-making. Recent studies estimate the cost per unit of health based on average displacement or outcome elasticity. We distinguish the types of point estimates reported in empirical work, including marginal productivity, average displacement, and outcome elasticity. Using this classification, we summarise the limitations of current approaches to threshold estimation in terms of theory, methods, and data. We highlight the questions that arise from alternative interpretations of thresholds and provide recommendations to policymakers seeking to use a supply-side threshold where the evidence base is emerging or incomplete. We recommend that: (1) policymakers must clearly define the scope of the application of a threshold, and the theoretical basis for empirical estimates should be consistent with that scope; (2) a process for the assessment of new evidence and for determining changes in the threshold to be applied in policy-making should be created; (3) decision-making processes should retain flexibility in the application of a threshold; and (4) policymakers should provide support for decision-makers relating to the use of thresholds and the implementation of decisions stemming from their application.
Keyphrases
  • healthcare
  • public health
  • mental health
  • decision making
  • primary care
  • machine learning
  • deep learning
  • electronic health record
  • social media
  • risk assessment