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Selection of end points in economic evaluations of coronary-heart-disease interventions

M F Drummond1, J Heyse, J Cook

  • 1Centre for Health Economics, University of York, UK.

Insights

Different economic evaluation methods for lowering cholesterol and blood pressure exist, impacting cost-effectiveness findings. Quality-adjusted life years (QALYs) and other outcome measures significantly alter cost-effectiveness ratios for interventions.

Area of Science:

  • Health Economics
  • Clinical Trial Analysis

Background:

  • Economic evaluations of interventions for lowering blood pressure or cholesterol utilize diverse outcome measures.
  • These measures range from physiologic improvements (e.g., mmHg reduction) to clinical events (e.g., coronary heart disease avoidance) and quality-adjusted life years (QALYs).

Purpose of the Study:

  • To illustrate the trade-offs between relevance, accuracy, and precision in economic evaluations.
  • To compare various outcome measures in the cost-effectiveness analysis of drug therapy for hypercholesterolemia.

Main Methods:

  • Evaluation of drug therapy for hypercholesterolemia in the United Kingdom.
  • Calculation of cost-effectiveness ratios using multiple end points: cost per percentage cholesterol reduction, cost per coronary heart disease (CHD) event avoided, cost per CHD-free year gained, cost per life year gained, and cost per quality-adjusted life year (QALY) gained.

Main Results:

  • Some outcome measures, like physiologic improvements, cannot be meaningfully discounted, hindering reflection of cost and outcome timing.
  • Incorporating quality-of-life adjustments significantly alters cost-effectiveness ratios.
  • The discount rate influences the pretreatment cholesterol level at which cost per life year gained equals cost per QALY gained.

Conclusions:

  • The choice of outcome measure critically impacts the perceived cost-effectiveness of health interventions.
  • Quality-of-life adjustments and discount rates are crucial factors in economic evaluations, particularly for chronic disease management.
  • Analytic perspective and data requirements vary significantly based on the chosen end points, influencing the assumptions and relevance of the findings.

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