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Cost-effectiveness with multiple outcomes.

Jakob Bjørner¹, Hans Keiding

¹National Institute of Occupational Health, Copenhagen, Denmark.

Health Economics

|September 24, 2004

Summary

This study introduces a new cost-effectiveness analysis method that avoids subjective outcome weighting. It uses Data Envelopment Analysis to identify superior health plans based on cost and multi-dimensional health outcomes.

Area of Science:

Health Economics
Decision Analysis
Operations Research

Background:

Healthcare activities require measurement of both cost and outcome.
Existing methods often use subjective weights for multi-dimensional health outcomes, leading to arbitrary indices.
A need exists for cost-effectiveness analysis methods that avoid artificial outcome aggregation.

Purpose of the Study:

To propose a novel approach to cost-effectiveness analysis that bypasses the need for subjective outcome weighting.
To introduce a method that assigns favorable weights to activities for comparison, ensuring inferior options are identified.
To apply this method to evaluate alternative health plans using real-world data.

Main Methods:

The study proposes an approach based on Data Envelopment Analysis (DEA), a technique from productivity theory.

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DEA is applied to cost-effectiveness evaluations by assigning the most favorable weights to each activity.

The method identifies activities that are efficient or inefficient relative to others without subjective aggregation of outcomes.

Main Results:

The proposed method successfully avoids artificial aggregation of multiple outcome measures.
Activities with poor scores under this method are demonstrably inferior to others.
The analysis of alternative health plans using Medical Outcome Study data showed varying cost-effectiveness based on the new approach.

Conclusions:

The Data Envelopment Analysis approach offers a robust alternative for cost-effectiveness analysis in healthcare.
This method provides a more objective way to compare health plans when outcomes are multi-dimensional.
It ensures that comparisons are based on the most favorable perspectives for each activity, leading to clearer identification of efficiency.