A FRAMEWORK FOR DIFFERENTIATING BETWEEN HEALTH IMPROVEMENTS AND IMPAIRMENTS IN QALY-ANALYSES

Purpose:

A wide range of studies in behavioural economics have established that the subjective magnitude of losses typically is greater than the corresponding magnitude of equal gains. Applied to health, if health state Y is better than X, movement X to Y should be associated with a smaller gain than the corresponding loss of Y to X. Furthermore, if X is better than Y on some health dimensions and worse on others, moving between X and Y would constitute both losses and gains. Contrary to the well-established preference-difference between losses and gains, QALY calculation from preference-based measures such as the EQ-5D is currently blind to the direction of change. We present a simple framework for implementing differentiated weighing for losses and gains in EQ-5D-based QALY calculation, and explore the practical and ethical consequences of implementation. 

Method:

For the EQ-5D, interaction terms in the value algorithm complicate differentiated weighing of losses and gains. We propose conceptualizing movements between any two health states as going through either the worst or best combination of the two. Going form state X (53111) to state Y (34211) can be seen as going through either state W (54211, worst combination) or state B (33111, best combination). For the movement through W, we have the QALY loss of X to W multiplied by a loss-aversion factor, followed by the gain of W to Y. We propose taking the average of movements through the worst and the best state. Given loss aversion factor Z, the weighed delta of X to Y would then be ((W-X)Z+(Y-W) + (B-X) + (Y-B)Z)/2. 

Result:

Implementing differential calculation of losses and gains in QALY-calculation corresponds to imposing a premium on dimension-wise health decrements from interventions, to changing patients’ health without improvement, and to variance in patient response. 

Conclusion:

The loss aversion factor would have to be determined empirically or normatively, but even small factors would likely influence cost-utility estimates, to the benefit of interventions with limited side-effects, variation in response, and patient risk. An informative analysis would be to vary the loss-aversion factor from 1 towards infinity to determine the point at which a suggested intervention is equal to its comparator. Applying differential weighing of losses and gains could improve decisions based on QALY analyses.