Investigating the relative value of health and social care related quality of life using a discrete choice experiment.

A key outcome in the evaluation of health technologies is the quality adjusted life year (QALY) which is often estimated using health measures such as the EuroQol instruments (EQ-5D-3L and EQ-5D-5L). The impacts of many interventions extend beyond a narrow definition of health to include non-health impacts such as social care related dimensions of quality of life (QoL). This means that there are circumstances where the QALY does not capture the full value of an intervention. In response to this, instruments with a wider measurement framework, such as the Adult Social Care Outcomes Toolkit (ASCOT), which measures social care related QoL, have been developed. Given the range of instruments available, it is important that decision-makers have tools to assess value for money comprehensively and consistently. To date, preference elicitation of different aspects of QoL combined within the same valuation procedure has not been tested. We investigate the relationship between health and social care aspects of QoL when assessed jointly by combining EQ-5D-5L and ASCOT in an online discrete choice experiment (DCE). In July 2016, 975 respondents recruited from internet panels completed 15 choice sets from an underlying design of 300. Conditional logit regression was used to estimate coefficient decrements for each attribute and examine their relative magnitude. Latent class and mixed logit modelling were used to understand preference heterogeneity. The results suggest trading across health and social care aspects indicated by coefficient estimates of differing magnitude. Dimensions with the largest disutility include four from EQ-5D-5L and one from ASCOT. There is evidence of preference heterogeneity at more severe dimension levels. We have used an established method to test the joint valuation of concepts measuring different aspects of QoL. The results have implications for the aspects of QoL that are included in QALY estimation and used in resource allocation decision-making.