THE DEVELOPMENT AND TESTING OF A DISCRETE CHOICE EXPERIMENT QUESTIONNAIRE TO MEASURE INDIVIDUALS' PREFERENCES FOR HEALTH OUTCOMES AND MEDICAL EXPENDITURES
To measure individuals’ preferences for potential baskets of health outcomes and medical expenditures, we designed a discrete choice experiment (DCE) questionnaire, pre-tested it for clarity of presentation, ease and feasibility of administration, and assessed its validity.
We recruited 31 participants by snowball sampling. The instrument included: (1) Information section in which we carefully described the context of the decision-making; (2) 10 choice tasks each including 2 generic alternatives (Situation A vs B) described by 2 attributes (total costs and health outcomes (in Healthy Years Equivalent (HYEs)); (3) Feedback type questions. In addition we added 2 tasks to test both dominance and stability properties. Participants’ preferences were estimated using different specifications of conditional logit model, and answers to the feedback questions were descriptively analysed. Predictive performance of the best choice model was investigated using a bootstrapping procedure with 1,000 replicates.
Overall difficulty of the questionnaire: 19.4% found it “very difficult”. The topic was considered interesting (“moderately to extremely”) by 80.7%. 73.3% considered the amount of information conveyed acceptable (“moderately to extremely”), only 3.2% considered it difficult (“very to extremely”) to understand. 87.1% and 74.2% declared taking into account HYEs and costs respectively, in all the vignettes when they made their choices. 3.2% of respondents failed stability test, 12.9% failed dominance and 3.2% failed both stability and dominance. The best specification of the choice model included an interaction effect between preferences for health outcomes and medical expenditures. The estimated preferences were in line with a prioriassumption regarding both the sign and magnitude of the estimates. Respondents positively valued increase in health outcomes (β=0.306) and negatively valued increase in level of medical expenditures (β=-0.113). The interaction effect was significant and negative. The mean predictive performance of this model was high: 80.84% [95% CI: 80.72-80.96].
The results of the pre-test for clarity of presentation, ease and feasibility of administration were positive; they also indicate that responses were valid. This questionnaire, once administered to a representative sample of the population, can generate a population based net loss or net benefit functions to be used, for example, in a framework recently published in Health Economics that describes how to assess and manage the risk of potential undesirable outcome in the context of resource allocation.