Discrete choice experiments to measure consumer preferences for health and healthcare

Abstract

To investigate the impact of health policies on individual well-being, estimate the value to society of new interventions or policies, or predict demand for healthcare, we need information about individuals' preferences. Economists usually use market-based data to analyze preferences, but such data are limited in the healthcare context. Discrete choice experiments are a potentially valuable tool for elicitation and analysis of preferences and thus, for economic analysis of health and health programs. This paper reviews the use of discrete choice experiments to measure consumers' preferences for health and healthcare. The paper provides an overview of the approach and discusses issues that arise when using discrete choice experiments to assess individuals' preferences for health and healthcare.

Sociocultural Determinants of Anticipated Vaccine Acceptance for Acute Watery Diarrhea in Early Childhood in Katanga Province, Democratic Republic of Congo

“…This finding is a general problem of studies in which costs are anticipated and no actual decision on expenditure has to be taken (hypothetical bias). 44 Nonetheless, the high rate of hypothetical acceptance supports that there is a high perceived need for a vaccine in the communities, which is supported by the fact that vaccines were considered the preferred way of prevention.…”

How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity

“…There has been no synthesis of the predictive abilities of DCEs in health, despite a substantive and recent increase in the number of studies using estimated choice probabilities from DCEs to predict choices, e.g., [9,41]. These studies implicitly assume that DCEs have sufficient external validity to provide meaningful results.…”

Personal privacy, public benefits, and biobanks: a conjoint analysis of policy priorities and public perceptions

“…For example, had we included one additional level in this study with respect to the disposition of participant's sample in the case of death, with the options of (i) retaining the sample in the biobank or (ii) destroying the sample, the participants would have been presented with 48 scenarios to rank (24 each under blanket and specific consent, respectively). Although fractional factorial designs 29 are used at times to reduce an excessive number of scenarios to a manageable number, we opted instead to limit the number of attributes from the outset to keep the number of scenarios at 12 (or 24, when the respondents completed the task under both blanket and specific consent). In so doing, however, we did not address other issues that are commonly discussed in the literature including commercialization, benefit sharing, disposition of samples after death, and so forth.…”