Stated preferences: a unique database composed of 1657 recent published articles in journals related to agriculture, environment, or health

Mahieu, Pierre-Alexandre; Andersson, Henrik; Beaumais, Olivier; Sourd, Romain Crastes dit; Hess, Stephane; Wolff, François‐Charles

doi:10.1007/s41130-017-0053-6

Cited by 26 publications

(19 citation statements)

References 51 publications

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“…blocking in experimental design and the type of qualitative methods used in a DCE). Third, our observation of less rapid growth in the number of empirical DCEs (compared to the growth observed in previous reviews) matches the trend in the preference research to focus on the broad range of stated preference methods available (rather than DCEs exclusively) [4, 5, 347].…”

Section: Discussionsupporting

confidence: 80%

“…3). This less consistent increase might be explained by the presence of many competing stated preference methods [4, 5, 347]. We hypothesise that other methods may be increasing in popularity or becoming more useful in health settings [348].…”

Section: Discussionmentioning

confidence: 99%

“…Example methods include interviews, focus groups and stated preference techniques such as the standard gamble or time trade-off. Studies by the Medical Device Innovation Consortium (MDIC) [4] and Mahieu et al [5] highlighted a wide variety of methods to measure both stated and revealed preferences in healthcare.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Choice Experiments in Health Economics: Past, Present and Future

et al. 2018

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Objectives Discrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990–2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges. Methods A systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated. Results Of the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels. Conclusions The use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers’ confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research. Electronic supplementary material The online version of this article (10.1007/s40273-018-0734-2) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

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Discrete Choice Experiments in Health Economics: Past, Present and Future

et al. 2018

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“…This kind of variation across studies is typical for valuation studies and an important motivation for meta‐analysis (Rolfe et al , ). In addition, we see that the number of choice experiment studies increased compared to contingent valuation methods studies over our data period (see also Mahieu et al , ). The descriptive analysis also showed that willingness to pay for grassland ES was higher for a reduction in grassland intensity levels in a mountain region and lower for a switch from cropland to grassland in other regions (Figure ).…”

Section: Resultsmentioning

confidence: 54%

A Meta‐analysis of the Willingness to Pay for Cultural Services from Grasslands in Europe

Huber¹,

Finger²

2019

J Agricultural Economics

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We investigate the willingness to pay (WTP) for cultural ecosystem services from grasslands using a meta‐analysis based on 32 eligible research papers that provide in total 79 estimates. The average WTP (corrected for purchasing power) across these studies is 38 Euros per person per year. Yet, our analysis reveals that the transfer of these results needs careful evaluation. More specifically, it is essential to frame the monetary valuation exercise in relation to the direction of grassland change. A switch from cropland to grassland reduces WTP by 90 Euros while an increase in less‐intensive land‐use in mountain regions raises WTP by 53 Euros. We conclude that for an adequate consideration of grasslands in holistic ecosystem services assessments, the direction of grassland change is important.

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“…3 The technique is mainstream in marketing, transport, and environmental economics, where it is used to predict individual and collective choices. 4,5 Its application in healthcare has grown exponentially 6,7 because the method is easy to apply and appears efficient. [8][9][10][11] DCEs in health economics are commonly used for valuing health and nonhealth outcomes, investigating trade-offs between health and nonhealth outcomes, and developing priority setting frameworks.…”

Section: Introductionmentioning

confidence: 99%

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

et al. 2019

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Background: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting .93% of choices). Conclusions: Our study shows that DCEs are able to predict choices-mimicking real-world decisions-if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts.

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Stated preferences: a unique database composed of 1657 recent published articles in journals related to agriculture, environment, or health

Cited by 26 publications

References 51 publications

Discrete Choice Experiments in Health Economics: Past, Present and Future

Discrete Choice Experiments in Health Economics: Past, Present and Future

A Meta‐analysis of the Willingness to Pay for Cultural Services from Grasslands in Europe

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

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