scholarly journals Ordinal preference elicitation methods in health economics and health services research: using discrete choice experiments and ranking methods

2012 ◽  
Vol 103 (1) ◽  
pp. 21-44 ◽  
Author(s):  
S. Ali ◽  
S. Ronaldson
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Anna Larsen ◽  
Albert Tele ◽  
Manasi Kumar

Abstract Background In designing, adapting, and integrating mental health interventions, it is pertinent to understand patients’ needs and their own perceptions and values in receiving care. Conjoint analysis (CA) and discrete choice experiments (DCEs) are survey-based preference-elicitation approaches that, when applied to healthcare settings, offer opportunities to quantify and rank the healthcare-related choices of patients, providers, and other stakeholders. However, a knowledge gap exists in characterizing the extent to which DCEs/CA have been used in designing mental health services for patients and providers. Methods We performed a scoping review from the past 20 years (2009–2019) to identify and describe applications of conjoint analysis and discrete choice experiments. We searched the following electronic databases: Pubmed, CINAHL, PsychInfo, Embase, Cochrane, and Web of Science to identify stakehold,er preferences for mental health services using Mesh terms. Studies were categorized according to pertaining to patients, providers and parents or caregivers. Results Among the 30 studies we reviewed, most were published after 2010 (24/30, 80%), the majority were conducted in the United States (11/30, 37%) or Canada (10/30, 33%), and all were conducted in high-income settings. Studies more frequently elicited preferences from patients or potential patients (21/30, 70%) as opposed to providers. About half of the studies used CA while the others utilized DCEs. Nearly half of the studies sought preferences for mental health services in general (14/30, 47%) while a quarter specifically evaluated preferences for unipolar depression services (8/30, 27%). Most of the studies sought stakeholder preferences for attributes of mental health care and treatment services (17/30, 57%). Conclusions Overall, preference elicitation approaches have been increasingly applied to mental health services globally in the past 20 years. To date, these methods have been exclusively applied to populations within the field of mental health in high-income countries. Prioritizing patients’ needs and preferences is a vital component of patient-centered care – one of the six domains of health care quality. Identifying patient preferences for mental health services may improve quality of care and, ultimately, increase acceptability and uptake of services among patients. Rigorous preference-elicitation approaches should be considered, especially in settings where mental health resources are scarce, to illuminate resource allocation toward preferred service characteristics especially within low-income settings.


2018 ◽  
Vol 37 (2) ◽  
pp. 201-226 ◽  
Author(s):  
Vikas Soekhai ◽  
Esther W. de Bekker-Grob ◽  
Alan R. Ellis ◽  
Caroline M. Vass

2014 ◽  
Vol 32 (9) ◽  
pp. 883-902 ◽  
Author(s):  
Michael D. Clark ◽  
Domino Determann ◽  
Stavros Petrou ◽  
Domenico Moro ◽  
Esther W. de Bekker-Grob

2009 ◽  
Vol 4 (4) ◽  
pp. 527-546 ◽  
Author(s):  
JORDAN J. LOUVIERE ◽  
EMILY LANCSAR

Abstract:Compared to many applied areas of economics, health economics has a strong tradition in eliciting and using stated preferences (SP) in policy analysis. Discrete choice experiments (DCEs) are one SP method increasingly used in this area. Literature on DCEs in health and more generally has grown rapidly since the mid-1990s. Applications of DCEs in health have come a long way, but to date few have been ‘best practice’, in part because ‘best practice’ has been somewhat of a moving target. The purpose of this paper is to briefly survey the history of DCEs and the state of current knowledge, identify and discuss knowledge gaps, and suggest potentially fruitful areas for future research to fill such gaps with the aim of moving the application of DCEs in health economics closer to best practice.


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