IntroductionBest–worst scaling (BWS) is becoming increasingly popular to elicit preferences in health care. However, little is known about current practice and trends in the use of BWS in health care. This study aimed to identify, review and critically appraise BWS in health care, and to identify trends over time in key aspects of BWS.MethodsA systematic review was conducted, using Medline (via Pubmed) and EMBASE to identify all English-language BWS studies published up until April 2016. Using a predefined extraction form, two reviewers independently selected articles and critically appraised the study quality, using the Purpose, Respondents, Explanation, Findings, Significance (PREFS) checklist. Trends over time periods (≤2010, 2011, 2012, 2013, 2014 and 2015) were assessed further.ResultsA total of 62 BWS studies were identified, of which 26 were BWS object case studies, 29 were BWS profile case studies and seven were BWS multi-profile case studies. About two thirds of the studies were performed in the last 2 years. Decreasing sample sizes and decreasing numbers of factors in BWS object case studies, as well as use of less complicated analytical methods, were observed in recent studies. The quality of the BWS studies was generally acceptable according to the PREFS checklist, except that most studies did not indicate whether the responders were similar to the non-responders.ConclusionUse of BWS object case and BWS profile case has drastically increased in health care, especially in the last 2 years. In contrast with previous discrete-choice experiment reviews, there is increasing use of less sophisticated analytical methods.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-016-0429-5) contains supplementary material, which is available to authorized users.

The recent endorsement of discrete-choice experiments (DCEs) and other stated-preference methods by regulatory and health technology assessment (HTA) agencies has placed a greater focus on demonstrating the validity and reliability of preference results. Areas covered: We present a practical overview of tests of validity and reliability that have been applied in the health DCE literature and explore other study qualities of DCEs. From the published literature, we identify a variety of methods to assess the validity and reliability of DCEs. We conceptualize these methods to create a conceptual model with four domains: measurement validity, measurement reliability, choice validity, and choice reliability. Each domain consists of three categories that can be assessed using one to four procedures (for a total of 24 tests). We present how these tests have been applied in the literature and direct readers to applications of these tests in the health DCE literature. Based on a stakeholder engagement exercise, we consider the importance of study characteristics beyond traditional concepts of validity and reliability. Expert commentary: We discuss study design considerations to assess the validity and reliability of a DCE, consider limitations to the current application of tests, and discuss future work to consider the quality of DCEs in healthcare.

Objective Persons with serious mental illness (SMI) have high rates of premature mortality from preventable medical conditions, but this group is underrepresented in epidemiologic surveys and we lack national estimates of the prevalence of conditions such as obesity and diabetes in this group. We performed a comprehensive review to synthesize estimates of the prevalence of 15 medical conditions among the population with SMI. Method We reviewed studies published in the peer-reviewed literature from January 2000-August 2012. Studies were included if they assessed prevalence in a sample of 100 or more US adults with schizophrenia or bipolar disorder. Results 57 studies were included in the review. For most medical conditions, the prevalence estimates varied considerably. For example, estimates of obesity prevalence ranged from 26% to 55%. This variation appeared to be due to differences in measurement (e.g. self-report versus clinical measures) and underlying differences in study populations. Few studies assessed prevalence in representative, community samples of persons with SMI. Conclusions In many studies, the prevalence of medical conditions among the population with SMI was higher than among the overall US population. Screening for and monitoring of these conditions should be common practice in clinical settings serving persons with SMI.

The framework for instrument development of a choice experiment included five stages of development and incorporated multiple stakeholder perspectives. Further comparisons of instrument development approaches are needed to identify best practices. To facilitate comparisons, researchers need to be encouraged to publish or discuss their instrument development strategies and findings.

These results are consistent with the idea that people have strong preferences for immediate consequences of medication. Despite efforts to produce recommendations, ambiguity surrounding good practices remains and various judgments need to be made when conducting stated-preference studies. To ensure transparency, these judgments should be described and justified.