Caroline Vass

Introduction Daily insulin administration can be burdensome for people with type 2 diabetes (PwT2D) and can impact treatment adherence. This study investigated preferences for once-weekly, long-acting basal insulin for treatment of PwT2D. Methods An online discrete-choice experiment was administered to PwT2D in the USA. Qualitative interviews informed the selection of six attributes: reduction in A1c level after 6 months, amount of time spent in optimal blood sugar range each day, number of serious low blood sugar events, number of nighttime low blood sugar events, change in weight because of the insulin over 6 months, and frequency of administration. Each participant completed eight questions offering a choice between two long-acting insulins; questions varied according to an experimental design. A fixed treatment choice question asked about preferences for daily versus weekly insulin, holding other treatment features constant. Data were analyzed using random-parameters logit models, and heterogeneity was explored through subgroup analyses. Results Four hundred sixty-six PwT2D completed the survey (mean age, 57; mean A1c, 7.5%; 59.0% female); 33.3% of these were currently on a basal/bolus regimen, 34.3% used basal only, and 32.4% were insulin naive. Respondents placed the most importance on avoiding a 10-pound weight change and equal importance on the largest change in the number of serious and nighttime low blood sugar events per year and achieving the longest time in range included in the choice questions. There was significant heterogeneity in preferences by experience: insulin-naive respondents had stronger preferences for scheduled and flexible weekly insulin over daily insulin; 67.6% preferred flexible weekly over daily insulin, all else being equal. Conclusion PwT2D valued insulin efficacy and reducing treatment-related adverse events, with heterogeneity in the relative importance of administration frequency. All else being equal, respondents preferred weekly over daily basal insulin. These findings provide insights into the preferences of PwT2D considering weekly long-acting insulin.

The interest in quantifying stated preferences for health and healthcare continues to grow, as does the technology available to support and improve health preference studies. Technological advancements in the last two decades have implications and opportunities for preference researchers designing, administering, analysing, interpreting and applying the results of stated preference surveys. In this paper, we summarise selected technologies and how these can benefit a preference study. We discuss empirical evaluations of the technology in preference research, with examples from health where possible. The technologies reviewed include serious games, virtual reality, eye tracking, innovative formats and decision aids with values clarification components. We conclude with a critical reflection on the benefits and limitations of implementing (often costly) technology alongside stated preference studies.

In health preference research (HPR) studies, data are generated by participants’/subjects’ decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects’ decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods’ advantages and limitations.

Introduction. This study aimed to understand the impact of alternative modes of information provision on the stated preferences of a sample of the public for attributes of newborn bloodspot screening (NBS) in the United Kingdom. Methods. An online discrete choice experiment survey was designed using 4 attributes to describe NBS (effect of treatment on the condition, time to receive results, whether the bloodspot is stored, false-positive rate). Survey respondents were randomized to 1 of 2 survey versions presenting the background training materials using text from a leaflet (leaflet version) or an animation (animation version). Heteroskedastic conditional logistic regression was used to estimate the effect of mode of information provision on error variance. Results. The survey was completed by 1,000 respondents (leaflet = 525; animation = 475). Preferences for the attributes in the DCE were the same in both groups, but the group receiving the animation version had 9% less error variance in their responses. Respondents completing the animation version gave higher ratings compared with the leaflet version in terms of ease of perceived understanding. Subgroup analysis suggested that the animation was particularly effective at reducing error variance for women (20%), people with previous children (16.5%), and people between the ages of 35 and 45 y (11.8%). Limitations. This study used simple DCE with 4 attributes, and the results may vary for more complex choice questions. Conclusion. This study provides evidence that that supplementing the information package offered to parents choosing to take part in NBS with an animation may aid them their decision making. Further research would be needed to test the animation in the health system. Implications. Researchers designing DCE should carefully consider the design of their training materials to improve the quality of data collected. Highlights: Prior to completing a discrete choice experiment about newborn bloodspot screening, respondents were shown information using either a leaflet-based or animated format. Respondents receiving information using an animation version reported that the information was slightly easier to understand and exhibited 9% less error variance in expressing their preferences for a newborn screening program. Using the animation version to present information appeared to have a larger impact in reducing the error variance of responses for specific respondents including women, individuals with children, individuals between the ages of 35 and 45 y, and individuals educated to degree level.

Objective: We conducted a survey‐based discrete‐choice experiment (DCE) to understand the test features that drive women's preferences for prenatal genomic testing, and explore variation across countries.Methods: Five test attributes were identified as being important for decision‐making through a literature review, qualitative interviews and quantitative scoring exercise. Twelve scenarios were constructed in which respondents choose between two invasive tests or no test. Women from eight countries who delivered a baby in the previous 24 months completed a DCE presenting these scenarios. Choices were modeled using conditional logit regression analysis.Results: Surveys from 1239 women (Australia: n = 178; China: n = 179; Denmark: n = 88; Netherlands: n = 177; Singapore: n = 90; Sweden: n = 178; UK: n = 174; USA: n = 175) were analyzed. The key attribute affecting preferences was a test with the highest diagnostic yield (p < 0.01). Women preferred tests with short turnaround times (p < 0.01), and tests reporting variants of uncertain significance (VUS; p < 0.01) and secondary findings (SFs; p < 0.01). Several country‐specific differences were identified, including time to get a result, who explains the result, and the return of VUS and SFs.Conclusion: Most women want maximum information from prenatal genomic tests, but our findings highlight country‐based differences. Global consensus on how to return uncertain results is not necessarily realistic or desirable.

Background Antibiotics have led to considerable increases in life expectancy. However, over time, antimicrobial resistance has accelerated and is now a significant global public health concern. Understanding societal preferences for the use of antibiotics as well as eliciting the willingness to pay for future research is crucial. Objective To investigate preferences for different strategies to optimize antibiotic use and to understand the willingness to pay for future research in antimicrobial resistance and antimicrobial drug development. Methods A discrete-choice experiment was administered to a sample of the UK general population. Respondents were asked to make nine choices, each offering three options—two hypothetical “doctor and antibiotics” and one “no doctor—no antibiotics”—defined by five attributes: treatment, days needed to recover, risk of bacterial infection that needs antibiotics, risk of common side effects, and risk of antimicrobial resistance by 2050. Data were analyzed using random parameters logit models. A double-bounded contingent valuation was also included in the survey to explore the willingness to pay for policies to contain antimicrobial resistance. Results Among the 2579 respondents who completed the survey, 1151 always selected “no doctor—no antibiotics” and 57 never varied their choices; therefore, 1371 responses were used in the analysis. Risk of antimicrobial resistance by 2050 was the most important attribute and the “treatment” was the least important attribute, although this was sensitive to a higher risk of bacterial infection. The aggregate annual willingness to pay for containing antimicrobial resistance was approximately £8.35 billion (~£5–£10 billion). Conclusions The antimicrobial resistance risk is relevant and important to the general public. The high willingness to pay suggests that large investments in policies or interventions to combat antimicrobial resistance are justified.

Background Literature reviews show stated-preference studies, used to understand the values individuals place on health and health care, are increasingly administered online, potentially maximising respondent access and allowing for enhanced response quality. Online respondents may often choose whether to use a desktop or laptop personal computer (PC), tablet or smartphone, all with different screen sizes and modes of data entry, to complete the survey. To avoid differences in measurement errors, frequently respondents are asked to complete the surveys on a PC despite evidence that handheld devices are increasingly used for internet browsing. As yet, it is unknown if or how the device used to access the survey affects responses and/or the subsequent valuations derived. Method This study uses data from a discrete choice experiment (DCE) administered online to elicit preferences of a general population sample of females for a national breast screening programme. The analysis explores differences in key outcomes such as completion rates, engagement with the survey materials, respondent characteristics, response time, failure of an internal validity test and health care preferences for (1) handheld devices and (2) PC users. Preferences were analysed using a fully correlated random parameter logit (RPL) model to allow for unexplained scale and preference heterogeneity. Results One thousand respondents completed the survey in its entirety. The most popular access devices were PCs (n = 785), including Windows (n = 705) and Macbooks (n = 69). Two-hundred and fifteen respondents accessed the survey on a handheld device. Most outcomes related to survey behaviour, including failure of a dominance check, ‘flat lining’, self-reported attribute non-attendance (ANA) or respondent-rated task difficulty, did not differ by device type (p > 0.100). Respondents accessing the survey using a PC were generally quicker (median time to completion 14.5 min compared with 16 min for those using handheld devices) and were significantly less likely to speed through a webpage. Although there was evidence of preference intensity (taste) or variability (scale) heterogeneity across respondents in the sample, it was not driven by the access device. Conclusion Overall, we find that neither preferences nor choice behaviour is associated with the type of access device, as long as respondents are presented with question formats that are easy to use on small touchscreens. Health preference researchers should optimise preference instruments for a range of devices and encourage respondents to complete the surveys using their preferred device. However, we suggest that access device characteristics should be gathered and included when reporting results.

Publications reporting discrete choice experiments of healthcare interventions rarely discuss whether patient and public involvement (PPI) activities have been conducted. This paper presents examples from the existing literature and a detailed case study from the National Institute for Health Research-funded PATHWAY programme that comprehensively included PPI activities at multiple stages of preference research. Reflecting on these examples, as well as the wider PPI literature, we describe the different stages at which it is possible to effectively incorporate PPI across preference research, including the design, recruitment and dissemination of projects. Benefits of PPI activities include gaining practical insights from a wider perspective, which can positively impact experiment design as well as survey materials. Further benefits included advice around recruitment and reaching a greater audience with dissemination activities, amongst others. There are challenges associated with PPI activities; examples include time, cost and outlining expectations. Overall, although we acknowledge practical difficulties associated with PPI, this work highlights that it is possible for preference researchers to implement PPI across preference research. Further research systematically comparing methods related to PPI in preference research and their associated impact on the methods and results of studies would strengthen the literature.