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

Making Use of Technology to Improve Stated Preference Studies

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.

Patient preferences in the treatment of chronic musculoskeletal pain: a systematic review of discrete choice experiments

ABSTRACT

Chronic musculoskeletal pain (CMP) is a preference-sensitive condition for which numerous treatment options are available, each with benefits and risks. Thus, patient preferences play a critical role in decision making. This study summarized evidence from discrete choice experiments (DCEs) to quantify patient preferences for CMP treatment and identified important treatment attributes. A systematic review of DCEs on patient preferences for CMP treatment was conducted. Studies were included if they used DCE to determine patient preferences for CMP. A previously described methodological assessment tool was used to assess the risk of bias. The treatment attributes were summarized and sorted according to the frequency of citation and relative weight. Subgroup analyses were conducted to explore the intervention-specific attributes. A total of 15 eligible studies with 4065 participants were included. We identified "capacity to realize daily life activities," "risk of adverse events," "effectiveness in pain reduction," and "out-of-pocket cost" as important attributes. Although "treatment frequency" and "onset of treatment efficacy" were less frequently mentioned, they were also important attributes. The attribute of "risk of adverse events" was especially important for drug treatment. The "out-of-pocket cost" and "treatment location and mode" were important attributes of exercise therapy. The attributes identified in this review will inform the design of future DCE studies, facilitate the translation of measurement-based care to value-based care, and provide the rationale to promote shared decision making and patient-centered care.

Preferences for pre-exposure prophylaxis (PrEP) among sexual and gender minorities: a discrete choice experiment in Brazil

Background

Men who have sex with men (MSM) and transgender women (TGW) are disproportionally affected by HIV infection in Latin America. This study aims to assess pre-exposure prophylaxis (PrEP) preferences among sexual and gender minorities (SGM) and identify attributes and levels that are related to PrEP uptake and adherence, both crucial for PrEP success.

Methods

We conducted a discrete choice experiment (DCE) among SGM from all Brazilian regions (September-December/2020). The survey was administered face-to-face (five Brazilian capitals) and online (entire country). We used a D-efficient zero-prior blocked experimental design to select 60 paired-profile DCE choice tasks.

Findings

The total sample size was 3924 (90.5% MSM; 7.2% TGW and 2.3% non-binary or gender diverse persons). In random-effects logit models, highest levels of protection and "no side effects" were the most important attribute levels. For "presentation", injectable and implant were preferred over oral. Participants were willing to accept a 4.1% protection reduction to receive injectable PrEP or a 4.2% reduction if PrEP were taken monthly. The largest class in the latent class models was defined predominantly by the preference for the highest HIV protection level (p < 0.005). Respondents in this class also preferred no side effects, injectable and implant presentations.

Interpretation

Higher HIV protection, no side effects, and presentation, whether injectable or implant, were the most important attributes in PrEP preferences. Protection against HIV was the most important attribute. PrEP programs should make available technologies such as long-acting presentations that could reunite the most desired attributes, thus maximizing acceptability and user-appropriateness.

Funding

Unitaid.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.

METHODS

An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.

RESULTS

Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.

CONCLUSIONS

Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

A Systematic Review of Discrete Choice Experiments in Oncology Treatments

BACKGROUND

As the number and type of cancer treatments available rises and patients live with the consequences of their disease and treatments for longer, understanding preferences for cancer care can help inform decisions about optimal treatment development, access, and care provision. Discrete choice experiments (DCEs) are commonly used as a tool to elicit stakeholder preferences; however, their implementation in oncology may be challenging if burdensome trade-offs (e.g. length of life versus quality of life) are involved and/or target populations are small.

OBJECTIVES

The aim of this review was to characterise DCEs relating to cancer treatments that were conducted between 1990 and March 2020.

DATA SOURCES

EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews were searched for relevant studies.

STUDY ELIGIBILITY CRITERIA

Studies were included if they implemented a DCE and reported outcomes of interest (i.e. quantitative outputs on participants' preferences for cancer treatments), but were excluded if they were not focused on pharmacological, radiological or surgical treatments (e.g. cancer screening or counselling services), were non-English, or were a secondary analysis of an included study.

ANALYSIS METHODS

Analysis followed a narrative synthesis, and quantitative data were summarised using descriptive statistics, including rankings of attribute importance.

RESULT

Seventy-nine studies were included in the review. The number of published DCEs relating to oncology grew over the review period. Studies were conducted in a range of indications (n = 19), most commonly breast (n =10, 13%) and prostate (n = 9, 11%) cancer, and most studies elicited preferences of patients (n = 59, 75%). Across reviewed studies, survival attributes were commonly ranked as most important, with overall survival (OS) and progression-free survival (PFS) ranked most important in 58% and 28% of models, respectively. Preferences varied between stakeholder groups, with patients and clinicians placing greater importance on survival outcomes, and general population samples valuing health-related quality of life (HRQoL). Despite the emphasis of guidelines on the importance of using qualitative research to inform attribute selection and DCE designs, reporting on instrument development was mixed.

LIMITATIONS

No formal assessment of bias was conducted, with the scope of the paper instead providing a descriptive characterisation. The review only included DCEs relating to cancer treatments, and no insight is provided into other health technologies such as cancer screening. Only DCEs were included.

CONCLUSIONS AND IMPLICATIONS

Although there was variation in attribute importance between responder types, survival attributes were consistently ranked as important by both patients and clinicians. Observed challenges included the risk of attribute dominance for survival outcomes, limited sample sizes in some indications, and a lack of reporting about instrument development processes.

PROTOCOL REGISTRATION

PROSPERO 2020 CRD42020184232.

Preferences for pre-exposure prophylaxis (PrEP) among men who have sex with men and transgender women at risk of HIV infection: a multicentre protocol for a discrete choice experiment in Brazil

INTRODUCTION

Pre-exposure prophylaxis (PrEP) is an important and well-established prevention strategy for sexual acquisition of HIV. In Brazil, transgender women (TGW) and men who have sex with men (MSM) bear the largest burden among key populations. Little is known about preferences for PrEP characteristics in these vulnerable populations in Latin America. The goal of this study is to investigate preferences of TGW and MSM with respect to PrEP characteristics, whether current user or not, and to assess any attributes and levels that may improve the decision to start using PrEP (uptake) and optimal continuity of use (adherence), which are important dimensions for PrEP success.

METHODS AND ANALYSIS

We hereby outline the protocol of a discrete choice experiment (DCE) to be conducted among TGW and MSM in Brazil. The study will be carried out in two phases. The first phase involves literature review and qualitative approaches including in-depth interviews to inform the development of the DCE (attributes and levels). The second phase entails the DCE survey and supporting questions pertaining to sociodemographic and risk behaviour information. The survey is aimed at current PrEP users and non-users, consisting of two modes of administration: face to face in five Brazilian capitals (Rio de Janeiro, Brasília, Manaus, Porto Alegre and Salvador) and online targeting the entire country. A D-efficient zero-prior blocked experimental design will be used to select 60 paired-profile DCE choice tasks, in which participants will be randomly assigned to one of four groups and presented with a set of 15 choice tasks. The planned sample size is 1000 volunteers.

ETHICS, TIMELINE AND DISSEMINATION

The study was approved by Comitê de Ética em Pesquisa-Instituto Nacional de Infectologia Evandro Chagas-INI/FIOCRUZ, CEP/INI, CAAE 28416220.2.1001.5262, approval number 3.979.759 in accordance with the Comissão Nacional de Ética em Pesquisa (CONEP-Brazilian National Board of Research Ethics). The study will be conducted between 2020 and 2021. The results will be disseminated to the scientific community and to the public in general through publications in published in peer-reviewed journals and in scientific conferences.

Utilising Patient and Public Involvement in Stated Preference Research in Health: Learning from the Existing Literature and a Case Study

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.

Can healthcare choice be predicted using stated preference data?

Lack of evidence about the external validity of Discrete Choice Experiments (DCEs)-sourced preferences inhibits greater use of DCEs in healthcare decision-making. This study examines the external validity of such preferences, unravels its determinants, and provides evidence of whether healthcare choice is predictable. We focused on influenza vaccination and used a six-step approach: i) literature study, ii) expert interviews, iii) focus groups, iv) survey including a DCE, v) field data, and vi) in-depth interviews with respondents who showed discordance between stated choices and actual healthcare utilization. Respondents without missing values in the survey and the actual healthcare utilization (377/499 = 76%) were included in the analyses. Random-utility-maximization and random-regret-minimization models were used to analyze the DCE data, whereas the in-depth interviews combined five scientific theories to explain discordance. When models took into account both scale and preference heterogeneity, real-world choices to opt for influenza vaccination were correctly predicted by DCE at an aggregate level, and 91% of choices were correctly predicted at an individual level. There was 13% (49/377) discordance between stated choices and actual healthcare utilization. In-depth interviews showed that several dimensions played a role in clarifying this discordance: attitude, social support, action of planning, barriers, and intention. Evidence was found that our DCE yields accurate actual healthcare choice predictions if at least scale and preference heterogeneity are taken into account. Analysis of discordant subjects showed that we can even do better. The DCE measures an important part of preferences by focusing on attribute tradeoffs that people make in their decision to participate in a healthcare intervention. Inhibitors may be among these attributes, but it is more likely that inhibitors have to do with exogenous factors like goals, religion, and social norms. Con-ducting upfront work on constraints/inhibitors of the focal behavior, not just what promotes the behavior, might further improve predictive ability.

Stated-preference research in HIV: A scoping review

Discrete choice experiments (DCE), conjoint analysis (CA), and best-worst scaling (BWS) are quantitative techniques for estimating consumer preferences for products or services. These methods are increasingly used in healthcare research, but their applications within the field of HIV research have not yet been described. The objective of this scoping review was to systematically map the extent and nature of published DCE, CA, and BWS studies in the field of HIV and identify priority areas where these methods can be used in the future. Online databases were searched to identify published HIV-related DCE, CA and BWS studies in any country and year as the primary outcome. After screening 1,496 citations, 57 studies were identified that were conducted in 26 countries from 2000-2017. The frequency of published studies increased over time and covered HIV themes relating to prevention (n = 25), counselling and testing (n = 10), service delivery (n = 10), and antiretroviral therapy (n = 12). Most studies were DCEs (63%) followed by CA (37%) and BWS (4%). The median [IQR] sample size was 288 [138-496] participants, and 74% of studies used primary qualitative data to develop attributes. Only 30% of studies were conducted in sub-Saharan Africa where the burden of HIV is highest. Moreover, few studies surveyed key populations including men who have sex with men, transgender people, pregnant and postpartum women, adolescents, and people who inject drugs. These populations represent priorities for future stated-preference research. This scoping review can help researchers, policy makers, program implementers, and health economists to better understand the various applications of stated-preference research methods in the field of HIV.

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

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.

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

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.