Patient preferences for conservative treatment of shoulder pain: a discrete choice experiment.

BACKGROUND: Shoulder pain is common amongst adults, but little is known about patients' preferences. OBJECTIVE: The aim of this study was to determine patients' preferences for treatment options offered for shoulder pain in primary care. METHODS: A discrete choice experiment was used to investigate these preferences. Adults with shoulder pain were asked to make 12 choices between two treatment options, or to opt-out. The attributes of the 12 treatment options were presented as varying in: treatment effectiveness (50%, 70%, or 90%), risk of relapse (10%, 20%, or 30%), time to pain reduction (2 or 6 weeks), prevention of relapse (yes/no), requiring injection (yes/no), and including physiotherapy (none, 6, or 12 sessions). A conditional logit model with latent class analysis was used for the analysis and a class assignment model. RESULTS: Three hundred and twelve participants completed the questionnaire with mean age of 52 ±â€…15.2 years. Latent class analysis revealed three groups. Group 1 preferred to opt-out, unless the attributes were highly favorable (90% effectiveness). Group 2 preferred treatment, but not an injection. Group 3 preferred to opt-out and did not opt for treatment. The likelihood of a participant belonging to one of these groups was 68.8%, 9.3%, and 21.9%, respectively. The class assignment was related to having previously received injection or physiotherapy, as they did not prefer that same treatment again. CONCLUSION: This study showed that most patients with shoulder pain prefer to opt-out, unless treatment attributes are highly favorable. Characteristics of influence on this decision was whether the patient had received an injection or physiotherapy before.

Preferences for preventive treatments for rheumatoid arthritis: discrete choice survey in the UK, Germany and Romania.

OBJECTIVE: To quantify preferences for preventive therapies for rheumatoid arthritis (RA) across three countries. METHODS: A web-based survey including a discrete choice experiment was administered to adults recruited via survey panels in the UK, Germany and Romania. Participants were asked to assume they were experiencing arthralgia and had a 60% chance of developing RA in the next 2 years and completed 15 choices between no treatment and two hypothetical preventive treatments. Treatments were defined by six attributes (effectiveness, risks and frequency/route of administration) with varying levels. Participants also completed a choice task with fixed profiles reflecting subjective estimates of candidate preventive treatments. Latent class models (LCMs) were conducted and the relative importance of attributes, benefit-risk trade-offs and predicted treatment uptake was subsequently calculated. RESULTS: Completed surveys from 2959 participants were included in the analysis. Most participants preferred treatment over no treatment and valued treatment effectiveness to reduce risk more than other attributes. A five-class LCM best fitted the data. Country, perceived risk of RA, health literacy and numeracy predicted class membership probability. Overall, the maximum acceptable risk for a 40% reduction in the chance of getting RA (60% to 20%) was 21.7%, 19.1% and 2.2% for mild side effects, serious infection and serious side effects, respectively. Predicted uptake of profiles reflecting candidate prevention therapies differed across classes. CONCLUSION: Effective preventive pharmacological treatments for RA were acceptable to most participants. The relative importance of treatment attributes and likely uptake of fixed treatment profiles were predicted by participant characteristics.

Illustrating Emerging Good Practices for Quantitative Benefit-Risk Assessment: A Hypothetical Case Study of Systemic Biologic Treatments for Plaque Psoriasis.

OBJECTIVES: Quantitative benefit-risk assessment (qBRA) is a structured process to evaluate the benefit-risk balance of treatment options to support decision making. The ISPOR qBRA Task Force was recently established to provide recommendations for the design, conduct, and reporting of qBRA. This report presents a hypothetical case study illustrating how to apply the Task Force's recommendations toward a qBRA to inform the benefit-risk assessment of brodalumab at the time of initial marketing approval. The qBRA evaluated 2 dosing regimens of brodalumab (210 mg or 140 mg twice weekly) compared with weight-based dosing of ustekinumab and placebo. METHODS: We followed the 5 steps recommended by the Task Force. Attributes included treatment response (≥75% improvement in Psoriasis Area and Severity Index), suicidal ideation and behavior, and infections. Performance data were drawn from pivotal clinical trials of brodalumab. The qBRA used multicriteria decision analysis and preference weights from a hypothetical discrete choice experiment. Sensitivity analyses examined the robustness of benefit-risk ranking to uncertainty in clinical effect and preference estimates, consideration of a subgroup (nail psoriasis), and the maintenance phase of treatment (52 weeks instead of 12). RESULTS: Results from this hypothetical qBRA suggest that brodalumab 210 mg had a more favorable benefit-risk profile compared with ustekinumab and placebo. Ranking of brodalumab compared with ustekinumab was dependent on brodalumab's dose. Sensitivity analyses demonstrated robustness of benefit-risk ranking to uncertainty in clinical effect and preference estimates, as well as choice of attributes and length of follow-up. CONCLUSION: This case study demonstrates how to implement the ISPOR Task Force's good practice recommendations on qBRA.

Quantitative Benefit-Risk Assessment in Medical Product Decision Making: A Good Practices Report of an ISPOR Task Force.

Benefit-risk assessment is commonly conducted by drug and medical device developers and regulators, to evaluate and communicate issues around benefit-risk balance of medical products. Quantitative benefit-risk assessment (qBRA) is a set of techniques that incorporate explicit outcome weighting within a formal analysis to evaluate the benefit-risk balance. This report describes emerging good practices for the 5 main steps of developing qBRAs based on the multicriteria decision analysis process. First, research question formulation needs to identify the needs of decision makers and requirements for preference data and specify the role of external experts. Second, the formal analysis model should be developed by selecting benefit and safety endpoints while eliminating double counting and considering attribute value dependence. Third, preference elicitation method needs to be chosen, attributes framed appropriately within the elicitation instrument, and quality of the data should be evaluated. Fourth, analysis may need to normalize the preference weights, base-case and sensitivity analyses should be conducted, and the effect of preference heterogeneity analyzed. Finally, results should be communicated efficiently to decision makers and other stakeholders. In addition to detailed recommendations, we provide a checklist for reporting qBRAs developed through a Delphi process conducted with 34 experts.

Suitability of Preference Methods Across the Medical Product Lifecycle: A Multicriteria Decision Analysis.

OBJECTIVES: This study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC. METHODS: Applying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix. RESULTS: Fifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, "estimating trade-offs between treatment characteristics" and "estimating weights for treatment characteristics" were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix. CONCLUSION: Although discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

Patients accept therapy using embryonic stem cells for Parkinson's disease: a discrete choice experiment.

BACKGROUND: New disease-modifying ways to treat Parkinson's disease (PD) may soon become a reality with intracerebral transplantation of cell products produced from human embryonic stem cells (hESCs). The aim of this study was to assess what factors influence preferences of patients with PD regarding stem-cell based therapies to treat PD in the future. METHODS: Patients with PD were invited to complete a web-based discrete choice experiment to assess the importance of the following attributes: (i) type of treatment, (ii) aim of treatment, (iii) available knowledge of the different types of treatments, (iv) effect on symptoms, and (v) risk for severe side effects. Latent class conditional logistic regression models were used to determine preference estimates and heterogeneity in respondents' preferences. RESULTS: A substantial difference in respondents' preferences was observed in three latent preference patterns (classes). "Effect on symptoms" was the most important attribute in class 1, closely followed by "type of treatment," with medications as preferred to other treatment alternatives. Effect on symptoms was also the most important attribute in class 2, with treatment with hESCs preferred over other treatment alternatives. Likewise for class 3, that mainly focused on "type of treatment" in the decision-making. Respondents' class membership was influenced by their experience in treatment, side effects, and advanced treatment therapy as well as religious beliefs. CONCLUSIONS: Most of the respondents would accept a treatment with products emanating from hESCs, regardless of views on the moral status of embryos. Preferences of patients with PD may provide guidance in clinical decision-making regarding treatments deriving from stem cells.

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries.

BACKGROUND: With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. OBJECTIVE: This study investigated public preferences for digital health data sharing. METHODS: A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets. RESULTS: A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. CONCLUSIONS: This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

The Role of Attribute Screening and Choice Set Formation in Health Discrete Choice Experiments: Modeling the Impact of Benefit and Risk Attributes.

OBJECTIVES: This study aimed to demonstrate the econometric modeling of benefit/risk-based choice set formation (CSF) within health-related discrete choice experiments. METHODS: In 4 different case studies, first, a trade-off model was fitted; building on this, a screening model was fitted; and finally, a full CSF model was estimated. This final model allows for attributes to be used first to screen out alternatives from choice tasks before respondents' trade-off attributes and make a choice among feasible alternatives. Educational level and health literacy of respondents were accounted for in all models. RESULTS: Model fit in terms of log likelihood, pseudo-R2, Akaike information criterion, and Bayesian information criterion improved from using only trade-off or screening models compared with CSF models in 3 of the 4 case studies. In those studies, significant screening behavior was identified that (1) affected trade-off inferences, (2) rejects the pure trade-off model, and (3) supports the existence of screening on the basis of benefit-risk profiles, and other attributes. Educational level and health literacy showed significant interactions with multiple attributes in all case studies. CONCLUSIONS: Choice modelers should pay close attention to noncompensatory respondent behavior when they include benefit or risk attributes in their discrete choice experiment. Further studies should investigate why and when respondents undertake screening behavior. Screening behavior in choice data analysis is always a possibility, so researchers should explore extensions of econometric models to reflect noncompensatory behavior. Assuming that benefit and risk attributes will only affect trade-off behavior is likely to lead to biased conclusions about benefit or risk-based behavior.

Preference Variation: Where Does Health Risk Attitude Come Into the Equation?

OBJECTIVES: Decisions about health often involve risk, and different decision makers interpret and value risk information differently. Furthermore, an individual's attitude toward health-specific risks can contribute to variation in health preferences and behavior. This study aimed to determine whether and how health-risk attitude and heterogeneity of health preferences are related. METHODS: To study the association between health-risk attitude and preference heterogeneity, we selected 3 discrete choice experiment case studies in the health domain that included risk attributes and accounted for preference heterogeneity. Health-risk attitude was measured using the 13-item Health-Risk Attitude Scale (HRAS-13). We analyzed 2 types of heterogeneity via panel latent class analyses, namely, how health-risk attitude relates to (1) stochastic class allocation and (2) systematic preference heterogeneity. RESULTS: Our study did not find evidence that health-risk attitude as measured by the HRAS-13 distinguishes people between classes. Nevertheless, we did find evidence that the HRAS-13 can distinguish people's preferences for risk attributes within classes. This phenomenon was more pronounced in the patient samples than in the general population sample. Moreover, we found that numeracy and health literacy did distinguish people between classes. CONCLUSIONS: Modeling health-risk attitude as an individual characteristic underlying preference heterogeneity has the potential to improve model fit and model interpretations. Nevertheless, the results of this study highlight the need for further research into the association between health-risk attitude and preference heterogeneity beyond class membership, a different measure of health-risk attitude, and the communication of risks.

Mimicking Real-Life Decision Making in Health: Allowing Respondents Time to Think in a Discrete Choice Experiment.

OBJECTIVE: To empirically test the impact of allowing respondents time to think (TTT) about their choice options on the outcomes of a discrete choice experiments (DCE). METHODS: In total, 613 participants of the Swedish CArdioPulmonary bioImage Study (SCAPIS) completed a DCE questionnaire that measured their preferences for receiving secondary findings of a genetic test. A Bayesian D-efficient design with 60 choice tasks divided over 4 questionnaires was used. Each choice task contained 2 scenarios with 4 attributes: type of disease, disease penetrance probability, preventive opportunities, and effectiveness of prevention. Respondents were randomly allocated to the TTT or no TTT (NTTT) sample. Latent class models (LCMs) were estimated to determine attribute-level values and their relative importance. In addition, choice certainty, attribute-level interpretation, choice consistency, and potential uptake rates were compared between samples. RESULTS: In the TTT sample, 92% of the respondents (245 of 267) indicated they used the TTT period to (1) read the information they received (72%) and (2) discuss with their family (24%). In both samples, respondents were very certain about their choices. A 3-class LCM was fitted for both samples. Preference reversals were found for 3 of the 4 attributes in one class in the NTTT sample (34% class-membership probability). Relative importance scores of the attributes differed between the 2 samples, and significant scale effects indicating higher choice consistency in TTT sample were found. CONCLUSIONS: Offering respondents TTT influences decision making and preferences. Developers of future DCEs regarding complex health-related decisions are advised to consider this approach to enhance the validity of the elicited preferences.

Research participants' preferences for receiving genetic risk information: a discrete choice experiment.

PURPOSE: This study aims to determine research participants' preferences for receiving genetic risk information when participating in a scientific study that uses genome sequencing. METHODS: A discrete choice experiment questionnaire was sent to 650 research participants (response rate 60.5%). Four attributes were selected for the questionnaire: type of disease, disease penetrance probability, preventive opportunity, and effectiveness of the preventive measure. Panel mixed logit models were used to determine attribute level estimates and the heterogeneity in preferences. Relative importance of the attribute and the predicted uptake for different information scenarios were calculated from the estimates. In addition, this study estimates predicted uptake for receiving genetic risk information in different scenarios. RESULTS: All characteristics influenced research participants' willingness to receive genetic risk information. The most important characteristic was the effectiveness of the preventive opportunity. Predicted uptake ranged between 28% and 98% depending on what preventive opportunities and levels of effectiveness were presented. CONCLUSION: Information about an effective preventive measure was most important for participants. They valued that attribute twice as much as the other attributes. Therefore, when there is an effective preventive measure, risk communication can be less concerned with the magnitude of the probability of developing disease.

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.

Understanding Patients' Preferences: A Systematic Review of Psychological Instruments Used in Patients' Preference and Decision Studies.

BACKGROUND: Research has been mainly focused on how to elicit patient preferences, with less attention on why patients form certain preferences. OBJECTIVES: To assess which psychological instruments are currently used and which psychological constructs are known to have an impact on patients' preferences and health-related decisions including the formation of preferences and preference heterogeneity. METHODS: A systematic database search was undertaken to identify relevant studies. From the selected studies, the following information was extracted: study objectives, study population, design, psychological dimensions investigated, and instruments used to measure psychological variables. RESULTS: Thirty-three studies were identified that described the association between a psychological construct, measured using a validated instrument, and patients' preferences or health-related decisions. We identified 33 psychological instruments and 18 constructs, and categorized the instruments into 5 groups, namely, motivational factors, cognitive factors, individual differences, emotion and mood, and health beliefs. CONCLUSIONS: This review provides an overview of the psychological factors and related instruments in the context of patients' preferences and decisions in healthcare settings. Our results indicate that measures of health literacy, numeracy, and locus of control have an impact on health-related preferences and decisions. Within the category of constructs that could explain preference and decision heterogeneity, health locus of control is a strong predictor of decisions in several healthcare contexts and is useful to consider when designing a patient preference study. Future research should continue to explore the association of psychological constructs with preference formation and heterogeneity to build on these initial recommendations.

Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review.

BACKGROUND: The inclusion of patient preferences (PP) in the medical product life cycle is a topic of growing interest to stakeholders such as academics, Health Technology Assessment (HTA) bodies, reimbursement agencies, industry, patients, physicians and regulators. This review aimed to understand the potential roles, reasons for using PP and the expectations, concerns and requirements associated with PP in industry processes, regulatory benefit-risk assessment (BRA) and marketing authorization (MA), and HTA and reimbursement decision-making. METHODS: A systematic review of peer-reviewed and grey literature published between January 2011 and March 2018 was performed. Consulted databases were EconLit, Embase, Guidelines International Network, PsycINFO and PubMed. A two-step strategy was used to select literature. Literature was analyzed using NVivo (QSR international). RESULTS: From 1015 initially identified documents, 72 were included. Most were written from an academic perspective (61%) and focused on PP in BRA/MA and/or HTA/reimbursement (73%). Using PP to improve understanding of patients' valuations of treatment outcomes, patients' benefit-risk trade-offs and preference heterogeneity were roles identified in all three decision-making contexts. Reasons for using PP relate to the unique insights and position of patients and the positive effect of including PP on the quality of the decision-making process. Concerns shared across decision-making contexts included methodological questions concerning the validity, reliability and cognitive burden of preference methods. In order to use PP, general, operational and quality requirements were identified, including recognition of the importance of PP and ensuring patient understanding in PP studies. CONCLUSIONS: Despite the array of opportunities and added value of using PP throughout the different steps of the MPLC identified in this review, their inclusion in decision-making is hampered by methodological challenges and lack of specific guidance on how to tackle these challenges when undertaking PP studies. To support the development of such guidance, more best practice PP studies and PP studies investigating the methodological issues identified in this review are critically needed.

Public awareness and individual responsibility needed for judicious use of antibiotics: a qualitative study of public beliefs and perceptions.

BACKGROUND: High consumption of antibiotics has been identified as an important driver for the increasing antibiotic resistance, considered to be one of the greatest threats to public health globally. Simply informing the public about this consequence is insufficient to induce behavioral change. This study explored beliefs and perceptions among Swedes, with the aim of identifying factors promoting and hindering a judicious approach to antibiotics use. The study focused primarily on the medical use of antibiotics, also considering other aspects connected with antibiotic resistance, such as travelling and food consumption. METHODS: Data were collected through focus group discussions at the end of 2016. Twenty-three Swedes were recruited using an area-based approach and purposive sampling, aiming for as heterogeneous groups as possible regarding gender (13 women, 10 men), age (range 20-81, mean 38), and education level. Interview transcripts were analyzed using qualitative content analysis. The Health Belief Model was used as a theoretical framework. RESULTS: Antibiotic resistance was identified by participants as a health threat with the potential for terrible consequences. The severity of the problem was perceived more strongly than the actual likelihood of being affected by it. Metaphors such as climate change were abundantly employed to describe antibiotic resistance as a slowly emerging problem. There was a tension between individual (egoistic) and collective (altruistic) reasons for engaging in judicious behavior. The individual effort needed and antibiotics overprescribing were considered major barriers to such behavior. In their discussions, participants stressed the need for empowerment, achieved through good health communication from authorities and family physicians. CONCLUSIONS: Knowledge about antibiotic consumption and resistance, as well as values such as altruism and trust in the health care system, has significant influence on both perceptions of individual responsibility and on behavior. This suggests that these factors should be emphasized in health education and health promotion. To instead frame antibiotic resistance as a slowly emerging disaster, risks diminish the public perception of being susceptible to it.

Survival or Mortality: Does Risk Attribute Framing Influence Decision-Making Behavior in a Discrete Choice Experiment?

OBJECTIVE: To test how attribute framing in a discrete choice experiment (DCE) affects respondents' decision-making behavior and their preferences. METHODS: Two versions of a DCE questionnaire containing nine choice tasks were distributed among a representative sample of the Dutch population aged 55 to 65 years. The DCE consisted of four attributes related to the decision regarding participation in genetic screening for colorectal cancer (CRC). The risk attribute included was framed positively as the probability of surviving CRC and negatively as the probability of dying from CRC. Panel mixed-logit models were used to estimate the relative importance of the attributes. The data of the positively and negatively framed DCE were compared on the basis of direct attribute ranking, dominant decision-making behavior, preferences, and importance scores. RESULTS: The majority (56%) of the respondents ranked survival as the most important attribute in the positively framed DCE, whereas only a minority (8%) of the respondents ranked mortality as the most important attribute in the negatively framed DCE. Respondents made dominant choices based on survival significantly more often than based on mortality. The framing of the risk attribute significantly influenced all attribute-level estimates and resulted in different preference structures among respondents in the positively and negatively framed data set. CONCLUSIONS: Risk framing affects how respondents value the presented risk. Positive risk framing led to increased dominant decision-making behavior, whereas negative risk framing led to risk-seeking behavior. Attribute framing should have a prominent part in the expert and focus group interviews, and different types of framing should be used in the pilot version of DCEs as well as in actual DCEs to estimate the magnitude of the effect of choosing different types of framing.

Exploring how individuals complete the choice tasks in a discrete choice experiment: an interview study.

BACKGROUND: To be able to make valid inferences on stated preference data from a Discrete Choice Experiment (DCE) it is essential that researchers know if participants were actively involved, understood and interpreted the provided information correctly and whether they used complex decision strategies to make their choices and thereby acted in accordance with the continuity axiom. METHODS: During structured interviews, we explored how 70 participants evaluated and completed four discrete choice tasks aloud. Hereafter, additional questions were asked to further explore if participants understood the information that was provided to them and whether they used complex decision strategies (continuity axiom) when making their choices. Two existing DCE questionnaires on rotavirus vaccination and prostate cancer-screening served as case studies. RESULTS: A large proportion of the participants was not able to repeat the exact definition of the risk attributes as explained to them in the introduction of the questionnaire. The majority of the participants preferred more optimal over less optimal risk attribute levels. Most participants (66%) mentioned three or more attributes when motivating their decisions, thereby acting in accordance with the continuity axiom. However, 16 out of 70 participants continuously mentioned less than three attributes when motivating their decision. Lower educated and less literate participants tended to mention less than three attributes when motivating their decision and used trading off between attributes less often as a decision-making strategy. CONCLUSION: The majority of the participants seemed to have understood the provided information about the choice tasks, the attributes, and the levels. They used complex decision strategies (continuity axiom) and are therefore capable to adequately complete a DCE. However, based on the participants' age, educational level and health literacy additional, actions should be undertaken to ensure that participants understand the choice tasks and complete the DCE as presumed.

Consistency between stated and revealed preferences: a discrete choice experiment and a behavioural experiment on vaccination behaviour compared.

BACKGROUND: Discrete Choice Experiments (DCEs) are increasingly used in studies in healthcare research but there is still little empirical evidence for the predictive value of these hypothetical situations in similar real life circumstances. The aim of this paper is to compare the stated preferences in a DCE and the accompanying questionnaire with the revealed preferences of young parents who have to decide whether to vaccinate their new born child against hepatitis B. METHODS: A DCE asking parents to decide in which scenario they would be more inclined to vaccinate their child against hepatitis B. The stated preference was estimated by comparing the per respondent utility of the most realistic scenario in which parents could choose to vaccinate their child against hepatitis B, with the utility of the opt-out, based on the mixed logit model from the DCE. This stated preference was compared with the actual behaviour of the parents concerning the vaccination of their new born child. RESULTS: In 80% of the respondents the stated and revealed preferences corresponded. The positive predictive value is 85% but the negative predictive value is 26%. CONCLUSIONS: The predictive value of the DCE in this study is satisfactory for predicting the positive choice but not for predicting the negative choice. However, the behaviour in this study is exceptional in the sense that most people chose to vaccinate. Future studies should focus on behaviours with a larger variance in the population.

The effect of out-of-pocket costs and financial rewards in a discrete choice experiment: an application to lifestyle programs.

BACKGROUND: Both out-of-pocket costs and financial rewards can be used to influence health related behavior. However, it is unclear which of these two has a larger effect on health related behavior. The aim of this study was to explore the possible difference in effect size between out-of-pocket costs and financial rewards on the willingness of diabetes mellitus type 2 (DM2) patients to participate in a lifestyle program. METHODS: A discrete choice experiment (DCE) questionnaire was sent to 767 DM2 patients in a geographically defined area (De Leidsche Rijn, Utrecht) in The Netherlands and completed by 206 of them. The questionnaire comprised of 18 choice tasks of which 9 contained a financial reward for lifestyle program completion, while the other 9 included out-of-pocket costs for program participation. In a second version of the questionnaire, the order of out-of-pocket cost and financial reward choice tasks was counterbalanced to reduce bias with respect to the position (first or second) of the two types of choice tasks. Panel-mixed-multinomial-logit models were used for data analysis. RESULTS: Increasing out-of-pocket costs were associated with a decreasing willingness to participate in a lifestyle program and, contrary to our expectations, increasing financial rewards were also associated with a decreasing willingness to participate in a lifestyle program. In addition, this willingness to participate changed to the same extent for both increasing out-of-pocket costs and increasing financial rewards. CONCLUSIONS: As expected, increasing out-of-pocket costs may prevent people from deciding to participate in a lifestyle program. However, offering a financial reward to persuade people to participate in a lifestyle program, may result in decreasing willingness to participate in a lifestyle program as well.

Differences in weight loss across different BMI classes: a meta-analysis of the effects of interventions with diet and exercise.

BACKGROUND: Lifestyle interventions are effective for weight loss and are recommended for persons with a body mass index (BMI) of 25-40 kg m(-2). However, this group is very heterogeneous, which could influence outcomes from lifestyle interventions. PURPOSE: In this systematic review, differences in 1-year weight change and percentage weight change after lifestyle interventions were investigated for participants varying in initial BMI using meta-analyses. METHOD: Twenty-two interventions with healthy Caucasian adults, a mean BMI between 25 and 40 kg m(-2), a dietary as well as a physical activity component aiming at weight loss, and at least five contact sessions guided by a professional health care provider were selected from a systematic search in the MEDLINE database. Participants in each intervention were divided into one of the three BMI classes: overweight (BMI of 25-29.99 kg m(-2)), class-I obesity (BMI of 30-34.99 kg m(-2)), and class-II obesity (BMI of 35-39.99 kg m(-2)). Differences in weight change and percentage weight change were analyzed and compared among different BMI classes within the same intervention by calculating standardized mean differences. RESULTS: Overweight participants lost 1.1 kg less (p < 0.01) than participants with class-I obesity and 1.5 kg less (p < 0.01) than participants with class-II obesity. For percentage weight change, no significant differences were found among the BMI classes. CONCLUSION: Average weight change during lifestyle interventions only differs to a small extent among people with BMI between 25 and 40 kg m(-2). This implies that these interventions are equally appropriate for these BMI classes.