Construct Validity, Reliability, and Responsiveness of the 10-Item Well-Being Instrument for Use in Economic Evaluation Studies.

OBJECTIVES: Economic evaluations of interventions in health and social care require outcome measures that capture their full benefits, including those beyond health. This study aimed to assess construct validity, test-retest reliability, and responsiveness of the newly developed 10-item Well-being instrument (WiX). METHODS: Data were gathered via an online survey in a representative sample of the adult general population in The Netherlands (N = 1045). Construct validity was assessed by inspecting convergent, structural, and discriminant validity, following the COnsensus-based Standards for the selection of health status Measurement INstruments methodology. Regression analyses of the WiX and its items on other validated measures of well-being were performed to assess the convergent validity of the instrument and the relevance of its items. Dimensionality of the WiX was assessed using exploratory factor analysis. To assess discriminant validity, several hypotheses in terms of well-being differences were assessed. Finally, a second survey was sent out 2 weeks after the initial survey (n = 563; 53.9% response rate) to assess the test-retest reliability and responsiveness of the WiX. RESULTS: The WiX showed to be correlated with alternative well-being measures as expected and able to sufficiently differentiate between relevant subgroups in the population. Moreover, the dimensionality analysis indicated that the WiX captures a broad array of elements relevant to well-being, including physical and mental health. The test-retest reliability was good, with an intraclass correlation coefficient of 0.82. CONCLUSIONS: The results regarding the WiX are favorable and indicate that this new instrument may be a promising alternative for existing measures of well-being for evaluating interventions in health and social care.

A socially interdependent choice framework for social influences in healthcare decision-making: a study protocol.

OBJECTIVES: Current choice models in healthcare (and beyond) can provide suboptimal predictions of healthcare users' decisions. One reason for such inaccuracy is that standard microeconomic theory assumes that decisions of healthcare users are made in a social vacuum. Healthcare choices, however, can in fact be (entirely) socially determined. To achieve more accurate choice predictions within healthcare and therefore better policy decisions, the social influences that affect healthcare user decision-making need to be identified and explicitly integrated into choice models. The purpose of this study is to develop a socially interdependent choice framework of healthcare user decision-making. DESIGN: A mixed-methods approach will be used. A systematic literature review will be conducted that identifies the social influences on healthcare user decision-making. Based on the outcomes of a systematic literature review, an interview guide will be developed that assesses which, and how, social influences affect healthcare user decision-making in four different medical fields. This guide will be used during two exploratory focus groups to assess the engagement of participants and clarity of questions and probes. The refined interview guide will be used to conduct the semistructured interviews with healthcare professionals and users. These interviews will explore in detail which, and how, social influences affect healthcare user decision-making. Focus group and interview transcripts will be analysed iteratively using a constant comparative approach based on a mix of inductive and deductive coding. Based on the outcomes, a social influence independent choice framework for healthcare user decision-making will be drafted. Finally, the Delphi technique will be employed to achieve consensus about the final version of this choice framework. ETHICS AND DISSEMINATION: This study was approved by the Erasmus School of Health Policy and Management Research Ethics Review Committee (ESHPM, Rotterdam, The Netherlands; reference ETH2122-0666).

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS II) statement: a validated Dutch translation.

OBJECTIVES: This study primarily aimed to develop a validated Dutch translation of the 28 items of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) II. A secondary aim was to provide a worked example of a scientifically valid translation process. METHODS: A four-step process was applied: (1) forward translation, (2) backward translation, (3) quantitative validation (two back-translated English versions vs. original English version), and (4) qualitative validation (one Dutch version vs. original English version), resulting in the final Dutch CHEERS II checklist. RESULTS: During quantitative validation, the average scores indicated high language comparability (1.88 (SD 0.70); 1.70 (SD 0.73)) and interpretation similarity (1.77 (SD 0.81); 1.54 (SD 0.74)). Four items required formal revision. In the qualitative validation step, feedback primarily focused on specific terms 'outcomes,' 'benefits and harms,' '(year of) conversion,' 'any,' and 'characterizing.' CONCLUSION: Despite English being the common language of science, translating research instruments remains relevant to enhance clarity, accessibility, and inclusivity. The Dutch translation can be used by students, regulators, researchers, or others to report and evaluate reporting of economic evaluations. Our detailed description of the applied methodology can facilitate future translations of research instruments.

How to Present a Decision Object in Health Preference Research: Attributes and Levels, the Decision Model, and the Descriptive Framework.

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.

Research trends in contemporary health economics: a scientometric analysis on collective content of specialty journals.

INTRODUCTION: Health economics is a thriving sub-discipline of economics. Applied health economics research is considered essential in the health care sector and is used extensively by public policy makers. For scholars, it is important to understand the history and status of health economics-when it emerged, the rate of research output, trending topics, and its temporal evolution-to ensure clarity and direction when formulating research questions. METHODS: Nearly 13,000 articles were analysed, which were found in the collective publications of the ten most specialised health economic journals. We explored this literature using patterns of term co-occurrence and document co-citation. RESULTS: The research output in this field is growing exponentially. Five main research divisions were identified: (i) macroeconomic evaluation, (ii) microeconomic evaluation, (iii) measurement and valuation of outcomes, (iv) monitoring mechanisms (evaluation), and (v) guidance and appraisal. Document co-citation analysis revealed eighteen major research streams and identified variation in the magnitude of activities in each of the streams. A recent emergence of research activities in health economics was seen in the Medicaid Expansion stream. Established research streams that continue to show high levels of activity include Child Health, Health-related Quality of Life (HRQoL) and Cost-effectiveness. Conversely, Patient Preference, Health Care Expenditure and Economic Evaluation are now past their peak of activity in specialised health economic journals. Analysis also identified several streams that emerged in the past but are no longer active. CONCLUSIONS: Health economics is a growing field, yet there is minimal evidence of creation of new research trends. Over the past 10 years, the average rate of annual increase in internationally collaborated publications is almost double that of domestic collaborations (8.4% vs 4.9%), but most of the top scholarly collaborations remain between six countries only.

Eliciting preferences of persons with dementia and informal caregivers to support ageing in place in the Netherlands: a protocol for a discrete choice experiment.

INTRODUCTION: Ageing in place (AIP) for persons with dementia is encouraged by European governments and societies. Healthcare packages may need reassessment to account for the preferences of care funders, patients and informal caregivers. By providing insight into people's preferences, discrete choice experiments (DCEs) can help develop consensus between stakeholders. This protocol paper outlines the development of a Dutch national study to cocreate a healthcare package design methodology built on DCEs that is person-centred and helps support informal caregivers and persons with dementia to AIP. A subpopulation analysis of persons with dementia with a migration background is planned due to their high risk for dementia and under-representation in research and care. METHODS AND ANALYSIS: The DCE is designed to understand how persons with dementia and informal caregivers choose between different healthcare packages. Qualitative methods are used to identify and prioritise important care components for persons with dementia to AIP. This will provide a list of care components that will be included in the DCE, to quantify the care needs and preferences of persons with dementia and informal caregivers. The DCE will identify individual and joint preferences to AIP. The relative importance of each attribute will be calculated. The DCE data will be analysed with the use of a random parameters logit model. ETHICS AND DISSEMINATION: Ethics approval was waived by the Amsterdam University Medical Center (W23_112 #23.137). A study summary will be available on the websites of Alzheimer Nederland, Pharos and Amsterdam Public Health institute. Results are expected to be presented at (inter)national conferences, peer-reviewed papers will be submitted, and a dissemination meeting will be held to bring stakeholders together. The study results will help improve healthcare package design for all stakeholders.

Discrete choice experiment versus swing-weighting: A head-to-head comparison of diabetic patient preferences for glucose-monitoring devices.

INTRODUCTION: Limited evidence exists for how patient preference elicitation methods compare directly. This study compares a discrete choice experiment (DCE) and swing-weighting (SW) by eliciting preferences for glucose-monitoring devices in a population of diabetes patients. METHODS: A sample of Dutch adults with type 1 or 2 diabetes (n = 459) completed an online survey assessing their preferences for glucose-monitoring devices, consisting of both a DCE and a SW exercise. Half the sample completed the DCE first; the other half completed the SW first. For the DCE, the relative importance of the attributes of the devices was determined using a mixed-logit model. For the SW, the relative importance of the attributes was based on ranks and points allocated to the 'swing' from the worst to the best level of the attribute. The preference outcomes and self-reported response burden were directly compared between the two methods. RESULTS: Participants reported they perceived the DCE to be easier to understand and answer compared to the SW. Both methods revealed that cost and precision of the device were the most important attributes. However, the DCE had a 14.9-fold difference between the most and least important attribute, while the SW had a 1.4-fold difference. The weights derived from the SW were almost evenly distributed between all attributes. CONCLUSIONS: The DCE was better received by participants, and generated larger weight differences between each attribute level, making it the more informative method in our case study. This method comparison provides further evidence of the degree of method suitability and trustworthiness.

Taking the Shortcut: Simplifying Heuristics in Discrete Choice Experiments.

Health-related discrete choice experiments (DCEs) information can be used to inform decision-making on the development, authorisation, reimbursement and marketing of drugs and devices as well as treatments in clinical practice. Discrete choice experiment is a stated preference method based on random utility theory (RUT), which imposes strong assumptions on respondent choice behaviour. However, respondents may use choice processes that do not adhere to the normative rationality assumptions implied by RUT, applying simplifying decision rules that are more selective in the amount and type of processed information (i.e., simplifying heuristics). An overview of commonly detected simplifying heuristics in health-related DCEs is lacking, making it unclear how to identify and deal with these heuristics; more specifically, how researchers might alter DCE design and modelling strategies to accommodate for the effects of heuristics. Therefore, the aim of this paper is three-fold: (1) provide an overview of common simplifying heuristics in health-related DCEs, (2) describe how choice task design and context as well as target population selection might impact the use of heuristics, (3) outline DCE design strategies that recognise the use of simplifying heuristics and develop modelling strategies to demonstrate the detection and impact of simplifying heuristics in DCE study outcomes.

Preferences of patients, clinicians, and healthy controls for the management of a Bethesda III thyroid nodule.

BACKGROUND: Active surveillance is propagated as an alternative for hemithyroidectomy in the management of Bethesda III thyroid nodules. METHODS: A cross-sectional survey questioned respondents on their willingness to accept risks related to active surveillance and hemithyroidectomy. RESULTS: In case of active surveillance, respondents (129 patients, 46 clinicians, and 66 healthy controls) were willing to accept a risk of 10%-15% for thyroid cancer and 15% for needing more extensive surgery in the future. Respondents were willing to accept a risk of 22.5%-30% for hypothyroidism after hemithyroidectomy. Patients and controls were willing to accept a higher risk on permanent voice changes compared with clinicians (10% vs. 3%, p < 0.001). CONCLUSION: Real-life risks associated which active surveillance and hemithyroidectomy for Bethesda III nodules are equivalent or less than the risks people are willing to accept. Clinicians accepted less risk for permanent voice changes.

Towards machine learning for moral choice analysis in health economics: A literature review and research agenda.

BACKGROUND: Discrete choice models (DCMs) for moral choice analysis will likely lead to erroneous model outcomes and misguided policy recommendations, as only some characteristics of moral decision-making are considered. Machine learning (ML) is recently gaining interest in the field of discrete choice modelling. This paper explores the potential of combining DCMs and ML to study moral decision-making more accurately and better inform policy decisions in healthcare. METHODS: An interdisciplinary literature search across four databases - PubMed, Scopus, Web of Science, and Arxiv - was conducted to gather papers. Based on the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) guideline, studies were screened for eligibility on inclusion criteria and extracted attributes from eligible papers. Of the 6285 articles, we included 277 studies. RESULTS: DCMs have shortcomings in studying moral decision-making. Whilst the DCMs' mathematical elegance and behavioural appeal hold clear interpretations, the models do not account for the 'moral' cost and benefit in an individual's utility calculation. The literature showed that ML obtains higher predictive power, model flexibility, and ability to handle large and unstructured datasets. Combining the strengths of ML methods with DCMs has the potential for studying moral decision-making. CONCLUSIONS: By providing a research agenda, this paper highlights that ML has clear potential to i) find and deepen the utility specification of DCMs, and ii) enrich the insights extracted from DCMs by considering the intrapersonal determinants of moral decision-making.

A Roadmap for Increasing the Usefulness and Impact of Patient-Preference Studies in Decision Making in Health: A Good Practices Report of an ISPOR Task Force.

Many qualitative and quantitative methods are readily available to study patient preferences in health. These methods are now being used to inform a wide variety of decisions, and there is a growing body of evidence showing studies of patient preferences can be used for decision making in a wide variety of contexts. This ISPOR Task Force report synthesizes current good practices for increasing the usefulness and impact of patient-preference studies in decision making. We provide the ISPOR Roadmap for Patient Preferences in Decision Making that invites patient-preference researchers to work with decision makers, patients and patient groups, and other stakeholders to ensure that studies are useful and impactful. The ISPOR Roadmap consists of 5 key elements: (1) context, (2) purpose, (3) population, (4) method, and (5) impact. In this report, we define these 5 elements and provide good practices on how patient-preference researchers and others can actively contribute to increasing the usefulness and impact of patient-preference studies in decision making. We also present a set of key questions that can support researchers and other stakeholders (eg, funders, reviewers, readers) to assess efforts that promote the ongoing impact (both intended and unintended) of a particular preference study and additional studies in the future.

Comparing Outcomes of a Discrete Choice Experiment and Case 2 Best-Worst Scaling: An Application to Neuromuscular Disease Treatment.

BACKGROUND AND OBJECTIVES: Case 2 best-worst scaling (BWS-2) is an increasingly popular method to elicit patient preferences. Because BWS-2 potentially has a lower cognitive burden compared with discrete choice experiments, the aim of this study was to compare treatment preference weights and relative importance scores. METHODS: Patients with neuromuscular diseases completed an online survey at two different moments in time, completing one method per occasion. Patients were randomly assigned to either first a discrete choice experiment or BWS-2. Attributes included: muscle strength, energy endurance, balance, cognition, chance of blurry vision, and chance of liver damage. Multinomial logit was used to calculate overall relative importance scores and latent class logit was used to estimate heterogeneous preference weights and to calculate the relative importance scores of the attributes for each latent class. RESULTS: A total of 140 patients were included for analyses. Overall relative importance scores showed differences in attribute importance rankings between a discrete choice experiment and BWS-2. Latent class analyses indicated three latent classes for both methods, with a specific class in both the discrete choice experiment and BWS-2 in which (avoiding) liver damage was the most important attribute. Ex-post analyses showed that classes differed in sex, age, level of education, and disease status. The discrete choice experiment was easier to understand compared with BWS-2. CONCLUSIONS: This study showed that using a discrete choice experiment and BWS-2 leads to different outcomes, both in preference weights as well as in relative importance scores, which might have been caused by the different framing of risks in BWS-2. However, a latent class analysis revealed similar latent classes between methods. Careful consideration about method selection is required, while keeping the specific decision context in mind and pilot testing the methods.

The Impact of Video-Based Educational Materials with Voiceovers on Preferences for Glucose Monitoring Technology in Patients with Diabetes: A Randomised Study.

INTRODUCTION: Ensuring patients have enough information about healthcare choices prior to completing a preference study is necessary to support the validity of the findings. Patients are commonly informed using text-based information with supporting graphics. Video-based information may be more engaging for the general patient population. This study aimed to assess (1) the impact that educating patients using video-based educational materials with a voiceover has on patient preferences compared to traditional text, and (2) whether this impact is consistent between two countries. MATERIALS AND METHODS: A video-based educational tool was developed to inform patients prior to completing a discrete choice experiment assessing preferences for glucose monitors. Patients with diabetes from the Netherlands and Poland were recruited through an online research panel. Respondents were randomised to receive information in either a text or a video with animations and a voiceover. Data were analysed using a mixed-logit model. RESULTS: N = 981 completed surveys were analysed from the Netherlands (n = 459) and Poland (n = 522). Differences were found between the countries, but no interpretable pattern of differences was found between the two types of educational materials. Patients spent less time in the educational material than would be necessary to fully review all of the content. CONCLUSIONS: Simply providing educational material in a video with animations and voiceovers does not necessarily lead to better engagement from respondents or different preference outcomes in a sample of diabetes patients when compared to text. Increasing engagement with educational materials should be a topic of future research for those conducting patient preference research as no amount of educational material will be helpful if respondents do not access it.

Diabetes patient preferences for glucose-monitoring technologies: results from a discrete choice experiment in Poland and the Netherlands.

INTRODUCTION: New glucose-monitoring technologies have different cost-benefit profiles compared with traditional finger-prick tests, resulting in a preference-sensitive situation for patients. This study aimed to assess the relative value adults with diabetes assign to device attributes in two countries. RESEARCH DESIGN AND METHODS: Adults with type 1 or 2 diabetes from the Netherlands (n=226) and Poland (n=261) completed an online discrete choice experiment. Respondents choose between hypothetical glucose monitors described using seven attributes: precision, effort to check, number of finger pricks required, risk of skin irritation, information provided, alarm function and out-of-pocket costs. Panel mixed logit models were used to determine attribute relative importance and to calculate expected uptake rates and willingness to pay (WTP). RESULTS: The most important attribute for both countries was monthly out-of-pocket costs. Polish respondents were more likely than Dutch respondents to choose a glucose-monitoring device over a standard finger prick and had higher WTP for a device. Dutch respondents had higher WTP for device improvements in an effort to check and reduce the number of finger pricks a device requires. CONCLUSION: Costs are the primary concern of patients in both countries when choosing a glucose monitor and would likely hamper real-world uptake. The costs-benefit profiles of such devices should be critically reviewed.

Dyadic Discrete Choice Experiments Enable Persons with Dementia and Informal Caregivers to Participate in Health Care Decision Making: A Mixed Methods Study.

BACKGROUND: Discrete choice experiments (DCEs) may facilitate persons with dementia and informal caregivers to state care preferences. DCEs can be cognitively challenging for persons with dementia. OBJECTIVE: This study aims to design a dementia friendly dyadic DCE that enables persons with dementia and informal caregivers to provide input individually and jointly, by testing the number of attributes and choice tasks persons with dementia can complete and providing insight in their DCE decision-making process. METHODS: This study included three DCE rounds: 1) persons with dementia, 2) informal caregivers, and 3) persons with dementia and informal caregivers together. A flexible DCE design was employed, with increasing choice task complexity to explore cognitive limitations in decision-making. Summary statistics and bivariate comparisons were calculated. A qualitative think-aloud approach was used to gain insight in the DCE decision-making processes. Transcripts were analyzed using thematic analysis. RESULTS: Fifteen person with dementia, 15 informal caregiver, and 14 dyadic DCEs were conducted. In the individual DCE, persons with dementia completed six choice tasks (median), and 80% could complete a choice task with least three attributes. In the dyadic DCE persons with dementia completed eight choice tasks (median) and could handle slightly more attributes. Qualitative results included themes of core components in DCE decision-making such as: understanding the choice task, attribute and level perception, option attractiveness evaluation, decision rule selection, and preference adaptation. CONCLUSION: Persons with dementia can use simple DCE designs. The dyadic DCE was promising for dyads to identify overlapping and discrepant care preferences while reaching consensus.

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.

Does It Matter How You Ask? Assessing the Impact of Failure or Effectiveness Framing on Preferences for Antibiotic Treatments in a Discrete Choice Experiment.

Purpose: Studies assessing framing effects in discrete choice experiments (DCE) primarily focused on attributes related to mortality/survival information. Little is known about framing effects for other attributes in health-related DCEs. This study aimed to investigate how framing treatment outcome as effective, failure, or a combined frame impacts respondent choices and DCE outcomes. Patients and Methods: Three Bayesian D-efficient designed DCE surveys measuring preferences for antibiotic treatments were randomly distributed to a representative sample of the Swedish population aged 18-65 years (n=1119). Antibiotic treatments were described using five attributes. Four attributes were static: Contribution to Antibiotic Resistance, Treatment Duration, Likelihood of Side-Effects, and Costs. A fifth treatment attribute was framed in three ways: Effectiveness, Failure Rate, or both. Mixed logit models were used to analyze attribute level estimates, importance value, and choice predictions. Results: Significant differences between the frames were found for the parameter estimates of the attributes of Treatment Duration and Likelihood of Side-Effects, but not Treatment Outcome which was the alternatively framed attribute. Contribution to Antibiotic Resistance and Costs were the most important attributes for all participants regardless of framing. Choice predictions for the "best option" antibiotic only slightly differed between the groups based on the frame seen (95.2-92.4%). Conclusion: Our study showed that attribute framing can impact preferences regardless of the attribute's importance value in alternative valuation. However, the practical implication of this effect may be limited. A theoretical discussion is needed to identify how researchers should accommodate and report any potential framing effect in their studies.

Can healthcare choice be predicted using stated preference data? The role of model complexity in a discrete choice experiment about colorectal cancer screening.

INTRODUCTION: The validity of discrete choice experiments (DCEs) is crucial to its usage in healthcare decision-making, but there is only a limited number of health contexts in which external validity is demonstrated. This study aims to assess the internal and external validity of the DCE in the context of colorectal cancer (CRC) screening, and gather insights into the discrepancy between stated and revealed preferences. METHODS: Stated and revealed preferences were elicited on an individual level from Dutch residents eligible for CRC screening in a DCE and a field experiment, respectively (N = 568). To identify the determinants of CRC screening participation and their relative importance, five random utility maximisation models that varied in complexity were used. We assessed the accuracy with which the models based on stated preferences predict individual-level screening choice in a holdout task (internal validity) and in the actual screening choice (external validity). Insights into the discrepancy between stated and revealed preferences were gathered by comparing groups of respondents. RESULTS: Our findings show high internal and external validity. Choices could be accurately predicted for 95% of the respondents in the holdout task, and 90% in the actual screening choice. When scale and preference heterogeneity were taken into account model fit improved; individual-level prediction accuracy slightly increased for the holdout task but not for the actual screening choice. Respondents for whom stated preferences matched revealed preferences were generally in better health and found the GP's support for their screening decision more important. DISCUSSION: Evidence was found that revealed preferences can be predicted accurately on an individual level. Incorporating heterogeneity improved internal validity but not external validity. Differences between stated and revealed preferences can be attributed to respondents' health and the support of their GP. We suggest researchers to continue investigating the internal and external validity of discrete choice experiments, and the role of model complexity.

Identifying the impact of social influences in health-related discrete choice experiments.

Several disciplines, among them health, sociology, and economics, provide strong evidence that social context is important to individual choices. It is therefore surprising that relatively little research has been focused on integrating the effect of social influence into choice models, especially given the importance of such choices in healthcare. This study developed and empirically tested a choice model that accounts for social network influences in a discrete choice experiment (DCE). We focused on maternal choices for childhood vaccination in Australia, and used an econometric choice model that explicitly 1) incorporated vaccine schedule characteristics, benefits and costs, and 2) represented up to ten different identifiable key influencer types (e.g., partner, parents, friends, healthcare professionals, inter alia), allowing for the attribution of directional importance of each influencer on the gravid woman's decision to adhere to or reject childhood vaccination. Pregnant women (N = 604) aged 18 years and older recruited from an online panel completed a survey, including a DCE and questions about key influencers. A two-class ordered latent class model was conducted to analyse the DCE data, which assumes that the underlying latent driver (in our case the WHO vaccine hesitancy scale) is ordered, to give a practical interpretation of the meaning of the classes. When the choice model considered both childhood vaccination attributes and key influencers, a very high model fit was reached. The impact of key influencers on maternal choice for childhood vaccination was massive compared to the impact of childhood vaccination attributes. The marginal impact differed between key influencers. Our DCE study showed that the maternal decision for childhood vaccination was essentially almost completely socially driven, suggesting that the potential impact of social network influences can and should be considered in health-related DCEs, particular those where there are likely to be strong underlying social norms dictating decision maker behaviour.

Applications of discrete choice experiments in COVID-19 research: Disparity in survey qualities between health and transport fields.

Published choice experiments linked to various aspects of the COVID-19 pandemic are analysed in a rapid review. The aim is to (i) document the diversity of topics as well as their temporal and geographical patterns of emergence, (ii) compare various elements of design quality across different sectors of applied economics, and (iii) identify potential signs of convergent validity across findings of comparable experiments. Of the N = 43 published choice experiments during the first two years of the pandemic, the majority identifies with health applications (n = 30), followed by transport-related applications (n = 10). Nearly 100,000 people across the world responded to pandemic-related discrete choice surveys. Within health applications, while the dominant theme, up until June 2020, was lockdown relaxation and tracing measures, the focus shifted abruptly to vaccine preference since then. Geographical origins of the health surveys were not diverse. Nearly 50% of all health surveys were conducted in only three countries, namely US, China and The Netherlands. Health applications exhibited stronger pre-testing and larger sample sizes compared to transport applications. Limited signs of convergent validity were identifiable. Within some applications, issues of temporal instability as well as hypothetical bias attributable to social desirability, protest response or policy consequentiality seemed likely to have affected the findings. Nevertheless, very few of the experiments implemented measures of hypothetical bias mitigation and those were limited to health studies. Our main conclusion is that swift administration of pandemic-related choice experiments has overall resulted in certain degrees of compromise in study quality, but this has been more so the case in relation to transport topics than health topics.