Using a Discrete-Choice Experiment Involving Cost to Value a Classification System Measuring the Quality-of-Life Impact of Self-Management for Diabetes

The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review

INTRODUCTION

Stakeholder preference evaluations are increasingly emphasized in healthcare policy and health technology assessment. Discrete choice experiments (DCEs) are the most common method for quantifying preferences among patients, the public, and healthcare professionals. While prior reviews (1990-2017) have examined DCE trends, no comprehensive synthesis exists for studies published since 2018. This updated review (2018-2023) provides critical insights into evolving methodologies and global trends in health-related DCEs.

METHODS

A systematic search (2018-2023) of Medline, Embase, and Web of Science identified relevant studies. Studies were screened for inclusion and data were extracted, including details on DCE design and analysis. To enable trend comparisons, the search strategy and extraction items aligned with previous reviews.

RESULTS

Of 2663 identified papers, 1279 met the inclusion criteria, reflecting a significant rise in published DCEs over time. DCEs were conducted globally, with a remarkable increase in publications from Asia and Africa compared with previous reviews. Experimental designs and econometric models have advanced, continuing prior trends. Notably, most recent DCEs were administered online.

DISCUSSION

The rapid growth of DCE applications underscores their importance in health research. While the methodology is advancing rapidly, it is crucial that researchers provide full transparency in reporting their methods, particularly in detailing experimental designs and validity tests, which are too often overlooked. Key recommendations include improving reporting of experimental designs, applying validity tests, following good practices for presenting benefit-risk attributes, and adopting open science practices. Ensuring methodological rigor will maximize the impact and reproducibility of DCE research in health economics.

A discrete choice experiment to understand preferences of patients with type 2 diabetes about the attributes of GLP1 receptor agonists in Spain

OBJECTIVE

To determine the preferences regarding injection, medication frequency and complexity of GLP1 receptor agonists among patients with type 2 diabetes, treatment-naïve for such drugs in Spain. Additionally, patients' willingness to pay according to these attributes was evaluated.

METHODS

A discrete-choice experiment survey designed to evaluate patients' preferences over three attributes discriminating by age, sex and patients experience with previous injectable treatment was fulfilled by patients. The resulting model was analyzed using a conditional (fixed-effects) logistic regression.

RESULTS

A total of 180 patients (63.35 ± 11.49 years, 63.28% men, 48.41% with previous cardiovascular disease, 54.69% with a time of evolution of diabetes >10 years) recruited from 5 health care centers in Spain completed the survey. Patients viewed positively weekly injections (vs daily injections), but rated negatively a complex preparation of the dose (vs simple preparation). Whereas naïve patients for injectable medications did not consider administration timing of importance, no naïve patients considered it relevant. No relevant differences were observed according to age or gender. Patients were willing to pay 83.25€for a "no preparation required" dose. No naïve and naïve patients were willing to pay 34.61€ and 14.35€; p = 0.000, to change daily injection for a weekly injection.

CONCLUSIONS

Patients highly valued the avoidance of injections, with weekly dosing clearly preferred over daily dosing, as well as reducing the treatment complexity. These findings may provide a better understanding of what patients prefer and value in their treatment and provide guidance for clinicians making therapeutic decisions regarding treatments of patients with type 2 diabetes.

Elicitation of societal preferences for chronic lymphocytic leukemia's treatments: a discrete choice experiment

The overall value of treatments for chronic lymphocytic leukemia (CLL) depends on several factors, including preferences of the general population, who contributes to the financing of health systems. This study investigated societal preferences for attributes of CLL treatments in Italy. An online large-scale survey was designed using a discrete choice experiment (DCE) methodology and delivered to the Italian adult general population. Ten treatment attributes were identified, covering efficacy, safety, operational aspects and (hypothetical) out-of-pocket cost. DCE data were analyzed using a mixed logit regression model, estimating the willingness-to-pay for attribute levels' change. The general population significantly preferred more effective treatments, with shorter duration, administered orally rather than orally + intravenously. Changes in therapy duration, frequency of checkups and organ damage risk had the greatest impact on preferences. The integration of societal preferences in the value judgments of CLL therapies may help health authorities in establishing priority setting and taking pricing-reimbursement decisions.

Does Willingness to Pay Differ for Mental and Physical Health?

OBJECTIVES

The urban environment can have a significant impact on mental and physical health. Health impact appraisal of new developments should address these issues. However, transferable economic valuation evidence for urban planners in the United Kingdom is thin, especially around mental health, making it harder to estimate the cost-efficiency of public health interventions to address these conditions. A further complication is that mental health may be perceived differently from physical health. This study examines willingness to pay (WTP) to avoid depression and lower back pain.

METHODS

WTP estimates were obtained by applying contingent valuation tasks in an online survey with a representative sample in the United Kingdom (N = 1553). Interval regression models were used to estimate the effects of disease severity, payment frequency, and respondent characteristics on WTP.

RESULTS

Respondents' WTP to avoid both conditions was relatively high (around 5%-6% of stated income to return to current health state). Depression was rated as being twice as burdensome on quality of life than pain, and bids to avoid depression were 20% to 30% more than pain. Analysis of motivation responses suggests mental health treatment is perceived as less easy to access and less effective than the equivalent for pain, and respondents expect a larger burden on their family and relationships as they try to manage their condition themselves.

CONCLUSIONS

Results suggest that depression bids may be affected by uncertainty around access to effective treatment in the healthcare system. This has implications for how mental illness may be prioritized in resource allocation toward public health interventions.

A scoping review to create a framework for the steps in developing condition-specific preference-based instruments de novo or from an existing non-preference-based instrument: use of item response theory or Rasch analysis

BACKGROUND

There is no widely accepted framework to guide the development of condition-specific preference-based instruments (CSPBIs) that includes both de novo and from existing non-preference-based instruments. The purpose of this study was to address this gap by reviewing the published literature on CSPBIs, with particular attention to the application of item response theory (IRT) and Rasch analysis in their development.

METHODS

A scoping review of the literature covering the concepts of all phases of CSPBI development and evaluation was performed from MEDLINE, Embase, PsychInfo, CINAHL, and the Cochrane Library, from inception to December 30, 2022.

RESULTS

The titles and abstracts of 1,967 unique references were reviewed. After retrieving and reviewing 154 full-text articles, data were extracted from 109 articles, representing 41 CSPBIs covering 21 diseases or conditions. The development of CSPBIs was conceptualized as a 15-step framework, covering four phases: 1) develop initial questionnaire items (when no suitable non-preference-based instrument exists), 2) establish the dimensional structure, 3) reduce items per dimension, 4) value and model health state utilities. Thirty-nine instruments used a type of Rasch model and two instruments used IRT models in phase 3.

CONCLUSION

We present an expanded framework that outlines the development of CSPBIs, both from existing non-preference-based instruments and de novo when no suitable non-preference-based instrument exists, using IRT and Rasch analysis. For items that fit the Rasch model, developers selected one item per dimension and explored item response level reduction. This framework will guide researchers who are developing or assessing CSPBIs.

Estimating preferences and willingness to pay for pharmacogenetic testing in populations who are medically underserved: a discrete choice experiment

Background: The implementation of pharmacogenetic (PGx) testing may contribute to health disparities if access to testing is inequitable, as medically underserved patients are prescribed higher rates of drugs with PGx guidelines and often experience the benefits of emerging health technologies last. Limited research has evaluated potential implementation of PGx testing in populations who are medically underserved and none have evaluated their preferences regarding PGx test characteristics and cost. Our study endeavored to assess the willingness to pay for PGx testing and key PGx test preferences in a nationwide cohort of medically underserved respondents. Methods: A survey was developed to assess willingness to pay and preferences for PGx testing through a discrete choice experiment (DCE). Five attributes of PGx tests were included in the DCE: doctor recommendation, wait time, number of actionable results, benefit of the test (avoid a side effect or address a health problem), and out-of-pocket cost. A convenience sample of U.S. adults with an average yearly household income of $42,000 or less was collected utilizing an online survey fielded by Qualtrics Research Services (Provo, UT). For the DCE analysis, conditional logit and mixed-logit regression models were utilized to determine relative utility of attributes and levels, conditional relative importance for each attribute, and marginal willingness to pay. Results: Respondents completed the survey with an 83.1% response completion rate. Following quality control procedures, 1,060 respondents were included in the final nationwide cohort. Approximately, 82% of respondents were willing to pay less than $100 for PGx testing, and a strong price ceiling was identified at $200. Out-of-pocket cost was the attribute identified as having the greatest relative importance on choice, while wait time had the lowest importance. Greater utility was observed if the PGx test was doctor recommended, had a higher number of actionable results, and resolved major or minor health problems compared with avoiding side effects. Conclusion: This first-of-its-kind study provides important insights into the willingness to pay for PGx testing and PGx test preferences of a large medically underserved population. Applying these findings can potentially lead to improvements in the successful implementation of PGx testing in this population.

A Preference-Based Value Assessment of the Fear of COVID-19 Contagion

Purpose

To assess the preference-based value of the fear of COVID-19 contagion.

Patients and Methods

We conducted a web-based, cross-sectional discrete choice experiment among 544 US adults. We used a Bayesian efficient design to generate choice sets. Each choice set comprised two hypothetical COVID-19 vaccine options characterized by seven attributes: chance of COVID-19 infection, chance of having severe symptoms from COVID-19 infection, vaccine protection duration, chance of mild to moderate adverse events from vaccination, chance of serious adverse events from vaccination, chance of future exposure to COVID-19 after vaccination, and out-of-pocket cost. We used mixed logit (ML) and latent class (LC) models to analyze data. Furthermore, we calculated the willingness-to-pay for eliminating the chance of future exposure to COVID-19, shedding light on the value attributed to the fear of contagion.

Results

The ML model demonstrated all attributes, including the chance of future exposure to COVID-19, were statistically significant. The participants were willing to pay approximately $13,046 to eliminate the chance of future exposure to COVID-19 or their fear of contagion when COVID-19 was still pandemic. The LC model unveiled two participant classes with distinct preference weights for the chance of future exposure to COVID-19 and out-of-pocket cost attributes. Nevertheless, the chance of future exposure to COVID-19 exposure held a significant degree of importance in both classes.

Conclusion

The chance of future exposure to COVID-19 exposure or fear of contagion was a significant element in the value assessment of COVID-19 vaccines. Further studies should be conducted to verify the value of fear of contagion and include it in the value assessment of healthcare technologies for infectious diseases.

Development of a discrete choice experiment questionnaire to elicit preferences by pregnant women and policymakers for the expansion of non-invasive prenatal screening

OBJECTIVE

An instrument for measuring intervention preferences applicable to both patients and policymakers would make it possible to better confront the needs of the supply and demand sides of the health care system. This study aimed to develop a discrete choice experiments (DCE) questionnaire to elicit the preferences of patients and policymakers. The instrument was specifically developed to estimate preferences for new conditions to be added to a screening program for fetal chromosomal anomalies.

METHODS

A DCE development study was conducted. The methods employed included a literature review, a qualitative study (based on individual semi-structured interviews, consultations, and a focus group discussion) with pregnant women and policymakers, and a pilot project with 33 pregnant women to validate the first version of the instrument and test the feasibility of its administration.

RESULTS

An initial list of 10 attributes was built based on a literature review and the qualitative research components of the study. Five attributes were built based on the responses provided by the participants from both groups. Eight attributes were consensually retained. A pilot project performed on 33 pregnant women led to a final instrument containing seven attributes: 'conditions to be screened', 'test performance', 'moment at gestational age to obtain the test result', 'degree of test result certainty to the severity of the disability', 'test sufficiency', 'information provided from test result', and 'cost related to the test'.

CONCLUSION

It is possible to reach a consensus on the construction of a DCE instrument intended to be administered to pregnant women and policymakers. However, complete validation of the consensual instrument is limited because there are too few voting members of health technology assessment agencies committees to statistically ascertain the relevance of the attributes and their levels.

Keeping an eye on cost: What can eye tracking tell us about attention to cost information in discrete choice experiments?

Concern has been expressed about including a cost attribute within discrete choice experiments (DCEs) when individuals do not have to pay at the point of consumption. We use eye tracking to investigate attention to cost when valuing publicly financed health care. One-hundred and four individuals completed a DCE concerned with preferences for UK general practitioner appointments: 51 responded to a DCE with cost included and 53 to the same DCE without cost. Eye-movements were tracked whilst respondents completed the DCE. We assessed if respondents pay attention to cost. We then compare fixation time (FT) on attributes, eye movement patterns and mental effort across the experimental groups. Results are encouraging for the inclusion of cost in DCEs valuing publicly provided healthcare. Most respondents gave visual attention to the cost attribute most of the time. Average FT on multi-attribute tasks increased by 44% in the cost DCE, with attention to non-monetary attributes increasing by 22%. Including cost led to more structured decision-making and did not increase mental effort. Acceptability of the cost attribute and difficulty of choice tasks were predictors of cost information processing, highlighting the importance of both motivating the cost attribute and considering difficulty of the tasks when developing DCEs.

Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics

PROBLEM

Policymakers must decide on interventions to control the pandemic. These decisions are driven by weighing the risks and benefits of various non-pharmaceutical intervention alternatives. Due to the nature of the pandemic, these decisions are not based on sufficient evidence regarding the effects, nor are decision-makers informed about the willingness of populations to accept the economic and health risks associated with different policy options. This empirical study seeks to reduce uncertainty by measuring population preferences for non-pharmaceutical interventions.

METHODS

An online-based discrete choice experiment (DCE) was conducted to elicit population preferences. Respondents were asked to choose between three pandemic scenarios with different interventions and impacts of the Corona pandemic. In addition, Best-worst scaling (BWS) was used to analyze the impact of the duration of individual interventions on people's acceptance. The marginal rate of substitution was applied to estimate willingness-to-accept (WTA) for each intervention and effect by risk of infection.

RESULTS

Data from 3006 respondents were included in the analysis. The DCE showed, economic effect of non-pharmaceutical measures had a large impact on choice decisions for or against specific lockdown scenarios. Individual income decreases had the most impact. Excess mortality and individual risk of infection were also important factors influencing choice decisions. Curfews, contact restrictions, facility closures, personal data transmissions, and mandatory masking in public had a lesser impact. However, significant standard deviations in the random parameter logit model (RPL) indicated heterogeneities in the study population. The BWS results showed that short-term restrictions were more likely to be accepted than long-term restrictions. According to WTA estimates, people would be willing to accept a greater risk of infection to avoid loss of income.

DISCUSSION

The results can be used to determine which consequences of pandemic measures would be more severe for the population. For example, the results show that citizens want to limit the decline in individual income during pandemic measures. Participation in preference studies can also inform citizens about potential tradeoffs that decision-makers face in current and future decisions during a pandemic. Knowledge of the population's preferences will help inform decisions that consider people's perspectives and expectations for the future. Survey results can inform decision-makers about the extent to which the population is willing to accept certain lockdown measures, such as curfews, contact restrictions, lockdowns, or mandatory masks.

Assessing social preferences in reimbursement negotiations for new Pharmaceuticals in Oncology: an experimental design to analyse willingness to pay and willingness to accept

Background

Price negotiations for specialty pharmaceuticals take place in a complex market setting. The determination of the added value of new treatments and the related societal willingness to pay are of increasing importance in policy reform debates. From a behavioural economics perspective, potential cognitive biases and other-regarding concerns affecting outcomes of reimbursement negotiations are of interest. An experimental setting to investigate social preferences in reimbursement negotiations for novel, oncology pharmaceuticals was used. Of interest were differences in social preferences caused by incremental changes of the patient outcome.

Methods

An online experiment was conducted in two separate runs (n = 202, n = 404) on the Amazon Mechanical Turk (MTurk) platform. Populations were split into two (run one) and four (run two) equally sized treatment groups for hypothetical reimbursement decisions. Participants were randomly assigned to the role of a public price regulator for pharmaceuticals (buyer) or a representative of a pharmaceutical company (seller). In run two, role groups were further split into two different price magnitude framings (“real world” vs unconverted “real payoff” prices). Decisions had real monetary effects on other participants (in the role of premium payers or investors) and via charitable donations to a patient organisation (patient benefit).

Results

56 (run one) and 59 (run two) percent of participants stated strictly monotone preferences for incremental patient benefit. The mean incremental cost-effectiveness ratio (ICER) against standard of care (SoC) was higher than the initial ICER of the SoC against no care. Regulators stated lower reservation prices in the “real world” prices group compared to their colleagues in the unconverted payoff group. No price group showed any reluctance to trade. Overall, regulators rated the relevance of the patient for their decision higher and the relevance of their own role lower compared to sellers.

Conclusions

The price magnitude of current oncology treatments affects stated preferences for incremental survival, and assigned responsibilities lead to different opinions on the relevance of affected stakeholders. The design is useful to further assess effects of reimbursement negotiations on societal outcomes like affordability (cost) or availability (access) of new pharmaceuticals and test behavioural policy interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06231-8.

To pay or not to pay? Cost information processing in the valuation of publicly funded healthcare

Discrete choice experiments (DCEs) commonly include a monetary attribute. This enables willingness to pay (WTP), a monetary measure of benefit, to be estimated for non-monetary attributes. There has been concern that the inclusion of a cost attribute challenges the credibility of the experiment when valuing publicly funded healthcare systems. However, very little research has explored this issue. Using a UK sample, we allocated participants across two versions of a DCE: one including a cost attribute and the other excluding a cost attribute. The DCE was identical in all other respects. We find no significant difference in response time across the two surveys, monotonicity was higher for the COST DCE and cost was stated as the most commonly ignored attribute in the COST DCE. Whilst the inclusion of a cost attribute did not alter the structure of preferences, it resulted in a lower level of choice consistency. Using an unrestricted latent class model, we find evidence of a credibility effect: respondents with experience of paying for health services and who perceive the choices as realistic are less likely to ignore cost. Further, respondents with a higher response time are less likely to be cost minimisers. Results are robust across different model specifications and choice formats. DCE practitioners should give due consideration to cost credibility when including a cost attribute, ensuring participants engage with the cost attribute. Ways to do this are suggested, including careful motivation of the cost attribute, consideration to the appropriate payment vehicle and careful consideration to the cost attribute when developing and piloting the survey. Failure to do this will result in an invalid willingness to pay estimates and thus policy recommendations.

Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment

Background

Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19.

Objective

This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future.

Methods

A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes.

Results

A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes.

Conclusions

Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

A discrete choice experiment to understand preferences of patients with type 2 diabetes treated with injectable non-insulinic agents

OBJECTIVE

To identify and evaluate the Spanish diabetes mellitus type 2 patients' preferences on injection and medication frequency and complexity of the treatment of diabetes. Additionally, patients' willingness to pay is evaluated.

METHODS

A total of 180 patients recruited from five health care centres in Spain completed a discrete choice experiment survey designed to evaluate patients' preferences over three attributes discriminating by age, sex and patients experience with previous treatment. The resulting model was analysed using a conditional (fixed-effects) logistic regression.

RESULTS

Naïve and non-naïve patients were willing to pay 83.25€ for a 'no preparation required' dose. In addition, both groups of patients were willing to pay 44.30€ for a 'simple preparation' dose. In terms of treatment frequency, no-naïve patients preferred a daily injection with freedom of timing before a daily scheduled injection, willing to pay 22.20€. In addition, no-naïve patients were willing to pay 34.61€ for a weekly injection. Finally, the most valued treatment change in naïve patients was to exchange a daily scheduled injection for a weekly injection, willing to pay 14.35€ for that change.

CONCLUSIONS

This study shows that patients highly value the avoidance of injections, with weekly dosing clearly preferred over daily dosing. Of the other attributes, a 'no preparation required' dose is clearly preferred over a 'simple preparation' dose. These findings may provide a better understanding of what patients prefer and value in their treatment and provide guidance for clinicians making therapeutic decisions regarding T2DM treatments.

Stated Preferences for Attributes of a CYP2C19 Pharmacogenetic Test Among the General Population Presented with a Hypothetical Acute Coronary Syndrome Scenario

Background

Pharmacogenetic (PGx) testing identifies pharmacotherapeutic risks to permit personalized therapy. Identifying the genetic profile of patients with acute coronary syndrome (ACS) who are considered for therapy with clopidogrel (P2Y₁₂ receptor blockers) and acetylsalicylic acid (ASA) contributes to the treatment paradigm. Patient preferences would inform a collaborative framework and by extension inform healthcare policy formulation.

Purpose

To quantify stated preferences (willingness to pay) for attributes of a novel point-of-care PGx (CYP2C19) test using a discrete choice experiment (DCE) from the general public in Ontario, Canada, and to identify starting point bias of the cost attribute.

Methods

A web survey was created and included a questionnaire, decision board, and a DCE. DCE choice sets include the following attributes (levels): sample collection (blood, finger prick, and cheek swab), turnaround time for results (1 hr, 3 days, and 1 week), and cost in additional insurance premiums. The presence of starting point bias (cost attribute levels of $0, $1, $5 or $0, $2, $10) in the estimation of willingness to pay (WTP) was tested.

Results

Estimates for turnaround time and cost attributes were statistically significant. Coefficients related to the starting point bias were also significant. Approximately 67% of survey participants chose the PGx test compared to status quo treatment options. WTP for a 1 hr turnaround time compared to a 1-week turnaround time was $10.77 (95% CI 9.58 -12.25).

Conclusion

This translational study shows preference for a point of care PGx test.

Assessment of the psychometric properties and refinement of the Health and Self-Management in Diabetes Questionnaire (HASMID)

Background

The Health And Self-Management In Diabetes (HASMID^v1) questionnaire consists of 8 attributes, 4 about quality of life, and 4 about self-management. The overall aim of this study was to rigorously examine the psychometric properties of the HASMID^v1 questionnaire.

Methods

The study comprised two phases. Phase 1 identified items of the HASMID^v1 questionnaire that potentially required rewording through consultation with a patient involvement panel and two focus groups of people with diabetes. Phase 2 involved a cross-sectional longitudinal survey where HASMID, EQ-5D-5L, health, treatment and sociodemographic questions were administered using both paper and online versions to people with diabetes. Participants were asked to complete the survey again approximately 3 months later. Psychometric analyses were undertaken to examine floor and ceiling effects, item distributions, known group differences and internal consistency. Rasch analysis was undertaken to assess differential item functioning and disordered thresholds.

Results

Phase 1 derived five alternative wordings to items: Irritable, Affects Mealtimes, Daily Routine, Social Activities and Problem. Phase 2 achieved 2835 responses at time point 1 (n = 1944 online, n = 891 paper version) and 1243 at time point 2 (n = 533 online, n = 710 paper version). Overall the HASMID items performed well, though two alternative worded items (Irritable and Social Activities) provided additional information not fully captured by the original HASMID items.

Conclusion

Psychometric evaluation and Rasch analysis were used in conjunction with expert opinion to determine the final questionnaire. The application of psychometric analyses or Rasch analysis alone to inform item selection would have resulted in different items being selected for the final instrument. The benefit of a combined approach has produced an instrument which has a broader evaluation of self-management. The final validated HASMID-10 is a short self-report PRO that can be used to evaluate the impact of self-management for people living with diabetes. HASMID-10 can be scored using total summative scores, with utility and monetary values also available for use in cost-utility and cost-benefit analyses.

PROM Validation Using Paper-Based or Online Surveys: Data Collection Methods Affect the Sociodemographic and Health Profile of the Sample

OBJECTIVE

This study examines the impact of data collection method on the sociodemographic and health profile of samples of people with diabetes who complete either an online or postal patient-reported outcome measure (PROM) validation survey.

METHODS

A longitudinal survey of people with diabetes was conducted using online and postal survey versions. The survey consisted of sociodemographic and health questions, a health and self-management PROM (Health and Self-Management in Diabetes [HASMID]), and 5-level version of EQ-5D. Dose adjustment for normal eating Online, Diabetes UK, and social media were used to recruit online survey participants. A panel of patients at a local National Health Service Trust was randomly allocated to participate in either survey version (two-thirds to postal version). Participants were asked to complete the survey again approximately 3 months later.

RESULTS

A total of 2784 participants completed the survey (1908 online, 876 postal). The samples (online versus postal) differed; the online sample was younger, with a larger proportion of women and respondents with type 1 diabetes. There were significant differences in sociodemographic characteristics by type of diabetes across data collection mode. The proportion of respondents who responded again at point 2 was higher in the postal sample (525 postal, 698 online).

CONCLUSION

The sociodemographic and health profile of samples of people with diabetes differed depending on whether they completed the online or postal survey. Differences are likely due to different recruitment methods and differences in those choosing to respond to different survey versions. Future PROM validation surveys should select data collection methods carefully because these can affect sample characteristics and results.

Producing a preference-based quality of life measure for people with Duchenne muscular dystrophy: a mixed-methods study protocol

INTRODUCTION

Preference-based measures (PBMs) of health-related quality of life (HRQoL) are used to generate quality-adjusted life years, which are necessary for cost-effectiveness evaluations of health interventions via cost-utility analysis. These measures of health can be generic (ie, pandiagnostic) or condition specific. No condition-specific PBM of HRQoL in Duchenne muscular dystrophy (DMD) exists, yet there are concerns that standard generic measures lack the specificity to assess aspects of HRQoL that are especially important to people with DMD. This study has been designed to produce a condition-specific PBM of HRQoL in DMD.

METHODS AND ANALYSIS

This mixed-methods study proceeds through three stages. In the first stage (concept elicitation), semistructured interviews will be conducted with boys and men diagnosed with DMD, and analysed with framework to produce a draft health state descriptive system for HRQoL in DMD. In the second stage (refining the descriptive system), patients, clinicians and primary caregivers of people with DMD will assess the face validity of the descriptive system. This will be followed by a quantitative survey on a larger sample of patients, which will be analysed with psychometric analyses to produce a refined descriptive system. In the third stage (valuation and econometric modelling), an online discrete choice experiment with duration will be administered to a general public sample to generate utility values for the new measure.

ETHICS AND DISSEMINATION

This study has received ethical approval from the National Health Service (REC reference: 18/SW/0055). The primary output of this research will be a condition-specific PBM (or 'bolt-on' to an existing generic PBM) in people with DMD and an associated value set. Results will be disseminated through international conferences and open-access journals.

Symptoms and feelings valued by patients after a percutaneous coronary intervention: a discrete-choice experiment to inform development of a new patient-reported outcome

OBJECTIVE

To inform the development of a patient-reported outcome measure, the aim of this study was to identify which symptoms and feelings following percutaneous coronary intervention (PCI) are most important to patients.

DESIGN

Discrete-choice experiment consisting of two hypothetical scenarios of 10 symptoms and feelings (pain or discomfort; shortness of breath; concern/worry about heart problems; tiredness; confidence to do usual activities; ability to do usual activities; happiness; sleep disturbance; dizziness or light-headedness and bruising) experienced after PCI, described by three levels (never, some of the time, most of the time). Preference weights were estimated using a conditional logit model.

SETTING

Four Australian public hospitals that contribute to the Victorian Cardiac Outcomes Registry (VCOR) and a private insurer's claim database.

PARTICIPANTS

138 people aged >18 years who had undergone a PCI in the previous 6 months.

MAIN OUTCOME MEASURES

Patient preferences via trade-offs between 10 feelings and symptoms.

RESULTS

Of the 138 individuals recruited, 129 (93%) completed all 16 choice sets. Conditional logit parameter estimates were mostly monotonic (eg, moving to worse levels for each individual symptom and feeling made the option less attractive). When comparing the magnitude of the coefficients (based on the coefficient of the worst level relative to best level in each item), feeling unhappy was the symptom or feeling that most influenced perception of a least-preferred PCI outcome (OR 0.42, 95% CI 0.34 to 0.51, p<0.0001) and the least influential was bruising (OR 0.81, 95% CI 0.67 to 0.99, p=0.04).

CONCLUSION

This study provides new insights into how patients value symptoms and feelings they experience following a PCI.

Estimating a Preference-Based Single Index Measuring the Quality-of-Life Impact of Self-Management for Diabetes

OBJECTIVE

Self-management is becoming increasingly important in diabetes but is neglected in conventional preference-based measures. The objective of this paper was to generate health state utility values for a novel classification system measuring the quality-of-life impact of self-management for diabetes, which can be used to generate quality-adjusted life years (QALYs).

METHODS

A large online survey was conducted using a discrete choice experiment (DCE), with duration as an additional attribute, on members of the UK general population ( n = 1,493) to elicit values for health (social limitations, mood, vitality, hypoglycaemia) and non-health (stress, hassle, control, support) aspects of self-management in diabetes. The data were modelled using a conditional fixed-effects logit model and utility estimates were anchored on the one to zero (full health to dead) scale.

RESULTS

The model produced significant and consistent coefficients, with one logical inconsistency and 3 insignificant coefficients for the milder levels of some attributes. The anchored utilities ranged from 1 for the best state to -0.029 for the worst state (meaning worse than dead) defined by the classification system.

CONCLUSION

The results presented here can potentially be used to generate utility values capturing the day to day impact of interventions in diabetes on both health and self-management. These utility values can potentially be used to generate QALYs for economic models of the cost-effectiveness of interventions in diabetes.