Search

OBJECTIVES: The EQ Health and Wellbeing Short (EQ-HWB-S) is a new generic measure that covers health and wellbeing developed for use in economic evaluation in health and social care. The aim was to test the feasibility of using composite time trade-off (cTTO) and a discrete choice experiment (DCE) based on an international protocol to derive utilities for the EQ-HWB-S and to generate a pilot value set. METHODS: A representative UK general population was recruited. Online videoconference interviews were undertaken where cTTO and DCE tasks were administered using EuroQol Portable Valuation Technology. Quality control (QC) was used to assess interviewers' performance. Data were modeled using Tobit, probit, and hybrid models. Feasibility was assessed based on the distribution of data, participants, and reports of understanding from the interviewer, QC and modeling results. RESULTS: cTTO and DCE data were available for 520 participants. Demographic characteristics were broadly representative of the UK general population. Interviewers met QC requirements. cTTO values ranged between -1 to 1 with increasing disutility associated with more severe states. Participants understood the tasks and the EQ-HWB-S states; and the interviewers reported high levels of understanding and engagement. The hybrid Tobit heteroscedastic model was selected for the pilot value set with values ranging from -0.384 to 1. Pain, mobility, daily activities, and sad/depressed had the largest disutilities, followed by loneliness, anxiety, exhaustion, control, and cognition in the selected model. CONCLUSIONS: EQ-HWB-S can be valued using cTTO and DCE. Further methodological work is recommended to develop a valuation protocol specific to the EQ-HWB-S.

Item does not contain fulltext, BACKGROUND: Assessment of patient-reported outcome measures (PROMs), including quality of life (QoL), is essential in diabetes research and care. However, a recent review concluded that current hypoglycaemia-specific PROMs have limited evidence of validity, reliability and responsiveness for assessing the impact of hypoglycaemia on QoL in people living with diabetes. None of the PROMs identified could be used directly to inform the cost-effectiveness of treatments and interventions. There is a need for a new hypoglycaemia-specific QoL PROM, which can be used directly to inform economic evaluations. AIMS: This project has three aims: (a) To develop draft PROM content for measuring the impact of hypoglycaemia on QoL in adults with diabetes. (b) To refine the draft content using cognitive debriefing interviews and psychometrics. This will result in a condition-specific PROM that can be used to quantify the impact of hypoglycaemia upon QoL. (c) To generate a preference-based measure (PBM) that will enable utility values to be calculated for economic evaluation. METHODS: A mixed-methods, three-stage design is used: (a) Qualitative interviews will inform the draft PROM content. (b) Cognitive debriefing interview data will be used to refine the draft PROM content. The PROM will be administered in a large-scale survey to enable psychometric validation. Final item selection for the PROM will be informed by psychometric performance, translatability assessment and input from stakeholder groups. (c) A classification system will be generated, comprising a reduced number of items from the PROM. A valuation survey will be conducted to derive a value set for the PBM.

Objectives: The development of measures such as the EQ-HWB (EQ Health and Wellbeing) requires selection of items. This study explored the psychometric performance of candidate items, testing their validity in patients, social carer users, and carers. Methods: Article and online surveys that included candidate items (N = 64) were conducted in Argentina, Australia, China, Germany, United Kingdom, and the United States. Psychometric assessment on missing data, response distributions, and known group differences was undertaken. Dimensionality was explored using exploratory and confirmatory factor analysis. Poorly fitting items were identified using information functions, and the function of each response category was assessed using category characteristic curves from item response theory (IRT) models. Differential item functioning was tested across key subgroups. Results: There were 4879 respondents (Argentina = 508, Australia = 514, China = 497, Germany = 502, United Kingdom = 1955, United States = 903). Where missing data were allowed, it was low (UK article survey 2.3%; US survey 0.6%). Most items had responses distributed across all levels. Most items could discriminate between groups with known health conditions with moderate to large effect sizes. Items were less able to discriminate across carers. Factor analysis found positive and negative measurement factors alongside the constructs of interest. For most of the countries apart from China, the confirmatory factor analysis model had good fit with some minor modifications. IRT indicated that most items had well-functioning response categories but there was some evidence of differential item functioning in many items. Conclusions: Items performed well in classical psychometric testing and IRT. This large 6-country collaboration provided evidence to inform item selection for the EQ-HWB measure.

Objectives: This article aims to describe the generation and selection of items (stage 2) and face validation (stage 3) of a large international (multilingual) project to develop a new generic measure, the EQ-HWB (EQ Health and Wellbeing), for use in economic evaluation across health, social care, and public health to estimate quality-adjusted life-years. Methods: Items from commonly used generic, carer, social care, and mental health quality of life measures were mapped onto domains or subdomains identified from a literature review. Potential terms and items were reviewed and refined to ensure coverage of the construct of the domains/subdomain (stage 2). Input on the potential item pool, response options, and recall period was sought from 3 key stakeholder groups. The pool of candidate items was tested in qualitative interviews with potential future users in an international face validation study (stage 3). Results: Stage 2 resulted in the generation of 687 items. Predetermined selection criteria were applied by the research team resulting in 598 items being dropped, leaving 89 items that were reviewed by key stakeholder groups. Face validation (stage 3) tested 97 draft items and 4 response scales. A total of 47 items were retained and 14 were modified, whereas 3 were added to the candidate pool of items. This resulted in a 64-item set. Conclusions: This international multiculture, multilingual study with a common methodology identified many items that performed well across all countries. These were taken to the psychometric testing along with modified and new items for the EQ-HWB.

Introduction Standard valuation methods, such as TTO and DCE are inefficient. They require data from hundreds if not thousands of participants to generate value sets. Here, we present the Online elicitation of Personal Utility Functions (OPUF) tool; a new type of online survey for valuing EQ-5D-5L health states using more efficient, compositional elicitation methods, which even allow estimating value sets on the individual level. The aims of this study are to report on the development of the tool, and to test the feasibility of using it to obtain individual-level value sets for the EQ-5D-5L. Methods We applied an iterative design approach to adapt the PUF method, previously developed by Devlin et al., for use as a standalone online tool. Five rounds of qualitative interviews, and one quantitative pre-pilot were conducted to get feedback on the different tasks. After each round, the tool was refined and re-evaluated. The final version was piloted in a sample of 50 participants from the UK. A demo of the EQ-5D-5L OPUF survey is available at: https://eq5d5l.me Results On average, it took participants about seven minutes to complete the OPUF Tool. Based on the responses, we were able to construct a personal EQ-5D-5L value set for each of the 50 participants. These value sets predicted a participants' choices in a discrete choice experiment with an accuracy of 80%. Overall, the results revealed that health state preferences vary considerably on the individual-level. Nevertheless, we were able to estimate a group-level value set for all 50 participants with reasonable precision. Discussion We successfully piloted the OPUF Tool and showed that it can be used to derive a group-level as well as personal value sets for the EQ-5D-5L. Although the development of the online tool is still in an early stage, there are multiple potential avenues for further research.

OBJECTIVES: To identify the themes to inform the content of a new generic measure, the EQ-HWB (EQ Health and Wellbeing), that can be used in economic evaluation across health, social care, and public health, based on the views of users and beneficiaries of these services including informal carers. METHODS: A qualitative review was undertaken. Systematic and citation searches were undertaken focusing on qualitative evidence of the impact on quality of life from reviews for selected health conditions, informal carers, social care users, and primary qualitative work used in the development of selected measures. A subset of studies was included in the review. Framework analysis and synthesis were undertaken based on a conceptual model. RESULTS: A total of 42 reviews and 24 primary studies were selected for inclusion in the review. Extraction and synthesis resulted in 7 high-level themes (with subthemes): (1) feelings and emotions (sadness, anxiety, hope, frustration, safety, guilt/shame); (2) cognition (concentration, memory, confusion, thinking clearly); (3) self-identity (dignity/respect, self-esteem); (4) "coping, autonomy, and control" relationships; (5) social connections (loneliness, social engagement, stigma, support, friendship, belonging, burden); (6) physical sensations (pain, discomfort, sleep, fatigue); and (7) activity (self-care, meaningful activities, mobility, communication, hearing, vision). Apart from physical sensations, most of the other themes and subthemes were relevant across both health and social care, including for informal carers. CONCLUSIONS: The findings from this broad review identified themes that go beyond health and that are relevant to patients, informal carers, and social care users. The themes and subthemes informed the domains for the EQ-HWB.

OBJECTIVES: Existing measures for estimating quality-adjusted life-years are mostly limited to health-related quality of life. This article presents an overview of the development the EQ-HWB (EQ Health and Wellbeing), which is a measure that encompasses health and wellbeing. METHODS: Stages: (1) Establishing domains through reviews of the qualitative literature informed by a conceptual framework. (2) Generation and selection of items to cover the domains. (3) Face validation of these items through qualitative interviews with 168 patients, social care users, general population, and carers across 6 countries (Argentina, Australia, China, Germany, United Kingdom, United States). (4) Extensive psychometric testing of candidate items (using classical, factor analysis, and item response theory methods) on > 4000 respondents in the 6 countries. Stakeholders were consulted throughout. RESULTS: A total of 32 subdomains grouped into 7 high-level domains were identified from the qualitative literature and 97 items generated to cover them. Face validation eliminated 36 items, modified 14, and added 3. Psychometric testing of 64 items found little difference in missing data or problems with response distribution, the conceptual model was confirmed except in China, and most items performed well in the item response theory in all countries. Evidence was presented to stakeholders in 2 rounds of consultation to inform the final selection of items for the EQ-HWB (25-item) and the short version of EQ-HWB (9-items). CONCLUSIONS: EQ-HWB measures have been developed internationally for evaluating interventions in health, public health, and social care including the impact on patients, social care users, and carers.

Quantification of the relative ellipsoid zone reflectivity (rEZR) might be a structural surrogate parameter for an early disease progression in the context of age-related macular degeneration (AMD). Within the European multicenter, cross-sectional MACUSTAR study, we have devised an automatic approach to determine the mean rEZR [arbitrary units, AU] at two independent visits in SD-OCT volume scans in study participants. Linear mixed-effects models were applied to analyze the association of AMD stage and AMD associated high-risk features including presence of pigmentary abnormalities, reticular pseudodrusen (RPD), volume of the retinal-pigment-epithelial–drusenoid-complex (RPEDC) with the rEZR. Intra-class correlation coefficients (ICC) were determined for rEZR reliability analysis. Within the overall study cohort (301 participants), we could observe decreased rEZR values (coefficient estimate ± standard error) of − 8.05 ± 2.44 AU (p = 0.0011) in the intermediate and of − 22.35 ± 3.28 AU (p < 0.0001) in the late AMD group. RPD presence was significantly associated with the rEZR in iAMD eyes (− 6.49 ± 3.14 AU; p = 0.0403), while there was a good ICC of 0.846 (95% confidence interval: 0.809; 0.876) in the overall study cohort. This study showed an association of rEZR with increasing disease severity and the presence of iAMD high-risk features. Further studies are necessary to evaluate the rEZR’s value as a novel biomarker for AMD and disease progression.

BACKGROUND: The SF-6Dv2 classification system assesses health states in six domains-physical functioning, role function, bodily pain, vitality, social functioning, and mental health. Scores have previously been derived from the SF-36v2® Health Survey. We aimed to develop a six-item stand-alone SF-6Dv2 Health Utility Survey (SF-6Dv2 HUS) and evaluate its comprehensibility. METHODS: Two forms of a stand-alone SF-6Dv2 HUS were developed for evaluation. Form A had 6 questions with 5-6 response choices, while Form B used 6 headings and 5-6 statements describing the health levels within each domain. The two forms were evaluated by 40 participants, recruited from the general population. Participants were randomized to debrief one form of the stand-alone SF-6Dv2 HUS during a 75-min interview, using think-aloud techniques followed by an interviewer-led detailed review. Participants then reviewed the other form of SF-6Dv2 and determined which they preferred. Any issues or confusion with items was recorded, as was as overall preference. Data were analyzed using Microsoft Excel and NVivo Software (v12). RESULTS: Participants were able to easily complete both forms. Participant feedback supported the comprehensibility of the SF-6Dv2 HUS. When comparing forms, 25/40 participants preferred Form A, finding it clearer and easier to answer when presented in question/response format. The numbered questions and underlining of key words in Form A fostered quick and easy comprehension and completion of the survey. However, despite an overall preference for Form A, almost half of participants (n = 19) preferred the physical functioning item in Form B, with more descriptive response choices. CONCLUSION: The results support using Form A, with modifications to the physical functioning item, as the stand-alone SF-6Dv2 HUS. The stand-alone SF-6Dv2 HUS is brief, easy to administer, and comprehensible to the general population.

OBJECTIVE: This study aimed to gain insight into decision-making strategies individuals used when evaluating pairs of SF-6Dv2 health states in discrete choice experiments (DCEs). METHODS: This qualitative, cross-sectional, noninterventional study asked participants to use a think-aloud approach to compare SF-6Dv2 health states in DCEs. Thematic analysis focused on comprehension and cognitive strategies used to compare health states and make decisions. RESULTS: Participants (N = 40) used 3 main strategies when completing DCEs: (1) trading, (2) reinterpretation, and (3) relying on previous experience. Trading was the most common strategy, used by everyone at least once, and involved prioritizing key attributes, such as preferring a health state with significant depression but no bodily pain. Reinterpretation was used by 17 participants and involved reconstructing health states by changing underlying assumptions (eg, rationalizing selecting a health state with significant pain because they could take pain medications). Finally, some (n = 13) relied on previous experience when making decisions on some choice tasks. Participants with experience dealing with pain, for instance, prioritized health states with the least impact in this dimension. CONCLUSIONS: Qualitatively evaluating the decision-making strategies used in DCEs allows researchers to evaluate whether the tasks and attributes are interpreted accurately. The findings from this study add to the understanding of the generation of SF-6Dv2 health utility weights and the validity of these weights (e.g., reinterpreting health states could undermine the validity of DCEs and utility weights), and the overall usefulness of the SF-6Dv2. The methodology described in this study can and should be carried forth in valuing other health utility measures, not just the SF-6Dv2.

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991–0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757–0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD. Trial registration: ClinicalTrials.gov NCT03349801. Registered on 22 November 2017.