The EQ-HWB: Overview of the Development of a Measure of Health and Wellbeing and Key Results

What is measured by the composite, single-item pain/discomfort dimension of the EQ-5D-5L? An exploratory analysis

PURPOSE

This study examines the EQ-5D-5L pain/discomfort dimension by drawing comparisons with five other pain and discomfort items (pain severity, discomfort severity, pain frequency, discomfort frequency and pain interference) collected in the Australian psychometric study for the EQ Health and Wellbeing instrument.

METHODS

Participants, recruited via a market research company, completed an online survey. Methods of analyses included the assessment of descriptive statistics, variation in reporting patterns using chi-square tests and cross-tabulations, correlation analyses, ordered univariate logistic regression, and discriminatory power analyses (Shannon index (H') and Shannon Evenness index (J')).

RESULTS

Survey data from 514 participants were used. Compared with EQ-5D-5L pain/discomfort, there was a higher proportion of respondents reporting some level of impairment on at least one of the pain severity and discomfort severity items (74% versus 81%). Correlation with EQ-5D-5L pain/discomfort was strongest for pain severity (r = 0.83) and weakest for discomfort frequency (r = 0.41); the same inferences were drawn for predictive ability. Adding any additional pain or discomfort items to the EQ-5D-5L increased the absolute informativity (H') but not the relative informativity (J'). When replacing EQ-5D-5L pain/discomfort with separate pain and/or discomfort items - i.e., adding items to a modified 'EQ-4D-5L'-absolute informativity increased, while relative informativity increased only when pain interference and frequency-related items (independently or in combination) were added.

CONCLUSION

The EQ-5D-5L pain/discomfort dimension captures aspects of pain more than aspects of discomfort. Potential reasons include the absence of descriptors or because pain is mentioned first in the composite item.

Analyzing the Pain/Discomfort and Anxiety/Depression Composite Domains and the Meaning of Discomfort in the EQ-5D: A Mixed-Methods Study

OBJECTIVES

The EQ-5D has 2 composite domains: pain/discomfort (PD) and anxiety/depression (AD). This study aims to explore how respondents use the composites to self-report health and what the meaning of discomfort is in the EQ-5D for the general public.

METHODS

Both qualitative and quantitative data were collected in an online cross-sectional survey involving a nationally representative general population sample in Hungary (n = 1700). Respondents completed the 5-level version of EQ-5D, followed by the composites split into individual subdomains. Open-ended questions were asked to explore respondents' interpretations and experiences of discomfort.

RESULTS

Six different response behaviors were identified in the composites: "uniform" (21%-32%), "most severe" (30%-34%), "least severe" (16%-23%), "average" (2%-4%), "synergistic" (4%-5%), and "inconsistent" (13%-15%). Compared with the individual subdomains, many respondents under-reported their problems on both composites (PD 16%-22% and AD 6%-13%, P < .05). In respondents who scored differently in the 2 separate domains, mainly problems with the first subdomain determined responses in the composites (PD 66% and AD 61%). The discomfort subdomain in the EQ-5D captured more than 100 different problems, including pain, nonpain physical discomfort (eg, tiredness, dizziness, and nausea), and psychological discomfort (eg, anxiety, nervousness, and sadness). Women, older adults, and those in worse general health status more often considered discomfort as pain (P < .05).

CONCLUSIONS

We found empirical evidence of measurement error in the composite responses on the EQ-5D, including under- and inconsistent reporting, ordering effects, potential differential item functioning, and interdomain dependency. Our findings contribute new knowledge to the development of new and refinement of existing self-reported health status instruments, also beyond the EQ-5D.

Where do measures of health, social care and wellbeing fit within a wider measurement framework? Implications for the measurement of quality of life and the identification of bolt-ons

BACKGROUND

There is variability across studies in the dimensionality i.e., set of latent variables to which health, social care and wellbeing measures relate. This variability may impact the development of new measures and the identification of bolt-on dimensions. We examine the dimensionality of commonly used measures and identify a set of potential bolt-ons for the EQ-5D-5L.

METHODS

We used the OMS dataset, an online survey of health, social care and wellbeing measures in patients and members of the general public. A content analysis provided a theoretical framework for results interpretation. Quantitative analyses were based on a pool of 79 items from 7 measures. Confirmatory factor analysis was used to assess health, social care and wellbeing measures dimensionality and their contribution to quality of life. The relationship between EQ-5D-5L items and the identified factors was used for bolt-ons identification.

RESULTS

The dimensionality comprised of seven factors, namely physical functioning, psychological symptoms, energy/sleep, physical pain, social functioning, needs and satisfaction. Health measures covered five of the seven factors identified, wellbeing measures three and the social care measure one. A list of candidate bolt-on items for the EQ-5D-5L was presented e.g., cognition, energy, dignity.

CONCLUSIONS

This study provides evidence on the dimensionality of health, social care and wellbeing measures and presents a list of candidate bolt-ons for the EQ-5D-5L.

China Health Related Outcomes Measures (CHROME): Development of a New Generic Preference-Based Measure for the Chinese Population

BACKGROUND AND OBJECTIVES

Existing generic preference-based measures were all developed in Western countries. Evidence shows that the Chinese population may have different perceptions about health and health-related quality of life. This study aimed at developing a descriptive system of a new generic preference-based measure under the initiative of China Health Related Outcomes Measures (CHROME).

METHODS

Qualitative data were collected through semi-structured interviews conducted in-person or online. Respondents were recruited from both the general public and populations with chronic diseases. Open-ended questions about the respondent's perception of general health and important aspects of health-related quality of life were asked. Probing questions based on a systematic review of existing generic preference-based measures were also used. The framework analysis was used to synthesize the qualitative data. Candidate items for the descriptive system were selected following the ISPOR and COSMIN guidelines. Expert panel review and cognitive debriefings were conducted for further revisions.

RESULTS

Qualitative interviews were conducted among 68 respondents, with 48.5% male and a mean age of 47.8 years (range 18-81 years). In total, 1558 codes were identified and then aggregated to 31 sub-themes and corresponding six themes to inform the development of the initial version of the descriptive system. Feedback from the expert panel survey and meeting (n = 15) and the cognitive debriefing interviews (n = 30) was incorporated into the revised version of the measure. Finally, the generic version of CHROME (CHROME-G) included 12 items across six domains, namely, pain, fatigue, appetite, mobility, vision, hearing, sleeping, daily activities, depression, worry, memory, and social interactions. The descriptive system used a mix of four-level and five-level response options and a 7-day recall period.

CONCLUSIONS

The CHROME-G is the first generic preference-based measure to be developed based on the inputs from the Chinese populations.

The role of patient and public involvement and engagement (PPIE) within the development of the EQ Health and Wellbeing (EQ-HWB)

Objectives

The value of patient and public involvement and engagement (PPIE) within the development and refinement of outcome measures is becoming increasingly recognized. The aim of this paper is to provide an overview of how PPIE was integrated within the development of a new measure designed for use in economic evaluations across health and social care, the EQ Health and Wellbeing (EQ-HWB™).

Methods

Four PPIE sessions were held at key stages. Discussions from each session and the outcome of any tasks were shared with the wider research team and used to help inform decision-making.

Results and discussion

PPIE covered several components of outcome measure development including; review of conceptual model; discussion on sub-domain inclusion; item refinement and reduction; pre-testing of items; selection of items for the measure; and design of the measure. Key learning points for future projects were highlighted including; consideration of practicalities, resources and logistics of PPIE activities; how sessions and activities are managed effectively; and how to managing expectations and communication from both researcher and PPIE perspectives.

Conclusions

The PPIE group provided invaluable insight into perspectives of future patients and carers. Their input was fed into a number of developmental stages. The formal involvement from the PPIE group meant that the voice of the general public was heard. This helped ensure the appropriateness of the design of the final measure.

Developing a New Generic Health and Wellbeing Measure: Psychometric Survey Results for the EQ-HWB

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.