Preference-based Measures Research Articles

PCN436 RESPONSIVENESS OF THE EQ-5D-5L AND EORTC QLU-C10D IN CANCER PATIENTS

EORTC QLU-C10D is a preference-based measure derived from the widely used profile-based measure, EORTC QLQ-C30. This study aimed to compare the responsiveness of EQ-5D-5L with QLU-C10D in cancer patients. We recruited patients with cancer being treated or followed at National Cancer Centre Singapore. Each patient was interviewed twice face-to-face during two different outpatient visits using EQ-5D-5L and EORTC QLQ-C30 questionnaires. Overall health and QOL at baseline and follow-up surveys were assessed using a single-item scale. Multiple country-specific value sets were used to calculate the EQ-5D-5L index. QLU-C10D index was derived using its Australian value set. Responsiveness of the indices was assessed using the standardized effect size (SES), standardized response mean (SRM) and F-statistic. The analysis included 158 cancer patients (mean age: 58 years; male: 52%) who completed both baseline and follow-up visits (median 62 days apart). At the follow-up visit, 39% and 23% reported improvement and deterioration in overall health, respectively. Based on data from deteriorated patients, the SES, SRM and the F-statistic values for QLU-C10D were -0.69, -0.48, and 7.4, respectively; those values for EQ-5D-5L indices were much lower (SES range: -0.02 to -0.05; SRM range: -0.02 to -0.06; F-statistic range: 0.02 to 0.11). Based on data from improved patients, the SES, SRM and the F-statistic values for QLU-C10D were 0.28, 0.35, and 7.2, respectively, which were either similar or slightly larger than those for EQ-5D-5L indices (SES range: 0.21 to 0.30; SRM range: 0.21 to 0.31; F-statistic range: 2.6 to 6.0). Similar results were observed when improvement and deterioration were defined by change in overall QOL at the follow-up visit. It appears that EQ-5D-5L is non-inferior in terms of responsiveness to improvement in health and QOL compared to QLU-C10D. However, QLU-C10D may be more responsive than EQ-5D-5L for assessing deterioration in health and QOL.

Mapping function from FACT-B to EQ-5D-5\u2009L using multiple modelling approaches: data from breast cancer patients in China

BackgroundThe Functional Assessment of Cancer Therapy-Breast (FACT-B) is the most commonly used scale for assessing quality of life in patients with breast cancer. The lack of preference-based measures limits the cost-utility of breast cancer in China. The goal of this study was to explore whether a mapping function can be established from the FACT-B to the EQ-5D-5 L when the EQ-5D health-utility index is not available.MethodsA cross-sectional survey of adults with breast cancer was conducted in China. All patients included in the study completed the EQ-5D-5 L and the disease-specific FACT-B questionnaire, and demographic and clinical data were also collected. The Chinese tariff value was used to calculate the EQ-5D-5 L utility scores. Five models were evaluated using three different modelling approaches: the ordinary least squares (OLS) model, the Tobit model and the two-part model (TPM). Total scores, domain scores, squared terms and interaction terms were introduced into models. The goodness of fit, signs of the estimated coefficients, and normality of prediction errors of the model were also assessed. The normality of the prediction error is determined by calculating the root mean squared error (RMSE), the mean absolute deviation (MAD), and the mean absolute error (MAE). Akaike information criteria (AIC) and Bayes information criteria (BIC) were also used to assess models and predictive performances. The OLS model was followed by simple linear equating to avoid regression to the mean.ResultsThe performance of the models was improved after the introduction of the squared terms and the interaction terms. The OLS model, including the squared terms and the interaction terms, performed best for mapping the EQ-5D-5 L. The explanatory power of the OLS model was 70.0%. The AIC and BIC of this model were the smallest (AIC = -705.106, BIC = -643.601). The RMSE, MAD and MAE of the OLS model, Tobit model and TPM were similar. The MAE values of the 5-fold cross-validation of the multiple models in this study were 0.07155~0.08509; meanwhile, the MAE of the TPM was the smallest, followed by that of the OLS model. The OLS regression proved to be the most accurate for the mean, and linearly equated scores were much closer to observed scores.ConclusionsThis study establishes a mapping algorithm based on the Chinese population to estimate the EQ-5D-5 L index of the FACT-B and confirms that OLS models have higher explanatory power and that TPMs have lower prediction error. Given the accuracy of the mean prediction and the simplicity of the model, we recommend using the OLS model. The algorithm can be used to calculate EQ-5D scores when EQ-5D data are not directly collected in a study.

What Difference Does It Make? A Comparison of Health State Preferences Elicited From the General Population and From People With Multiple Sclerosis

BackgroundA major debate in the quality-adjusted life-year (QALY) literature concerns whose preferences should be used to estimate health state values (HSVs) and to calculate QALYs. ObjectivesThis study explores differences between public and patient values for multiple sclerosis (MS) health states, described using an MS-specific classification system (Multiple Sclerosis Impact Scale–8 Dimensions [MSIS-8D]). MethodsThe MSIS-8D is an existing preference-based measure of health-related quality of life in MS, which has 2 tariffs of HSVs, based on the preferences of a representative sample of the UK general population (n = 1702) and of people with MS living in the United Kingdom (n = 1635), elicited using the time trade-off technique. Here, we explore differences between HSVs by sample type, using descriptive statistics and multivariate regression methods. ResultsOverall, the survey of people with MS produced significantly higher HSVs; estimated values ranged from 0.079 to 0.883 for the general population survey and from 0.138 to 0.894 for the MS survey. Differences in HSVs were more pronounced for severe health states. The difference between patient and public values varied across the dimensions of the MSIS-8D. People with MS placed greater importance on cognition than the general population, leading to lower HSVs when impairment was at a worse level; the reverse was true for the daily activities, fatigue, and depression dimensions. ConclusionsWe identified significant differences in HSVs by sample type. Using patient rather than public values may influence the results of economic evaluations, depending on the dimensions of health-related quality of life affected by the intervention being assessed, and may therefore have important consequences for reimbursement decisions.

Health-related quality of life in oncology drug reimbursement submissions in Canada: A review of submissions to the pan-Canadian Oncology Drug Review.

In Canada, the Canadian Agency for Drugs and Technologies in Health (CADTH) evaluates and makes recommendations for the reimbursement of cancer drugs. One component of its recommendation is based on an economic evaluation, which typically takes the form of a cost-utility analysis. A cost-utility analysis measures the effects of competing therapies with quality-adjusted life-years (QALYs). The data for this calculation typically come from generic, preference-based measures of health-related quality of life (HRQOL). The objective of this review is to determine the frequency at which HRQOL data are collected alongside cancer drug trials and used in the cost-utility analysis submitted to theCADTH pan-Canadian Oncology Drug Review (pCODR). Submissions between 2015 and 2018 to pCODR, the group charged with evaluating cancer drug submissions at CADTH, were reviewed. All pCODR submissions, either in progress or completed, were publicly available online. The search was restricted to completed evaluations. Forty-three submissions met the inclusion criteria. The incremental gain in QALYs in most submissions from the new technology was small (median incremental gain, 0.86; interquartile range, 0.6-1.39). More than half of the submissions (56%) did not include original data on HRQOL, with most relying on previous studies of variable relevance and quality. Re-analyses by pCODR based on concerns over HRQOL data used in the submitted model were common (52%). Drug manufacturers do not consistently collect data on HRQOL alongside clinical trials and instead rely on evidence generated in previous studies to inform cost-utility analyses. These findings should induce manufacturers to collect original HRQOL data that are simultaneously relevant to patients and decision makers.

Developing a cerebral palsy-specific preference-based measure for a six-dimensional classification system (CP-6D): protocol for a valuation study

IntroductionCerebral palsy (CP) is a lifelong condition. The CP quality of life (CPQOL) instrument is a frequently used disease-specific instrument to assess health-related quality of life (HRQoL) in people with...

How Should We Capture Health State Utility in Dementia? Comparisons of DEMQOL-Proxy-U and of Self- and Proxy-Completed EQ-5D-5L

BackgroundDementia-specific and proxy-completed preference-based measures have been proposed for use in intervention studies involving people living in residential care, in instances where generic, self-reported preference-based measures have been deemed inappropriate. ObjectiveThis study was conducted to investigate the construct validity, criterion validity, and responsiveness of DEMQOL-Proxy-U and of self- and proxy-completed EQ-5D-5L. MethodsThe analysis used a 3-wave, individual-level data set of 1004 people living with dementia in residential care that included self-completed EQ-5D-5L and formal-carer and informal-carer proxy-completed EQ-5D-5L and DEMQOL-Proxy-U utility values, in addition to other nonutility cognitive measures (Functional Assessment Staging [FAST], Clinical Dementia Rating [CDR], Cohen-Mansfield Agitation Inventory [CMAI]) and health-related quality of life (HRQOL) measures (nursing home version of the Quality of Life with Alzheimer's disease scale [QOL-AD-NH], Quality of Life in Late-Stage Dementia [QUALID] scale). Construct validity, criterion validity, and responsiveness were assessed using correlation, Bland-Altman plots, and panel data regression models. ResultsSelf-completed EQ-5D-5L failed to reflect clinically important differences and changes in FAST, CDR, and CMAI but did capture the resident's own view of HRQOL (QOL-AD-NH). As dementia severity increased, collection of EQ-5D-5L-proxy and DEMQOL-Proxy-U data was more feasible than collection of self-completed EQ-5D-5L. These formal-carer and informal-carer proxy measures also better reflected changes in FAST, CDR, and CMAI but did not capture the resident's own view of HRQOL (QOL-AD-NH), despite adequately capturing the proxy's own view of the resident's HRQOL (QUALID). This indicates discrepancies between a proxy's view and resident's view of the impact that tangible declines in health, cognition, or functional abilities have on HRQOL. The EQ-5D-5L-proxy and DEMQOL-Proxy-U were generally poor substitutes. Regardless of which proxy completed it, the EQ-5D-5L-proxy was typically more responsive than the DEMQOL-Proxy-U to changes in CDR, FAST, and CMAI, indicating that use of the DEMQOL-Proxy-U is not always justified. ConclusionDisparities in the measurement properties of different utility measures mean that choices about how to measure utility in trials could affect economic evaluation outcomes and hence how resources are allocated for dementia care.

Developing a preference-based utility scoring algorithm for the Psoriasis Area Severity Index (PASI)

Introduction: It is challenging to identify health state utilities associated with psoriasis because generic preference-based measures may not capture the impact of dermatological symptoms. The Psoriasis Area Severity Index (PASI) is one of the most commonly used psoriasis rating scales in clinical trials. The purpose of this study was to develop a utility scoring algorithm for the PASI.Methods: Forty health states were developed based on PASI scores of 40 clinical trial patients. Health states were valued in time trade-off interviews with UK general population participants. Regression models were conducted to crosswalk from PASI scores to utilities (e.g. OLS linear, random effects, mean, robust, spline, quadratic).Results: A total of 245 participants completed utility interviews (51.4% female; mean age = 45.3 years). Models predicting utility based on the four PASI location scores (head, upper limbs, trunk, lower limbs) had better fit/accuracy (e.g. R2, mean absolute error [MAE]) than models using the PASI total score. Head/upper limb scores were more strongly associated with utility than trunk/lower limb. The recommended model is the OLS linear model based on the four PASI location scores (R2 = 0.13; MAE = 0.03). An alternative is recommended for situations when it is necessary to estimate utility based on the PASI total score.Conclusions: The derived scoring algorithm may be used to estimate utilities based on PASI scores of any treatment group with psoriasis. Because the PASI is commonly used in psoriasis clinical trials, this scoring algorithm greatly expands options for quantifying treatment outcomes in cost-effectiveness analyses of psoriasis therapies. Results indicate that psoriasis of the head/upper limbs could be more important than trunk/lower limbs, suggesting reconsideration of the standard PASI scoring approach.

Using the Fatigue Severity Scale to inform healthcare decision-making in multiple sclerosis: mapping to three quality-adjusted life-year measures (EQ-5D-3L, SF-6D, MSIS-8D)

BackgroundFatigue has a major influence on the quality of life of people with multiple sclerosis. The Fatigue Severity Scale is a frequently used patient-reported measure of fatigue impact, but does not generate the health state utility values required to inform cost-effectiveness analysis, limiting its applicability within decision-making contexts. The objective of this study was to use statistical mapping methods to convert Fatigue Severity Scale scores to health state utility values from three preference-based measures: the EQ-5D-3L, SF-6D and Multiple Sclerosis Impact Scale-8D.MethodsThe relationships between the measures were estimated through regression analysis using cohort data from 1056 people with multiple sclerosis in South West England. Estimation errors were assessed and predictive performance of the best models as tested in a separate sample (n = 352).ResultsFor the EQ-5D and the Multiple Sclerosis Impact Scale-8D, the best performing models used a censored least absolute deviation specification, with Fatigue Severity Scale total score, age and gender as predictors. For the SF-6D, the best performing model used an ordinary least squares specification, with Fatigue Severity Scale total score as the only predictor.ConclusionsHere we present algorithms to convert Fatigue Severity Scales scores to health state utility values based on three preference-based measures. These values may be used to estimate quality-adjusted life-years for use in cost-effectiveness analyses and to consider the health-related quality of life of people with multiple sclerosis, thereby informing health policy decisions.

Health-related quality of life: population epidemiology and concordance in Australian children aged 11-12 years and their parents.

ObjectivesTo describe the distribution of health-related quality of life (HRQL) in a national sample of Australian children aged 11–12 years and their parents, and examine associations within parent–child dyads.DesignThe Child Health CheckPoint, a population-based cross-sectional study nested between waves 6 and 7 of the Longitudinal Study of Australian Children (LSAC).SettingAssessment centres in seven Australian cities and eight regional towns, or home visit; February 2015 to March 2016.ParticipantsOf all participating CheckPoint families (n=1874), 1853 children (49.0% girls) and 1863 parents (87.7% mothers) with HRQL data were included (1786 pairs).Outcome measuresHRQL was self-reported using preference-based (Child Health Utility 9Dimension, CHU9D) and non-preference-based (Pediatric Quality of Life, PedsQL V.4.0) measures for children and preference-based measures for parents (CHU9D; Assessment of Quality of Life 8 Dimension, AQoL-8D). Utility scores from preference-based measures were calculated using existing Australian algorithms to present a score on a 0–1 scale, where 1 represents full health. Parent–child concordance was assessed using Pearson’s correlation coefficients and adjusted linear regression models. Survey weights and methods were applied to account for LSAC’s complex sample design, stratification and clustering within postcodes.ResultsChildren’s means and SD were 0.81 (SD 0.16) for CHU9D and 78.3 (SD 13.03) for PedsQL. In adults, mean HRQL for AQoL-8D and CHU9D were 0.78 (SD 0.16) and 0.89 (SD 0.10), respectively. Mean HRQL was similar for boys and girls, but slightly higher for fathers than mothers. The Pearson correlation coefficient for parent–child CHU9D values was 0.13 (95% CI 0.09 to 0.18). Percentiles and concordance are presented for both samples for males and females separately and together.ConclusionsWe provide Australian paediatric population values for HRQL measures, and the first national CHU9D values for mid-life adults. At age 11–12 years in this relatively healthy sample, parent–child concordance in HRQL was small.

Disease-specific Preference-based Measure of Glaucoma Health States: HUG-5 Psychometric Validation

Glaucoma is the second leading cause of irreversible blindness in the world, with 60 million people worldwide estimated to suffer from the condition. Health utility is an important outcome measure of treatment effect in economic outcomes. The available tools for the measurement of health utility in glaucoma patients do not support a preference-based algorithm required to estimate health utility. To resolve this gap in the literature, the HUG-5 (Health Utility for Glaucoma-5 dimensions) was developed. The objective of the present study was to validate the HUG-5 with accepted measures of health state and vision-specific quality of life. The HUG-5 measures patient self-reported levels of visual discomfort, mobility, daily life activities, emotion, and social activities, as affected by the progression and management of glaucoma. To evaluate the psychometric properties, the HUG-5 was assessed for construct validity between similar and dissimilar dimensions of the National Eye Institute Visual Function Questionnaire-25 (NEI-VFQ-25) and the EuroQol's 5 Dimensions. The HUG-5 was evaluated for test-retest reliability after a 2-week period. The HUG-5 composite distributions of mild/moderate and advanced glaucoma patients were tested for differences to measure sensitivity. A total of 124 patients with glaucoma were administered the NEI-VFQ-25, the HUG-5, and the EuroQol's 5 Dimensions. The HUG-5 demonstrated construct validity, with convergent and discriminant support for visual discomfort, mobility, daily life activities, emotional distress, and social activities. The HUG-5 concurrently measured health-related quality of life associated with best-eye visual field loss (r=0.63, P<0.001). The HUG-5 measured health state consistently with test-retest reliability (intraclass correlation=0.91, P<0.001). The HUG-5 was established to be sensitive in detecting differences between patients with mild/moderate glaucoma and those with advanced glaucoma with a rank-sum test with continuity correction (W=693.5, P<0.001). This study demonstrates promising results for the HUG-5's response range and relationship with the NEI-VFQ-25 and best-eye visual field loss, highlighting the value of disease-specific preference-based scoring systems in measuring health state changes in glaucoma patients.

Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample.

Mapping algorithms have been indicated as a second-best solution for estimating health state utilities for the calculation of quality-adjusted life-years within cost-utility analysis when no generic preference-based measure is incorporated into the study. However, the predictive performance of these algorithms may be variable and hence it is important to assess their external validity before application in different settings. The aim of this study was to assess the external validity and generalisability of existing mapping algorithms for predicting preference-based Child Health Utility 9D (CHU9D) utilities from non-preference-based Pediatric Quality of Life Inventory (PedsQL) scores among children and adolescents living with or without disabilities or health conditions. Five existing mapping algorithms, three developed using data from an Australian community population and two using data from a UK population with one or more self-reported health conditions, were externally validated on data from the Longitudinal Study of Australian Children (n = 6623). The predictive accuracy of each mapping algorithm was assessed using the mean absolute error (MAE) and the mean squared error (MSE). Values for the MAE (0.0741-0.2302) for all validations were within the range of published estimates. In general, across all ages, the algorithms amongst children and adolescents with disabilities/health conditions (Australia MAE: 0.2085-0.2302; UK MAE: 0.0854-0.1162) performed worse relative to those amongst children and adolescents without disabilities/health conditions (Australia MAE: 0.1424-0.1645; UK MAE: 0.0741-0.0931). The published mapping algorithms have acceptable predictive accuracy as measured by MAE and MSE. The findings of this study indicate that the choice of the most appropriate mapping algorithm to apply may vary according to the population under consideration.

Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children.

Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman's rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study. SDQ and CHU9D were moderately correlated (ρ = - 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation. The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.

Social and environmental preferences: measuring how people make tradeoffs among themselves, others, and collective goods

Social preferences like social value orientation are considered a promising solution to social dilemmas, such as mitigating anthropogenic climate change. However, evidence on the relationship between social preferences and environmental concerns is mixed, possibly because these constructs have commonly been measured by distinct methods that do not facilitate direct comparisons. We address this gap by introducing an incentivized preference-based measurement approach, extending a subject’s concerns for the wellbeing of others to a subject’s willingness to support environmental and humanitarian endeavors, based on a simple social preferences utility function. In this measurement approach, subjects make resource allocation choices with real consequences and the design ensures comparability of different revealed preferences (i.e., people’s willingness to make tradeoffs between themselves and others via donations to NGOs supporting different environmental and social causes). We then use this measurement method in an exploratory fashion to consistently assess preferences for environmental and humanitarian concerns in a laboratory experiment. We find that social and environmental value orientations are robustly interrelated, and further that people are generally more willing to pay to benefit people in need, compared to abstract environmental causes. We conclude that interventions to nudge people towards proenvironmental behavior will have a greater impact if human suffering resulting from global climate change is made more salient.

The burden of informal caregiving in Hungary, Poland and Slovenia: results from national representative surveys

BackgroundWe aimed to investigate the burden of informal care in Hungary (HU), Poland (PL) and Slovenia (SI).MethodsA cross-sectional online survey was performed involving representative samples of 1000 respondents per country. Caregiving situations were explored; health status of informal caregivers/care recipients and care-related quality of life were assessed using the EQ-5D-5L and CarerQol-7D.ResultsThe proportion of caregivers was (HU/PL/SI) 14.9, 15.0 and 9.6%, respectively. Their mean age was 56.1, 45.6 and 48.0, and the average time spent on informal care was 27.6, 35.5 and 28.8 h/week. Chronic care was dominant (> 1 year: 78.5%, 72.0%, 74.0%) and care recipients were mainly (own/in-law) parents. Average EQ-5D-5L scores of care recipients were 0.53, 0.49 and 0.52. For Poland and Slovenia, EQ-5D-5L scores of informal care providers were significantly lower than of other respondents. Average CarerQol-7D scores were (HU/PL/SI) 76.0, 69.6 and 70.9, and CarerQol-VAS was 6.8, 6.4 and 6.6, respectively. Overall, 89, 87, and 84% of caregivers felt some or a lot fulfilment related to caring. Problems with combining tasks with daily activities were most important in Hungary and Slovenia. Women had a higher probability of being a caregiver in Hungary. CarerQol-7D scores were significantly associated with caregivers’ EQ-5D-5L scores. In Hungary and Poland, living in a larger household was positively, while caring for patients with mental health problems was negatively associated with CarerQol-7D scores.ConclusionsThese first results from the Central and Eastern European region using preference-based measures for the evaluation of informal care can serve as a valuable input for health economic analyses.

Health-related quality of life in adolescents and young adults with cancer - Including a focus on economic evaluation.

Health-related quality of life (HRQL) is an amalgam of three elements - the opportunities that a person's health status affords, the constraints that it places upon the person and the value that a person places on his/her health status. HRQL measures are specific, for example for a disease, or generic with broad applicability. The latter include preference-based measures that can be used to generate quality-adjusted life years and so contribute to economic evaluation. Measures of HRQL in adolescents and young adults (AYAs) with cancer may fail to capture some important dimensions, for example sexual health. However, the use of HRQL measures in this population has identified burdens of morbidity according to disease, treatment status and duration of follow-up. There are few economic evaluations of the treatment of cancer in AYAs but preliminary evidence suggests that this is a cost-effective undertaking. Opportunities abound to include measurement of HRQL in routine clinical care.

Estimating loss in quality of life associated with asthma-related crisis events (ESQUARE): a cohort, observational study

BackgroundEvidence of quality of life implications of asthma attacks are limited, particularly when measured on a utility scale, which enables calculating Quality-Adjusted Life-Years (QALYs) and comparisons with other health conditions and services. Therefore, this study sought to estimate the utility loss associated with an asthma-related crisis event (accident and emergency (A&E) attendance or hospital admission).MethodsParticipants were recruited in a cohort study from A&E and hospital admissions at three UK hospitals. They completed the EuroQol-5 Dimensions 5-Level (EQ-5D-5 L), Asthma Quality of Life Questionnaire (AQLQ), Time trade-off (TTO), and peak flow and symptom diary over 8 weeks, where three different methods (EQ-5D-5 L, AQLQ, and TTO), were used to estimate utilities. The mean difference between two time points were estimated using the Wilcoxon signed rank test.ResultsFrom baseline to week 8, mean increases (95% CI) were estimated to be 0.086 (0.019–0.153), 0.154 (0.112–0.196) and 0.132 (0.063–0.201) for EQ-5D-5 L, AQL-5D (preference-based measure derived from AQLQ), and TTO respectively over 8 weeks (p < 0.01).ConclusionAsthma crisis events are estimated to be associated with a mean utility loss of between 0.086 and 0.132. The utility decrement can be used to assign values to asthma-related crisis events, which can enhance economic evaluations.Trial registrationNCT02771678. Registered 13 May 2016.

Spillover Effects on Caregivers' and Family Members' Utility: A Systematic Review of the Literature.

A growing body of research has identified health-related quality-of-life effects for caregivers and family members of ill patients (i.e. 'spillover effects'), yet these are rarely considered in cost-effectiveness analyses (CEAs). The objective of this study was to catalog spillover-related health utilities to facilitate their consideration in CEAs. We systematically reviewed the medical and economic literatures (MEDLINE, EMBASE, and EconLit, from inception through 3 April 2018) to identify articles that reported preference-based measures of spillover effects. We used keywords for utility measures combined with caregivers, family members, and burden. Of 3695 articles identified, 80 remained after screening: 8(10%) reported spillover utility per se, as utility or disutility (i.e. utility loss); 25 (30%) reported a comparison group, either population values (n = 9) or matched, non-caregiver/family member or unaffected individuals' utilities (n = 16; 3 reported both spillover and a comparison group); and 50 (63%) reported caregiver/family member utilities only. Alzheimer's disease/dementia was the most commonly studied disease/condition, and the EQ-5D was the most commonly used measurement instrument. This comprehensive catalog of utilities showcases the spectrum of diseases and conditions for which caregiver and family members' spillover effects have been measured, and the variation in measurement methods used. In general, utilities indicated a loss in quality of life associated with being a caregiver or family member of an ill relative. Most studies reported caregiver/family member utility without any comparator, limiting the ability to infer spillover effects. Nevertheless, these values provide a starting point for considering spillover effects in the context of CEA, opening the door for more comprehensive analyses.

A comparison of the reliability and validity of SF-6D, EQ-5D and HUI3 utility measures in patients with schizophrenia and patients with depression in Singapore

There is limited evidence of a direct comparison of the psychometric performance of generic preference-based measures in patients with mental illness in an Asian patient population. The current study aimed to compare the test–retest reliability, convergent and known-group validity and magnitude of change in scores of the EuroQol Five-Dimension, Health Utility Index Mark 3 (HUI3) and Short-Form Six-Dimension (SF-6D) measures in patients with depression and patients with schizophrenia spectrum disorder. 500 patients were recruited from a tertiary psychiatric institution in Singapore. The Schizophrenia Quality of Life Scale (SQLS), 8-item Patient Health Questionnaire, Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q), and Positive and Negative Syndrome Scale were also included. In the schizophrenia sample, the SF-6D was found to have higher test–retest validity, convergent validity with SQLS domain scores, known-group validity and magnitude of change in scores over 6-month follow up than other measures. In the depression sample, the HUI3 was found to have higher test–retest reliability, convergent validity with Q-LES-Q, known group validity and magnitude of change in scores than other measures. Results suggest that the SF-6D and HUI3 to be more suitable as a utility measure for patients with schizophrenia and depression in an Asian patient population.

Evaluating the content validity of generic preference-based measures for use in Parkinson's disease

BackgroundGeneric preference-based measures are used to assess the cost-utility of different interventions in Parkinson's disease (PD). However, the difficulty of using them in people with a particular health condition is that these measures may not encapsulate all of the domains that are impacted by the disease. ObjectiveTo contribute evidence towards the content validity of generic preference-based measures in people with PD. MethodsParticipants with PD were interviewed on a personalized measure of quality of life, the Patient Generated Index (PGI). The domains identified with the PGI were then categorized using the WHO's International Classification of Functioning, Disability and Health. The extent to which the generic measures (EQ-5D-5L, SF-6D, HUI II, and HUI III) included domains important to persons with PD was qualitatively evaluated. ResultsThe study included seventy-six participants with PD (mean age 69 years). Dexterity, the top domain nominated by participants, was only included in 1 out of 4 of the generic measures. Fatigue, another commonly reported problem, was not included in 3 out of 4 measures. Sleep, dyskinesia and bowel/bladder problems were not included in any of the measures. ConclusionContent validity is an essential step in the evaluation of whether a questionnaire truly measures the construct it purports to measure, in this case the construct being health related quality of life (HRQL). This study evaluated the content validity of the EQ-5D-5L, SF-6D, HUI II and HUI III in people with PD, and demonstrated that several important PD specific domains are missing from these measures.

Is Successful Aging Declining with Age? The Age Profile of Objective, Subjective, and Preference-Based Measures of Successful Aging in Europe

This note compares the age profile based on standard objective and subjective measures of successful aging with the age profile based on a novel preference-based measure proposed by Decancq et al. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE) for 2013, we find that the share of persons between 50 and 90 who are aging successfully in Europe declines with age according to the objective and preference-based measures of successful aging. At any point in the age distribution, the share of older persons who are aging successfully according to the preference-based measure is significantly larger than the share according to the objective measure. The subjective measure of successful aging, however, shows a remarkably stable age profile.