Search

Background. To incorporate the spillover effects experienced by carers providing informal care in health policy decisions, new carer-related preference-based measures have been developed for use in economic evaluation, which include the Adult Social Care Outcomes Toolkit for Carers (ASCOT-Carer), Carer Experience Scale (CES), and Care-Related Quality of Life (CarerQoL). The aim of this study was to investigate the extent to which these 3 instruments measure complementary or overlapping constructs. Methods. Data were derived from an online survey undertaken with carers residing in Australia. An exploratory factor analysis was conducted to ascertain the underlying latent constructs of the 3 measures. Results. Data from 351 informal carers yielded a 5-factor model describing general quality of life outside caring, problems due to caring, fulfilment from caring, social support with caring, and relationship with the care recipient. Most of the ASCOT-Carer and the CarerQol items loaded onto the first and second factors, respectively. The greatest overlap was observed between CarerQol and CES items loading onto the other 3 shared common factors. Limitations. Online data collection resulted in inconsistent responses, which had to be removed to yield logical data. A convenience sampling approach may have compromised the generalizability of study findings. Conclusion. Although some overlap was observed, the 3 carer-related preference-based measures seem to tap into different constructs of carer-related quality of life and caring experiences and cannot be used interchangeably. [ABSTRACT FROM AUTHOR]

Background. Researchers often justify excluding some responses in studies eliciting valuations of health states as not representing respondents' true preferences. Here, we examine the effects of applying 8 common exclusion criteria on societal utility estimates. Setting. An online survey of a US nationally representative sample (N = 1164) used the standard gamble method to elicit preferences for health states defined by 7 health domains from the Patient-Reported Outcomes Measurement Information System (PROMIS®). Methods. We estimate the impacts of applying 8 commonly used exclusion criteria on mean utility values for each domain, using beta regression, a form of analysis suited to double-bounded scales, such as utility. Results. Exclusion criteria have varied effects on the utility functions for the different PROMIS health domains. As a result, applying those criteria would have varied effects on the value of treatments (and side effects) that change health status on those domains. Limitations. Although our method could be applied to any health utility judgments, the present estimates reflect the features of the study that produced them. Those features include the selected health domains, standard gamble method, and an online format that excluded some groups (e.g., visually impaired and illiterate individuals). We also examined only a subset of all possible exclusion criteria, selected to represent the space of possibilities, as characterized in a companion article. Conclusions. Exclusion criteria can affect estimates of the societal utility of health states. We use those effects, in conjunction with the results of the companion article, to make suggestions for selecting exclusion criteria in future studies. [ABSTRACT FROM AUTHOR]

Background. In a systematic review, Engel et al. found large variation in the exclusion criteria used to remove responses held not to represent genuine preferences in health state valuation studies. We offer an empirical approach to characterizing the similarities and differences among such criteria. Setting. Our analyses use data from an online survey that elicited preferences for health states defined by domains from the Patient-Reported Outcomes Measurement Information System (PROMIS®), with a U.S. nationally representative sample (N = 1164). Methods. We use multidimensional scaling to investigate how 10 commonly used exclusion criteria classify participants and their responses. Results. We find that the effects of exclusion criteria do not always match the reasons advanced for applying them. For example, excluding very high and very low values has been justified as removing aberrant responses. However, people who give very high and very low values prove to be systematically different in ways suggesting that such responses may reflect different processes. Conclusions. Exclusion criteria intended to remove low-quality responses from health state valuation studies may actually remove deliberate but unusual ones. A companion article examines the effects of the exclusion criteria on societal utility estimates. [ABSTRACT FROM AUTHOR]

Objective. To compare and contrast EQ-5D-5L (5L) and EQ-5D-3L (3L) health state values derived from a common sample. Methods. Data from the 2017 US EQ-5D valuation study were analyzed. Value sets were estimated with random-effects linear regression based on composite time trade-off (cTTO) valuations for 3L and 5L health states with 2 approaches to model specification: main effects only and additional N3/N45 terms. Properties of the descriptive system and value set characteristics were compared by examining distributions of predicted index scores, ceiling effects, and single-level transition values from adjacent corner health states. Mean transition values were calculated for all predicted 3L and 5L health states and plotted against baseline index scores. Results. A total of 1062 respondents were included in the analysis. The observed mean cTTO values for the worst possible 3L and 5L health states were −0.423 and −0.343, respectively. The range of scale was larger with the 3L, compared to the 5L, for both main effects and N term models. Values for the mildest 5L health states (range, 0.857−0.924) were similar to 11111 for the 3L. Parameter estimates for matched dimension levels differed by <|0.07| except for the most severe level of Mobility. For the main effects model, 3L mean transition values were greater for more severe baseline 3L index scores, whereas 5L mean transition values remained constant irrespective of the baseline index score. Conclusions. Compared to the 3L, the 5L exhibited a lower ceiling effect and improved measurement properties. There was a larger range of scale for the 3L compared to 5L; however, this difference was driven by differences in preference for the most severe level of problems in Mobility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background. Existing methods to link preference-based and profile-based health-related quality of life (HRQoL) questionnaires have their limitations. Hence, we developed a new mapping method (the mean rank method, MRM) and applied it to map the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) to the EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L). We then compared the new MRM with current methods; i.e., regression-mapped (OLS method) and equipercentile method (EPM). Methods. Singapore residents, aged ≥21 y, were recruited from the general population and 2 outpatient clinics in acute care hospitals. Performance of the MRM was evaluated using both simulation and split-sample validation (n = 658 in training and n = 657 in validation samples). Using the training sample, we derived 3 sets of mapped EQ-5D-5L utilities based on MRM, OLS method and EPM. Using simulation and the validation sample, we compared the performance of the mapping methods in terms of distribution parameters, mean utility by strata, association with health covariates, and prediction errors at the individual level, among others. Results. The WHOQOL-BREF Physical Health domain is the only domain significantly associated with EQ-5D-5L utilities. Simulation showed that MRM more accurately reproduced the variance and percentiles of the distribution of the observed utilities than did the OLS method or EPM. OLS method tended to underestimate the mean utility of good health states, overestimate the mean utility of poor health states, and underestimate the association with covariates. An analysis of validation sample gave similar results. Conclusion. In scenarios similar to the mapping of WHOQOL-BREF to the EQ-5D-5L, the MRM outperformed the OLS method and EPM in important—though not all—aspects. The simplicity and reproducibility of the MRM makes it an attractive alternative to current methods. [ABSTRACT FROM AUTHOR]

Introduction. Health state valuation is a key input in many economic evaluations that inform resource allocation across competing healthcare interventions. Empirical evidence has shown that, in preference elicitation surveys, respondents may value a health state differently if they are aware of the condition causing it (‘labeling effects’). This study investigates the impact of including a multiple sclerosis (MS) label for valuation of MS health states. Methods. Health state values for MS were elicited using two internet-based surveys in representative samples of the UK population (n = 1702; n = 1788). In one survey respondents were not informed that health states were caused by MS. The second survey included a condition label for MS. Surveys were identical in all other ways. Health states were described using a MS-specific eight-dimensional classification system (MSIS-8D), and the time trade-off valuation technique was used. Differences between values for labeled and unlabeled states were assessed using descriptive statistics and multivariate regression methods. Results. Adding a MS condition label had a statistically significant effect on mean health state values, resulting in lower values for labeled MS states v. unlabeled states. The data suggest that the MS label had a more significant effect on values for less severe states, and no significant effect on values for the most severe states. The inclusion of the MS label had a differential impact across the dimensions of the MSIS-8D. Across the MSIS-8D, predicted values ranged from 0.079 to 0.883 for unlabeled states, and 0.066 to 0.861 for labeled states. Conclusion. Differences reported in health state values, using labeled and unlabeled states, demonstrate that condition labels affect the results of valuation studies, and can have important implications in decision-analytic modelling and in economic evaluations. [ABSTRACT FROM AUTHOR]

Background. Health state valuation data are often excluded from studies that aim to provide a nationally representative set of values for preference-based health-related quality of life (HRQoL) instruments. The purpose was to provide a systematic examination of exclusion criteria used in the derivation of societal scoring algorithms for preference-based HRQoL instruments. Methods. Data sources included MEDLINE, official instrument websites, and publication reference lists. Analyses that used data from national valuation studies and reported a scoring algorithm for a generic preference-based HRQoL instrument were included. Data extraction included exclusion criteria and associated justifications, exclusion rates, the characteristics of excluded respondents, and analyses that explored consequential implications of exclusion criteria on the respective national tariff. Results. Seventy-six analyses (from 70 papers) met the inclusion criteria. In addition to being excluded for logical inconsistencies, respondents were often excluded if they valued fewer than 3 health states or if they gave the same value to all health states. Numerous other exclusion criteria were identified, with varying degrees of justification, often based on an assumption that respondents did not understand the task or as a consequence of the chosen statistical modeling techniques. Rates of exclusion ranged from 0% to 65%, with excluded respondents more likely to be older, less educated, and less healthy. Limitations included that the database search was confined to MEDLINE; study selection focused on national valuation studies that used standard gamble, time tradeoff, and/or visual analog scale techniques; and only English-language studies were included. Conclusion. Exclusion criteria used in national valuation studies vary considerably. Further consideration is necessary in this important and influential area of research, from the design stage to the reporting of results. [ABSTRACT FROM AUTHOR]

Background. Many descriptions of health used in vignettes and condition-specific measures name the medical condition. This article assesses the impact of referring to the medical condition in the descriptions of health states valued by members of the general population. Methods. A valuation study was conducted using face-to-face interviews involving the time trade-off valuation technique. All respondents valued essentially the same health states, but for each respondent, the descriptions featured an irritable bowel syndrome (IBS) label, a cancer label, or no label. Random effects generalized least squares regressions were used to estimate the impact of each label and experience of the condition on health state values. Data. A sample of 241 members of the UK general population each valued 8 states, generating 1910 observations (response rate = 39%, completion rate = 99% for all states). Results. The authors find no significant difference between health state values when the description contains no label or an IBS label. They find that the inclusion of a cancer label in health state descriptions affects health state values and that the impact is dependent on the severity of the state, with a significant reduction in values for more severe health states (up to −0.25 for the worst possible state) but no significant difference for mild states. Conclusions. A condition label can affect health state values, but this is dependent on the specific condition and severity. The authors recommend avoiding condition labels in health state descriptions (where possible) to ensure that values are not affected by prior knowledge or preconception of the condition that may distort the health state being valued. [ABSTRACT FROM PUBLISHER]