Medical Decision Making

Background. Health services that affect quality of life (QoL) are increasingly evaluated using cost utility analyses (CUA). These commonly employ one of a small number of multiattribute utility instruments (MAUI) to assess the effects of the health service on utility. However, the MAUI differ significantly, and the choice of instrument may alter the outcome of an evaluation. Aims. The present article has 2 objectives: 1) to compare the results of 3 measures of the sensitivity of 6 MAUI and the results of 6 tests of construct validity in 7 disease areas and 2) to rank the MAUI by each of the test results in each disease area and by an overall composite index constructed from the tests. Methods. Patients and the general public were administered a battery of instruments, which included the 6 MAUI, disease-specific QoL instruments (DSI), and 6 other comparator instruments. In each disease area, instrument sensitivity was measured 3 ways: by the unadjusted mean difference in utility between public and patient groups, by the value of the effect size, and by the correlation between MAUI and DSI scores. Content and convergent validity were tested by comparison of MAUI utilities and scores from the 6 comparator instruments. These included 2 measures of health state preferences, measures of subjective well-being and capabilities, and generic measures of physical and mental QoL derived from the SF-36. Results. The apparent sensitivity of instruments varied significantly with the measurement method and by disease area. Validation test results varied with the comparator instruments. Notwithstanding this variability, the 15D, AQoL-8D, and the SF-6D generally achieved better test results than the QWB and EQ-5D-5L.

Background: Cost-utility analyses commonly employ a multiattribute utility (MAU) instrument to estimate the health state utilities, which are needed to calculate quality-adjusted life years. Different MAU instruments predict significantly different utilities, which makes comparison of results from different evaluation studies problematical. Aim: This article presents mapping functions (“crosswalks”) from 6 MAU instruments (EQ-5D-5L, SF-6D, Health Utilities Index 3 [HUI 3], 15D, Quality of Well-Being [QWB], and Assessment of Quality of Life 8D [AQoL-8D]) to each of the other 5 instruments in the study: a total of 30 mapping functions. Methods: Data were obtained from a multi-instrument comparison survey of the public and patients in 7 disease areas conducted in 6 countries (Australia, Canada, Germany, Norway, United Kingdom, and United States). The 8022 respondents were administered each of the 6 study instruments. Mapping equations between each instrument pair were estimated using 4 econometric techniques: ordinary least squares, generalized linear model, censored least absolute deviations, and, for the first time, a robust MM-estimator. Results: Goodness-of-fit indicators for each of the results are within the range of published studies. Transformations reduced discrepancies between predicted utilities. Incremental utilities, which determine the value of quality-related health benefits, are almost perfectly aligned at the sample means. Conclusion: Transformations presented here align the measurement scales of MAU instruments. Their use will increase confidence in the comparability of evaluation studies, which have employed different MAU instruments.

Health care interventions may affect the health of patients’ family networks. It has been suggested that these “health spillovers” should be included in economic evaluation, but there is not a systematic method for doing this. In this article, we develop a framework for including health spillovers in economic evaluation. We focus on extra-welfarist economic evaluations where the objective is to maximize health benefits from a health care budget (the “health care perspective”). Our framework involves adapting the conventional cost-effectiveness decision rule to include 2 multiplier effects to internalize the spillover effects. These multiplier effects express the ratio of total health effects (for patients and their family networks) to patient health effects. One multiplier effect is specified for health benefit generated from providing a new intervention, one for health benefit displaced by funding this intervention. We show that using multiplier effects to internalize health spillovers could change the optimal funding decisions and generate additional health benefits to society.

Whether respondents incorporate altruistic preferences in time tradeoff (TTO) exercises remains understudied. We present an extended quality-adjusted life-year model incorporating altruism. We derive that altruism may affect TTO values in 2 directions. First, “longevity altruists” may wish to prolong life for the sake of their loved ones (to avoid being missed). Second, “quality-of-life altruists” may have a preference to avoid bad health states resulting in being a burden to loved ones. The existence and influence of these preferences in a TTO were empirically confirmed in a sample of 1690 respondents from the general public. We classified respondents as “longevity altruists” or “quality-of-life altruists” based on their reasoning behind inclusion of loved ones in their TTO responses. In line with expectations, longevity altruists traded fewer years than quality-of-life altruists. Nonaltruists gave intermediate values.

Objective. To assess preferences between child behavioral problems and estimate their value on a quality-adjusted life year (QALY) scale. Methods. Respondents, age 18 or older, drawn from a nationally representative panel between August 2012 and February 2013 completed a series of paired comparisons, each involving a choice between 2 different behavioral problems described using the Behavioral Problems Index (BPI), a 28-item instrument with 6 domains (Anxious/Depressed, Headstrong, Hyperactive, Immature Dependency, Anti-social, and Peer Conflict/Social Withdrawal). Each behavioral problem lasted 1 or 2 years for an unnamed child, age 7 or 10 years, with no suggested relationship to the respondent. Generalized linear model analyses estimated the value of each problem on a QALY scale, considering its duration and the child’s age. Results. Among 5207 eligible respondents, 4155 (80%) completed all questions. Across the 6 domains, problems relating to antisocial behavior were the least preferred, particularly the items related to cheating, lying, bullying, and cruelty to others. Conclusions. The findings are the first to produce a preference-based summary measure of child behavioral problems on a QALY scale. The results may inform both clinical practice and resource allocation decisions by enhancing our understanding of difficult tradeoffs in how adults view child behavioral problems. Understanding US values also promotes national health surveillance by complementing conventional measures of surveillance, survival, and diagnoses.

Objectives . Recent proposals for value-based assessment, made by the National Institute of Health and Care Excellence (NICE) in the United Kingdom, recommended that burden of illness (BOI) should replace end of life (EOL) as a factor for consideration when deciding on new health technologies. This article reports on a study eliciting societal preferences for 1) BOI from a medical condition, defined as quality-adjusted life year (QALY) loss due to premature mortality and prospective morbidity, and 2) EOL, defined as expected life expectancy of less than 2 years and expected life expectancy gain from new treatment of 3 months or more. Methods . A discrete choice experiment survey was conducted with an online UK general population sample. Respondents chose whether they thought the health service should treat patient group A or B: life expectancy and health-related quality of life (HRQOL) with current treatment or life expectancy and HRQOL gains from new treatment, respectively. These attributes were used to derive BOI, QALY gain, and EOL. The respondents’ choices were analyzed using conditional logistic regression with a range of specifications examined, including BOI or EOL, QALY gain and QALY gain squared, and robustness. QALY weights were estimated. Results . The sample of 3669 respondents was representative of the UK population for age and sex. QALY gain had a positive and significant coefficient across all models. QALY gain squared term was negative and significant across all models, indicating a diminishing marginal social value from QALY gains. When included, the BOI coefficient was generally small, positive, and significant, but this was not consistent across the different life expectancy variants. EOL was always positive and significant. Conclusions . The social value of a QALY gain is not equal between recipients but depends on whether they are end of life, and it may depend on the prospective burden of illness.

Background. Parameter uncertainty in EQ-5D value sets is routinely ignored. Sources of parameter uncertainty include uncertainty in the estimated regression coefficients of the scoring algorithm and uncertainty that arises from the need to use a nonsaturated functional form when creating the scoring algorithm. We hypothesize that this latter source is the major contributor to parameter uncertainty in the value sets. Methods. We used data from the United States EQ-5D-3L valuation study to assess the extent of parameter uncertainty in the value set. We refitted the US scoring algorithm to quantify contributors to the mean square prediction errors and used a Bayesian approach to estimate the predictive distribution of the mean utilities. The impact of parameter uncertainty in the value set was assessed using survey data. Results. Parameter uncertainty in the estimated regression coefficients explained 16% of the mean squared prediction error; uncertainty in the functional form explained the remaining 84%. The median width of the 95% credible intervals for the mean utilities was 0.15. In estimating mean utility in our survey population, parameter uncertainty in the value set was responsible for 93% of the total variance, with sampling variation in the survey population being responsible for the remaining 7%. Conclusion. EQ-5D-3L value sets are estimated subject to considerable parameter uncertainty; the median credible interval width is large compared with reported values of the minimum important difference for the EQ-5D-3L, which have been reported to be as small as 0.03. Other countries’ scoring algorithms are based on smaller studies and are hence subject to greater uncertainty. This uncertainty should be accounted for when using EQ-5D health utilities in economic evaluations.

Objective. To determine health-state utility (HSU) values for type 2 diabetes (T2D) patients with various clinical conditions in China, and to examine how these conditions are correlated with HSU values. Methods. A consecutive sample of T2D patients was recruited from a tertiary hospital in Suzhou, China. HSU values of T2D patients were measured using the EQ-5D-5L, a new version of the EQ-5D. T2D patients’ clinical data were collected from the survey and the clinical examination; sociodemographic data were obtained from the survey. Ordinary least square (OLS) models were employed to estimate the impact of clinical conditions on HSU values controlling for sociodemographic conditions. Nonparametric biased-corrected and accelerated (BCA) bootstrap was used to calculate the 95% confidence intervals (CIs). Results. A total of 289 T2D patients provided data. The mean HSU value of the sample was 0.876 (standard deviation: 0.14), with a median of 0.895 (range: −0.063 to 1). Using the OLS model and BCA bootstrap, the impact of significant conditions on HSU values and 95% CIs are as follows: neuropathy (−0.057, [−0.093, −0.017]), heart disease (−0.074, [−0.122, −0.018]), cerebrovascular disease (−0.160, [−0.287, −0.030]), and increasing diabetes duration (−0.004, [−0.007, −0.001]). Conclusion. The HSU values estimated can be used to assess the cost-effectiveness of health care interventions for T2D patients in China.

Background. Logical inconsistency for health states preferences occurs when one logically worse health state, in terms of quality of life, is ranked higher than a logically better health state. Objective. This study explores the presence of inconsistent responses for the EQ-5D health states valuations in a Brazilian population survey. It compares the level of inconsistency in 3 preference-based methods: ranking, visual analog scale (VAS), and time tradeoff (TTO). The influence of EQ-5D health state descriptions is explored by examining the distance between states using a city-block metric as an indicator of proximity. Moreover, it examines the association between formal education and the presence of inconsistencies, as well as the effect of removing inconsistent respondents on the estimation of social value sets from TTO and VAS. Methods. Data came from a valuation study with 3362 literate individuals aged between 18 and 64 years living in urban areas of Minas Gerais state, Brazil. Logical inconsistency was assessed using the percentage of inconsistent respondents and inconsistency rate. A logistic model was estimated to assess the association between formal education and the logical inconsistency. Societal preferences were estimated excluding inconsistent respondents considering city-block metric. Results. The percentage of inconsistent respondents and inconsistency rate are similar for TTO and ranking and lower for VAS. The probability of being inconsistent is higher among less educated groups in ranking and TTO. Inconsistency decreases with distance for all 3 methods. The removal of inconsistent individuals by considering city-block distance improves TTO estimation of social value sets. Conclusion. Findings suggest that removal of inconsistencies in TTO should consider city-block distance. For VAS, inconsistencies are not associated with formal education and do not affect social value set estimation.

Background. Most EQ-5D-3L valuation studies include the same sample of health states that was used in the protocol of the original UK Measurement and Valuation of Health (MVH) study. Thus far, no studies using a time tradeoff utility elicitation method have been carried out using all 243 EQ-5D health states. Because the values and preferences regarding health outcomes differ among countries, it is essential to have country-specific data to enable local high-level decisions regarding resource allocation. This study developed a country-specific set of values for EQ-5D-3L health states. Methods. A multicentric study was conducted in 4 Brazilian areas. A probabilistic sample of the general population, aged 18 to 64 y, stratified by age and gender, was surveyed. The interview followed a revised version of the MVH protocol, in which all 243 health states were valued. Each respondent ranked and valued 7 health states using the TTO in a home interview. Results. Data were collected from 9148 subjects. The best-fitting regression model was an individual-level mixed-effects model without any interaction terms. The dimensions “Mobility” and “Usual Activities” were associated with higher losses in health state utility value. The “Anxiety/Depression” dimension was the domain that contributed to lower losses in health state utility value. Conclusions. This study generated significant insight into the Brazilian population’s health preferences that can be applied to health technology assessment and economic analyses in Brazil. This information represents an important new tool that can be used in Brazilian health policy creation and evaluation.

Importance: Many cost-utility analyses rely on generic utility measures for estimates of disease impact. Commonly used generic preference-based indexes may generate different absolute estimates of disease burden despite sharing anchors of dead at 0 and full health at 1.0. Objective: We compare the impact of 16 prevalent chronic health conditions using 6 utility-based indexes of health and a visual analog scale. Design: Data were from the National Health Measurement Study (NHMS), a cross-sectional telephone survey of 3844 adults aged 35 to 89 years in the United States. Main Outcome Measures: The NHMS included the EuroQol-5D-3L, Health and Activities Limitation Index (HALex), Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), preference-based scoring for the SF-36v2 (SF-6D), Quality of Well-Being Scale, and visual analog scale. Respondents self-reported 16 chronic conditions. Survey-weighted regression analyses for each index with all health conditions, age, and sex were used to estimate health condition impact estimates in terms of quality-adjusted life years (QALYs) lost over 10 years. All analyses were stratified by ages 35 to 69 and 70 to 89 years. Results: There were significant differences between the indexes for estimates of the absolute impact of most conditions. On average, condition impacts were the smallest with the SF-6D and EQ-5D-3L and the largest with the HALex and HUI3. Likewise, the estimated loss of QALYs varied across indexes. Condition impact estimates for EQ-5D-3L, HUI2, HUI3, and SF-6D generally had strong Spearman correlations across conditions (i.e., >0.69). Limitations: This analysis uses cross-sectional data and lacks health condition severity information. Conclusions: Health condition impact estimates vary substantially across the indexes. These results imply that it is difficult to standardize results across cost-utility analyses that use different utility measures.