Liv Ariane Augestad

Comparison of hypothetical and experienced EQ-5D valuations: relative weights of the five dimensions

PurposeEQ-5D tariffs are typically based on general population valuations studies, but whether valuations of experienced health (EH) or hypothetical health (HH) are more appropriate is disputed. Previous comparisons of valuations of EH and HH have focused on absolute differences in dimension-specific regression coefficients. We examined differences in the relative importance attributed to the EQ-5D dimensions between EH and HH valuations of EQ-5D states in the United States.MethodsWe used the regression model from the US EQ-5D valuation study on EH ratings from the 2000–2003 Medical Expenditure Panel Survey and on HH ratings from the US EQ-5D valuation study conducted in 2001. We then compared patterns in the relative magnitudes of coefficients that corresponded to the five dimensions.ResultsIn the HH model, self-care and pain/discomfort were the most important dimensions, while usual activities were the least important. In the EH model, usual activities were the most important dimension, while self-care was one of the least important.DiscussionThe findings reveal considerable differences between stated preferences for HH and ratings of EH, particularly for self-care and usual activities. The findings accentuate the importance of the debate about which groups’ values should be used in medical priority setting.

Learning Effects in Time Trade-Off Based Valuation of EQ-5D Health States

OBJECTIVES In EuroQol five-dimensional questionnaire valuation studies, each participant typically assesses more than 10 hypothetical health states by using the time trade-off (TTO) method. We wanted to explore potential learning effects when using the TTO method, that is, whether the valuations were affected by the number of previously rated health states (the sequence number). METHODS We included 3773 respondents from the US EQ-5D valuation study, each of whom valued 12 health states (plus unconscious) in random order. With linear regression, we used sequence number to predict mean and standard deviations across all health states. We repeated the analysis separately for TTO responses indicating a state better than death and a state worse than death. Each TTO value requires a specific number of choice iterations. To test whether respondents used fewer iterations with experience, we used linear regression with sequence number as the independent variable and number of iterations as the dependent variable. RESULTS Mean TTO values were fairly stable across the sequence number, but analyzing state better than death and state worse than death values separately revealed a tendency toward more extreme values: state better than death values increased by 0.02, while state worse than death values decreased by 0.21 (P < 0.0001) over the full sequence. The standard deviations increased slightly, while the number of choice iterations was the same over the sequence number. The findings were stable across the levels of health state severity, age, and sex. CONCLUSIONS TTO values become more extreme with increasing experience. Because of the randomized valuation order, these effects do not bias specific health states; however, they reduce the overall validity and reliability of TTO values.

A Shortcut to Mean-Based Time Tradeoff Tariffs for the EQ-5D?

Background EQ-5D valuation studies are usually performed using the time tradeoff (TTO) method, which is costly and time consuming. We focused on 2 properties that particularly characterize TTO: the initial choice task categorizing health states as better than death (BTD), worse than death (WTD), or equal to death (ETD), and unwillingness to trade (UTT) lifetime to improve health. The aim of this study was to estimate the value of the information to be gained from continuing the conventional TTO tasks beyond the initial question and the extent to which mean-based EQ-5D tariff values could be predicted through a simplified method of categorizing health states into BTD, WTD, ETD, and UTT. Methods We used data from the UK EQ-5D valuation study (n = 2997). We designed an abbreviated system with only 4 values (collapsed TTO [cTTO]) based on the 4 response categories and assigned values as follows: WTD = −.5, ETD = 0, BTD = .5, and UTT = 1. Based on the mean cTTO scores for the valued health states, we created a regression-based cTTO tariff, which was compared with the conventional (full) TTO tariff (fTTO) by regressing 1) the fTTO means on cTTO means and 2) the fTTO tariff on the cTTO tariff. Results WTD values were unrelated to health state severity. Correlation between the means of fTTO and means of cTTO was >.999, and tariff values from fTTO correlated with tariff values from cTTO at r > .999. Conclusions Once respondents have classified health states as UTT, BTD, ETD, or WTD, the TTO procedure adds little further information to the tariff values. The WTD task fails to discriminate between good and bad health states. TTO valuation could likely be simplified using cTTO.

“When I saw walking I just kind of took it as wheeling”: interpretations of mobility-related items in generic, preference-based health state instruments in the context of spinal cord injury

BackgroundIn health economic analyses, health states are typically valued using instruments with few items per dimension. Due to the generic (and often reductionist) nature of such instruments, certain groups of respondents may experience challenges in describing their health state. This study is concerned with generic, preference-based health state instruments that provide information for decisions about the allocation of resources in health care. Unlike physical measurement instruments, preference-based health state instruments provide health state values that are dependent on how respondents interpret the items. This study investigates how individuals with spinal cord injury (SCI) interpret mobility-related items contained within six preference-based health state instruments.MethodsSecondary analysis of focus group transcripts originally collected in Vancouver, Canada, explored individuals’ perceptions and interpretations of mobility-related items contained within the 15D, Assessment of Quality of Life 8-dimension (AQoL-8D), EQ-5D-5L, Health Utilities Index (HUI), Quality of Well-Being Scale Self-Administered (QWB-SA), and the 36-item Short Form health survey version 2 (SF-36v2). Ritchie and Spencer’s ‘Framework Approach’ was used to perform thematic analysis that focused on participants’ comments concerning the mobility-related items only.ResultsFifteen individuals participated in three focus groups (five per focus group). Four themes emerged: wording of mobility (e.g., ‘getting around’ vs ‘walking’), reference to aids and appliances, lack of suitable response options, and reframing of items (e.g., replacing ‘walking’ with ‘wheeling’). These themes reflected item features that respondents perceived as relevant in enabling them to describe their mobility, and response strategies that respondents could use when faced with inaccessible items.ConclusionInvestigating perceptions to mobility-related items within the context of SCI highlights substantial variation in item interpretation across six preference-based health state instruments. Studying respondents’ interpretations of items can help to understand discrepancies in the health state descriptions and values obtained from different instruments. This line of research warrants closer attention in the health economics and quality of life literature.

Impact of Transformation of Negative Values and Regression Models on Differences Between the UK and US EQ-5D Time Trade-Off Value Sets

BackgroundNational EQ-5D value sets are developed because preferences for health may vary in different populations. UK values are lower than US values for most of the 243 possible EQ-5D health states. Although similar protocols were used for data collection, analytic choices regarding how to model values from the collected data may also influence national value sets. Participants in the UK and US studies assessed the same subset of 42 EQ-5D health states using the time trade-off (TTO) method. However, different methods were used to transform negative values to a range bounded by 0 and −1, and values for all 243 health states were estimated using two different regression models. The transformation of negative values is inconsistent with expected utility theory, and the choice of which transformation method to use lacks a theoretical foundation.ObjectivesOur objectives were to assess how much of the observed difference between the UK and US EQ-5D value sets may be explained by the choice of transformation method for negative values relative to the choice of regression model and the differences between elicited TTO values in the respective national studies (datasets).MethodsWe applied both transformation methods and both regression models to each of the two datasets, resulting in eight comparable value sets. We arranged these value sets in pairs in which one source of difference (transformation method, regression model or dataset) was varied. For each of these paired value sets, we calculated the mean difference between the two matching values for each of the 243 health states. Finally, we calculated the mean utility gain for all possible transitions between pairs of EQ-5D health states within each value set and used the difference in transition scores as a measure of impact from changing transformation method, regression model or dataset.ResultsThe mean absolute difference in values was 1.5 times larger when changing the transformation method than when using different datasets. The choice of transformation method had a 3.2 times larger effect on the mean health gain (transition score) than the choice of dataset. The mean health gain in the UK value set was 0.09 higher than in the US value set. Using the UK transformation method on the US dataset reduced this absolute difference to 0.02. The choice of regression model had little overall impact on the differences between the value sets.ConclusionsMost of the observed differences between the UK and US value sets were caused by the use of different transformation methods for negative values, rather than differences between the two study populations as reflected in the datasets. Changing the regression model had little impact on the differences between the value sets.

Time Trade-Off and Ranking Exercises Are Sensitive to Different Dimensions of EQ-5D Health States

BACKGROUND One method suggested for creating preference-based tariffs for the new five-level EuroQol five-dimensional (EQ-5D) questionnaire is combining time trade-off (TTO) and discrete choice exercises. Rank values from previous valuation studies can be used as proxies for discrete choice exercises. This study examined rank and TTO data to determine whether the methods differ in sensitivity to the EQ-5D questionnaire dimensions. METHODS We used rank and TTO data for 42 EQ-5D questionnaire health states from the US and UK three-level EQ-5D questionnaire valuation studies, extracting overall ranks of mean TTO and mean rank values, ranging from 1 (best) to 42 (worst). We identified pairs of health states with reversed overall ranks between TTO and rank data and regressed overall rank differences (TTO - ranking) on dummy variables representing impairments on EQ-5D questionnaire dimensions. RESULTS Forty-three (US) and 41 (UK) health state pairs displayed reversed rank order. Both US and UK regression models on rank differences indicated that respondents rated impairments involving pain/discomfort and anxiety/depression as relatively worse in TTO than in the ranking task. DISCUSSION Different dimension sensitivity between TTO and ranking methods suggests that combining them could lead to inconsistent tariffs. Differences could be caused by respondents focusing on the first presented dimensions when ranking states or could be related to the longest endurable time for health states involving pain/discomfort or anxiety/depression. The observed differences call into question which method best represents the preferences of the population.

A critical re-evaluation of the regression model specification in the US D1 EQ-5D value function

BackgroundThe EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses. EQ-5D health states are assigned values on a scale anchored in perfect health (1) and death (0).The dominant procedure for defining values for EQ-5D health states involves regression modeling. These regression models have typically included a constant term, interpreted as the utility loss associated with any movement away from perfect health. The authors of the United States EQ-5D valuation study replaced this constant with a variable, D1, which corresponds to the number of impaired dimensions beyond the first. The aim of this study was to illustrate how the use of the D1 variable in place of a constant is problematic.MethodsWe compared the original D1 regression model with a mathematically equivalent model with a constant term. Comparisons included implications for the magnitude and statistical significance of the coefficients, multicollinearity (variance inflation factors, or VIFs), number of calculation steps needed to determine tariff values, and consequences for tariff interpretation.ResultsUsing the D1 variable in place of a constant shifted all dummy variable coefficients away from zero by the value of the constant, greatly increased the multicollinearity of the model (maximum VIF of 113.2 vs. 21.2), and increased the mean number of calculation steps required to determine health state values.DiscussionUsing the D1 variable in place of a constant constitutes an unnecessary complication of the model, obscures the fact that at least two of the main effect dummy variables are statistically nonsignificant, and complicates and biases interpretation of the tariff algorithm.

Influenced from the start: anchoring bias in time trade-off valuations

PurposeThe de facto standard method for valuing EQ-5D health states is the time trade-off (TTO), an iterative choice procedure. The TTO requires a starting point (SP), an initial offer of time in full health which is compared to a fixed offer of time in impaired health. From the SP, the time in full health is manipulated until preferential indifference. The SP is arbitrary, but may influence respondents, an effect known as anchoring bias. The aim of the study was to explore the potential anchoring effect and its magnitude in TTO experiments.MethodsA total of 1249 respondents valued 8 EQ-5D health states in a Web study. We used the lead time TTO (LT-TTO) which allows eliciting negative and positive values with a uniform method. Respondents were randomized to 11 different SPs. Anchoring bias was assessed using OLS regression with SP as the independent variable. In a secondary experiment, we compared two different SPs in the UK EQ-5D valuation study TTO protocol.ResultsA 1-year increase in the SP, corresponding to an increase in TTO value of 0.1, resulted in 0.02 higher recorded LT-TTO value. SP had little impact on the relative distance and ordering of the eight health states. Results were similar to the secondary experiment.ConclusionThe anchoring effect may bias TTO values. In this Web-based valuation study, the observed anchoring effect was substantial. Further studies are needed to determine whether the effect is present in face-to-face experiments.

Comparing 15D Valuation Studies in Norway and Finland—Challenges When Combining Information from Several Valuation Tasks

BACKGROUND The 15D is a generic preference-based health-related quality-of-life instrument developed in Finland. Values for the 15D instrument are estimated by combining responses to three distinct valuation tasks. The impact of how these tasks are combined is relatively unexplored. OBJECTIVES To compare 15D valuation studies conducted in Norway and Finland in terms of scores assigned in the valuation tasks and resulting value algorithms, and to discuss the contributions of each task and the algorithm estimation procedure to observed differences. METHODS Norwegian and Finnish scores from the three valuation tasks were compared using independent samples t tests and Lin concordance correlation coefficients. Covariance between tasks was assessed using Pearson product-moment correlations. Norwegian and Finnish value algorithms were compared using concordance correlation coefficients, total ranges, and ranges for individual dimensions. Observed differences were assessed using minimal important difference. RESULTS Mean scores in the main valuation task were strikingly similar between the two countries, whereas the final value algorithms were less similar. The largest differences between Norway and Finland were observed for depression, vision, and mental function. CONCLUSIONS 15D algorithms are a product of combining scores from three valuation tasks by use of methods involving multiplication. This procedure used to combine scores from the three tasks by multiplication serves to amplify variance from each task. From relatively similar responses in Norway and Finland, diverging value algorithms are created. We propose to simplify the 15D algorithm estimation procedure by using only one of the valuation tasks.

Seasonally adjusted birth frequencies follow the Poisson distribution.

BACKGROUND Variations in birth frequencies have an impact on activity planning in maternity wards. Previous studies of this phenomenon have commonly included elective births. A Danish study of spontaneous births found that birth frequencies were well modelled by a Poisson process. Somewhat unexpectedly, there were also weekly variations in the frequency of spontaneous births. Another study claimed that birth frequencies follow the Benford distribution. Our objective was to test these results. MATERIAL AND METHOD We analysed 50,017 spontaneous births at Akershus University Hospital in the period 1999-2014. To investigate the Poisson distribution of these births, we plotted their variance over a sliding average. We specified various Poisson regression models, with the number of births on a given day as the outcome variable. The explanatory variables included various combinations of years, months, days of the week and the digit sum of the date. RESULTS The relationship between the variance and the average fits well with an underlying Poisson process. A Benford distribution was disproved by a goodness-of-fit test (p < 0.01). The fundamental model with year and month as explanatory variables is significantly improved (p < 0.001) by adding day of the week as an explanatory variable. Altogether 7.5% more children are born on Tuesdays than on Sundays. The digit sum of the date is non-significant as an explanatory variable (p = 0.23), nor does it increase the explained variance. INERPRETATION: Spontaneous births are well modelled by a time-dependent Poisson process when monthly and day-of-the-week variation is included. The frequency is highest in summer towards June and July, Friday and Tuesday stand out as particularly busy days, and the activity level is at its lowest during weekends.