Populating an economic model with health state utility values: moving towards better practice

Background: When estimating health state utility values (HSUV) for multiple health conditions, the alternative models used to combine these data can produce very different values. Results generated using a baseline of perfect health are not comparable with those generated using a baseline adjusted for not having the health condition taking into account age and gender. Despite this, there is no guidance on the preferred techniques that should be used and very little research describing the effect on cost per QALY results. Methods: Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health state utility profiles (perfect health, individuals with no history of CVD, general population) in conjunction with three models (minimum, additive, multiplicative) frequently used to estimate proxy scores for multiple health conditions. Results: Assuming a baseline of perfect health ignores the natural decline in quality of life associated with co-morbidities, over-estimating the benefits of treatment to such an extent it could potentially influence a threshold policy decision. The minimum model biases results in favour of younger aged cohorts while the additive and multiplicative technique produced similar results. Although further research in additional health conditions is required to support our findings, this pilot study highlights the urgent need for analysts to conform to an agreed reference case and provides initial recommendations for better practice. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation

C*-algebras of separated graphs

The construction of the C*-algebra associated to a directed graph $E$ is extended to incorporate a family $C$ consisting of partitions of the sets of edges emanating from the vertices of $E$. These C*-algebras $C^*(E,C)$ are analyzed in terms of their ideal theory and K-theory, mainly in the case of partitions by finite sets. The groups $K_0(C^*(E,C))$ and $K_1(C^*(E,C))$ are completely described via a map built from an adjacency matrix associated to $(E,C)$. One application determines the K-theory of the C*-algebras $U^{\text{nc}}_{m,n}$, confirming a conjecture of McClanahan. A reduced C*-algebra \Cstred(E,C) is also introduced and studied. A key tool in its construction is the existence of canonical faithful conditional expectations from the C*-algebra of any row-finite graph to the C*-subalgebra generated by its vertices. Differences between \Cstred(E,C) and $C^*(E,C)$, such as simplicity versus non-simplicity, are exhibited in various examples, related to some algebras studied by McClanahan.Comment: 29 pages. Revised version, to appear in J. Functional Analysi

Estimating health state utility values for comorbid health conditions: a synopsis of the current evidence base

Background: Analysts frequently estimate the health state utility values (HSUVs) for combined health conditions (CHCs) using data from cohorts with single health conditions. The methods used to estimated the HSUVs can produce very different results and there is currently no consensus on the most appropriate technique that should be used. Objective: To conduct a detailed critical review of existing empirical literature to gain an understanding of the reasons for differences in results and identify where uncertainty remains that may be addressed by further research. Results: Of the eleven studies identified, ten assessed the additive method, ten the multiplicative method, seven the minimum method, and three the combination model. Two studies evaluated just one of the techniques while the others compared results generated using two or more. The range of the HSUVs can influence general findings and methods are sometimes compared using descriptive statistics that may not be appropriate for assessing predictive ability. None of the proposed methods gave consistently accurate results across the full range of possible HSUVs and the values assigned to normal health influence the accuracy of the methods. Conclusions: While there is no unequivocal evidence for supporting one particular method, the combination linear model appeared to give more accurate results in the studies reviewed. However, before a method can be recommended, research is required in datasets covering the full range of the preference-based indices and health conditions typically defined in decision analytic models. The methods used to assess performance and the statistics used when reporting results require improvement in general

Using health state utility values from the general population to approximate baselines in decision analytic models when condition specific data are not available

Decision analytic models in healthcare require baseline health related quality of life (HRQoL) data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per QALY thresholds. The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition-specific data are not available. Methods: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status. Results: Over 45% of respondents (n=41,174) reported at least one health condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one health condition. In these instances, if condition-specific data are not available, data from respondents who report they do not have a prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on HRQoL may not be constant across ages for all conditions and these relationships may be condition-specific. Additional research is required to validate our findings.health state utility values; baseline; quality of life; EQ-5D; age-adjusted

Populating an economic model with health state utility values: moving towards better practice

Background: When estimating health state utility values (HSUV) for multiple health conditions, the alternative models used to combine these data can produce very different values. Results generated using a baseline of perfect health are not comparable with those generated using a baseline adjusted for not having the health condition taking into account age and gender. Despite this, there is no guidance on the preferred techniques that should be used and very little research describing the effect on cost per QALY results. Methods: Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health state utility profiles (perfect health, individuals with no history of CVD, general population) in conjunction with three models (minimum, additive, multiplicative) frequently used to estimate proxy scores for multiple health conditions. Results: Assuming a baseline of perfect health ignores the natural decline in quality of life associated with co-morbidities, over-estimating the benefits of treatment to such an extent it could potentially influence a threshold policy decision. The minimum model biases results in favour of younger aged cohorts while the additive and multiplicative technique produced similar results. Although further research in additional health conditions is required to support our findings, this pilot study highlights the urgent need for analysts to conform to an agreed reference case and provides initial recommendations for better practice. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation

Using health state utility values from the general population to approximate baselines in decision analytic models when condition specific data are not available

Decision analytic models in healthcare require baseline health related quality of life (HRQoL) data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per QALY thresholds. The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition specific data are not available. Methods: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status. Results: Over 45% of respondents (n=41,174) reported at least one health condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one health condition. In these instances, if condition specific data are not available, data from respondents who report they do not have a prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on HRQoL may not be constant across ages for all conditions and these relationships may be condition specific. Additional research is required to validate our findings

Tails from the Peak District: adjusted censored mixture models of EQ-5D health state utility values

Health state utility data generated using the EQ-5D instrument are typically right bounded at one with a substantial gap to the next set of observations, left bounded by some negative value, and are multi modal. These features present challenges to the estimation of the e¤ect of clinical and socioeconomic characteristics on health utilities. We present an adjusted censored model and then use this in a flexible, mixture modelling framework to address these issues. We demonstrate superior performance of this model compared to linear regression and Tobit censored regression using a dataset from repeated observations of patients with rheumatoid arthritis. We �nd that three latent classes are appropriate in estimating EQ-5D from function, pain and sociodemographic factors. Analysis of utility data should apply methods that recognise the distributional features of the data