Tessa Peasgood

An updated systematic review of Health State Utility Values for osteoporosis related conditions

IntroductionAn important component of cost effectiveness models in the field of osteoporosis is the set of Health State Utility Values (HSUVs) used for key fracture outcomes. This paper presents a review of HSUVs for key osteoporotic states (hip, wrist, shoulder, clinical, and morphometric vertebral fractures, established osteoporosis, and interaction of several fractures). It provides an update to the systematic review conducted by Brazier et al. (Osteoporos Int 13(10):768–776, 2002).Materials and methodsA systematic search was undertaken of the main literature databases for HSUVs for established osteoporosis, vertebral, hip, wrist, and shoulder fractures were identified. Studies meeting the inclusion criteria were reviewed in terms of the patient population, the method of describing health (if not obtained directly from patients), the method of valuing health states and the source of values.ResultsEstimates of Health State Utility Values were found across the osteoporosis conditions from 27 studies. A wide range of empirical estimates were found, partly due to differences in valuation technique (VAS, SG, TTO), descriptive system and differences in respondents (population or patient), the perspective of the task (own health or a scenario), sample size, and study quality.ConclusionThe paper provides a set of multipliers representing the loss in HSUVs for use as a “reference case” in cost-effectiveness models.

Health-state utility values in breast cancer

Health-related quality of life is an important issue in the treatment of breast cancer and health-state utility values are essential for cost–utility analysis. A literature review was conducted to identify published values for common health states for breast cancer. In total, 13 databases were searched and 49 articles were identified providing 476 unique utility values. Where possible mean utility estimates were pooled using ordinary least squares with utilities clustered within study group and weighted by both number of respondents and inverse of the variance of each utility. Regressions included controls for disease state, utility assessment method and other features of study design. Utility values found in the review were summarized for six categories: screening-related states; preventative states; adverse events in breast cancer and its treatment; nonspecific breast cancer; metastatic breast cancer states; and early breast cancer states. The large number of values identified for metastatic breast cancer and early breast cancer states enabled data to be synthesized by meta-regression. Utilities were found to vary significantly between valuation methods and depending on who conducted the valuation. For metastatic breast cancer, values significantly varied by severity of condition, treatment and side-effects. Despite the numerous studies it is not feasible to generate a definitive list of health-state utility values that can be used in future economic evaluations owing to the complexity of the health states involved and the variety of methods used to obtain values. Future research into quality of life in breast cancer should make greater use of validated generic preference-based measures for which public preferences exist.

A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures

BACKGROUND Generic preference-based measures of health like the EQ-5D and SF-6D(®) are increasingly being used in economic evaluation and outcome assessment. However, there are concerns as to whether or not these generic measures are appropriate for use in people with mental health problems. OBJECTIVES The EQ-5D and SF-36(®) (including its derivatives the SF-12(®) and SF-6D) were assessed using the psychometric criteria of validity and responsiveness using quantitative and qualitative methods. Another aim was to estimate mapping functions between the EQ-5D and SF-6D and condition-specific measures, where appropriate. DESIGN Four studies were undertaken to examine the appropriateness of the measures: (1) a systematic review of quantitative evidence on validity and responsiveness; (2) a further quantitative assessment of these criteria using existing data sets; (3) a review of qualitative research on the quality of life of people with mental health problems; and (4) qualitative semistructured interviews of people with a full range of problems. A fifth study estimated mapping functions between mental health-specific measures and the EQ-5D and SF-6D. SETTING A choice of venue was offered for the interviews including the participants own home, a room at the university or a centre frequently used by mental health services. PARTICIPANTS The interviews were undertaken with 19 people with a broad range of mental health problems at varying levels of severity. MAIN OUTCOME MEASURES The reviews included the EQ-5D and SF-36 (and the SF-12 and SF-6D). The psychometric analysis included the Hospital Anxiety and Depression Scale (HADS), Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM), Generalised Anxiety Disorder Assessment (GAD-7), General Health Questionnaire (GHQ-12) and Patient Health Questionnaire (PHQ-9). RESULTS (1) and (2) The EQ-5D and SF-36 achieved an adequate level of performance in depression, and to some extent in anxiety and personality disorder. Results from the psychometric analyses in schizophrenia and bipolar disorder have been more mixed. (3) A framework analysis of 13 studies identified six major themes. (4) The interview data fitted the themes from the review well and resulted in minor modifications to the themes. The final set of themes comprised: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; hope and hopelessness; and physical health. CONCLUSIONS The EQ-5D and SF-36 achieved mixed results in the quantitative testing against psychometric criteria. The qualitative analysis suggests this is because they provide a very limited coverage of themes identified by people with mental health problems. Recommendations for future work include the development of new preference-based measures in mental health that are based on, or substantially revise, an existing measure. FUNDING The Medical Research Council.

Measuring Well-Being for Public Policy: Preferences or Experiences?

Policy makers seeking to enhance well‐being are faced with a choice of possible measures that may offer contrasting views about how well an individual’s life is going. We suggest that choice of well‐being measure should be based on three general criteria: (1) the measure must be conceptually appropriate (that is, are we measuring the right sort of concept for public policy?), (2) it must be valid (that is, is it a good measure of that concept?), and (3) it must be empirically useful (that is, does it provide information in a format that can be readily used by policy makers?). Preference‐based measures (as represented by income) are compared to experience‐based measures (as represented by subjective evaluations of life) according to these criteria. Neither set of measures meets ideal standards, but experiences do fare at least as well as preferences, and subjective evaluations perform much better than income alone as a measure of well‐being.

Losing Sight of the Wood for the Trees Some Issues in Describing and Valuing Health, and Another Possible Approach

AbstractBackground and Objective: The ability to value health in a way that allows the comparison of different conditions across a range of population groups is central to determining priorities in healthcare. This paper considers some of the concerns with the ‘received wisdom’ in valuing health — to describe it using a generic descriptive system and to value it using the hypothetical preferences of the general public. Methods: The literature on the dimensions of health that matter most to people was reviewed and this paper discusses the use of global measures of subjective well-being (SWB) as a possible alternative. New analysis of the British Household Panel Survey was conducted to explore the relationship between life satisfaction and the preference-based quality-of-life measure the SF-6D. The impact on life satisfaction of each level for each dimension of the SF-6D is estimated through a linear model predicting life satisfaction with the SF-6D levels as determinants. Results: Valuing changes in the health of the general population via changes in life satisfaction would lead to different weights being attached to the different dimensions of health, as compared to a well used utility score in which weights are taken from general population preferences. If preferences elicited via standard gamble exercises are based only on a prediction of what it would be like to live in a particular health state, then these results suggest that reductions in physical functioning matter less than people imagine and reductions in mental health impact upon our lives more than preferences would suggest. Conclusions: Using data from the British Household Panel Survey, it is shown that a focus on SWB would place greater emphasis on mental health conditions. The implications for health policy are considered.

Is Meta-Analysis for Utility Values Appropriate Given the Potential Impact Different Elicitation Methods Have on Values?

A growing number of published articles report estimates from meta-analysis or meta-regression on health state utility values (HSUVs), with a view to providing input into decision-analytic models. Pooling HSUVs is problematic because of the fact that different valuation methods and different preference-based measures (PBMs) can generate different values on exactly the same clinical health state. Existing meta-analyses of HSUVs are characterised by high levels of heterogeneity, and meta-regressions have identified significant (and substantial) impacts arising from the elicitation method used. The use of meta-regression with few utility values and inclusion criteria that extend beyond the required utility value has not helped. There is the potential to explore greater use of mapping between different PBMs and valuation methods prior to data synthesis, which could support greater use of pooling values. Researchers wishing to populate decision-analytic models have a responsibility to incorporate all high-quality evidence available. In relation to HSUVs, greater understanding of the differences between different methods and greater consistency of methodology is required before this can be achieved.

The Identification, Review and Synthesis of Health State Utility Values from the Literature

Systematic literature reviews of health-related quality of life (HRQoL) evidence that are to inform economic models can be challenging due to the volume of hits identified in searches using generic terms for HRQoL. Nevertheless, a robust review of the literature is required to ensure that the health state utility values (HSUVs) used in the economic model are the most appropriate available. This article provides a synopsis of literature relating to identifying, reviewing and synthesising HSUVs. The process begins with scoping the needs of the economic model, including the definitions of health states and the requirements of any reimbursement agencies. A sequence of searches may be required as the economic model evolves. The terminology used for HRQoL measures may be problematic, and as there is no robust HRQoL filter [equivalent to that applied for randomised control trial (RCTs)], sifting the results of sensitive searches can be resource intensive. Alternative approaches such as forward and backward citation searches may reduce the resources required, while maintaining the integrity of the search. Any included studies should be assessed in terms of quality using a recommended checklist, and insufficient detail in the primary studies should be noted as a short-coming in this exercise. Subject to homogeneity (similar populations, same measure and preference weights) evidence can be pooled in some way, although methodological research into the appropriateness of alternative techniques for meta-analysis is in its infancy. Reporting standards are key and as a minimum should include details on searches, inclusion/exclusion criteria (together with rationale for exclusion at each stage), assessment of quality and relevance of included studies, and justification for the choice of final HSUVs.

The Impact of Diabetes-Related Complications on Preference-Based Measures of Health-Related Quality of Life in Adults with Type I Diabetes

Introduction. This study estimates health-related quality of life (HRQoL) or utility decrements associated with type 1 diabetes mellitus (T1DM) using data from a UK research program on the Dose Adjustment For Normal Eating (DAFNE) education program. Methods. A wide range of data was collected from 2341 individuals who undertook a DAFNE course in 2009–2012, at baseline and for 2 subsequent years. We use fixed- and random-effects linear models to generate utility estimates for T1DM using different instruments: EQ-5D, SF-6D, and EQ-VAS. We show models with and without controls for HbA1c and depression, which may be endogenous (if, for example, there is reverse causality in operation). Results. We find strong evidence of an unobserved individual effect, suggesting the superiority of the fixed-effects model. Depression shows the greatest decrement across all the models in the preferred fixed-effects model. The fixed-effects EQ-5D model also finds a significant decrement from retinopathy, body mass index, and HbA1c (%). Estimating a decrement using the fixed-effects model is not possible for some conditions where there are few new cases. In the random-effects model, diabetic foot disease shows substantial utility decrements, yet these are not significant in the fixed-effects models. Conclusion. Utility decrements have been calculated for a wide variety of health states in T1DM that can be used in economic analyses. However, despite the large data set, the low incidence of several complications leads to uncertainty in calculating the utility weights. Depression and diabetic foot disease result in a substantial loss in HRQoL for patients with T1DM. HbA1c (%) appears to have an independent negative impact on HRQoL, although concerns remain regarding the potential endogeneity of this variable.

Experience-based utility and own health state valuation for a health state classification system: why and how to do it

In the estimation of population value sets for health state classification systems such as the EuroQOL five dimensions questionnaire (EQ-5D), there is increasing interest in asking respondents to value their own health state, sometimes referred to as “experience-based utility values” or, more correctly, own rather than hypothetical health states. Own health state values differ to hypothetical health state values, and this may be attributable to many reasons. This paper critically examines whose values matter; why there is a difference between own and hypothetical values; how to measure own health state values; and why to use own health state values. Finally, the paper examines other ways that own health state values can be taken into account, such as including the use of informed general population preferences that may better take into account experience-based values.

Sourcing and Using Appropriate Health State Utility Values in Economic Models in Health Care

Decision analytic models (DAMs) in health care are generally used to assess different interventions to determine which provides the best value for money [1]. These models explore both the costs and benefits accrued by patients receiving the interventions, and results are presented in terms of the incremental cost per benefit of the intervention under evaluation [2]. While not all policy decision makers and reimbursement authorities conform to the same methodology [3], there has been a substantial growth in the use of incremental cost per quality-adjusted life-year (QALY) over the last two decades [4]. As the incremental QALY is the denominator in the outputs of the model, results can be sensitive to both the health state utility values (HSUVs) used and the methods and techniques used to deploy these values within the model [5]. The different components or stages involved in sourcing appropriate HSUVs (Fig. 1) are rarely independent and the process is generally iterative and often challenging [6]. In addition to identifying an appropriate preference-based measure for the particular condition (Fig. 1), and satisfying any associated reimbursement agency requirements, factors such as the advantages (or disadvantages) of collecting utility evidence in randomised clinical trials and other sources [7], the potential need to use a mapping function to predict the required HSUVs [8], and satisfying the exact and often evolving health states used within the DAM all require consideration. Once the most appropriate HSUVs available have been identified, these may not match the requirements of the DAM exactly and analysts may then need to make a series of methodological decisions or assumptions related to the practicalities of using this evidence within the DAM. These may include adjustments to account for age, sex or adverse events, evidence for the baseline or counterfactual (i.e. the trajectory of HSUVs for people who do not have a particular health condition or clinical event), estimate HSUVs for comorbidities (when a second health condition is present concurrently with the primary health condition), and characterising uncertainty appropriately (when using summary statistics sourced from different studies) (Fig. 1).