3,571 research outputs found

    Current state of the art in preference-based measures of health and avenues for further research

    Get PDF
    Preference-based measures of health (PBMH) have been developed primarily for use in economic evaluation. They have two components: a standardised, multidimensional system for classifying health states and a set of preference weights or scores that generate a single index score for each health state defined by the classification, where full health is one and zero is equivalent to death. A health state can have a score of less than zero if regarded as worse than being dead. These PMBH can be distinguished from non-preference-based measures by the way the scoring algorithms have been developed, in that they are estimated from the values people place on different aspects of health rather than a simple summative scoring procedure or weights obtained from techniques based on item response patterns (e.g. factor analysis or Rasch analysis). The use of PBMH has grown considerably over the last decade with the increasing use of economic evaluation to inform health policy, for example through the establishment of bodies such as the National Institute for Clinical Excellence in England and Wales, the Health Technology Board in Scotland, and similar agencies in Australia and Canada. Preference-based measures have become a common means of generating health state values for calculating quality-adjusted life years (QALY). The status of PBMH was considerably enhanced by the recommendations of the U.S. Public Health Service Panel on Cost-Effectiveness in Health and Medicine to use them in economic evaluation (6). A key requirement for PBHM in economic evaluation is that they allow comparison across programs. While PBMH have been developed primarily for use in economic evaluation, they have also been used to measure health in populations. PBHM provide a better means than a profile measure of determining whether there has been an overall improvement in self-perceived health. The preference-based nature of their scoring algorithms also offers an advantage over non-preference-based measures since the overall summary score reflects what is important to the general population. A non-preference-based measure does not provide an indication to policy makers of the overall importance of health differences between groups or of changes over time. The purpose of this paper is to critically review methods of designing preference-based measures. The paper begins by reviewing approaches to deriving preference weights for PBMH, and this is followed by a brief description and comparison of five common PBMH. The main part of the paper then critically reviews the core components of these measures, namely the classifications for describing health states, the source of their values, and the methods for estimating the scoring algorithm. The final section proposes future research priorities for this field

    Current state of the art in preference-based measures of health and avenues for further research

    Get PDF
    Preference-based measures of health (PBMH) have been developed primarily for use in economic evaluation. They have two components, a standardized, multidimensional system for classifying health states and a set of preference weights or scores that generate a single index score for each health state defined by the classification, where full health is one and zero is equivalent to death. A health state can have a score of less than zero if regarded as worse than being dead. These PMBH can be distinguished from non-preference-based measures by the way the scoring algorithms have been developed, in that they are estimated from the values people place on different aspects of health rather than a simple summative scoring procedure or weights obtained from techniques based on item response patterns (e.g., factor analysis or Rasch analysis). The use of PBMH has grown considerably over the last decade with the increasing use of economic evaluation to inform health policy. Preference-based measures have become a common means of generating health state values for calculating quality-adjusted life years (QALY). The status of PBMH was considerably enhanced by the recommendations of the U.S. Public Health Service Panel on Cost-Effectiveness in Health and Medicine to use them in economic evaluation. A key requirement for PBHM in economic evaluation is that they allow comparison across programmes. While PBMH have been developed primarily for use in economic evaluation, they have also been used to measure health in populations. PBHM provide a better means than a profile measure of determining whether there has been an overall improvement in self-perceived health. The preference-based nature of their scoring algorithms also offers an advantage over non-preference-based measures since the overall summary score reflects what is important to the general population. A non-preference-based measure does not provide an indication to policy makers of the overall importance of health differences between groups or of changes over time. The purpose of this paper is to critically review methods of designing preference based measures. The paper begins by reviewing approaches to deriving preference weights for PBMH, and this is followed by a brief description and comparison of five common PBMH. The main part of the paper then critically reviews the core components of these measures, namely the classifications for describing health states, the source of their values, and the methods for estimating the scoring algorithm. The final section proposes future research priorities for this field.preference-based health measures

    Quality of life evidence for patients with Alzheimer’s disease: use of existing quality of life evidence from the ADENA trials to estimate the utility impact of Exelon®

    Get PDF
    This paper utilises the Mini-Mental State Examination (MMSE) score of patients with Alzheimer’s disease to establish a relationship between disease progression and quality of life measures, and the author also compares his results to findings from the literature review about Alzheimer’s patient utility.Alzheimer's disease; quality of life

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

    Get PDF
    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

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

    Get PDF
    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

    Exploring the relationship between two health state classification systems and happiness using a large patient data set

    Get PDF
    The economic evaluation of health care technologies employs a standard economic approach based on preferences to provide utility information. This paper investigates an alternative approach that uses happiness to weight the health states of two preference-based measures (EQ-5D and SF-6D) in a follow-up of a large hospital patient sample (N=15,184). Logit models relating the health state classifications of these two measures to happiness suggests a different weighting across dimensions to that from preference elicitation techniques such as time trade-off. While mental health (depression and anxiety), vitality and social functioning were found to have a large significant association to a patient’s own happiness assessment, pain was less so and physical health had none. The implications of these results for health policy are discussed

    Evidence of preference construction in a comparison of variants of the standard gamble method

    Get PDF
    An increasingly important debate has emerged around the extent to which techniques such as the standard gamble, which is used, amongst other things, to value health states, actually serve to construct respondents' preferences rather than simply elicit them. According to standard theory, the variant used should have no bearing on the numbers elicited from respondents, i.e. procedural invariance should hold. This study addresses this debate by comparing two variants of standard gamble in the valuation of health states. It is a mixed methods study that combines a quantitative comparison with the probing of respondents in order to ascertain possible reasons for the differences that emerged. Significant differences were found between variants and, furthermore, there was evidence of an ordering effect. Respondents' responses to probing suggested that they were influenced by the method of elicitation

    Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

    Get PDF
    Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems

    A comparison of the EQ-5D and the SF-6D across seven patient groups

    Get PDF
    As the number of preference-based instruments grows, it becomes increasingly important to compare different preference-based measures of health in order to inform an important debate on the choice of instrument. This paper presents a comparison of two of them, the EQ-5D and the SF-6D (recently developed from the SF-36) across seven patient/population groups (chronic obstructive airways disease, osteoarthritis, irritable bowel syndrome, lower back pain, leg ulcers, post menopausal women and elderly). The mean SF-6D index value was found to exceed the EQ-5D by 0.045 and the intraclass correlation coefficient between them was 0.51. Whilst this convergence lends some support for the validity of these measures, the modest difference at the aggregate level masks more significant differences in agreement across the patient groups and over severity of illness, with the SF-6D having a smaller range and lower variance in values. There is evidence for floor effects in the SF-6D and ceiling effects in the EQ-5D. These discrepancies arise from differences in their health state classifications and the methods used to value them. Further research is required to fully understand the respective roles of the descriptive systems and the valuation methods and to examine the implications for estimates of the impact of health care interventions
    • …
    corecore