The Role and Activities of the IFLA Libraries for the Blind Section

The purpose of this article is to introduce some of the issues that blind and other print disabled people face in connection with reading and to explain how this situation influences the role of libraries for the blind. It goes on to describe the structure and purpose of the International Federation of Library Associations and Institutions (IFLA) and its Libraries for the Blind Section, and to highlight the Section???s challenges, goals, and activities contained in its latest strategic plan.published or submitted for publicatio

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

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

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®

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

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

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

The Syrophoenician Woman

Matthew 15:21-28

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

To Dance Again

Exodus 15:1-11,20-21

Five Strong Women Charged with

Exodus

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

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