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

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 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

To Dance Again

Exodus 15:1-11,20-21

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

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

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

A review of studies mapping (or cross walking) from non-preference based measures of health to generic preference-based measures

This paper presents a systematic review of current practice in mapping between nonpreference based measures and generic preference-based measures. It reviews the studies identified by a systematic search of the published literature and the grey literature. This review seeks to address the feasibility and overall validity of this approach, the circumstances when it should be considered and to bring together any lessons for future mapping studies

A view from the Bridge: agreement between the SF-6D utility algorithm and the Health utilities Index

BACKGROUND: The SF-6D is a new health state classification and utility scoring system based on 6 dimensions (‘6D’) of the Short Form 36, and permits a ‘‘bridging’’ transformation between SF-36 responses and utilities. The Health Utilities Index, mark 3 (HUI3) is a valid and reliable multi-attribute health utility scale that is widely used. We assessed within-subject agreement between SF-6D utilities and those from HUI3. METHODS: Patients at increased risk of sudden cardiac death and participating in a randomized trial of implantable defibrillator therapy completed both instruments at baseline. Score distributions were inspected by scatterplot and histogram and mean score differences compared by paired t-test. Pearson correlation was computed between instrument scores and also between dimension scores within instruments. Between-instrument agreement was by intra-class correlation coefficient (ICC). RESULTS: SF-6D and HUI3 forms were available from 246 patients. Mean scores for HUI3 and SF-6D were 0.61 (95% CI 0.60–0.63) and 0.58 (95% CI 0.54–0.62) respectively; a difference of 0.03 (p50.03). Score intervals for HUI3 and SF-6D were (-0.21 to 1.0) and (0.30–0.95). Correlation between the instrument scores was 0.58 (95% CI 0.48–0.68) and agreement by ICC was 0.42 (95% CI 0.31–0.52). Correlations between dimensions of SF-6D were higher than for HUI3. CONCLUSIONS: Our study casts doubt on the whether utilities and QALYs estimated via SF-6D are comparable with those from HUI3. Utility differences may be due to differences in underlying concepts of health being measured, or different measurement approaches, or both. No gold standard exists for utility measurement and the SF-6D is a valuable addition that permits SF-36 data to be transformed into utilities to estimate QALYs. The challenge is developing a better understanding as to why these classification-based utility instruments differ so markedly in their distributions and point estimates of derived utilities

Sample sizes for the SF-6D preference based measure of health from the SF-36: a practical guide

Background Health Related Quality of Life (HRQoL) measures are becoming more frequently used in clinical trials and health services research, both as primary and secondary endpoints. Investigators are now asking statisticians for advice on how to plan and analyse studies using HRQoL measures, which includes questions on sample size. Sample size requirements are critically dependent on the aims of the study, the outcome measure and its summary measure, the effect size and the method of calculating the test statistic. The SF-6D is a new single summary preference-based measure of health derived from the SF-36 suitable for use clinical trials and in the economic evaluation of health technologies. Objectives To describe and compare two methods of calculating sample sizes when using the SF-6D in comparative clinical trials and to give pragmatic guidance to researchers on what method to use. Methods We describe two main methods of sample size estimation. The parametric (t-test) method assumes the SF-6D data is continuous and normally distributed and that the effect size is the difference between two means. The non-parametric (Mann-Whitney MW) method assumes the data are continuous and not normally distributed and the effect size is defined in terms of the probability that an observation drawn at random from population Y would exceed an observation drawn at random from population X. We used bootstrap computer simulation to compare the power of the two methods for detecting a shift in location. Results This paper describes the SF-6D and retrospectively calculated parametric and nonparametric effect sizes for the SF-6D from a variety of studies that had previously used the SF-36. Computer simulation suggested that if the distribution of the SF-6D is reasonably symmetric then the t-test appears to be more powerful than the MW test at detecting differences in means. Therefore if the distribution of the SF-6D is symmetric or expected to be reasonably symmetric then parametric methods should be used for sample size calculations and analysis. If the distribution of the SF-6D is skewed then the MW test appears to be more powerful at detecting a location shift (difference in means) than the t-test. However, the differences in power (between the t and MW tests) are small and decrease as the sample size increases. Conclusions We have provided a clear description of the distribution of the SF-6D and believe that the mean is an appropriate summary measure for the SF-6D when it is to be used in clinical trials and the economic evaluation of new health technologies. Therefore pragmatically we would recommend that parametric methods be used for sample size calculation and analysis when using the SF-6D.sample size; health-related quality of life; SF-36; preference-based measures of health; bootstrap simulation

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

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