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

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

Health care policy evaluation: empirical analysis of the restrictions implied by Quality Adjusted Life Years, CHERE Working Paper 2006/10

This paper investigates the nature of the utility function for health care, defined over the probability of survival, survival duration, health state and cost of treatment. A discrete choice experiment, involving treatment choice for a hypothetical health condition is used to test restrictions on preferences in the QALY model. We find that preferences do not conform to expected utility, and there are significant interactions between health state and survival duration. Individual characteristics are significant, implying substantial differences in valuations of health states across the population. The results suggest the QALY approach distorts valuations of health outcomes.Discrete choice experiment, Qalys, preferences, health state valuation

The estimation of a preference-based measure of health from the SF-36

This paper reports on the findings of a study to derive a preference-based measure of health from the SF-36 for use in economic evaluation. The SF-36 was revised into a six-dimensional health state classification called the SF-6D. A sample of 249 states defined by the SF-6D have been valued by a representative sample of 611 members of the UK general population, using standard gamble. Models are estimated for predicting health state valuations for all 18,000 states defined by the SF-6D. The econometric modelling had to cope with the hierarchical nature of the data and its skewed distribution. The recommended models have produced significant coefficients for levels of the SF-6D, which are robust across model specification. However, there are concerns with some inconsistent estimates and over prediction of the value of the poorest health states. These problems must be weighed against the rich descriptive ability of the SF-6D, and the potential application of these models to existing and future SF-36 data set

The relative value of different QALY types

The oft-applied assumption in the use of Quality Adjusted Life Years (QALYs) in economic evaluation, that all QALYs are valued equally, has been questioned from the outset. The literature has focused on differential values of a QALY based on equity considerations such as the characteristics of the beneficiaries of the QALYs. However, a key characteristic which may affect the value of a QALY is the type of QALY itself. QALY gains can be generated purely by gains in survival, purely by improvements in quality of life, or by changes in both. Using a discrete choice experiment and a new methodological approach to the derivation of relative weights, we undertake the first direct and systematic exploration of the relative weight accorded different QALY types and do so in the presence of equity considerations; age and severity. Results provide new evidence against the normative starting point that all QALYs are valued equally.This study was funded by an Australian National Health and Medical Research Council project grant APP1047788

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

A comparison of five multi attribute utility instruments

Abstract This paper presents the results of the validation study carried out to evaluate the Assessment of Quality of Life (AQoL) Instrument for the measurement of health related quality of life and utility. It involves, inter alia, the largest comparison of utility instruments that has been carried out to date. The five instruments included in the study are the AQoL, the Canadian HUI III, the Finnish 15D, the EuroQoL (EQ5D) and the SF36 with UK utility weights as quantified by Brazier (1998). The paper compares: (i) the absolute utility score obtained by different sub-populations; (ii) instrument sensitivity; (iii) the incremental differences in utility between different health states; (iv) the structural properties of descriptive systems; and (v) a limited comparison with a Time Trade-Off (TTO) assessment of own health by individuals. Using these criteria the AQoL performs very well. Its predicted utilities are very similar to those obtained from the HUI. There is evidence that the AQoL has greater sensitivity to health states than other instruments and its psychometric properties, as usually judged, are excellent. Despite this, it is concluded that, at present, no single MAU system can claim to be the gold standard and that researchers should select an instrument that is sensitive to the health states which they are investigating and that caution should be exercised in treating any of the instrument results as representing a utility score which truly represents a trade-off between life and health related quality of life

Choice modelling in the development of natural resource management strategies in NSW

Protecting environmental services generates social benefits. At the same time, private landholders supplying these benefits may face some costs. To provide these services efficiently, policy makers need information about community values for the environment as well as landholders’ costs. This study explores how choice modelling (a non-market valuation technique) is used to estimate comment values. These include use and non-use values for increasing environmental quality in NSW catchments. Non-market valuation techniques for estimating environmental values are reviewed. This is followed by a discussion of methodological aspects of the choice modelling technique and its potential as a regional planning tool for Catchment Management Authorities (CMA’s)Nonmarket valuation, choice modelling, trade-offs, bio-physical modelling, Environmental Economics and Policy, Land Economics/Use,

Hospitality healthscapes: a conjoint analysis approach to understanding patient responses to hotel-like hospital rooms

In an increasingly competitive market, healthcare providers are incorporating best practices from the hospitality industry to improve the patient experience. The present study offers a model of hospitality healthscapes to provide a patient-based perspective of the infusion of hospitality into healthcare. A study of 406 respondents examined the hotel-like attributes that patients prefer in hospital rooms and the effect of their provision on patients’ well-being and willingness to pay higher out-of-pocket expenses. Using conjoint analysis and 3D visual representations of hospital rooms, the study found that high-end material finishes and hospitality-certified healthcare staff were the two greatest influences on patient choice. The study also found some differences between the preferences of “less healthy” and “more healthy” patients, with the less healthy patients willing to pay, on average, 13% higher out-of-pocket expenses for hotel-like hospital rooms than the more healthy patients. This study represents the first attempt in the evidence-based design literature to holistically and empirically examine the infusion of hospitality into healthcare by emphasizing the “patient as customer.” The findings have important marketing implications for healthcare providers who wish to enhance the patient experience