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In most societies resources are insufficient to provide everyone with all the health care they want. In practice, this means that some people are given priority over others. On what basis should priority be given? In this paper we are interested in the general public's views on this question. We set out to synthesis what the literature has found as a whole regarding which attributes or factors the general public think should count in priority setting and what weight they should receive. A systematic review was undertaken (in August 2014) to address these questions based on empirical studies that elicited stated preferences from the general public. Sixty four studies, applying eight methods, spanning five continents met the inclusion criteria. Discrete Choice Experiment (DCE) and Person Trade-off (PTO) were the most popular standard methods for preference elicitation, but only 34% of all studies calculated distributional weights, mainly using PTO. While there is heterogeneity, results suggest the young are favoured over the old, the more severely ill are favoured over the less severely ill, and people with self-induced illness or high socioeconomic status tend to receive lower priority. In those studies that considered health gain, larger gain is universally preferred, but at a diminishing rate. Evidence from the small number of studies that explored preferences over different components of health gain suggests life extension is favoured over quality of life enhancement; however this may be reversed at the end of life. The majority of studies that investigated end of life care found weak/no support for providing a premium for such care. The review highlights considerable heterogeneity in both methods and results. Further methodological work is needed to achieve the goal of deriving robust distributional weights for use in health care priority setting.

Purpose Evidence surrounding utilities for health states, derived either directly from the application of preference-based valuation methods or indirectly from the application of preference-based quality of life instruments, is increasingly being utilised to inform the cost-effectiveness of child health interventions. Proxy (parent or health professional) assessments are common in this area. This study sought to investigate the degree of convergence in childhood utilities generated directly or indirectly within dyad child and proxy assessments. Methods A systematic literature review was conducted following PRISMA guidelines. A comprehensive literature search strategy conducted across six search engines (PubMed, Embase, Web of Science, PsychoINFO, EconLit, CINAHL and Cochrane Library). Original peer-reviewed articles that reported utilities derived directly or indirectly using simultaneous dyad child and proxy assessments were extracted. Mean and median utilities, correlation coefficients and levels of agreement were extracted, catalogued and assessed. Results A total of 35 studies that reported utilities for two or more respondent types were identified. Of these, 29 studies reported dyad childhood self-report and proxy utilities whilst six studies reported levels of agreement and/or correlations only without documenting overall utilities. Proxy assessment was most often conducted by parents with the HUI3 representing the most commonly applied instrument across a range of health conditions. The utilities derived from child and parent proxy assessment were bidirectional with parental proxies tending to underestimate and health professional proxies tending to overestimate relative to child self-reports. Inter-rater agreement between child self-reports and parent-proxy reports were poorer for more subjective attributes (cognition, emotion and pain), relative to physical attributes (mobility, self-care, speech, vision) of health-related quality of life. Conclusions Childhood utilities derived from children or proxies are not interchangeable. The choice of self or proxy assessor may have potentially significant implications for economic evaluations of child health interventions.

Purpose and setting Despite the label “generic” health state utility instruments (HSUIs), empirical evidence shows that different HSUIs generate different estimates of Health-Related Quality of Life (HRQoL) in the same person. Once a HSUI is used to generate a QALY, the difference between HSUIs is often ignored, and decision-makers act as if ‘a QALY is a QALY is a QALY’. Complementing evidence that different generic HSUIs produce different empirical values, this study addresses an important gap by exploring how HSUIs differ, and processes that produced this difference. 15 developers of six generic HSUIs used for estimating the QOL component of QALYs: Quality of Well-Being (QWB) scale; 15 Dimension instrument (15D); Health Utilities Index (HUI); EuroQol EQ-5D; Short Form-6 Dimension (SF-6D), and the Assessment of Quality of Life (AQoL) were interviewed in 2012–2013. Principal findings We identified key factors involved in shaping each instrument, and the rationale for similarities and differences across measures. While HSUIs have a common purpose, they are distinctly discrete constructs. Developers recalled complex developmental processes, grounded in unique histories, and these backgrounds help to explain different pathways taken at key decision points during the HSUI development. The basis for the HSUIs was commonly not equivalent conceptually: differently valued concepts and goals drove instrument design and development, according to each HSUI's defined purpose. Developers drew from different sources of knowledge to develop their measure depending on their conceptualisation of HRQoL. Major conclusions/contribution to knowledge We generated and analysed first-hand accounts of the development of the HSUIs to provide insight, beyond face value, about how and why such instruments differ. Findings enhance our understanding of why the six instruments developed the way they did, from the perspective of key developers of those instruments. Importantly, we provide additional, original explanation for why a QALY is not a QALY is not a QALY.

A key objective of discrete choice experiments is to obtain sufficient quantity of high quality choice data to estimate choice models to be used to explore various policy/clinically relevant issues. This paper focuses on a relatively new form of choice experiment, ‘Best Worst Discrete Choice Experiments’ (BWDCEs) and their relevance to health research as a new way to meet such an objective. We explain what BWDCEs are, how and when to apply them and we present several analytical approaches to model the resulting data. We demonstrate this preference elicitation approach in an empirical application exploring preferences of 898 members of the general public in Edmonton and Calgary, Canada for treatment of cardiac arrest occurring in a public place and show the gains achieved compared to traditional analysis of first best data. We suggest that BWDCEs are a valuable way to investigate preferences in the health sector and discuss implications for task design, analysis and areas for future research. Refereed/Peer-reviewed

There is growing use of discrete choice experiments (DCEs) to investigate preferences for products and programs and for the attributes that make up such products and programs. However, a fundamental issue overlooked in the interpretation of many choice experiments is that attribute parameters estimated from DCE response data are confounded with the underlying subjective scale of the utilities, and strictly speaking cannot be interpreted as the relative "weight" or "impact" of the attributes, as is frequently done in the health economics literature. As such, relative attribute impact cannot be compared using attribute parameter size and significance. Instead, to investigate the relative impact of each attribute requires commensurable measurement units; that is, a common, comparable scale. We present and demonstrate empirically a menu of five methods that allow such comparisons: (1) partial log-likelihood analysis; (2) the marginal rate of substitution for non-linear models; (3) Hicksian welfare measures; (4) probability analysis; and (5) best-worst attribute scaling. We discuss the advantages and disadvantages of each method and suggest circumstances in which each is appropriate. © 2006 Elsevier Ltd. All rights reserved.