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Background: In cost-effectiveness analyses in healthcare, Quality-Adjusted Life Years are often used as outcome measure of effectiveness. However, there is an ongoing debate concerning the appropriateness of its use for decision-making in palliative care., Aim: To systematically map pros and cons of using the Quality-Adjusted Life Year to inform decisions on resource allocation among palliative care interventions, as brought forward in the debate, and to discuss the Quality-Adjusted Life Year's value for palliative care., Design: The integrative review method of Whittemore and Knafl was followed. Theoretical arguments and empirical findings were mapped., Data Sources: A literature search was conducted in PubMed, EMBASE, and CINAHL, in which MeSH (Medical Subject Headings) terms were Palliative Care, Cost-Benefit Analysis, Quality of Life, and Quality-Adjusted Life Years., Findings: Three themes regarding the pros and cons were identified: (1) restrictions in life years gained, (2) conceptualization of quality of life and its measurement, including suggestions to adapt this, and (3) valuation and additivity of time, referring to changing valuation of time. The debate is recognized in empirical studies, but alternatives not yet applied., Conclusion: The Quality-Adjusted Life Year might be more valuable for palliative care if specific issues are taken into account. Despite restrictions in life years gained, Quality-Adjusted Life Years can be achieved in palliative care. However, in measuring quality of life, we recommend to-in addition to the EQ-5D- make use of quality of life or capability instruments specifically for palliative care. Also, we suggest exploring the possibility of integrating valuation of time in a non-linear way in the Quality-Adjusted Life Year.

Cost-effectiveness analysis has become a widely accepted tool for decision making in health care. The standard textbook cost-effectiveness analysis focuses on whether to make the switch from an old or common practice technology to an innovative technology, and in doing so, it takes a global perspective. In this article, we are interested in a local perspective, and we look at the questions of whether and when the switch from old to new should be made. A new approach to cost-effectiveness from a local (e.g., a hospital) perspective, by means of a mathematical model for cost-effectiveness that explicitly incorporates time, is proposed. A decision rule is derived for establishing whether a new technology should be adopted, as well as a general rule for establishing when it pays to postpone adoption by 1 more period, and a set of decision rules that can be used to determine the optimal timing of adoption. Finally, a simple example is presented to illustrate our model and how it leads to optimal decision making in a number of cases.

The purpose was to explore the impact of response shift on quality-of-life measurement and consequently on cost-effectiveness analysis. This was done by using data from an earlier study. Adaptation to illness provides a probable explanation for the occurrence of response shift. In the case of proxy respondents, it is likely that the adaptation effect is absent. Therefore, two hypotheses were tested: 1) retrospective quality-of-life measurement does not differ from ratings given by proxies; and 2) proxy ratings differ from prospectively assessed pretreatment quality-of-life scores. Quality of life was assessed using a visual analog scale (VAS), time tradeoff (TTO), and the standard gamble (SG). Analyses revealed that neither hypothesis could be rejected, indicating that prospectively assessed pretreatment quality of life is enhanced by adaptation to the imperfect health state during the pretreatment period. Consequently, the cost-effectiveness ratio is different when using proxy measures or retrospective assessments of pretreatment quality of life, compared with using assessments of the quality of life in currently ill patients.