This paper argues that the Quality Adjusted Life Year or QALY is fatally flawed as a way of priority setting in health care and of dealing with the problem of scarce resources. In addition to showing why this is so the paper sets out a view of the moral constraints that govern the allocation of health resources and suggests reasons for a new attitude to the health budget.

Quality of life is a difficult concept to define and to measure. An hypothesis is proposed which suggests that the quality of life measures the difference, or the gap, at a particular period of time between the hopes and expectations of the individual and that individual's present experiences. Quality of life can only be described by the individual, and must take into account many aspects of life. The approach is goal-orientated, and one of task analysis. The hypothesis is developed in (...) a diagramatic way, and several methods of testing the hypothesis suggested. (shrink)

Medical, scientific and societal progress has been such that, in a universalist humanist perspective such as the WHO’s, it has become an ethical imperative for the primary endpoints in evidence based health care research to be expressed in e.g. Quality Adjusted Life Years (QALYs). The classical endpoints of discrete health‐related functions and duration of survival are increasingly perceived as unacceptably reductionistic. The major problem in ‘felicitometrics’ is the measurement of the ‘quality’ term in QALYs. That the mental, physical and social (...) domains, each containing many dimensions and items, all contribute to QOL is uncontroversial. What is controversial, is the weight of the different dimensions in overall QOL. It has been shown to be very different between different patient populations. In human individuals, assuredly complex systems, the many dimensions and items of QOL observably interact, probably sometimes also in chaotic ways. In these conditions, the weights of isolated items in individuals become for all practical purposes meaningless. Therefore, the much used multi‐item questionnaires at best describe, but do not evaluate QOL, neither in individuals, nor in populations. For example, allergic patients treated with cetirizine scored better than those a placebo on all dimensions of the SF‐36, a standard QOL questionnaire. Here there is no serious doubt that the treatment improved QOL, because it is highly unlikely that any important dimension on which the patient groups would have scored otherwise is missing in the SF‐36. However, whether piracetam treatment of acute stroke, which improved the surrogate endpoints neurological and functional scores, also improved QOL is plausible, but will be proven only when comprehensive QOL measurement will have been done. And suppose in randomised populations of end‐stage metastatic solid cancer patients, one would compare palliative last‐line chemotherapy with only palliative care, and one would, as can be expected, find no significant differences in average survival, and chemotherapy superior for the mental domain, but inferior for the physical comfort domain: we would not know which treatment, on aggregate, would be the better. The problem is that QOL is an individual and emergent construct, the resultant of a great many intractions, and of a different order than its contributing components. Overall QOL can therefore best be captured only as the Gestalt of a global self‐assessment. Just as people in everyday life, while acting under uncertainty, make global assessments all the time, so they can seriously answer the serious question: ‘How have you been?’ A solemn, practical, non peer‐relativistic, non‐cultural, experiential, and well tolerated way to obtain such responses is Anamnestic Comparative Self Assessment (ACSA), in which the subjects' memories of the best and the worst times in their life experience define their individual scale of QOL. ACSA is thus both exquisitely idiosyncratic, and yet can in a universalist humanistic perspective be considered generic. Using both a multi‐item questionnaire and a global assessment allows by one logistic regression, to estimate the weights of the dimensions and items in populations, and thus identify those whose improvement would most contribute to the QOL of the greatest number. A combined approach to measurement of QOL is necessary to maximise the utility of QOL interventions. (shrink)