Early summary measures of the health status of populations were often measures of a single variable which stood as a more or less crude measure of the health of a population, or as a surrogate for a measure of the health of a population, such as life expectancy or infant mortality. For some purposes, and in the absence of more fine-grained data about population health, these single variable measures can sometimes provide useful information. Health interventions can also be evaluated for the impact they have on increasing life expectancy or reducing infant mortality. Since virtually no one disagrees that it is desirable to reduce infant mortality rates, we can evaluate interventions for their effects in doing so without raising the problem of how to assign relative values to different health outcomes.

The usefulness of life expectancy or infant mortality rates is clearly very limited, however, because they give us information about only one of the aims of health interventions— extending life or preventing premature loss of life—and they provide only limited information about that aim. They give us no information about another aim, at least as important, that of health interventions to improve or protect the quality of life by treating or preventing suffering and disability. Multi-attribute measures like the Sickness Impact Profile 5 and the MOS 36 6 provide measures of different aspects of overall HRQL on which a particular population can be mapped, and an intervention assessed for its impact on these different components of health, or HRQL. Because these measures do not assign different relative value or importance to the different aspects or attributes of HRQL, they do not provide a single overall summary measure of HRQL. Thus, if one of two populations or health interventions scores higher in some respect(s) but lower in others, no conclusion can be drawn about whether the overall HRQL of one population, or from one intervention, is better than the other.

This limitation may not be serious in some contexts. For example, when evaluating some alternative interventions or pharmaceuticals in clinical trials, the impacts of the different interventions may be clustered in a limited domain of HRQL, and the different impacts in that domain of one intervention may be uniformly, or nearly uniformly, better than those of alternative interventions. Nevertheless, even in many clinical trials the outcomes of different interventions may be more multidimensional and conflicting in the sense that no one alternative dominates or is better than all the others in all of its effects on HRQL. For comparing overall population health between and among countries, states, or regions, and for resource prioritization or allocation at national, state, or regional levels, assignments of different relative value or importance to different health-related outcomes or effects are necessary.

Measures like QALYs and DALYs have the important advantage that they address both length of life and health-related quality of life, and they provide a basis for assigning relative value to length versus quality of life, as well as to different impacts on quality of life. The construction of any measure like the QALY or DALY requires a two-step process: first, different states of disability or conditions limiting HRQL are described; second, different relative values are assigned to those different conditions. Instruments like the HUI and the QWB have been developed to play this role. The determination of people’s different health-related conditions both before and after a particular health intervention is an empirical question that should be answered by appeal to relevant data regarding the burden of a particular disease and the reduction in that burden that a particular health intervention can be expected to produce; the overall HRQL

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