Age and Time in the Measurement of the Burden of Disease

A basic principle of the Global Burden of Disease (GBD) studies is that all units of health loss have the same value. However, in earlier iterations of the GBD studies this principle had some qualifications. One was that the value of the same health loss may differ between individuals of different ages; that is, DALYs were age-weighted. Age-weighting has now been removed for ethical reasons. A year of healthy life has the same value regardless of the age of the person living it. Contrary to this claim, however, the author argues that a form of implicit age-weighting is still implied by the GBD methodology. But the author also argues that this kind of age-weighting can be defended on moral grounds.

The Uses of Burden-of-Disease Data for Priority Setting

The generation of burden-of-disease (BOD) data has been driven by a desire to inform policy making and priority setting. This chapter examines the uses of BOD data for priority setting. The authors first introduce the most relevant BOD metrics and describe how these have been linked to priority setting. They next discuss the problems with using BOD data to directly guide priorities. They stress that BOD data are about problems, while priority setting is about solutions, and describe how priority setting based on BOD fails to capture cost-effectiveness and fairness in the distribution of benefits. Against this background, the authors argue that BOD can play many important roles in priority setting, but that these are all indirect. They outline multiple indirect roles, with illustrations from a recent priority setting proposal in Norway. They conclude with general recommendations for how BOD-related data can be made more useful for priority setting.

Years of Good Life Based on Consumption and Health

This chapter proposes a practical measure of individual well-being to facilitate the economic evaluation of public policies. The authors propose to evaluate policies in terms of years of good life gained, in a practical and flexible way that complements and builds upon the standard outcome measures used in cost-effectiveness and cost–benefit analysis. The authors show how to do this by adjusting years of life lived for consumption-related quality of life—that is, the material standard of living—as well as health-related quality of life. This is a straightforward extension of the quality-adjusted life year metric used in health economics for measuring years of healthy life. The authors’ approach allows for differences between people in the marginal value of money. It also permits distributional impact analysis in terms of lifetime well-being—that is, how many good years of life different people can expect over the course of their lives. The authors aim to show how years of good life could be measured in practice by harnessing readily available data on three important elements of individual well-being: consumption, health-related quality of life, and mortality. They also aim to identify the main ethical assumptions needed to use this measure.

Healthy Nails versus Long Lives

How should governments balance saving people from very large individual disease burdens (such as an early death) against saving them from middling burdens (such as erectile dysfunction) and minor burdens (such as nail fungus)? This chapter considers this question through an analysis of a priority-setting proposal in the Netherlands, on which avoiding a multitude of middling burdens takes priority over saving one person from early death, but no number of very small burdens can take priority over avoiding one death. It argues that there is some, albeit imperfect, evidence of substantial public support for such a policy. Furthermore, it provides a principled rationale for it in terms of respect for the person who faces the largest burden. However, it also argues that the threshold for what counts as a minor burden should be set substantially lower than in the Dutch proposal.

Discounting for Uncertainty in Health

When modeling future health outcomes, there are several reasons one might apply a discount function. Setting aside questions of whether health is intrinsically or instrumentally preferred at different times, one can still use a discount function to account for various unmodeled factors. Since it is infeasible to track all possible future trajectories for society, this could be a good pragmatic approach. How this should be done in a health context can be explored; in particular there is a reasonable case that a higher discount rate should be used for years lived with disability than years of life lost. Discounting for uncertainty adds robustness and can be used to dissolve the “eradication paradox” and “research paradox.”

Causal Contribution

Causal language in everyday life and even in policymaking contexts encourages a certain kind of mistake: given some quantifiable outcome (say, the total number of years of life lost in a certain community, over a certain time period), it may be taken for granted that it makes sense to ask, How much of this outcome was due to its various causes? But only in very rare circumstances—when the causal factors in question act additively—is this question well posed. This chapter explains what this additivity requirement amounts to, why it is almost never met, and why an alternative that some have found beguiling—draw on the game-theoretic concept of “Shapley values”—provides no refuge. In discussions of the global burden of disease, the question of what percentage of a given outcome each of its causes “contributed” to that outcome should be rejected as meaningless.

Does GBD 2010 Succeed in Measuring Health?

This chapter responds to Joshua Salomon’s argument in Chapter 5 of this volume that the Global Burden of Disease 2010 (GBD) study succeeds in measuring health (Salomon et al. 2012a,b).. It begins by criticizing some detailed features of the GBD 2010’s surveys that make it unlikely that they succeed in measuring what they aim to measure. The chapter also comments on the consistency in the survey responses across very different nations, which may appear to support the claim that GBD 2010 succeeds in measuring health. Finally, the author argues that there is an interpretation of the results that is at least equally plausible, whereby GBD 2010 measures the values people assign to health rather than health itself.

Health Can Be Measured

This chapter defends the view that it is possible to measure the quantity of health and not simply the value of health and that the 2010 Global Burden of Disease Study (GBD) shows how this might be done. The author begins by offering a brief introduction to the GBD, focusing in particular on the evolution of measurement constructs and approaches pertaining to disability weights over the first several iterations of the GBD. The author then describes the new approach to disability weights measurement taken in the GBD 2010 study. Based on this, the chapter presents a conceptual framework and empirical evidence to support the claim that it is possible to measure quantities of health.

GBD

This chapter describes the motivation behind and history of the Global Burden of Disease Study (GBD) and the approach it uses to measure population health. It discusses several of the value choices involved in the GBD (including choice of life expectancy, disability weights, comorbidity, and incidence vs. prevalence perspective), technical details involved in producing GBD results (including modeling techniques, data synthesis, and data analysis), several key metrics used by the GBD, and the GBD’s online data visualization tools. It concludes by briefly discussing some important empirical results from the most recent GBD study, before reflecting on the future of the GBD.

Causal Attribution, Counterfactuals, and Disease Interventions

This chapter explores a number of interrelated issues that affect assessment of the global burden of disease including what can be learned from causal attributions of particular episodes of death and disability to specific diseases (such as cancer and stroke) about the effects of interventions to remove or reduce the incidence of these diseases (disease interventions). It also explores the use of counterfactuals in epidemiological causal reasoning—a methodology that is employed in the Global Burden of Disease project. It is argued that the effects of such disease interventions can be reliably predicted from causal attribution data alone only when strong additional “independence” assumptions are satisfied. It also argued that in many realistic circumstances these assumptions are unlikely to hold and that when they do not, additional information besides that provided by causal attribution data is needed to predict the effects of disease interventions. Among other things, one needs to explicitly model the causal relationships among different diseases or causes of death. This, in turn, requires frameworks (e.g., structural equations and directed graphs) that explicitly incorporate counterfactual information about what would happen if one were to intervene in various ways. Later sections discuss the sorts of variables that can or should figure in causal claims in epidemiology and the relevance of the epidemiological notions of excess and etiological fractions to predicting the results of disease interventions.