On the comparable quantification of health risks: Lessons from the Global Burden of Disease Study

Extensive discussion and comments on the Global Burden of Disease Study fin dings have suggested the need to examine more carefully the basis for compa ring the magnitude of different health risks. Attributable burden can be de fined as the difference between burden currently observed and burden that w ould have been observed under an alternative population distribution of exp osure. Population distributions of exposure may be defined over many differ ent levels and intensities of exposure (such as systolic or diastolic blood pressure on a continuous scale), and the comparison distribution of exposu re need not be zero. Avoidable burden is defined as the reduction in the fu ture burden of disease if the current levels of exposure to a risk factor w ere reduced to those specified by the counterfactual distribution of exposu re. Choosing the alternative population distribution for a variable, the co unterfactual distribution of exposure, is the critical step in developing a more general and standardized concept of comparable, attributable, or avoi dable burden. We have identified four types of distributions of exposure th at could be used as the counterfactual distributions: theoretical minimum r isk, plausible minimum risk, feasible minimum risk, and cost-effective mini mum risk. Using tobacco and alcohol as examples, we explore the implication s of using these different types of counterfactual distributions to define attributable and avoidable burden. The ten risk factor assessments included in the Global Burden of Disease Study reflect a range of methods and count erfactual distributions. We recommend that future assessments should focus on avoidable and attributable burden based on the plausible minimum risk co unterfactual distribution of exposure.