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 findings have suggested the need to examine more carefully the basis for comparing the magnitude of different health risks. Attributable burden can be defined as the difference between burden currently observed and burden that would have been observed under an alternative population distribution of exposure. Population distributions of exposure may be defined over many different levels and intensities of exposure (such as systolic or diastolic blood pressure on a continuous scale), and the comparison distribution of exposure need not be zero. Avoidable burden is defined as the reduction in the future burden of disease if the current levels of exposure to a risk factor were reduced to those specified by the counterfactual distribution of exposure. Choosing the alternative population distribution for a variable, the counterfactual distribution of exposure, is the critical step in developing a more general and standardized concept of comparable, attributable, or avoidable burden. We have identified four types of distributions of exposure that could be used as the counterfactual distributions: theoretical minimum risk, plausible minimum risk, feasible minimum risk, and cost-effective minimum risk. Using tobacco and alcohol as examples, we explore the implications 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 counterfactual distributions. We recommend that future assessments should focus on avoidable and attributable burden based on the plausible minimum risk counterfactual distribution of exposure.
Murray
C J
CJ
Harvard School of Public Health, Harvard University, Cambridge, MA 02138, USA.
Lopez
A D
AD
D015984:Causality; D017281:Cost of Illness; D004194:Disease / Q000209:etiology*; D014943:Global Health; D006801:Humans; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods