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Background: Disability Adjusted Life Years (DALYs) quantify the loss of healthy years of life due to dying prematurely and due to living with diseases and injuries. Current methods of attributing DALYs to underlying risk factors fall short on two main points. First, risk factor attribution methods often unjustly apply incidence-based population attributable fractions (PAFs) to prevalence-based data. Second, it mixes two conceptually distinct approaches targeting different goals, namely an attribution method aiming to attribute uniquely to a single cause, and an elimination method aiming to describe a counterfactual situation without exposure. In this paper we describe dynamic modeling as an alternative, completely counterfactual approach and compare this to the approach used in the Global Burden of Disease 2010 study (GBD2010).Methods: Using data on smoking in the Netherlands in 2011, we demonstrate how an alternative method of risk factor attribution using a pure counterfactual approach results in different estimates for DALYs. This alternative method is carried out using the dynamic multistate disease table model DYNAMO-HIA. We investigate the differences between our alternative method and the method used by the GBD2010 by doing additional analyses using data from a synthetic population in steady state.Results: We observed important differences between the outcomes of the two methods: in an artificial situation where dynamics play a limited role, DALYs are a third lower as compared to those calculated with the GBD2010 method (398,000 versus 607,000 DALYs). The most important factor is newly occurring morbidity in life years gained that is ignored in the GBD2010 approach. Age-dependent relative risks and exposures lead to additional differences between methods as they distort the results of prevalence-based DALY calculations, but the direction and magnitude of the distortions depend on the particular situation.Conclusions: We argue that the GBD2010 approach is a hybrid of an attributional and counterfactual approach, making the end result hard to understand, while dynamic modelling uses a purely counterfactual approach and thus yields better interpretable results. [ABSTRACT FROM AUTHOR]