OBJECTIVES: Epidemiological models have been widely used to estimate how increased uptakes of medical and surgical treatments affect mortality and related outcomes. Standard methods rely on the estimate of the case fatality, defined as the risk of death in the absence of the treatment. Because most patients receive some treatment, mortality rates where some treatment is present are often used instead of case fatality rates, leading to biased results. A method that does not rely on case fatality estimates is needed. METHODS: We borrow the mechanism used for the calculation of the Potent Impact Fraction (PIF), an epidemiological measure that is equal to the proportional reduction in the incidence of a disease or mortality, resulting from a specific change in the distribution of a risk factor in the population, and apply it to the estimation of the relative reduction of mortality caused by the increase of treatment uptake in the population at risk. We apply this method to estimate the reduction of cardiovascular disease deaths in Ontario, if treatment rates for CHD interventions were to be increased from 2005 levels to the recommended benchmark utilization of 90%. The Mant-Hicks model for polypharmacy is adopted, while the uptakes of multiple treatments are assumed to be independent from each other. RESULTS: Using the proposed PIF-based method, we estimated that increasing treatment to benchmark levels uptake results in a reduction of cardiovascular mortality of 22.5%. The standard method gives a reduction of 17%, probably due to underestimation of the case fatality. CONCLUSIONS: Here we present an alternative method for the estimation of the effect of treatment uptake increase to the reduction of mortality. Our example suggests that the magnitude of bias associated with the standard method may be substantial. This approach may be a useful tool for epidemiological and health care research.