Performance-based payment schemes are nothing new for not-for-profit organizations that use commercial business models, such as social marketing and social franchise programs. Perhaps because of their familiarity with using output-based budgeting processes, these programs have advanced the development of different types of modeling techniques for describing impacts on health outcomes, and applied these estimates to monitor programs worldwide. Tried and true estimates of health status have been used in new ways, and innovative estimates of equity are reported. Each of these developments is discussed in this special supplement, and each has something to say about the metrics that are used, and the usefulness of the results they produce. The disability-adjusted life year metric (DALY) is a wellknown measure of a population’s disease burden, most commonly known for its use in estimating the global burden of disease. The construction of the DALY statistic has not been without controversy, particularly the relative valuation of some interventions over others and the specification of the weights that are assigned for the measurement of health losses. The Global Burden of Disease Study 2010 provided fresh re-estimations of these weights using large-scale, population-based data sources, representing a major advancement with the DALY metric [1,2]. The variation of the DALY metric to suggest the effectiveness of programs, the DALYs averted metric, is an attractive theoretical premise that several of the papers in this special supplement explore. Through the use of large-scale program performance data across multiple countries, these papers carefully develop original applications of the DALYs averted metric, tracking impact of a single program, and comparing programs across widely different settings. In the process, several interesting issues arise that are surely causing conundrums for managers. For example, the shift from counting the number of products sold to estimating health impact showed that it is the level of disease prevalence (and mortality rates) that will drive the DALYs averted count; smaller programs in high disease burden settings have a larger impact in terms of DALYs averted than large programs in settings with low disease prevalence [3,4]. There are a number of conceptual leaps made in the models being reported, particularly as they move from using products and service utilization statistics to estimating the numbers of DALYs averted based upon the predicted impact of behavior change interventions. The application of these analytic methods across a wide range of services and products within the framework of routine monitoring are innovative achievements, and represent advances to the field of measuring health impact. However, underlying the models are the ubiquitous problems with data quality - even in closely managed social marketing and franchise programs. The broader use of modeling DALYs averted counts will be challenging in settings where the availability of reliable and valid data has been a constraint to capturing accurate measures of performance. The modeling of DALYs averted is a leap in analytic sophistication for programs accustomed to reporting tallies of outputs, as well as more standard rates or ratios as measures of coverage and impact. The requisite skills in complex statistical modeling will be a constraint to the wide-scale diffusion of the DALYs averted technique for monitoring impact. An alternative approach to measuring a program’s impact is to assess its contribution to national health goals, rather than suggest attribution to changes in population health status. Family planning programs are assessed on an aggregate level by changes in the population’s contraceptive prevalence and on a programmatic level by couple-years of protection (CYPs). CYPs convert the number of contraceptives distributed into numbers of contraceptive users. The Marie Stopes International Impact 2 model goes the next step by using an innovative technique that estimates a service delivery organization’ sm arginal contribution to population-level contraceptive prevalence rates (CPR) that is