Objective Examining the power (ability) of classical epidemiological estimators to rate inequality in health in univariate and composite ways. Methods Ecological study. Ratio, excess risk, attributable risk (AR) and relative difference were the estimators used for showing disparities; all of them were weighted by population size. Kappa concordance coefficient was used between weighted estimators and weighted Gini coefficients for each health outcome used. Cumulative variance at first factor in principal component analysis was used for determining the estimators' suitability for use in a composite index. 24 high-income OECD (Organisation for Economical Cooperation and Development) countries' data for 1998-2002 were included. Such data was obtained from OECD health data for 2004 (3rd edition). Data concerning child mortality and gross domestic product (GDP) was obtained from World Development Indicators for 2005 on CD-ROM.The main outcomes compared amongst countries were: maternal mortality, child mortality, infant mortality, low birth-weight, life-expectancy, measles' immunisation and DTP immunisation. Results Ratio and AR ranked maternal mortality as being the condition having the most disparity; risk excess ranked vaccination programmes and relative difference ranked low birth-weight as being the worst conditions. There was concordance in the ranking of inequities amongst ratio, AR and Gini coefficients (p<0.05). Cumulative variance in the first factor was higher for ratio and AR when they were used for constructing a composite index. Conclusions Ratio and AR were better than risk excess and relative difference for measuring disparities in health and constructing composite inequity in health indexes.