OBJECTIVES Examining the power (ability) of classical epidemiological estimators to rate inequalities in health in univariate and composite ways. METHODS Ecological study. Estimators used to show disparities were ratio, excess risk, attributable risk (AR) and relative difference. 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. Twenty-four high-income OECD (Organisation for Economical Cooperation and Development) countries, between 1998 to 2002 were included. Data were obtained from OECD health data for 2004 (3rd edition). Data concerning child mortality and gross domestic product (GDP) were obtained from World Development Indicators for 2005 on CD-ROM. Main outcomes compared among 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 condition. There was concordance in the ranking of inequities among 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.