KEY EMPIRICAL FINDINGS
Our approach identified groups, assessed intergroup inequities and examined risk factors associated with such inequities. To group countries by mortality, we used cluster analysis, which tests whether individual members (countries in our analysis) within clusters are similar to each other (homogeneous or compact with respect to certain characteristics) and different from members of other clusters (with respect to the same characteristics). Cluster analysis is a statistical method well suited for classifying data into cluster groups, and has several basic science and medical applications, such as the classification of elements of the periodic table and of diseases for research on etiology and treatment, but had not been used for health inequity analysis. We used a K-means clustering technique to minimize variability within clusters and to maximize variability between clusters. For adult and child mortality, we sought the K-means cluster analysis that produced the most discriminatory results.
Our analysis revealed that inequities in child and adult mortality have been large and growing, and were related to several economic, social and health sector variables. Specifically, we found that 29 countries had high adult mortality and 23 had high child mortality. All these countries were in western and sub‐Saharan Africa and Afghanistan. Between 1960 and 2000, adult male mortality in countries with high mortality increased at >4 times the rate in countries with low mortality. For child mortality, the worse‐off group made slower progress in reducing under five mortality than the better‐off group. Our empirical results uncover vast inequities in health across societies, which reflect societal choices of public policy and institutions. Efforts to address this problem require attention to worse‐off countries, geographic concentrations, and a multidimensional approach to development.