Measuring the performance of the health systems is an important challenge at international level. The main objective of this work is to analyze the outcomes of the Kazakhstan Health Care System in order to establish the main causes of avoidable mortality in the country. Also, to identify benchmarking possibilities that may support public policy decisions to improve the results.

To calculate the avoidable mortality indicators due to preventable and treatable causes, the methodology agreed by the OECD and Eurostat based on the International Classification of Diseases, ICD-10 was applied. Starting from the mortality database of the World Health Organization, the standardized indicators of avoidable mortality was calculated for those countries that had available data based on this classification. Based on the outcomes obtained, a “Two-Step” Cluster Analysis was used to identify and characterize the different clusters of countries that present similar results to identify possible affinities and detect benchmarking possibilities.

The main causes of mortality from treatable diseases in Kazakhstan are those related to the circulatory system, followed by different types of cancer and respiratory diseases.

Applying the cluster analysis in the international context, we find important differences between the different clusters, both in the standardized ratios of avoidable mortality and in its causes. Notable differences have also been identified between Kazakhstan and the countries that make up its cluster. Overall, Kazakhstan presents better avoidable mortality results, both from preventable and treatable causes, than the average of the cluster to which it belongs. However, in some causes of death, it presents worse results and high mortality rates, as in the case of those related to the circulatory and respiratory systems or different types of injuries.

The cluster analysis based on the avoidable mortality indicators reveals different conglomerates of countries that show important similarities between them and also some significant differences. Groups of avoidable diseases that characterize each cluster and subcluster, provide key information for the benchmarking and the design of future actions.