the clustering of world countries regarding causes of death and health risk factors

Background: we aimed to determine how many clusters, who member countries would be grouped based on the causal rates of disease-specific deaths and preventable risk factors, and evaluated the cluster memberships using some sociodemographic and socioeconomic factors. methods: we constructed a dataset relating to 146 who countries using reports and some official websites. an explanatory factor analysis was implemented to reveal the underlying patterns of the dataset. the ward hierarchical clustering method and gap statistical analyses were used to group countries that have similar causes of death. clusters were then compared using subgroup analysis based on some socioeconomic and sociodemographic indicators. results: we divided 146 countries into six meaningful clusters. in a comparative analysis, the differences between clusters were found to be statistically significant according to disease-specific causes of death, risk factors, socioeconomic, and sociodemographic indicators (p<0.001). conclusion: income levels, expenditure rates on health, educational levels, and causes of death in a country are directly proportional to one another. furthermore, it was surprising that the country clusters regarding causes of death and health risk factors showed regional distributions.