Specific and older age-associated comorbidities increase mortality risk in severe forms of coronavirus disease (COVID-19). We matched COVID-19 comorbidities with causes of death in 28 EU countries for the total population and for the population above 65 years and applied a machine-learning-based tree clustering algorithm on shares of death for COVID-19 comorbidities and for influenza and on their growth rates between 2011 and 2016. We distributed EU countries in clusters and drew a map of the EU populations’ vulnerabilities to COVID-19 comorbidities and to influenza. Noncommunicable diseases had impressive shares of death in the EU but with substantial differences between eastern and western countries. The tree clustering algorithm accurately indicated the presence of western and eastern country clusters, with significantly different patterns of disease shares of death and growth rates. Western populations displayed higher vulnerability to malignancy, blood-related diseases, and diabetes mellitus and lower respiratory diseases, while eastern countries’ populations suffered more from ischaemic heart, cerebrovascular, and circulatory diseases. Dissimilarities between EU countries were also present when influenza was considered. The heat maps of EU populations’ vulnerability to diseases based on mortality indicators constitute the basis for more targeted health policy strategies in a collaborative effort at the EU level.
【저자키워드】 COVID-19, Europe, Mortality, Comorbidities, eastern countries, western countries, 【초록키워드】 coronavirus disease, Diseases, Influenza, Diabetes Mellitus, Comorbidity, respiratory diseases, Population, Health, Algorithm, Clustering, Clusters, death, Cluster, disease, mortality risk, malignancy, growth rates, growth rate, Older, effort, total population, Specific, country, heat map, significantly, indicated, form, applied, suffered, cause, 【제목키워드】 vulnerability, Indicator, Map,