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European Countries, Clinical Factors, Epidemiological Factors, COVID-19, Cluster Analysis
Background and aim of the work
European COVID-19 statistics showed differentiation between mortality and new cases. Some studies suggested several factors including migration, cancer incidence, life expectancy and health system capacity maybe associated with differentiations. Up to now, impact of those factors in different European societies is not discussed and compared. Aim of the present study was to perform the cluster analysis in European countries in attention to clinical and epidemiological factors due to covid-19.
We collected some appropriate extreme data of COVID-19 to access the situations by ANOVA post-hoc test in 3 scenarios, as well as to estimate regression coefficients in simple linear regression, and a cluster analysis using average linkage. Covid-19 Statistics were considered in all analyses until April 24, 2020.
Among 39 European countries, several countries reported highest rate of confirmed cases included of Italy (current statues=2270.52) and Spain (current status=2616.24). The highest rate of mortality was seen in France (current status=242.16), Italy (current status=305.52). Life expectancy (female) (P=0.01, 95%Cl=1521.27,15264.58), migration (P<0.001, 95%Cl=41.42,96.72) had significant association with confirmed cases and death. Overall cancer death (P<0.001, 95%Cl=0.36,0.68; P<0.001, 95%Cl=0.01,0.07) and lung cancer death (P<0.001, 95%Cl=1.97,3.56; P<0.001, 95%Cl=0.09,0.37) associated with confirmed cases and mortality, too. We were also determined 5 clusters which more than 30 countries were categorized in the first cluster.
Demographic factors, including population, life expectancy and migration, underlying disorders, such as several types of cancers, especially lung cancers lead to various distribution of COVID-19 in terms of prevalence and mortality, across European counties.
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