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10.23750/abm.v92i1.10090 http://scihub22266oqcxt.onion/10.23750/abm.v92i1.10090 33682803!7975970!33682803     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Acta+Biomed 2020 ; 92 (1): e2021022 Nephropedia Template TP {{tp|p=33682803|t=2020. A cluster analysis of epidemiological and clinical factors associated with the accumulation process of the burden of COVID-19 in European countries |pdf=|usr=}}{{33682803}} gab.com Text A cluster analysis of epidemiological and clinical factors associated with the accumulation process of the burden of COVID-19 in European countries JO: Acta Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33682803 #FOAvip 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. Methods 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. Results 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. Conclusions 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. Twit Text FOAVip A cluster analysis of epidemiological and clinical factors associated with the accumulation process of the burden of COVID-19 in European countries ????Acta Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33682803 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33682803 #FOAvip English Wikipedia <ref>{{Cite pmid|33682803}}</ref> A cluster analysis of epidemiological and clinical factors associated with the accumulation process of the burden of COVID-19 in European countries #MMPMID33682803 Shojaee S; Eslami P; Dooghaie Moghadam A; Pourhoseingholi MA; Ashtari S; Vahedian-Azimi A; Zali MR Acta Biomed 2020[Nov]; 92 (1): e2021022 PMID33682803show ga 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. Methods 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. Results 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. Conclusions 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. |*SARS-CoV-2[MESH] |COVID-19/*epidemiology/mortality[MESH] |Cluster Analysis[MESH] |Emigration and Immigration[MESH] |Europe/epidemiology[MESH] |Humans[MESH]A cluster analysis of epidemiological and clinical factors associated (epmc).pdf DeepDyve Pubget Overpricing