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10.24171/j.phrp.2021.12.2.03 http://scihub22266oqcxt.onion/10.24171/j.phrp.2021.12.2.03 33979996!8102879!33979996     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Osong+Public+Health+Res+Perspect 2021 ; 12 (2): 64-72 Nephropedia Template TP {{tp|p=33979996|t=2021. Global variation of COVID-19 mortality rates in the initial phase |pdf=|usr=}}{{33979996}} gab.com Text Global variation of COVID-19 mortality rates in the initial phase JO: Osong Public Health Res Perspect http://www.kidney.de/pget.php?&tw=1&pmid=33979996 #FOAvip OBJECTIVE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and less-affected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden. METHODS: We identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation-maximization) clustering algorithm. RESULTS: Statistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p < 0.05). CONCLUSION: Countries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis. Twit Text FOAVip Global variation of COVID-19 mortality rates in the initial phase ????Osong Public Health Res Perspect http://www.kidney.de/pget.php?&tw=1&pmid=33979996 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33979996 #FOAvip English Wikipedia <ref>{{Cite pmid|33979996}}</ref> Global variation of COVID-19 mortality rates in the initial phase #MMPMID33979996 Siddiqui SH; Sarfraz A; Rizvi A; Shaheen F; Yousafzai MT; Ali SA Osong Public Health Res Perspect 2021[Apr]; 12 (2): 64-72 PMID33979996show ga OBJECTIVE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and less-affected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden. METHODS: We identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation-maximization) clustering algorithm. RESULTS: Statistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p < 0.05). CONCLUSION: Countries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis. äGlobal variation of COVID-19 mortality rates in the initial phase (epmc).pdf DeepDyve Pubget Overpricing