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10.1371/journal.pone.0233255 http://scihub22266oqcxt.onion/10.1371/journal.pone.0233255 32407411!7224552!32407411     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (5): e0233255 Nephropedia Template TP {{tp|p=32407411|t=2020. Geo-temporal distribution of 1,688 Chinese healthcare workers infected with COVID-19 in severe conditions-A secondary data analysis |pdf=|usr=}}{{32407411}} gab.com Text Geo-temporal distribution of 1,688 Chinese healthcare workers infected with COVID-19 in severe conditions-A secondary data analysis JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32407411 #FOAvip INTRODUCTION: The COVID-19 outbreak is posing an unprecedented challenge to healthcare workers. This study analyzes the geo-temporal effects on disease severity for the 1,688 Chinese healthcare workers infected with COVID-19. METHODS: Using the descriptive results recently reported by the Chinese CDC, we compare the percentage of infected healthcare workers in severe conditions over time and across three areas in China, and the fatality rate of infected healthcare workers with all the infected individuals in China aged 22-59 years. RESULTS: Among the infected Chinese healthcare workers whose symptoms onset appeared during the same ten-day period, the percentage of those in severe conditions decreased significantly from 19.7% (Jan 11-20) to 14.4% (Jan 21-31) to 8.7% (Feb 1-11). Across the country, there was also a significant difference in the disease severity, with Wuhan being the most severe (17.3%), followed by Hubei Province (10.2%), and the rest of China (6.6%). The case fatality rate for the 1,688 infected Chinese healthcare workers was significantly lower than that for the 29,798 infected patients aged 20-59 years-0.3% (5/1,688) vs. 0.65% (193/29,798), respectively. CONCLUSION: The disease severity among infected healthcare workers improved considerably over a short period of time in China. The more severe conditions in Wuhan compared to the rest of the country may be attributable to the draconian lockdown. The clinical outcomes of infected Chinese healthcare workers may represent a more accurate estimation of the severity of COVID-19 for those who have access to quality healthcare. Twit Text FOAVip Geo-temporal distribution of 1,688 Chinese healthcare workers infected with COVID-19 in severe conditions-A secondary data analysis ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32407411 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32407411 #FOAvip English Wikipedia <ref>{{Cite pmid|32407411}}</ref> Geo-temporal distribution of 1,688 Chinese healthcare workers infected with COVID-19 in severe conditions-A secondary data analysis #MMPMID32407411 Gao W; Sanna M; Tsai MK; Wen CP PLoS One 2020[]; 15 (5): e0233255 PMID32407411show ga INTRODUCTION: The COVID-19 outbreak is posing an unprecedented challenge to healthcare workers. This study analyzes the geo-temporal effects on disease severity for the 1,688 Chinese healthcare workers infected with COVID-19. METHODS: Using the descriptive results recently reported by the Chinese CDC, we compare the percentage of infected healthcare workers in severe conditions over time and across three areas in China, and the fatality rate of infected healthcare workers with all the infected individuals in China aged 22-59 years. RESULTS: Among the infected Chinese healthcare workers whose symptoms onset appeared during the same ten-day period, the percentage of those in severe conditions decreased significantly from 19.7% (Jan 11-20) to 14.4% (Jan 21-31) to 8.7% (Feb 1-11). Across the country, there was also a significant difference in the disease severity, with Wuhan being the most severe (17.3%), followed by Hubei Province (10.2%), and the rest of China (6.6%). The case fatality rate for the 1,688 infected Chinese healthcare workers was significantly lower than that for the 29,798 infected patients aged 20-59 years-0.3% (5/1,688) vs. 0.65% (193/29,798), respectively. CONCLUSION: The disease severity among infected healthcare workers improved considerably over a short period of time in China. The more severe conditions in Wuhan compared to the rest of the country may be attributable to the draconian lockdown. The clinical outcomes of infected Chinese healthcare workers may represent a more accurate estimation of the severity of COVID-19 for those who have access to quality healthcare. |Adult[MESH] |Asian People[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*mortality[MESH] |Health Personnel/*statistics & numerical data[MESH] |Humans[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*mortality[MESH] |SARS-CoV-2[MESH] |Spatio-Temporal Analysis[MESH]Geo-temporal distribution of 1,688 Chinese healthcare workers infected(epmc).pdf DeepDyve Pubget Overpricing