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10.1017/dmp.2020.298 http://scihub22266oqcxt.onion/10.1017/dmp.2020.298 32782048!7511835!32782048     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Disaster+Med+Public+Health+Prep 2020 ; 14 (3): 364-371 Nephropedia Template TP {{tp|p=32782048|t=2020. Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation |pdf=|usr=}}{{32782048}} gab.com Text Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation JO: Disaster Med Public Health Prep http://www.kidney.de/pget.php?&tw=1&pmid=32782048 #FOAvip In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission. Twit Text FOAVip Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation ????Disaster Med Public Health Prep http://www.kidney.de/pget.php?&tw=1&pmid=32782048 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32782048 #FOAvip English Wikipedia <ref>{{Cite pmid|32782048}}</ref> Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation #MMPMID32782048 Brown RB Disaster Med Public Health Prep 2020[Jun]; 14 (3): 364-371 PMID32782048show ga In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission. |*Bias[MESH] |COVID-19[MESH] |Congresses as Topic/legislation & jurisprudence[MESH] |Coronavirus Infections/epidemiology/*mortality[MESH] |Humans[MESH] |Mortality/*trends[MESH] |Pandemics[MESH] |Pneumonia, Viral/epidemiology/*mortality[MESH] |Public Health/methods/trends[MESH]Public Health Lessons Learned From Biases in Coronavirus Mortality Ov(epmc).pdf DeepDyve Pubget Overpricing