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10.1017/S0950268820003076 http://scihub22266oqcxt.onion/10.1017/S0950268820003076 33413705!7804083!33413705     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 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 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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Infect 2020 ; 149 (ä): e1 Nephropedia Template TP {{tp|p=33413705|t=2020. Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries |pdf=|usr=}}{{33413705}} gab.com Text Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries JO: Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=33413705 #FOAvip Although testing is widely regarded as critical to fighting the COVID-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics - population testing number and testing coverage - to population mortality outcomes and identify a benchmark for testing adequacy. We aggregated publicly available data through 12 April on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. We found that testing coverage, but not population testing number, was highly correlated with population mortality (rs = -0.79, P = 5.975 x 10-9vs. rs = -0.3, P = 0.05) and case fatality rate (rs = -0.67, P = 9.067 x 10-6vs. rs = -0.21, P = 0.20). A testing coverage threshold of 15-45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality; above 45, increased testing did not yield significant incremental mortality benefit. Taken together, testing coverage was better than population testing number in explaining country performance and can serve as an early and sensitive indicator of testing adequacy and disease burden. Twit Text FOAVip Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries ????Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=33413705 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33413705 #FOAvip English Wikipedia <ref>{{Cite pmid|33413705}}</ref> Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries #MMPMID33413705 Wei C; Lee CC; Hsu TC; Hsu WT; Chan CC; Chen SC; Chen CJ Epidemiol Infect 2020[Dec]; 149 (ä): e1 PMID33413705show ga Although testing is widely regarded as critical to fighting the COVID-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics - population testing number and testing coverage - to population mortality outcomes and identify a benchmark for testing adequacy. We aggregated publicly available data through 12 April on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. We found that testing coverage, but not population testing number, was highly correlated with population mortality (rs = -0.79, P = 5.975 x 10-9vs. rs = -0.3, P = 0.05) and case fatality rate (rs = -0.67, P = 9.067 x 10-6vs. rs = -0.21, P = 0.20). A testing coverage threshold of 15-45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality; above 45, increased testing did not yield significant incremental mortality benefit. Taken together, testing coverage was better than population testing number in explaining country performance and can serve as an early and sensitive indicator of testing adequacy and disease burden. |*Global Health[MESH] |*SARS-CoV-2[MESH] |COVID-19 Testing/*statistics & numerical data[MESH] |COVID-19/*epidemiology/*mortality[MESH] |Humans[MESH]Correlation of population mortality of COVID-19 and testing coverage: (epmc).pdf DeepDyve Pubget Overpricing