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10.1093/aje/kwab058 http://scihub22266oqcxt.onion/10.1093/aje/kwab058 33710318!7989606!33710318     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       Am+J+Epidemiol 2021 ; 190 (8): 1447-1451 Nephropedia Template TP {{tp|p=33710318|t=2021. Schnake-Mahl and Bilal Respond to "Structural Racism and COVID-19 Mortality in the US" |pdf=|usr=}}{{33710318}} gab.com Text Schnake-Mahl and Bilal Respond to "Structural Racism and COVID-19 Mortality in the US" JO: Am J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=33710318 #FOAvip In their commentary, Zalla et al. (Am J Epidemiol. 2021;190(8):1439-1446) argue that the approach taken by the Centers for Disease Control and Prevention, comparing the proportion of coronavirus disease 2019 (COVID-19) deaths by race/ethnicity with a weighted population distribution, ignores how systemic racism structures the composition of places. While the Centers for Disease Control and Prevention have abandoned their measure, they did so because of the changing geographic distribution of COVID-19, not because the measure underestimates racial disparities. We further Zalla et al.'s argument, advocating for a relational approach to estimating COVID-19 racial inequities that integrates the reciprocal relationship between context and composition through the interaction of places and people over time. To support our argument, we present a series of figures exploring the heterogeneous relationships between places, people, and time, using publicly available, US county-level COVID-19 mortality data from February to December 2020 from Johns Hopkins University. Longitudinal and more geographically granular data that allows for disaggregation by person, place, and time will improve our estimation and understanding of inequities in COVID-19. Twit Text FOAVip Schnake-Mahl and Bilal Respond to "Structural Racism and COVID-19 Mortality in the US" ????Am J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=33710318 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33710318 #FOAvip English Wikipedia <ref>{{Cite pmid|33710318}}</ref> Schnake-Mahl and Bilal Respond to "Structural Racism and COVID-19 Mortality in the US" #MMPMID33710318 Schnake-Mahl AS; Bilal U Am J Epidemiol 2021[Aug]; 190 (8): 1447-1451 PMID33710318show ga In their commentary, Zalla et al. (Am J Epidemiol. 2021;190(8):1439-1446) argue that the approach taken by the Centers for Disease Control and Prevention, comparing the proportion of coronavirus disease 2019 (COVID-19) deaths by race/ethnicity with a weighted population distribution, ignores how systemic racism structures the composition of places. While the Centers for Disease Control and Prevention have abandoned their measure, they did so because of the changing geographic distribution of COVID-19, not because the measure underestimates racial disparities. We further Zalla et al.'s argument, advocating for a relational approach to estimating COVID-19 racial inequities that integrates the reciprocal relationship between context and composition through the interaction of places and people over time. To support our argument, we present a series of figures exploring the heterogeneous relationships between places, people, and time, using publicly available, US county-level COVID-19 mortality data from February to December 2020 from Johns Hopkins University. Longitudinal and more geographically granular data that allows for disaggregation by person, place, and time will improve our estimation and understanding of inequities in COVID-19. |*COVID-19[MESH] |*Racism[MESH] |Ethnicity[MESH] |Health Status Disparities[MESH] |Humans[MESH]Schnake-Mahl and Bilal Respond to "Structural Racism and COVID-19 Mort(epmc).pdf DeepDyve Pubget Overpricing