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10.3390/ijerph18094829 http://scihub22266oqcxt.onion/10.3390/ijerph18094829 33946561!8124803!33946561     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 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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2021 ; 18 (9): ä Nephropedia Template TP {{tp|p=33946561|t=2021. COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making |pdf=|usr=}}{{33946561}} gab.com Text COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33946561 #FOAvip This article reports the outcome of a project to develop and assess a predictive model of vulnerability indicators for COVID-19 infection in Los Angeles County. Multiple data sources were used to construct four indicators for zip code tabulation areas: (1) pre-existing health condition, (2) barriers to accessing health care, (3) built environment risk, and (4) the CDC's social vulnerability. The assessment of the indicators finds that the most vulnerable neighborhoods are characterized by significant clustering of racial minorities. An overwhelming 73% of Blacks reside in the neighborhoods with the two highest levels of pre-existing health conditions. For the barriers to accessing health care indicator, 40% of Latinx reside in the highest vulnerability places. The built environment indicator finds that selected Asian ethnic groups (63%), Latinx (55%), and Blacks (53%) reside in the neighborhoods designated as high or the highest vulnerability. The social vulnerability indicator finds 42% of Blacks and Latinx and 38% of selected Asian ethnic group residing in neighborhoods of high vulnerability. The vulnerability indicators can be adopted nationally to respond to COVID-19. The metrics can be utilized in data-driven decision making of re-openings or resource distribution such as testing, vaccine distribution and other pandemic-related resources to ensure equity for the most vulnerable. Twit Text FOAVip COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33946561 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33946561 #FOAvip English Wikipedia <ref>{{Cite pmid|33946561}}</ref> COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making #MMPMID33946561 Ong PM; Pech C; Gutierrez NR; Mays VM Int J Environ Res Public Health 2021[Apr]; 18 (9): ä PMID33946561show ga This article reports the outcome of a project to develop and assess a predictive model of vulnerability indicators for COVID-19 infection in Los Angeles County. Multiple data sources were used to construct four indicators for zip code tabulation areas: (1) pre-existing health condition, (2) barriers to accessing health care, (3) built environment risk, and (4) the CDC's social vulnerability. The assessment of the indicators finds that the most vulnerable neighborhoods are characterized by significant clustering of racial minorities. An overwhelming 73% of Blacks reside in the neighborhoods with the two highest levels of pre-existing health conditions. For the barriers to accessing health care indicator, 40% of Latinx reside in the highest vulnerability places. The built environment indicator finds that selected Asian ethnic groups (63%), Latinx (55%), and Blacks (53%) reside in the neighborhoods designated as high or the highest vulnerability. The social vulnerability indicator finds 42% of Blacks and Latinx and 38% of selected Asian ethnic group residing in neighborhoods of high vulnerability. The vulnerability indicators can be adopted nationally to respond to COVID-19. The metrics can be utilized in data-driven decision making of re-openings or resource distribution such as testing, vaccine distribution and other pandemic-related resources to ensure equity for the most vulnerable. |*COVID-19[MESH] |Decision Making[MESH] |Humans[MESH] |Pandemics[MESH] |Public Health[MESH]COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Mo(epmc).pdf DeepDyve Pubget Overpricing