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10.1097/PHH.0000000000001263 http://scihub22266oqcxt.onion/10.1097/PHH.0000000000001263 32956299!ä!32956299 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32956299.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Public+Health+Manag+Pract 2021 ; 27 Suppl 1, COVID-19 and Public Health: Looking Back, Moving Forward (ä): S43-S56 Nephropedia Template TP {{tp|p=32956299|t=2021. Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses |pdf=|usr=}}{{32956299}} gab.com Text Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses JO: J Public Health Manag Pract http://www.kidney.de/pget.php?&tw=1&pmid=32956299 #FOAvip OBJECTIVE: To overcome the absence of national, state, and local public health data on the unequal economic and social burden of COVID-19 in the United States. DESIGN: We analyze US county COVID-19 deaths and confirmed COVID-19 cases and positive COVID-19 tests in Illinois and New York City zip codes by area percent poverty, percent crowding, percent population of color, and the Index of Concentration at the Extremes. SETTING: US counties and zip codes in Illinois and New York City, as of May 5, 2020. MAIN OUTCOME MEASURES: Rates, rate differences, and rate ratios of COVID-19 mortality, confirmed cases, and positive tests by category of county and zip code-level area-based socioeconomic measures. RESULTS: As of May 5, 2020, the COVID-19 death rate per 100 000 person-years equaled the following: 143.2 (95% confidence interval [CI]: 140.9, 145.5) vs 83.3 (95% CI: 78.3, 88.4) in high versus low poverty counties (>/=20% vs <5% of persons below poverty); 124.4 (95% CI: 122.7, 126.0) versus 48.2 (95% CI: 47.2, 49.2) in counties in the top versus bottom quintile for household crowding; and 127.7 (95% CI: 126.0, 129.4) versus 25.9 (95% CI: 25.1, 26.6) for counties in the top versus bottom quintile for the percentage of persons who are people of color. Socioeconomic gradients in Illinois confirmed cases and New York City positive tests by zip code-level area-based socioeconomic measures were also observed. CONCLUSIONS: Stark social inequities exist in the United States for COVID-19 outcomes. We recommend that public health departments use these straightforward cost-effective methods to report on social inequities in COVID-19 outcomes to provide an evidence base for policy and resource allocation. Twit Text FOAVip Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses ????J Public Health Manag Pract http://www.kidney.de/pget.php?&tw=1&pmid=32956299 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32956299 #FOAvip English Wikipedia <ref>{{Cite pmid|32956299}}</ref> Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses #MMPMID32956299 Chen JT; Krieger N J Public Health Manag Pract 2021[Jan]; 27 Suppl 1, COVID-19 and Public Health: Looking Back, Moving Forward (ä): S43-S56 PMID32956299show ga OBJECTIVE: To overcome the absence of national, state, and local public health data on the unequal economic and social burden of COVID-19 in the United States. DESIGN: We analyze US county COVID-19 deaths and confirmed COVID-19 cases and positive COVID-19 tests in Illinois and New York City zip codes by area percent poverty, percent crowding, percent population of color, and the Index of Concentration at the Extremes. SETTING: US counties and zip codes in Illinois and New York City, as of May 5, 2020. MAIN OUTCOME MEASURES: Rates, rate differences, and rate ratios of COVID-19 mortality, confirmed cases, and positive tests by category of county and zip code-level area-based socioeconomic measures. RESULTS: As of May 5, 2020, the COVID-19 death rate per 100 000 person-years equaled the following: 143.2 (95% confidence interval [CI]: 140.9, 145.5) vs 83.3 (95% CI: 78.3, 88.4) in high versus low poverty counties (>/=20% vs <5% of persons below poverty); 124.4 (95% CI: 122.7, 126.0) versus 48.2 (95% CI: 47.2, 49.2) in counties in the top versus bottom quintile for household crowding; and 127.7 (95% CI: 126.0, 129.4) versus 25.9 (95% CI: 25.1, 26.6) for counties in the top versus bottom quintile for the percentage of persons who are people of color. Socioeconomic gradients in Illinois confirmed cases and New York City positive tests by zip code-level area-based socioeconomic measures were also observed. CONCLUSIONS: Stark social inequities exist in the United States for COVID-19 outcomes. We recommend that public health departments use these straightforward cost-effective methods to report on social inequities in COVID-19 outcomes to provide an evidence base for policy and resource allocation. |*Cost of Illness[MESH] |*Local Government[MESH] |*Socioeconomic Factors[MESH] |COVID-19/*epidemiology[MESH] |Cross-Sectional Studies[MESH] |Ethnicity/*statistics & numerical data[MESH] |Humans[MESH] |Illinois/epidemiology[MESH] |Income/*statistics & numerical data[MESH] |New York City/epidemiology[MESH] |Pandemics/*statistics & numerical data[MESH] |Race Factors[MESH] |Residence Characteristics/*statistics & numerical data[MESH]Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, an(epmc).pdf DeepDyve Pubget Overpricing