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10.1371/journal.pone.0246548 http://scihub22266oqcxt.onion/10.1371/journal.pone.0246548 33534870!7857563!33534870     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 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 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 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (2): e0246548 Nephropedia Template TP {{tp|p=33534870|t=2021. Urban-rural differences in COVID-19 exposures and outcomes in the South: A preliminary analysis of South Carolina |pdf=|usr=}}{{33534870}} gab.com Text Urban-rural differences in COVID-19 exposures and outcomes in the South: A preliminary analysis of South Carolina JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33534870 #FOAvip As the COVID-19 pandemic moved beyond the initial heavily impacted and urbanized Northeast region of the United States, hotspots of cases in other urban areas ensued across the country in early 2020. In South Carolina, the spatial and temporal patterns were different, initially concentrating in small towns within metro counties, then diffusing to centralized urban areas and rural areas. When mitigation restrictions were relaxed, hotspots reappeared in the major cities. This paper examines the county-scale spatial and temporal patterns of confirmed cases of COVID-19 for South Carolina from March 1st-September 5th, 2020. We first describe the initial diffusion of the new confirmed cases per week across the state, which remained under 2,000 cases until Memorial Day weekend (epi week 23) then dramatically increased, peaking in mid-July (epi week 29), and slowly declining thereafter. Second, we found significant differences in cases and deaths between urban and rural counties, partially related to the timing of the number of confirmed cases and deaths and the implementation of state and local mitigations. Third, we found that the case rates and mortality rates positively correlated with pre-existing social vulnerability. There was also a negative correlation between mortality rates and county resilience patterns, as expected, suggesting that counties with higher levels of inherent resilience had fewer deaths per 100,000 population. Twit Text FOAVip Urban-rural differences in COVID-19 exposures and outcomes in the South: A preliminary analysis of South Carolina ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33534870 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33534870 #FOAvip English Wikipedia <ref>{{Cite pmid|33534870}}</ref> Urban-rural differences in COVID-19 exposures and outcomes in the South: A preliminary analysis of South Carolina #MMPMID33534870 Huang Q; Jackson S; Derakhshan S; Lee L; Pham E; Jackson A; Cutter SL PLoS One 2021[]; 16 (2): e0246548 PMID33534870show ga As the COVID-19 pandemic moved beyond the initial heavily impacted and urbanized Northeast region of the United States, hotspots of cases in other urban areas ensued across the country in early 2020. In South Carolina, the spatial and temporal patterns were different, initially concentrating in small towns within metro counties, then diffusing to centralized urban areas and rural areas. When mitigation restrictions were relaxed, hotspots reappeared in the major cities. This paper examines the county-scale spatial and temporal patterns of confirmed cases of COVID-19 for South Carolina from March 1st-September 5th, 2020. We first describe the initial diffusion of the new confirmed cases per week across the state, which remained under 2,000 cases until Memorial Day weekend (epi week 23) then dramatically increased, peaking in mid-July (epi week 29), and slowly declining thereafter. Second, we found significant differences in cases and deaths between urban and rural counties, partially related to the timing of the number of confirmed cases and deaths and the implementation of state and local mitigations. Third, we found that the case rates and mortality rates positively correlated with pre-existing social vulnerability. There was also a negative correlation between mortality rates and county resilience patterns, as expected, suggesting that counties with higher levels of inherent resilience had fewer deaths per 100,000 population. |*Healthcare Disparities[MESH] |COVID-19/*epidemiology/mortality/pathology/virology[MESH] |Databases, Factual[MESH] |Humans[MESH] |Rural Population[MESH] |SARS-CoV-2/isolation & purification[MESH] |South Carolina/epidemiology[MESH] |Survival Analysis[MESH]Urban-rural differences in COVID-19 exposures and outcomes in the Sout(epmc).pdf DeepDyve Pubget Overpricing