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10.1111/jrh.12486 http://scihub22266oqcxt.onion/10.1111/jrh.12486 32602983!7361905!32602983     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 243.2 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       J+Rural+Health 2020 ; 36 (4): 591-601 Nephropedia Template TP {{tp|p=32602983|t=2020. Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates |pdf=|usr=}}{{32602983}} gab.com Text Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates JO: J Rural Health http://www.kidney.de/pget.php?&tw=1&pmid=32602983 #FOAvip PURPOSE: There are growing signs that the COVID-19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county-level change in prevalence rates of COVID-19 by rural-urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates. METHODS: We used crowdsourced data on COVID-19 and linked them to county-level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R-studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis-Ord local statistics. FINDINGS: In the rural counties, the mean prevalence of COVID-19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID-19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID-19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25-49 years had increased COVID-19 prevalence rates. CONCLUSIONS: Our findings show a rapid spread of COVID-19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID-19 prevalence. Twit Text FOAVip Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates ????J Rural Health http://www.kidney.de/pget.php?&tw=1&pmid=32602983 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32602983 #FOAvip English Wikipedia <ref>{{Cite pmid|32602983}}</ref> Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates #MMPMID32602983 Paul R; Arif AA; Adeyemi O; Ghosh S; Han D J Rural Health 2020[Sep]; 36 (4): 591-601 PMID32602983show ga PURPOSE: There are growing signs that the COVID-19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county-level change in prevalence rates of COVID-19 by rural-urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates. METHODS: We used crowdsourced data on COVID-19 and linked them to county-level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R-studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis-Ord local statistics. FINDINGS: In the rural counties, the mean prevalence of COVID-19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID-19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID-19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25-49 years had increased COVID-19 prevalence rates. CONCLUSIONS: Our findings show a rapid spread of COVID-19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID-19 prevalence. |*Betacoronavirus[MESH] |*Health Status Disparities[MESH] |Bayes Theorem[MESH] |COVID-19[MESH] |Coronavirus Infections/diagnosis/*epidemiology[MESH] |Female[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/diagnosis/*epidemiology[MESH] |Population Surveillance[MESH] |Prevalence[MESH] |Prognosis[MESH] |Risk Factors[MESH] |Rural Population/*statistics & numerical data[MESH] |SARS-CoV-2[MESH] |United States[MESH]Progression of COVID-19 From Urban to Rural Areas in the United States(epmc).pdf DeepDyve Pubget Overpricing