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10.1590/1413-812320202512.34472020 http://scihub22266oqcxt.onion/10.1590/1413-812320202512.34472020 33295514!ä!33295514 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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cien+Saude+Colet 2020 ; 25 (12): 4957-4967 Nephropedia Template TP {{tp|p=33295514|t=2020. Analise espacial das condicoes de enfrentamento a COVID-19: uma proposta de Indice da Infraestrutura da Saude do Brasil |pdf=|usr=}}{{33295514}} gab.com Text Analise espacial das condicoes de enfrentamento a COVID-19: uma proposta de Indice da Infraestrutura da Saude do Brasil JO: Cien Saude Colet http://www.kidney.de/pget.php?&tw=1&pmid=33295514 #FOAvip One of the concerns linked to the COVID-19 pandemic is the capacity of health systems to respond to the demand for care for people with the disease. The objective of this study was to create a COVID-19 response Healthcare Infrastructure Index (HII), calculate the index for each state in Brazil, and determine its spatial distribution within and across regions. The HII was constructed using principal component factor analysis. The adequacy of the statistical model was tested using the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity. The spatial distribution of the HII was analyzed using exploratory spatial data analysis. The data were obtained from DATASUS, the Federal Nursing Council, Ministry of Health, Government Procurement Portal, and the Transparency Portal. The nine states in the country's North and Northeast regions showed the lowest indices, while the five states from the Southeast and South regions showed the highest indices. Low-low clusters were observed in Amazonas and Para and high-high clusters were found in Minas Gerais, Rio de Janeiro, Sao Paulo, and Parana. Twit Text FOAVip Analise espacial das condicoes de enfrentamento a COVID-19: uma proposta de Indice da Infraestrutura da Saude do Brasil ????Cien Saude Colet http://www.kidney.de/pget.php?&tw=1&pmid=33295514 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33295514 #FOAvip English Wikipedia <ref>{{Cite pmid|33295514}}</ref> Analise espacial das condicoes de enfrentamento a COVID-19: uma proposta de Indice da Infraestrutura da Saude do Brasil #MMPMID33295514 Bezerra ECD; Santos PSD; Lisbinski FC; Dias LC Cien Saude Colet 2020[Dec]; 25 (12): 4957-4967 PMID33295514show ga One of the concerns linked to the COVID-19 pandemic is the capacity of health systems to respond to the demand for care for people with the disease. The objective of this study was to create a COVID-19 response Healthcare Infrastructure Index (HII), calculate the index for each state in Brazil, and determine its spatial distribution within and across regions. The HII was constructed using principal component factor analysis. The adequacy of the statistical model was tested using the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity. The spatial distribution of the HII was analyzed using exploratory spatial data analysis. The data were obtained from DATASUS, the Federal Nursing Council, Ministry of Health, Government Procurement Portal, and the Transparency Portal. The nine states in the country's North and Northeast regions showed the lowest indices, while the five states from the Southeast and South regions showed the highest indices. Low-low clusters were observed in Amazonas and Para and high-high clusters were found in Minas Gerais, Rio de Janeiro, Sao Paulo, and Parana. |*Health Services Accessibility[MESH] |*SARS-CoV-2[MESH] |*Spatial Analysis[MESH] |Brazil/epidemiology[MESH] |COVID-19/epidemiology/*therapy[MESH] |Factor Analysis, Statistical[MESH] |Health Facilities/*supply & distribution[MESH] |Health Workforce/statistics & numerical data[MESH] |Humans[MESH] |Multivariate Analysis[MESH] |Pandemics[MESH]Analise espacial das condicoes de enfrentamento a COVID-19: uma propos(epmc).pdf DeepDyve Pubget Overpricing