Baseline factors associated with death in a COVID-19 hospital cohort, Sao Paulo, 2020.

This study aimed to verify socio-demographic and baseline clinical factors associated with death in a hospital cohort of patients with COVID-19. A retrospective cohort study was conducted between February and December 2020 in a university hospital in the city of São Paulo, using Hospital Epidemiology Center data. RT-PCR-positive patients were selected to compose the sample (n = 1,034). At the end of the study, 362 (32%) patients died. In this cohort, age equal to or greater than sixty years (HR = 1.49) and liver disease (HR = 1.81) were independent risk factors for death from COVID-19 associated with higher in-hospital mortality.

Hospital-Acquired SARS-CoV-2 infection among patients admitted to a university hospital.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency, as it is a highly contagious disease, health services had to adapt to the high demand for hospitalizations in order to contain hospital outbreaks. We aimed to identify the impact of nosocomial transmission of severe acute respiratory coronavirus virus 2 among inpatients at a university hospital in São Paulo, Brazil. Among 455 inpatients diagnosed with coronavirus disease 2019 in March-May, 2020, nosocomial infection was implicated in 42 (9.2%), of whom 23 (54.7%) died. becoming routine, especially when community transmission occur with high levels of incidence. It was possible to observe with this study that the nosocomial transmission by SARS-CoV-2 was present even with these measures instituted, and some of the damages caused by these infections are intangible.

Impact of In-Hospital infection with SARS-CoV-2 among Inpatients at a university hospital.

OBJECTIVE: The objective of this study was to evaluate the incidence of nosocomial infection and the impact of cross-transmission of SARS-CoV-2 among inpatients at a tertiary care teaching hospital. METHODS: This was a retrospective cohort study involving inpatients admitted to a tertiary university hospital in the city of São Paulo, Brazil, between March 2020 and February 2021. Cases were identified on the basis of a positive reverse-transcription polymerase chain reaction result for SARS-CoV-2 and the review of electronic medical records. Nosocomial transmission was defined by applying the criteria established by the Brazilian National Health Regulatory Agency. RESULTS: We identified 2146 cases of SARS-CoV-2 infection, 185 (8.6%) of which were considered cases of nosocomial transmission. The mean age was 58.3 years. The incidence density was 1.78 cases per 1,000 patient-days on the general wards, being highest on the cardiac surgery ward, and only 0.16 per 1,000 patient-days on the COVID-19 wards. Of the 185 patients evaluated, 115 (62.2%) were men, 150 (81.1%) cases had at least one comorbidity, and 104 (56.2%) evolved to death. CONCLUSIONS: Despite the preventive measures taken, nosocomial transmission of SARS-CoV-2 occurred throughout our hospital. Such measures should be intensified when the incidence of community transmission peaks.

The correspondence between the structure of the terrestrial mobility network and the spreading of COVID-19 in Brazil

The inter-cities mobility network is of great importance in understanding outbreaks, especially in Brazil, a continental-dimension country. We adopt the data from the Brazilian Ministry of Health and the terrestrial flow of people between cities from the Brazilian Institute of Geography and Statistics database in two scales: cities from Brazil, without the North region, and from the São Paulo State. Grounded on the complex networks approach, and considering that the mobility network serves as a proxy for the SARS-CoV-2 spreading, the nodes and edges represent cities and flows, respectively. Network centrality measures such as strength and degree are ranked and compared to the list of cities, ordered according to the day that they confirmed the first case of COVID-19. The strength measure captures the cities with a higher vulnerability of receiving new cases. Besides, it follows the interiorization process of SARS-CoV-2 in the São Paulo State when the network flows are above specific thresholds. Some countryside cities such as Feira de Santana (Bahia State), Ribeirão Preto (São Paulo State), and Caruaru (Pernambuco State) have strength comparable to states'capitals. Our analysis offers additional tools for understanding and decision support to inter-cities mobility interventions regarding the SARS-CoV-2 and other epidemics. A rede de mobilidade intermunicipal é de suma importância para a compreensão de surtos, sobretudo no Brasil, um país com dimensões continentais. Os autores adotaram os dados do Ministério da Saúde e informações sobre o fluxo de pessoas entre cidades, da base de dados do Instituto Brasileiro de Geografia e Estatística, em duas escalas: cidades brasileiras, sem a região Norte, e do Estado de São Paulo. Com base na abordagem de redes complexas, e considerando que a rede de mobilidade serve como proxy para a propagação do SARS-CoV-2, os nós e arestas representam cidades e fluxos, respectivamente. As medidas de centralidade de rede, como força e grau, são ranqueadas e comparadas à lista das cidades, de acordo com o dia da confirmação do primeiro caso de COVID-19. A medida de força capta as cidades com maior vulnerabilidade à pandemia, além de acompanhar o processo de interiorização do SARS-CoV-2 no Estado de São Paulo quando os fluxos de rede estão acima de limiares específicos. Algumas cidades do interior, como Feira de Santana (Bahia), Ribeirão Preto (São Paulo) e Caruaru (Pernambuco) mostram forças comparáveis às capitais estaduais. Nossa análise oferece ferramentas adicionais para a compreensão e o apoio para a tomada de decisões sobre intervenções na mobilidade intermunicipal em relação ao SARS-CoV-2 e outras epidemias. La red de movilidad entre ciudades es de vital importancia para la comprensión de los brotes, especialmente en Brasil, un país con dimensiones continentales. Conseguimos los datos del Ministerio de Salud Brasileño y el flujo terrestre de gente entre ciudades a partir de la base de datos del Instituto Brasileño de Geografía y Estadística en dos escalas: ciudades de Brasil, sin la región Norte, y Estado de São Paulo. Basado en un planteamiento de redes complejas, y considerando que la movilidad de la red sirve como un proxy para la propagación del SARS-CoV-2, los nodos y extremos representan ciudades y flujos, respectivamente. Las medidas de centralidad de la red como la fuerza y el grado se clasificaron y compararon con la lista de ciudades, ordenadas según el día en que confirmaron el primer caso de COVID-19. La medida de fuerza captura las ciudades con la mayor vulnerabilidad en recibir nuevos casos. Asimismo, le sigue la interiorización del proceso de SARS-CoV-2 en el Estado de São Paulo, cuando los flujos de la red están por encima de determinados umbrales. Algunas ciudades en áreas rurales como Feira de Santana (Estado de Bahía), Ribeirão Preto (Estado de São Paulo), y Caruaru (Estado de Pernambuco) poseen una fuerza comparable a las capitales de los estados. Nuestro análisis ofrece herramientas adicionales para la compresión y apoyo en la toma de decisiones, respecto a las intervenciones de movilidad entre ciudades, en relación con el SARS-CoV-2 y otras epidemias.

Forecast UTI: aplicación para prever camas en Unidades de Cuidados Intensivos en el contexto de la pandemia de COVID-19 Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic

Resumo Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente. Resumen En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente. In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic. / Forecast UTI: aplicativo para previsão de leitos de unidades de terapia intensiva no contexto da pandemia de COVID-19.

In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.

En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente.

The correspondence between the structure of the terrestrial mobility network and the spreading of COVID-19 in Brazil.

The inter-cities mobility network is of great importance in understanding outbreaks, especially in Brazil, a continental-dimension country. We adopt the data from the Brazilian Ministry of Health and the terrestrial flow of people between cities from the Brazilian Institute of Geography and Statistics database in two scales: cities from Brazil, without the North region, and from the São Paulo State. Grounded on the complex networks approach, and considering that the mobility network serves as a proxy for the SARS-CoV-2 spreading, the nodes and edges represent cities and flows, respectively. Network centrality measures such as strength and degree are ranked and compared to the list of cities, ordered according to the day that they confirmed the first case of COVID-19. The strength measure captures the cities with a higher vulnerability of receiving new cases. Besides, it follows the interiorization process of SARS-CoV-2 in the São Paulo State when the network flows are above specific thresholds. Some countryside cities such as Feira de Santana (Bahia State), Ribeirão Preto (São Paulo State), and Caruaru (Pernambuco State) have strength comparable to states' capitals. Our analysis offers additional tools for understanding and decision support to inter-cities mobility interventions regarding the SARS-CoV-2 and other epidemics.