Bed demand and occupancy within the Brazilian National Health System for the most common types of cancer in Brazil, from 2018 to 2021: a cross-sectional study.

OBJECTIVE: To analyze bed demand and occupancy within the Brazilian National Health System (Sistema Único de Saúde - SUS) for the main types of cancer in Brazil, from 2018 to 2021. METHODS: This was a descriptive cross-sectional study, using data from the Hospital Information System. Queuing theory model was used for calculating average admission rate, average hospitalization rate, probability of overload, and average number of people in the queue. RESULTS: The Southeast and South regions showed the highest average hospitalization rates, while the North region showed the lowest rates. The Southeast region presented a high probability of surgical bed overload, especially in the states of São Paulo (99.0%), Minas Gerais (97.0%) and Rio de Janeiro (97.0%). São Paulo state showed an overload above 95.0% in all types of beds analyzed. CONCLUSION: There was a high probability of oncology bed occupancy within the Brazilian National Health System, especially surgical and medical beds, and regional disparities in bed overload. MAIN RESULTS: The study found a high demand for hospital admissions to oncological bed in the Southeast region and a high probability of system overload in the states of the Southeast and Northeast regions of Brazil, thus highlighting the inequities in access to healthcare services in the country. IMPLICATIONS FOR SERVICES: This study presents a methodology for the improved allocation of resources and management of surgical and medical bed flows in areas with the highest bed overload and regions with low service availability. PERSPECTIVES: It is necessary to promote public policies that ensure the equitable supply of beds for oncological treatment within the SUS, especially in states with bed overload and healthcare service gaps.

Emprego do modelo matemático SEIHDR para auxiliar gestores na estimativa de ocupação de leitos de UTI em uma pandemia: simulação para pacientes com COVID-19 no município de Porto Alegre / Use of the SEIHDR mathematical model to assist health managers in estimating ICU bed occupancy in a pandemic: simulation for patients with COVID-19 in the city of Porto Alegre, Brazil

Introdução: A pandemia de COVID-19, no Brasil, constituiu uma ameaça ao sistema de saúde pelo risco de esgotamento dos leitos de Unidade de Terapia Intensiva (UTI). O objetivo do estudo foi projetar a ocupação de leitos de UTI com casos de COVID-19 no pico em Porto Alegre. Para isso, resolvemos utilizar uma ferramenta matemática com parâmetros da pandemia desta cidade.Métodos:Utilizamos o modelo matemático SEIHDR. Analisamos os casos de hospitalização por COVID-19 em Porto Alegre e RS até 3 de agosto de 2020 a fim de extrair os parâmetros locais para construir uma curva epidemiológica do total de casos prevalentes hospitalizados em UTI. Também analisamos as taxas de reprodução básica (R0) e reprodução efetiva (Re).Resultados: O modelo matemático projetou um pico de 344 casos prevalentes, em UTI, para o dia 22 de agosto de 2020. Calculamos 1,56 para o R0 e 1,08 no dia 3 de agosto para o Re.Conclusão: O modelo matemático simulou uma primeira onda de casos ocupando leitos de UTI muito próxima dos dados reais. Também indicou corretamente uma queda no número de casos nos dois meses subsequentes. Apesar das limitações, as estimativas do modelo matemático forneceram informações sobre as dimensões temporal e numérica de uma pandemia que poderiam ser usadas como auxílio aos gestores de saúde na tomada de decisões para a alocação de recursos frente a calamidades de saúde como o surto de COVID-19 no Brasil.

Introduction: The COVID-19 pandemic in Brazil has been a threat to health services due to the risk of bed shortage in the intensive care unit (ICU). This study aimed to estimate the bed occupancy at the ICU with patients with COVID-19 during the peak of the pandemic in Porto Alegre, capital of Rio Grande do Sul (RS), the southernmost state of Brazil. To this end, we used a mathematical model with pandemic parameters from the city.Methods: We used the SEIHDR mathematical model. We analyzed hospitalizations for COVID-19 in Porto Alegre and RS until August 3, 2020, to extract local parameters to create an epidemiological curve of the total number of prevalent cases in the ICU. We also analyzed the basic reproduction rate (R0) and effective reproduction rate (Re). Results: The mathematical model estimated a peak of 344 prevalent cases in the ICU on August 22, 2020. The model calculated an R0 of 1.56 and Re of 1.08 on August 3, 2020.Conclusion: The mathematical model accurately estimated the first peak of cases in the ICU. Also, it correctly indicated a drop in the number of cases in the following two months. Despite the limitations, the mathematical model estimates provided information on the temporal and numerical dimensions of a pandemic that could be used to assist health managers in making decisions on the allocation of resources in a state of public calamity such as the COVID-19 outbreak in Brazil.

Smart testing and critical care bed sharing for COVID-19 control.

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.

[Covid-19 in the Northeast of Brazil: from lockdown to the relaxation of social distancing measures]. / Covid-19 no nordeste do Brasil: entre o lockdown e o relaxamento das medidas de distanciamento social.

Even in the period when the Covid-19 pandemic was on the rise in the Northeast of Brazil, the relaxation of social distancing measures was introduced. The scope of the study is to assess, in the light of the epidemiological-sanitary situation in the region, the suitability of relaxation of social distancing measures. Based on the WHO guidelines for relaxation of social distancing, operational indicators were created and analyzed for each guideline in the context of the Northeast. To analyze the behavior of the epidemic, according to selected indicators, Joinpoint trend analysis techniques, heat maps, rate ratios and time trends between capitals and the state interior were compared. The weekly growth peak of the epidemic occurred in May-July 2020 (epidemiological weeks 19 to 31). In most capitals, there was no simultaneous downward trend in the number of cases and deaths in the 14 days prior to flexibilization. In all states the number of tests performed was insufficient. In epidemiological week 24, the state percentages of ICU/Covid-19 bed occupancy were close to or above 70%. The epidemiological situation of the nine Northeastern state capitals analyzed here did not meet criteria and parameters recommended by the World Health Organization for the relaxation of social distancing measures.

Mesmo no período em que a pandemia de Covid-19 encontrava-se em crescimento no Nordeste do Brasil, iniciou-se a adoção de medidas de flexibilização do distanciamento social. O objetivo do estudo é o de avaliar a pertinência das propostas de flexibilização, tomando-se em conta a situação da pandemia em cada local e o momento em que foram adotadas. Tendo como referência as diretrizes da OMS, foram construídos e analisados indicadores operacionais para cada diretriz, no contexto da região Nordeste. Para análise do comportamento da epidemia, conforme indicadores selecionados, foram usadas técnicas de Joinpoint Trend Analysis, mapas de calor, razão de taxas e comparação da tendência temporal entre capitais e interior dos estados. O pico do crescimento semanal ocorreu em maio-julho/2020 (semanas epidemiológicas 19 a 31). Na maioria das capitais não se observou tendência decrescente simultânea do número de casos e óbitos nos 14 dias prévios à flexibilização. Em todos os estados o quantitativo de testes realizados foi insuficiente. Na semana epidemiológica 24 os percentuais estaduais de ocupação de leitos de UTI/Covid-19 foram próximos ou superiores 70%. A situação epidemiológica das nove capitais dos estados do Nordeste, no momento em que a decisão de flexibilização foi tomada, mostra que nenhuma delas atendia aos critérios e parâmetros recomendados pela OMS.

Covid-19 no nordeste do Brasil: entre o lockdown e o relaxamento das medidas de distanciamento social / Covid-19 in the Northeast of Brazil: from lockdown to the relaxation of social distancing measures

Resumo Mesmo no período em que a pandemia de Covid-19 encontrava-se em crescimento no Nordeste do Brasil, iniciou-se a adoção de medidas de flexibilização do distanciamento social. O objetivo do estudo é o de avaliar a pertinência das propostas de flexibilização, tomando-se em conta a situação da pandemia em cada local e o momento em que foram adotadas. Tendo como referência as diretrizes da OMS, foram construídos e analisados indicadores operacionais para cada diretriz, no contexto da região Nordeste. Para análise do comportamento da epidemia, conforme indicadores selecionados, foram usadas técnicas de Joinpoint Trend Analysis, mapas de calor, razão de taxas e comparação da tendência temporal entre capitais e interior dos estados. O pico do crescimento semanal ocorreu em maio-julho/2020 (semanas epidemiológicas 19 a 31). Na maioria das capitais não se observou tendência decrescente simultânea do número de casos e óbitos nos 14 dias prévios à flexibilização. Em todos os estados o quantitativo de testes realizados foi insuficiente. Na semana epidemiológica 24 os percentuais estaduais de ocupação de leitos de UTI/Covid-19 foram próximos ou superiores 70%. A situação epidemiológica das nove capitais dos estados do Nordeste, no momento em que a decisão de flexibilização foi tomada, mostra que nenhuma delas atendia aos critérios e parâmetros recomendados pela OMS.

Abstract Even in the period when the Covid-19 pandemic was on the rise in the Northeast of Brazil, the relaxation of social distancing measures was introduced. The scope of the study is to assess, in the light of the epidemiological-sanitary situation in the region, the suitability of relaxation of social distancing measures. Based on the WHO guidelines for relaxation of social distancing, operational indicators were created and analyzed for each guideline in the context of the Northeast. To analyze the behavior of the epidemic, according to selected indicators, Joinpoint trend analysis techniques, heat maps, rate ratios and time trends between capitals and the state interior were compared. The weekly growth peak of the epidemic occurred in May-July 2020 (epidemiological weeks 19 to 31). In most capitals, there was no simultaneous downward trend in the number of cases and deaths in the 14 days prior to flexibilization. In all states the number of tests performed was insufficient. In epidemiological week 24, the state percentages of ICU/Covid-19 bed occupancy were close to or above 70%. The epidemiological situation of the nine Northeastern state capitals analyzed here did not meet criteria and parameters recommended by the World Health Organization for the relaxation of social distancing measures.

Estimating Brazilian states demands for intensive care unit and clinical hospital beds during the COVID-19 pandemic: development of a predictive model

ABSTRACT BACKGROUND: The fragility of healthcare systems worldwide had not been exposed by any pandemic until now. The lack of integrated methods for bed capacity planning compromises the effectiveness of public and private hospitals' services. OBJECTIVES: To estimate the impact of the COVID-19 pandemic on the provision of intensive care unit and clinical beds for Brazilian states, using an integrated model. DESIGN AND SETTING: Experimental study applying healthcare informatics to data on COVID-19 cases from the official electronic platform of the Brazilian Ministry of Health. METHODS: A predictive model based on the historical records of Brazilian states was developed to estimate the need for hospital beds during the COVID-19 pandemic. RESULTS: The proposed model projected in advance that there was a lack of 22,771 hospital beds for Brazilian states, of which 38.95% were ICU beds, and 61.05% were clinical beds. CONCLUSIONS: The proposed approach provides valuable information to help hospital managers anticipate actions for improving healthcare system capacity.

Estimating Brazilian states' demands for intensive care unit and clinical hospital beds during the COVID-19 pandemic: development of a predictive model.

BACKGROUND: The fragility of healthcare systems worldwide had not been exposed by any pandemic until now. The lack of integrated methods for bed capacity planning compromises the effectiveness of public and private hospitals' services. OBJECTIVES: To estimate the impact of the COVID-19 pandemic on the provision of intensive care unit and clinical beds for Brazilian states, using an integrated model. DESIGN AND SETTING: Experimental study applying healthcare informatics to data on COVID-19 cases from the official electronic platform of the Brazilian Ministry of Health. METHODS: A predictive model based on the historical records of Brazilian states was developed to estimate the need for hospital beds during the COVID-19 pandemic. RESULTS: The proposed model projected in advance that there was a lack of 22,771 hospital beds for Brazilian states, of which 38.95% were ICU beds, and 61.05% were clinical beds. CONCLUSIONS: The proposed approach provides valuable information to help hospital managers anticipate actions for improving healthcare system capacity.

Análisis de la demanda asistencial en la urgencia y unidad de hospitalización psiquiátrica en el contexto de pandemia por COVID-19 / Analysis of the demand for care in a psychiatric emergency room and an acute inpatient unit in the context of the COVID-19 pandemic

INTRODUCCIÓN: El objetivo consiste en analizar el impacto del COVID-19 en la demanda asistencial de las urgencias y en los ingresos psiquiátricos durante el primer mes de la pandemia. MÉTODOS: Realizamos un estudio transversal observacional retrospectivo en pacientes que acuden a urgencias psiquiátricas entre el 11 de marzo y el 11 de abril de 2019 y 2020 respectivamente. Se incluyeron variables sociodemográficas y clínicas en el estudio. Se realizaron las pruebas de Chi Cuadrado o Test exacto de Fisher para el contraste de hipótesis de variables categóricas y la prueba U Mann-Whitney para el contraste de variables cuantitativas. El nivel de significación estadística se estableció en p<0.05. Los análisis se realizaron con IBM SPSS Statistics. RESULTADOS: Se observa un descenso significativo de la media de pacientes atendidos al día en urgencias entre ambos periodos, siendo esta de 5,91 (±2,53) en 2019 y de 2,41 (±1,81) en 2020 (p<0.001). Se ha visto una disminución significativa de la ocupación media de camas en la UHB, ocupándose un 91,84% (±7,72) de camas en 2019 y un 58,85% (±13,81) en 2020 (p<0,001). En cuanto a la proporción de ingresos de los pacientes que acuden a urgencias, se ha visto un aumento significativo en el año 2020 respecto al año anterior. CONCLUSIONES: La demanda en la urgencia de pacientes psiquiátricos y la ocupación media de camas se ha reducido durante el primer mes tras la declaración de la pandemia. El miedo al contagio puede actuar como modulador de la demanda psiquiátrica.

INTRODUCTION: The aim is to analyze the impact of COVID-19 on the demand for emergency care and psychiatric admissions during the first month of the pandemic. METHODS: We conducted a retrospective, observational and cross-sectional study. We reviewed the clinical records of all patients attending the psychiatric emergency room (ER) between March 11th and April 11th, of both 2019 and 2020. Sociodemographic and clinical variables were included in the study. Chi-square test or Fisher's exact test were performed to compare categorical variables, while U Mann-Whitney U test was used for quantitative variables. The level of statistical significance was set at p<0.05. Analysis were conducted using IBM SPSS Statistics. RESULTS: The was a significant decrease in the number of patients attended in the ER. An average of 5.91 (±2.53) patients were treated per day in 2019 compared to 2.41 (±1.81) in 2020 (p<0.001). There was also a significant decrease in the occupancy rate at the inpatient psychiatric unit, with a 91.84% (±7.72) of beds occupied in 2019 and only 58.85% (±13.81) in 2020 (p<0.001). Regarding the percentage of patients admitted after assessment in the ER, there was a significant increase in 2020 compared to the previous year. CONCLUSIONS: The demand for care in the psychiatric emergency room and the average bed occupancy have decreased during the first month after the declaration of the pandemic. Fear of contagion may act as a modulator of psychiatric demand.

Impact of a prolonged COVID-19 lockdown on patterns of admission, mortality and performance indicators in a cardiovascular intensive care unit.

BACKGROUND: The effects of an early and prolonged lockdown during the coronavirus disease 2019 (COVID-19) pandemic on cardiovascular intensive care units (CICUs) are not well established. OBJECTIVES: This study analyses patterns of admission, mortality and performance indicators in a CICU before and during the Argentine lockdown in the COVID-19 pandemic. METHODS: This is a retrospective observational cross-sectional study of all consecutive patients aged 18 years or more admitted to the cardiac intensive care unit at a high-volume reference hospital in Buenos Aires, Argentina, comparing hospitalization rates, primary causes of admission, inpatient utilization indicators, pharmacy supplies' expenditures and in-hospital mortality between 5 March and 31 July 2020, with two corresponding control periods in 2019 and 2018. RESULTS: We included 722 female patients [mean age of 61.6 (SD 15.5) years; 237 (32.8%)]. Overall hospitalizations dropped 53.2% (95%CI: 45.3, 61.0%), from 295.5 patients/year over the periods 2018/2019 to 137 patients in 2020. Cardiovascular disease-related admissions dropped 59.9%, while admission for non-cardiac causes doubled its prevalence from 9.6% over the periods 2018/2019 to 22.6% in the study period (P < 0.001).In the period 2020, the bed occupancy rate fell from 82.2% to 77.4%, and the bed turnover rate dropped 50% from 7.88 to 3.91 monthly discharges/bed. The average length of stay doubled from 3.26 to 6.75 days, and the turnover interval increased from 3.8 to 8.39 days in 2020.Pharmacy supplies' expenditures per discharge increased 134% along with a rise in antibiotics usage from 6.5 to 11.4 vials/ampoules per discharge (P < 0.02).Overall mortality increased from 7% (n = 41) to 13.9% (n = 19) (P = 0.008) at the expense of non-cardiac-related admissions (3.6-19.4%, P = 0.01). CONCLUSIONS: This study found a significant reduction in overall and cardiovascular disease-related causes of admission to the cardiac intensive care unit, worse performance indicators and increased in-hospital mortality along the first 5 months of the early and long-lasting COVID-19 lockdown in Argentina. These results highlight the need to foster public awareness concerning the risks of avoiding hospital attendance. Moreover, health systems should follow strict screening protocols to prevent potential biases in the admission of patients with critical conditions unrelated to the COVID-19 pandemic.

In-hospital mortality from severe COVID-19 in a tertiary care center in Mexico City; causes of death, risk factors and the impact of hospital saturation.

BACKGROUND: As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has remained in Latin America, Mexico has become the third country with the highest death rate worldwide. Data regarding in-hospital mortality and its risk factors, as well as the impact of hospital overcrowding in Latin America has not been thoroughly explored. METHODS AND FINDINGS: In this prospective cohort study, we enrolled consecutive adult patients hospitalized with severe confirmed COVID-19 pneumonia at a SARS-CoV-2 referral center in Mexico City from February 26th, 2020, to June 5th, 2020. A total of 800 patients were admitted with confirmed diagnosis, mean age was 51.9 ± 13.9 years, 61% were males, 85% were either obese or overweight, 30% had hypertension and 26% type 2 diabetes. From those 800, 559 recovered (69.9%) and 241 died (30.1%). Among survivors, 101 (18%) received invasive mechanical ventilation (IMV) and 458 (82%) were managed outside the intensive care unit (ICU); mortality in the ICU was 49%. From the non-survivors, 45.6% (n = 110) did not receive full support due to lack of ICU bed availability. Within this subgroup the main cause of death was acute respiratory distress syndrome (ARDS) in 95% of the cases, whereas among the non-survivors who received full (n = 105) support the main cause of death was septic shock (45%) followed by ARDS (29%). The main risk factors associated with in-hospital death were male sex (RR 2.05, 95% CI 1.34-3.12), obesity (RR 1.62, 95% CI 1.14-2.32)-in particular morbid obesity (RR 3.38, 95%CI 1.63-7.00)-and oxygen saturation < 80% on admission (RR 4.8, 95%CI 3.26-7.31). CONCLUSIONS: In this study we found similar in-hospital and ICU mortality, as well as risk factors for mortality, compared to previous reports. However, 45% of the patients who did not survive justified admission to ICU but did not receive IMV / ICU care due to the unavailability of ICU beds. Furthermore, mortality rate over time was mainly due to the availability of ICU beds, indirectly suggesting that overcrowding was one of the main factors that contributed to hospital mortality.

Vigilância Epidemiológica estadual no enfrentamento da pandemia pela COVID-19 no Brasil: um relato de experiência / State Epidemiological Surveillance in combating the COVID-19 pandemic in Brazil: an experience report / Vigilancia Epidemiológica estatal en el combate a la pandemia por COVID-19 en Brasil: informe de experiência

Objetivo: sistematizar a experiência do estado do Espírito Santo no enfrentamento da COVID-19, baseando-se na vivência enquanto equipe gestora e operacional da vigilância epidemiológica estadual, no período de março de 2020 a março de 2021. Método: trata-se de um estudo descritivo, do tipo relato de experiência. Os dados foram obtidos por meio de canais oficiais, alimentados por um sistema de notificação em saúde adotado pelo estado do Espírito Santo e por planilhas enviadas diariamente pelos estabelecimentos de saúde. Resultados: observou-se que a aproximação entre a gestão estadual e municipal facilitou a implementação das orientações instituídas e a consolidação das medidas em todo território capixaba, vale salientar que outros órgãos governamentais auxiliaram nesse processo. Conclusão: os desdobramentos exigidos na gestão da pandemia evidenciam a importância da Vigilância em Saúde e o papel estratégico da Vigilância Epidemiológica no controle da pandemia, e na tomada de decisão e direcionamento de recursos humanos e financeiros.

Objective: to systematize the experience of the state of Espírito Santo in facing COVID-19, based on the experience as a manager and operational team of the state epidemiological surveillance, in the period from March 2020 to March 2021. Method: this is a descriptive study, of the experience report type. The data were obtained through official channels, fed by a health notification system adopted by the State of Espírito Santo and by spreadsheets sent daily by health establishments. Results: it was observed that the approximation between state and municipal management facilitated the implementation of the instituted guidelines and the consolidation of the measures in the entire territory of the state of Espírito Santo. Conclusion: it is concluded that the developments required in the management of the pandemic highlight the importance of Health Surveillance and the strategic role of the Epidemiological Surveillance in the control of the pandemic, and in the decision making and direction of human and financial resources

Objetivo: sistematizar la experiencia de estado de Espírito Santo en el enfrentamiento del COVID-19, a partir de la experiencia como equipo gestora y operacional de la vigilancia epidemiológica estatal, de marzo de 2020 a marzo de 2021. Método: se trata de un estudio descriptivo, tipo relato de experiencia. Los datos se obtuvieron a través de canales oficiales, alimentados por un sistema de notificación sanitaria adoptado por el Estado de Espírito Santo y por planillas enviadas diariamente por los establecimientos de salud. Resultados: se observó que la aproximación entre la gestión estatal y municipal facilitó la implementación de orientaciones instituidas y la consolidación de medidas en todo el territorio del estado de Espírito Santo, cabe mencionar que otras agencias gubernamentales asistieron en este proceso. Conclusión: se concluye que los desdoblamientos exigidos en la gestión de la pandemia evidenciaron la importancia de la Vigilancia en Salud y el rol estratégico de la Vigilancia Epidemiológica en el control de la pandemia y en la toma de decisiones y direccionamiento de recursos humanos y financieros

Restructuring of a pediatric hospital in the face of the COVID-19 pandemic.

Overview of the pandemic In December 2019, a new virus named SARS-CoV-2 was reported in Wuhan province, China. The first case of COVID-19 in Mexico was confirmed on February 28, 2020, and the World Health Organization declared the pandemic on March 11.

Evaluation of bed vacancy and occupancy times as governance parameters in patients admitted to a public intensive care unit. / Avaliação do tempo de desocupação e ocupação de leitos como parâmetros de governança em pacientes internados em unidade de terapia intensiva na rede pública.

OBJECTIVE: To evaluate the vacancy and occupancy times of intensive care unit beds; to analyze differences in these times between the day and night shifts and weekdays, weekends, and holidays; and to identify predictors of vacancy and occupancy times. METHODS: This was a cross-sectional, observational, descriptive, analytical, inferential study. A total of 700 vacancy-to-occupancy records from 54 beds of an adult intensive care unit of a public hospital in Sergipe, Brazil, dated between January and December 2018 were analyzed. The nonparametric Mann-Whitney test was used for comparisons between groups. Several predictive models of length of stay were constructed. The incidence rate ratio was used to estimate the effect size. RESULTS: During the study period, there were 13,477 requests for the 54 intensive care unit beds, and only 5% (700 patients) were granted. The vacancy-to-occupancy times were shorter when beds were occupied at night (incidence rate ratio of 0.658; 95%CI 0.550 - 0.787; p < 0.0001) or on weekends (incidence rate ratio of 0.566; 95%CI 0.382 - 0.838; p = 0.004). Female sex (incidence rate ratio of 0.749; 95%CI 0.657 - 0.856; p < 0.0001) was a predictor of shorter vacancy-to-occupancy time. This time tended to increase with patient age (incidence rate ratio of 1.006; 95% CI 1.003 - 1.009; p < 0.0001). CONCLUSION: Disparities in the waiting time for intensive care unit beds were identified, as the time was greater in the daytime and on weekdays, and women and younger patients experienced shorter vacancy-to-occupancy times.

OBJETIVO: Avaliar o tempo de desocupação e ocupação dos leitos na unidade de terapia intensiva; analisar os intervalos entre os tempos durante o período do dia e da noite, finais de semana e feriados e identificar preditores para os tempos de desocupação e ocupação. MÉTODOS: Estudo transversal, de natureza observacional, descritivo, analítico e inferencial. Foram analisados 700 registros de desocupação-ocupação em 54 leitos na unidade de terapia intensiva adulto de um hospital da rede pública de Sergipe, entre janeiro e dezembro de 2018. O teste não paramétrico de Mann-Whitney foi utilizado para comparações entre grupos. Diversos modelos preditivos de tempo de permanência foram elaborados. A razão de taxa de incidência foi utilizada como estimativa de tamanho do efeito. RESULTADOS: Durante o período do estudo, houve 13.477 solicitações de vaga na unidade de terapia intensiva para os 54 leitos, e apenas 5% (700 pacientes) conseguiram o acesso ao leito. Os tempos de desocupação-ocupação tiveram valores menores quando a ocupação do leito era realizada no período noturno (razão de taxa de incidência de 0,658; IC95% 0,550 - 0,787; p < 0,0001) e oferta nos finais de semana (razão de taxa de incidência de 0,566; IC95% 0,382 - 0,838; p = 0,004). O sexo feminino (razão de taxa de incidência de 0,749; IC95% 0,657 - 0,856; p < 0,0001) foi um preditor de menor tempo de desocupação-ocupação. Esse tempo tende a aumentar com a idade do paciente (razão de taxa de incidência de 1,006; IC95% 1,003 - 1,009; p < 0,0001). CONCLUSÃO: Identificaram-se disparidades no tempo de espera para a ocupação do leito, sendo maior no período diurno e em dias úteis. Mulheres e pacientes mais jovens são beneficiados por um processamento mais rápido no tempo de desocupação-ocupação.

Situação epidemiológica atual - 12 de outubro de 2020 / Current epidemiological situation - october 12, 2020

Boletim infográfico com o panorama da situação da pandemia COVID-19 em 12/10/2020, contempla informações sobre a distribuição de casos confirmados e número de óbitos no mundo, Brasil e na Bahia. Com relação ao Estado da Bahia apresenta detalhamento sobre: dados laboratoriais de COVID-19; porcentagem dos casos confirmados por faixa etária; distribuição dos casos confirmados por município; número de casos confirmados por profissionais de saúde por COVID-19; número de casos e percentual de confirmados por COVID-19 segundo raça/cor; número de casos ativos de COVID-19 por Núcleo Regional de Saúde; municípios com mais casos ativos; distribuição dos óbitos confirmados de COVID-19, segundo faixa etária; distribuição dos óbitos por sexo e faixa etária; distribuição da taxa de letalidade por faixa etária; distribuição dos óbitos por município de ocorrência; distribuição dos óbitos por município de residência; situação dos leitos COVID-19 ­ Bahia; taxa de ocupação por Núcleo Regional de Saúde

Situação epidemiológica atual - 14 de outubro de 2020 / Current epidemiological situation - october 14, 2020

O boletim descreve de forma detalhada a situação da COVID-19 no Estado da Bahia desde o inicio da pandemia. Contempla informações relacionadas ao registro de casos notificados da COVID-19, taxa de crescimento, distribuição de casos confirmados nos Núcleos Regionais Saúde, casos confirmados segundo raça/cor, ocupação de leitos de UTI, perfil dos casos de Síndrome Multissistêmica Pediátrica, número de curados, número de óbitos. Até 14/10/2020 no Estado da Bahia, o coeficiente de incidência foi de 2.217,34/100.000 habitantes

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.

COVID-19 - The Availability of ICU Beds in Brazil during the Onset of Pandemic.

Background: Brazil faces some challenges in the battle against the COVID-19 pandemic, including: the risks for cross-infection (community infection) increase in densely populated areas; low access to health services in areas where the number of beds in intensive care units (ICUs) is scarce and poorly distributed, mainly in states with low population density. Objective: To describe and intercorrelate epidemiology and geographic data from Brazil about the number of intensive care unit (ICU) beds at the onset of COVID-19 pandemic. Methods: The epidemiology and geographic data were correlated with the distribution of ICU beds (public and private health systems) and the number of beneficiaries of private health insurance using Pearson's Correlation Coefficient. The same data were correlated using partial correlation controlled by gross domestic product (GDP) and number of beneficiaries of private health insurance. Findings: Brazil has a large geographical area and diverse demographic and economic aspects. This diversity is also present in the states and the Federal District regarding the number of COVID-19 cases, deaths and case fatality rate. The effective management of severe COVID-19 patients requires ICU services, and the scenario was also dissimilar as for ICU beds and ICU beds/10,000 inhabitants for the public (SUS) and private health systems mainly at the onset of COVID-19 pandemic. The distribution of ICUs was uneven between public and private services, and most patients rely on SUS, which had the lowest number of ICU beds. In only a few states, the number of ICU beds at SUS was above 1 to 3 by 10,000 inhabitants, which is the number recommended by the World Health Organization (WHO). Conclusions: Brazil needed to improve the number of ICU beds units to deal with COVID-19 pandemic, mainly for the SUS showing a late involvement of government and health authorities to deal with the COVID-19 pandemic.

Avaliação do tempo de desocupação e ocupação de leitos como parâmetros de governança em pacientes internados em unidade de terapia intensiva na rede pública / Evaluation of bed vacancy and occupancy times as governance parameters in patients admitted to a public intensive care unit

RESUMO Objetivo: Avaliar o tempo de desocupação e ocupação dos leitos na unidade de terapia intensiva; analisar os intervalos entre os tempos durante o período do dia e da noite, finais de semana e feriados e identificar preditores para os tempos de desocupação e ocupação. Métodos: Estudo transversal, de natureza observacional, descritivo, analítico e inferencial. Foram analisados 700 registros de desocupação-ocupação em 54 leitos na unidade de terapia intensiva adulto de um hospital da rede pública de Sergipe, entre janeiro e dezembro de 2018. O teste não paramétrico de Mann-Whitney foi utilizado para comparações entre grupos. Diversos modelos preditivos de tempo de permanência foram elaborados. A razão de taxa de incidência foi utilizada como estimativa de tamanho do efeito. Resultados: Durante o período do estudo, houve 13.477 solicitações de vaga na unidade de terapia intensiva para os 54 leitos, e apenas 5% (700 pacientes) conseguiram o acesso ao leito. Os tempos de desocupação-ocupação tiveram valores menores quando a ocupação do leito era realizada no período noturno (razão de taxa de incidência de 0,658; IC95% 0,550 - 0,787; p < 0,0001) e oferta nos finais de semana (razão de taxa de incidência de 0,566; IC95% 0,382 - 0,838; p = 0,004). O sexo feminino (razão de taxa de incidência de 0,749; IC95% 0,657 - 0,856; p < 0,0001) foi um preditor de menor tempo de desocupação-ocupação. Esse tempo tende a aumentar com a idade do paciente (razão de taxa de incidência de 1,006; IC95% 1,003 - 1,009; p < 0,0001). Conclusão: Identificaram-se disparidades no tempo de espera para a ocupação do leito, sendo maior no período diurno e em dias úteis. Mulheres e pacientes mais jovens são beneficiados por um processamento mais rápido no tempo de desocupação-ocupação.

Abstract Objective: To evaluate the vacancy and occupancy times of intensive care unit beds; to analyze differences in these times between the day and night shifts and weekdays, weekends, and holidays; and to identify predictors of vacancy and occupancy times. Methods: This was a cross-sectional, observational, descriptive, analytical, inferential study. A total of 700 vacancy-to-occupancy records from 54 beds of an adult intensive care unit of a public hospital in Sergipe, Brazil, dated between January and December 2018 were analyzed. The nonparametric Mann-Whitney test was used for comparisons between groups. Several predictive models of length of stay were constructed. The incidence rate ratio was used to estimate the effect size. Results: During the study period, there were 13,477 requests for the 54 intensive care unit beds, and only 5% (700 patients) were granted. The vacancy-to-occupancy times were shorter when beds were occupied at night (incidence rate ratio of 0.658; 95%CI 0.550 - 0.787; p < 0.0001) or on weekends (incidence rate ratio of 0.566; 95%CI 0.382 - 0.838; p = 0.004). Female sex (incidence rate ratio of 0.749; 95%CI 0.657 - 0.856; p < 0.0001) was a predictor of shorter vacancy-to-occupancy time. This time tended to increase with patient age (incidence rate ratio of 1.006; 95% CI 1.003 - 1.009; p < 0.0001). Conclusion: Disparities in the waiting time for intensive care unit beds were identified, as the time was greater in the daytime and on weekdays, and women and younger patients experienced shorter vacancy-to-occupancy times.

Profile of general hospitals in the Unified Health System.

OBJECTIVE To characterize the organization of Brazilian general hospitals that provide services to the Unified Health System using indicators that describe the main dimensions of hospital care. METHODS A 2015 cross-sectional observational study, comprising the range of general hospitals that serve the Unified Health System. We constructed the hospital indicators from two national administrative databases: the National Registry of Health Facilities and the Hospital Information System of the Unified Health System. The indicators include the main dimensions associated with hospital care: public-private mix, production, production factors, performance, quality, case-mix and geographic coverage. Latent class analysis of indicators with bootstrapping was used to identify hospital profiles. RESULTS We identified three profiles, with hospital size being the variable with the highest degree of belonging. Small hospitals show low occupancy rates (21.36%) and high participation of hospitalizations that could have been solved with outpatient care, besides attending only medium complexity cases. They receive few non-residents, indicating that they are mainly dedicated to the local population. Medium-sized hospitals are more similar to small-sized ones: about 100% of the visits are of medium complexity, low occupancy rate (45.81%), high rate of hospitalizations for primary care sensitive conditions (17.10%) and relative importance in the healthcare provision of non-residents (26%). Large hospitals provide high complexity care, have an average occupancy rate of 64.73% and show greater geographical coverage. CONCLUSIONS The indicators point to three hospital profiles, characterized mainly by the production scale. Small hospitals show low performance, suggesting the need to reorganize hospital care provision, especially at the municipal level. The set of proposed indicators includes the main dimensions of hospital care, providing a tool that can help to plan and continuously monitor the hospital network of the Unified Health System.

Health system collapse 45 days after the detection of COVID-19 in Ceará, Northeast Brazil: a preliminary analysis.

INTRODUCTION: COVID-19 emerged in late 2019 and quickly became a serious public health problem worldwide. This study aim to describe the epidemiological course of cases and deaths due to COVID-19 and their impact on hospital bed occupancy rates in the first 45 days of the epidemic in the state of Ceará, Northeastern Brazil. METHODS: The study used an ecological design with data gathered from multiple government and health care sources. Data were analyzed using Epi Info software. RESULTS: The first cases were confirmed on March 15, 2020. After 45 days, 37,268 cases reported in 85.9% of Ceará's municipalities, with 1,019 deaths. Laboratory test positivity reached 84.8% at the end of April, a period in which more than 700 daily tests were processed. The average age of cases was 67 (<1 - 101) years, most occurred in a hospital environment (91.9%), and 58% required hospitalization in an ICU bed. The average time between the onset of symptoms and death was 18 (1 - 56) days. Patients who died in the hospital had spent an average of six (0 - 40) days hospitalized. Across Ceará, the bed occupancy rate reached 71.3% in the wards and 80.5% in the ICU. CONCLUSIONS: The first 45 days of the COVID-19 epidemic in Ceará revealed a large number of cases and deaths, spreading initially among the population with a high socioeconomic status. Despite the efforts by the health services and social isolation measures the health system still collapsed.