Update on the Epimed Monitor Adult ICU Database: 15 years of its use in national registries, quality improvement initiatives and clinical research.

In recent decades, several databases of critically ill patients have become available in both low-, middle-, and high-income countries from all continents. These databases are also rich sources of data for the surveillance of emerging diseases, intensive care unit performance evaluation and benchmarking, quality improvement projects and clinical research. The Epimed Monitor database is turning 15 years old in 2024 and has become one of the largest of these databases. In recent years, there has been rapid geographical expansion, an increase in the number of participating intensive care units and hospitals, and the addition of several new variables and scores, allowing a more complete characterization of patients to facilitate multicenter clinical studies. As of December 2023, the database was being used regularly for 23,852 beds in 1,723 intensive care units and 763 hospitals from ten countries, totaling more than 5.6 million admissions. In addition, critical care societies have adopted the system and its database to establish national registries and international collaborations. In the present review, we provide an updated description of the database; report experiences of its use in critical care for quality improvement initiatives, national registries and clinical research; and explore other potential future perspectives and developments.

Effectiveness of a multicomponent intervention to face the COVID-19 pandemic in Rio de Janeiro's favelas: difference-in-differences analysis.

INTRODUCTION: Few community-based interventions addressing the transmission control and clinical management of COVID-19 cases have been reported, especially in poor urban communities from low-income and middle-income countries. Here, we analyse the impact of a multicomponent intervention that combines community engagement, mobile surveillance, massive testing and telehealth on COVID-19 cases detection and mortality rates in a large vulnerable community (Complexo da Maré) in Rio de Janeiro, Brazil. METHODS: We performed a difference-in-differences (DID) analysis to estimate the impact of the multicomponent intervention in Maré, before (March-August 2020) and after the intervention (September 2020 to April 2021), compared with equivalent local vulnerable communities. We applied a negative binomial regression model to estimate the intervention effect in weekly cases and mortality rates in Maré. RESULTS: Before the intervention, Maré presented lower rates of reported COVID-19 cases compared with the control group (1373 vs 1579 cases/100 000 population), comparable mortality rates (309 vs 287 deaths/100 000 population) and higher case fatality rates (13.7% vs 12.2%). After the intervention, Maré displayed a 154% (95% CI 138.6% to 170.4%) relative increase in reported case rates. Relative changes in reported death rates were -60% (95% CI -69.0% to -47.9%) in Maré and -28% (95% CI -42.0% to -9.8%) in the control group. The case fatality rate was reduced by 77% (95% CI -93.1% to -21.1%) in Maré and 52% (95% CI -81.8% to -29.4%) in the control group. The DID showed a reduction of 46% (95% CI 17% to 65%) of weekly reported deaths and an increased 23% (95% CI 5% to 44%) of reported cases in Maré after intervention onset. CONCLUSION: An integrated intervention combining communication, surveillance and telehealth, with a strong community engagement component, could reduce COVID-19 mortality and increase case detection in a large vulnerable community in Rio de Janeiro. These findings show that investment in community-based interventions may reduce mortality and improve pandemic control in poor communities from low-income and middle-income countries.

Analysis of COVID-19 under-reporting in Brazil. / Análise da subnotificação de COVID-19 no Brasil.

OBJECTIVE: To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states. METHODS: We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil's expected case-fatality ratio was also adjusted by the population's age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths). RESULTS: The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%). CONCLUSION: The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

Progression of confirmed COVID-19 cases after the implementation of control measures. / Progressão dos casos confirmados de COVID-19 após implantação de medidas de controle.

OBJECTIVE: To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures. METHODS: The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular. RESULTS: We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases. The effectiveness of the measures taken by these countries could be seen after 1 to 2 weeks of their application. In Italy and Spain, control measures at the national level were taken at a late stage of the epidemic, which could have contributed to the high propagation of COVID-19. In Brazil, Rio de Janeiro and São Paulo adopted measures that could be effective in slowing the propagation of the virus. However, we only expect to see their effects on the growth of the curve in the coming days. CONCLUSION: Our results may help decisionmakers in countries in relatively early stages of the epidemic, especially Brazil, understand the importance of control measures in decelerating the growth curve of confirmed cases.

Análise da subnotificação de COVID-19 no Brasil / Analysis of COVID-19 under-reporting in Brazil

RESUMO Objetivo: Estimar as taxas de notificação de casos de doença pelo coronavírus 2019 (COVID-19) para o Brasil em geral e em todos os estados. Métodos: Estimamos o número real de casos de COVID-19 utilizando o número de óbitos notificados no Brasil e em cada estado e a proporção entre casos e letalidade, conforme a Organização Mundial da Saúde. A proporção entre casos e letalidade prevista para o Brasil foi também ajustada segundo a pirâmide de idade populacional. Assim, a taxa de notificações pode ser definida como o número de casos confirmados (informados pelo Ministério da Saúde) dividido pelo número de casos previstos (estimado a partir do número de óbitos). Resultados: A taxa de notificação de COVID-19 no Brasil foi estimada em 9,2% (IC95%: 8,8% - 9,5%), sendo que, em todos os estados, as taxas encontradas foram inferiores a 30%. São Paulo e Rio de Janeiro, os estados mais populosos do país, mostraram baixas taxas de notificação (8,9% e 7,2%, respectivamente). A taxa de notificação mais alta ocorreu em Roraima (31,7%) e a mais baixa na Paraíba (3,4%). Conclusão: Os resultados indicam que a notificação de casos confirmados no Brasil é muito abaixo da encontrada em outros países que avaliamos. Assim, os responsáveis pela tomada de decisões, inclusive os governos, não têm conhecimento da real dimensão da pandemia, o que pode prejudicar a determinação das medidas de controle.

ABSTRACT Objective: To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states. Methods: We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil's expected case-fatality ratio was also adjusted by the population's age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths). Results: The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%). Conclusion: The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

Progressão dos casos confirmados de COVID-19 após implantação de medidas de controle / Progression of confirmed COVID-19 cases after the implementation of control measures

RESUMO Objetivo: Analisar as medidas adotadas por países que demonstraram controle sobre a transmissão da doença pelo novo coronavírus 2019 (COVID-19) e também como cada curva de casos acumulados se comportou após a implantação dessas medidas. Métodos: A metodologia adotada para este estudo compreendeu três fases: sistematização das medidas de controle adotadas por diferentes países, identificação dos pontos de inflexão na curva do crescimento do número de casos nesses países e análise específica dos dados brasileiros. Resultados: Observamos que China (excluindo-se Hubei), Hubei e Coreia do Sul foram eficazes na desaceleração das taxas de crescimento dos casos de COVID-19. A eficácia das medidas tomadas por esses países pode ser observada após 1 ou 2 semanas de sua aplicação. Na Itália e Espanha, foram tomadas medidas de controle em nível nacional em uma fase tardia da epidemia, o que pode ter contribuído para a elevada propagação da COVID-19. No Brasil, Rio de Janeiro e São Paulo adotaram medidas que podem ter sido eficazes na redução da rapidez da propagação do vírus, entretanto, só temos expectativa de ver seus efeitos no crescimento da curva nos próximos dias. Conclusão: Nossos resultados podem ajudar os responsáveis pela tomada de decisões em países em estágios relativamente precoces da epidemia, especialmente no Brasil, a compreenderem a importância das medidas de controle para desaceleração da curva de crescimento de casos confirmados.

ABSTRACT Objective: To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures. Methods: The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular. Results: We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases. The effectiveness of the measures taken by these countries could be seen after 1 to 2 weeks of their application. In Italy and Spain, control measures at the national level were taken at a late stage of the epidemic, which could have contributed to the high propagation of COVID-19. In Brazil, Rio de Janeiro and São Paulo adopted measures that could be effective in slowing the propagation of the virus. However, we only expect to see their effects on the growth of the curve in the coming days. Conclusion: Our results may help decisionmakers in countries in relatively early stages of the epidemic, especially Brazil, understand the importance of control measures in decelerating the growth curve of confirmed cases.