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Peres IT, Ferrari GF, Quintairos A, Bastos LDSL, Salluh JIF. Validation of a new data-driven SLOSR ICU efficiency measure compared to the traditional SRU. Intensive Care Med 2023; 49:1546-1548. [PMID: 37922007 DOI: 10.1007/s00134-023-07255-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2023] [Indexed: 11/05/2023]
Affiliation(s)
- Igor Tona Peres
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Rio de Janeiro, Rio de Janeiro, 22451-900, Brazil.
| | - Guilherme Fonseca Ferrari
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Rio de Janeiro, Rio de Janeiro, 22451-900, Brazil
| | - Amanda Quintairos
- D'Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro, 30, Rio de Janeiro, Rio de Janeiro, 22281-100, Brazil
- Department of Critical and Intensive Care Medicine, Academic Hospital Fundación Santa Fe de Bogota, Carrera 7 117-15, Bogotá, Colombia
| | - Leonardo Dos Santos Lourenço Bastos
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Rio de Janeiro, Rio de Janeiro, 22451-900, Brazil
| | - Jorge Ibrain Figueira Salluh
- D'Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro, 30, Rio de Janeiro, Rio de Janeiro, 22281-100, Brazil
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Cerbino-Neto J, Peres IT, Varela MC, Brandão LGP, de Matos JA, Pinto LF, da Costa MD, Garcia MHDO, Soranz D, Maia MDLDS, Krieger MA, da Cunha RV, Camacho LAB, Ranzani O, Hamacher S, Bozza FA, Penna GO. Seroepidemiology of SARS-CoV-2 on a partially vaccinated island in Brazil: Determinants of infection and vaccine response. Front Public Health 2022; 10:1017337. [PMID: 36457326 PMCID: PMC9706255 DOI: 10.3389/fpubh.2022.1017337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background A vaccination campaign targeted adults in response to the pandemic in the City of Rio de Janeiro. Objective We aimed to evaluate the seroprevalence of SARS-CoV-2 antibodies and identify factors associated with seropositivity on vaccinated and unvaccinated residents. Methods We performed a seroepidemiologic survey in all residents of Paquetá Island, a neighborhood of Rio de Janeiro city, during the COVID-19 vaccine roll-out. Serological tests were performed from June 16 to June 19, 2021, and adjusted seropositivity rates were estimated by age and epidemiological variables. Logistic regression models were used to estimate adjusted ORs for risk factors to SARS-CoV-2 seropositivity in non-vaccinated individuals, and potential determinants of the magnitude of antibody responses in the seropositive population. Results We included in the study 3,016 residents of Paquetá (83.5% of the island population). The crude seroprevalence of COVID-19 antibodies in our sample was 53.6% (95% CI = 51.0, 56.3). The risk factors for SARS-CoV-2 seropositivity in non-vaccinated individuals were history of confirmed previous COVID-19 infection (OR = 4.74; 95% CI = 3.3, 7.0), being a household contact of a case (OR = 1.93; 95% CI = 1.5, 2.6) and in-person learning (OR = 2.01; 95% CI = 1.4, 3.0). Potential determinants of the magnitude of antibody responses among the seropositive were hybrid immunity, the type of vaccine received, and time since the last vaccine dose. Being vaccinated with Pfizer or AstraZeneca (Beta = 2.2; 95% CI = 1.8, 2.6) determined higher antibody titers than those observed with CoronaVac (Beta = 1.2; 95% CI = 0.9, 1.5). Conclusions Our study highlights the impact of vaccination on COVID-19 collective immunity even in a highly affected population, showing the difference in antibody titers achieved with different vaccines and how they wane with time, reinforcing how these factors should be considered when estimating effectiveness of a vaccination program at any given time. We also found that hybrid immunity was superior to both infection-induced and vaccine-induced immunity alone, and online learning protected students from COVID-19 exposure.
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Affiliation(s)
- José Cerbino-Neto
- Municipal Health Department of Rio de Janeiro, Rio de Janeiro City Government, Rio de Janeiro, Brazil,National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil,D'Or Institute for Research and Education, Rio de Janeiro, Brazil,*Correspondence: José Cerbino-Neto
| | - Igor Tona Peres
- Department of Industrial Engineering and Tecgraf Institute, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Margareth Catoia Varela
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Luciana Gomes Pedro Brandão
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Juliana Arruda de Matos
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil,National Institute of Traumatology and Orthopedics Jamil Haddad, Rio de Janeiro, Brazil
| | - Luiz Felipe Pinto
- School of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcellus Dias da Costa
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | | | - Daniel Soranz
- Municipal Health Department of Rio de Janeiro, Rio de Janeiro City Government, Rio de Janeiro, Brazil,National School of Public Health, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | | | - Marco Aurélio Krieger
- Vice Presidency of Production and Innovation in Health (VPPIS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Rivaldo Venâncio da Cunha
- Coordination of Health Surveillance and Reference Laboratories, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | | | - Otavio Ranzani
- Barcelona Institute for Global Health (ISGlobal), CIBER Epidemiología y Salud Pública, Universitat Pompeu Fabra, Barcelona, Spain,Pulmonary Division, Hospital das Clínicas (HCFMUSP), Heart Institute (InCor), Universidade de Sáo Paulo, Sáo Paulo, Brazil
| | - Silvio Hamacher
- Department of Industrial Engineering and Tecgraf Institute, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernando Augusto Bozza
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil,D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Gerson Oliveira Penna
- Tropical Medicine Centre - University of Brasília and Fiocruz School of Government, Brasília, Brazil
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Peres IT, Hamacher S, Oliveira FLC, Bozza FA, Salluh JIF. Data-driven methodology to predict the ICU length of stay: A multicentre study of 99,492 admissions in 109 Brazilian units. Anaesth Crit Care Pain Med 2022; 41:101142. [PMID: 35988701 DOI: 10.1016/j.accpm.2022.101142] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/25/2022] [Accepted: 06/25/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE The length of stay (LoS) is one of the most used metrics for resource use in Intensive Care Units (ICU). We propose a structured data-driven methodology to predict the ICU length of stay and the risk of prolonged stay, and its application in a large multicenter Brazilian ICU database. METHODS Demographic data, comorbidities, complications, laboratory data, and primary and secondary diagnosis were prospectively collected and retrospectively analysed by a data-driven methodology, which includes eight different machine learning models and a stacking model. The study setting included 109 mixed-type ICUs from 38 Brazilian hospitals and the external validation was performed by 93 medical-surgical ICUs of 55 hospitals in Brazil. RESULTS A cohort of 99,492 adult ICU admissions were included from the 01st of January to the 31st of December 2019. The stacking model combining Random Forests and Linear Regression presented the best results to predict ICU length of stay (RMSE = 3.82; MAE = 2.52; R² = 0.36). The prediction model for the risk of long stay were accurate to early identify prolonged stay patients (Brier Score = 0.04, AUC = 0.87, PPV = 0.83, NPV = 0.95). CONCLUSION The data-driven methodology to predict ICU length of stay and the risk of long-stay proved accurate in a large multicentre cohort of general ICU patients. The proposed models are helpful to predict the individual length of stay and to early identify patients with high risk of prolonged stay.
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Affiliation(s)
- Igor Tona Peres
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Silvio Hamacher
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | | | - Fernando Augusto Bozza
- Evandro Chagas National Institute of Infectious Disease, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil; IDOR, D'Or Institute for Research and Education, Rio de Janeiro, RJ, Brazil
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Kurtz P, Peres IT, Soares M, Salluh JIF, Bozza FA. Hospital Length of Stay and 30-Day Mortality Prediction in Stroke: A Machine Learning Analysis of 17,000 ICU Admissions in Brazil. Neurocrit Care 2022; 37:313-321. [PMID: 35381967 DOI: 10.1007/s12028-022-01486-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/07/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Hospital length of stay and mortality are associated with resource use and clinical severity, respectively, in patients admitted to the intensive care unit (ICU) with acute stroke. We proposed a structured data-driven methodology to develop length of stay and 30-day mortality prediction models in a large multicenter Brazilian ICU cohort. METHODS We analyzed data from 130 ICUs from 43 Brazilian hospitals. All consecutive adult patients admitted with stroke (ischemic or nontraumatic hemorrhagic) to the ICU from January 2011 to December 2020 were included. Demographic data, comorbidities, acute disease characteristics, organ support, and laboratory data were retrospectively analyzed by a data-driven methodology, which included seven different types of machine learning models applied to training and test sets of data. The best performing models, based on discrimination and calibration measures, are reported as the main results. Outcomes were hospital length of stay and 30-day in-hospital mortality. RESULTS Of 17,115 ICU admissions for stroke, 16,592 adult patients (13,258 ischemic and 3334 hemorrhagic) were analyzed; 4298 (26%) patients had a prolonged hospital length of stay (> 14 days), and 30-day mortality was 8% (n = 1392). Prolonged hospital length of stay was best predicted by the random forests model (Brier score = 0.17, area under the curve = 0.73, positive predictive value = 0.61, negative predictive value = 0.78). Mortality prediction also yielded the best discrimination and calibration through random forests (Brier score = 0.05, area under the curve = 0.90, positive predictive value = 0.66, negative predictive value = 0.94). Among the 20 strongest contributor variables in both models were (1) premorbid conditions (e.g., functional impairment), (2) multiple organ dysfunction parameters (e.g., hypotension, mechanical ventilation), and (3) acute neurological aspects of stroke (e.g., Glasgow coma scale score on admission, stroke type). CONCLUSIONS Hospital length of stay and 30-day mortality of patients admitted to the ICU with stroke were accurately predicted through machine learning methods, even in the absence of stroke-specific data, such as the National Institutes of Health Stroke Scale score or neuroimaging findings. The proposed methods using general intensive care databases may be used for resource use allocation planning and performance assessment of ICUs treating stroke. More detailed acute neurological and management data, as well as long-term functional outcomes, may improve the accuracy and applicability of future machine-learning-based prediction algorithms.
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Affiliation(s)
- Pedro Kurtz
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil.,Hospital Copa Star, Rio de Janeiro, Brazil.,Paulo Niemeyer State Brain Institute, Rio de Janeiro, Brazil
| | - Igor Tona Peres
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcio Soares
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Jorge I F Salluh
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil.,Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernando A Bozza
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil. .,National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.
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Peres IT, Hamacher S, Oliveira FLC, Bozza FA, Salluh JIF. Prediction of intensive care units length of stay: a concise review. Rev Bras Ter Intensiva 2021; 33:183-187. [PMID: 34231798 PMCID: PMC8275087 DOI: 10.5935/0103-507x.20210025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/10/2021] [Indexed: 11/20/2022] Open
Affiliation(s)
- Igor Tona Peres
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica do Rio de Janeiro - Rio de Janeiro (RJ), Brasil
| | - Silvio Hamacher
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica do Rio de Janeiro - Rio de Janeiro (RJ), Brasil
| | - Fernando Luiz Cyrino Oliveira
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica do Rio de Janeiro - Rio de Janeiro (RJ), Brasil
| | - Fernando Augusto Bozza
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz - Rio de Janeiro (RJ), Brasil
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Peres IT, Bastos LSL, Gelli JGM, Marchesi JF, Dantas LF, Antunes BBP, Maçaira PM, Baião FA, Hamacher S, Bozza FA. Sociodemographic factors associated with COVID-19 in-hospital mortality in Brazil. Public Health 2021; 192:15-20. [PMID: 33607516 PMCID: PMC7836512 DOI: 10.1016/j.puhe.2021.01.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/15/2020] [Accepted: 01/08/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES The coronavirus disease 2019 (COVID-19) pandemic has highlighted inequalities in access to healthcare systems, increasing racial disparities and worsening health outcomes in these populations. This study analysed the association between sociodemographic characteristics and COVID-19 in-hospital mortality in Brazil. STUDY DESIGN A retrospective analysis was conducted on quantitative reverse transcription polymerase chain reaction-confirmed hospitalised adult patients with COVID-19 with a defined outcome (i.e. hospital discharge or death) in Brazil. Data were retrieved from the national surveillance system database (SIVEP-Gripe) between February 16 and August 8, 2020. METHODS Clinical characteristics, sociodemographic variables, use of hospital resources and outcomes of hospitalised adult patients with COVID-19, stratified by self-reported race, were investigated. The primary outcome was in-hospital mortality. The association between self-reported race and in-hospital mortality, after adjusting for clinical characteristics and comorbidities, was evaluated using a logistic regression model. RESULTS During the study period, Brazil had 3,018,397 confirmed COVID-19 cases and 100,648 deaths. The study population included 228,196 COVID-19-positive adult in-hospital patients with a defined outcome; the median age was 61 years, 57% were men, 35% (79,914) self-reported as Black/Brown and 35.4% (80,853) self-reported as White. The total in-hospital mortality was 37% (85,171/228,196). Black/Brown patients showed higher in-hospital mortality than White patients (42% vs 37%, respectively), were admitted less frequently to the intensive care unit (ICU) (32% vs 36%, respectively) and used more invasive mechanical ventilation (21% vs 19%, respectively), especially outside the ICU (17% vs 11%, respectively). Black/Brown race was independently associated with high in-hospital mortality after adjusting for sex, age, level of education, region of residence and comorbidities (odds ratio = 1.15; 95% confidence interval = 1.09-1.22). CONCLUSIONS Among hospitalised Brazilian adults with COVID-19, Black/Brown patients showed higher in-hospital mortality, less frequently used hospital resources and had potentially more severe conditions than White patients. Racial disparities in health outcomes and access to health care highlight the need to actively implement strategies to reduce inequities caused by the wider health determinants, ultimately leading to a sustainable change in the health system.
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Affiliation(s)
- I T Peres
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - L S L Bastos
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - J G M Gelli
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - J F Marchesi
- Instituto Tecgraf, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - L F Dantas
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - B B P Antunes
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - P M Maçaira
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - F A Baião
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - S Hamacher
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - F A Bozza
- National Institute of Infectious Diseases Evandro Chagas (INI), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil; D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil.
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Peres IT, Hamacher S, Oliveira FLC, Thomé AMT, Bozza FA. What factors predict length of stay in the intensive care unit? Systematic review and meta-analysis. J Crit Care 2020; 60:183-194. [PMID: 32841815 DOI: 10.1016/j.jcrc.2020.08.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/02/2020] [Accepted: 08/02/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Studies have shown that a small percentage of ICU patients have prolonged length of stay (LoS) and account for a large proportion of resource use. Therefore, the identification of prolonged stay patients can improve unit efficiency. In this study, we performed a systematic review and meta-analysis to understand the risk factors of ICU LoS. MATERIALS AND METHODS We searched MEDLINE, Embase and Scopus databases from inception to November 2018. The searching process focused on papers presenting risk factors of ICU LoS. A meta-analysis was performed for studies reporting appropriate statistics. RESULTS From 6906 citations, 113 met the eligibility criteria and were reviewed. A meta-analysis was performed for six factors from 28 papers and concluded that patients with mechanical ventilation, hypomagnesemia, delirium, and malnutrition tend to have longer stay, and that age and gender were not significant factors. CONCLUSIONS This work suggested a list of risk factors that should be considered in prediction models for ICU LoS, as follows: severity scores, mechanical ventilation, hypomagnesemia, delirium, malnutrition, infection, trauma, red blood cells, and PaO2:FiO2. Our findings can be used by prediction models to improve their predictive capacity of prolonged stay patients, assisting in resource allocation, quality improvement actions, and benchmarking analysis.
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Affiliation(s)
- Igor Tona Peres
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - Silvio Hamacher
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | | | - Antônio Márcio Tavares Thomé
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - Fernando Augusto Bozza
- Evandro Chagas National Institute of Infectious Disease, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil; IDOR, D'Or Institute for Research and Education, Rio de Janeiro, RJ, Brazil.
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Prado MFD, Antunes BBDP, Bastos LDSL, Peres IT, Silva ADABD, Dantas LF, Baião FA, Maçaira P, Hamacher S, Bozza FA. Analysis of COVID-19 under-reporting in Brazil. Rev Bras Ter Intensiva 2020; 32:224-228. [PMID: 32667439 PMCID: PMC7405743 DOI: 10.5935/0103-507x.20200030] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 05/06/2020] [Indexed: 12/04/2022] Open
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.
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Affiliation(s)
| | | | | | - Igor Tona Peres
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | | | - Leila Figueiredo Dantas
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Fernanda Araújo Baião
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Paula Maçaira
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Silvio Hamacher
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Fernando Augusto Bozza
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brasil
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Antunes BBDP, Peres IT, Baião FA, Ranzani OT, Bastos LDSL, Silva ADABD, Souza GFGD, Marchesi JF, Dantas LF, Vargas SA, Maçaira P, Hamacher S, Bozza FA. Progression of confirmed COVID-19 cases after the implementation of control measures. Rev Bras Ter Intensiva 2020; 32:213-223. [PMID: 32667447 PMCID: PMC7405732 DOI: 10.5935/0103-507x.20200028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 04/03/2020] [Indexed: 11/20/2022] Open
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.
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Affiliation(s)
| | - Igor Tona Peres
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Fernanda Araújo Baião
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Otavio Tavares Ranzani
- Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brasil
| | | | | | | | | | - Leila Figueiredo Dantas
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Soraida Aguilar Vargas
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Paula Maçaira
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Silvio Hamacher
- Departamento de Engenharia Industrial, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brasil
| | - Fernando Augusto Bozza
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brasil
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