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OBJECTIVES: To measure the direct cost of treating acute ischemic stroke (IS) from the perspective of a public hospital in Brazil (HCFMB) and compare it with the reimbursement by the Unified Health System (SUS), through the Procedure Table Management System, Medicines, Orthoses/Prostheses and Special Materials of the Unified Health System (SIGTAP). METHODS: We performed a micro-costing study; four scenarios were evaluated: standard (1); alteplase (2); alteplase and mechanical thrombectomy (3); mechanical thrombectomy (4). Based on the number of patients hospitalized for ischemic stroke in 2019, hospital cost, and SUS billing were calculated for each scenario. Hospital costs were adjusted for inflation using CCEMG-EPPI-Centre Cost Converter. RESULTS: In 2019, 258 patients were hospitalized due to IS, 89.5% in scenario 1, 8% in scenario 2, 1.5% in scenario 3, 1% in scenario 4. From the hospital's perspective, the cost per patient was estimated at R$7780.13, R$15 741.23, R$28 988.49, R$25 739.79, for scenarios 1, 2, 3 and 4, respectively. The reimbursement by SIGTAP was estimated at R$3079.87, R$5417.21, R$10 901.92, R$10 286.28, respectively. If thrombectomy had been included in the SIGTAP, the last two values would be R$25 393.34 and R$24 248.89. CONCLUSIONS: The hospital cost of treating acute IS in 2019 was estimated at R$2 295 209, the SUS reimbursement at R$889 391.54. With the inclusion of thrombectomy at SIGTAP, this reimbursement would be R$975 282.44, and the loss in the cost of HCFMB per patient in relation to reimbursement by the SUS is greater in scenarios without this procedure.

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BACKGROUND: Although dementia has emerged as an important risk factor for severe SARS-CoV-2 infection, results on COVID-19-related complications and mortality are not consistent. We examined the clinical presentations and outcomes of COVID-19 in a multicentre cohort of in-hospital patients, comparing those with and without dementia. METHODS: This retrospective observational study comprises COVID-19 laboratory-confirmed patients aged ≥ 60 years admitted to 38 hospitals from 19 cities in Brazil. Data were obtained from electronic hospital records. A propensity score analysis was used to match patients with and without dementia (up to 3:1) according to age, sex, comorbidities, year, and hospital of admission. Our primary outcome was in-hospital mortality. We also assessed admission to the intensive care unit (ICU), invasive mechanical ventilation (IMV), kidney replacement therapy (KRT), sepsis, nosocomial infection, and thromboembolic events. RESULTS: Among 1,556 patients included in the study, 405 (4.5%) had a diagnosis of dementia and 1,151 were matched controls. When compared to matched controls, patients with dementia had a lower frequency of dyspnoea, cough, myalgia, headache, ageusia, and anosmia; and higher frequency of fever and delirium. They also had a lower frequency of ICU admission (32.7% vs. 47.1%, p < 0.001) and shorter ICU length of stay (7 vs. 9 days, p < 0.026), and a lower frequency of sepsis (17% vs. 24%, p = 0.005), KRT (6.4% vs. 13%, p < 0.001), and IVM (4.6% vs. 9.8%, p = 0.002). There were no differences in hospital mortality between groups. CONCLUSION: Clinical manifestations of COVID-19 differ between older inpatients with and without dementia. We observed that dementia alone could not explain the higher short-term mortality following severe COVID-19. Therefore, clinicians should consider other risk factors such as acute morbidity severity and baseline frailty when evaluating the prognosis of older adults with dementia hospitalised with COVID-19.

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The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48-71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

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The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (ß = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (ß = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (ß = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (ß = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

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Even though most current recommendations include the general use of masks to prevent community transmission of SARS-Cov-2, the effectiveness of this measure is still debated. The studies on this policy include physical filtering tests with inanimate microparticles, randomized clinical trials, observational studies, ecological analyses, and even computational modeling of epidemics. Much of the so-called evidence is inferred from studies on different respiratory viruses and epidemiological settings. Heterogeneity is a major factor limiting the generalization of inferences. In this article, we reviewed the empirical and rational bases of mask use and how to understand these recommendations compared to other policies of social distancing, restrictions on non-essential services, and lockdown. We conclude that recent studies suggest a synergistic effect of the use of masks and social distancing rather than opposing effects of the two recommendations. Developing social communication approaches that clarify the need to combine different strategies is a challenge for public health authorities.

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Abstract Even though most current recommendations include the general use of masks to prevent community transmission of SARS-Cov-2, the effectiveness of this measure is still debated. The studies on this policy include physical filtering tests with inanimate microparticles, randomized clinical trials, observational studies, ecological analyses, and even computational modeling of epidemics. Much of the so-called evidence is inferred from studies on different respiratory viruses and epidemiological settings. Heterogeneity is a major factor limiting the generalization of inferences. In this article, we reviewed the empirical and rational bases of mask use and how to understand these recommendations compared to other policies of social distancing, restrictions on non-essential services, and lockdown. We conclude that recent studies suggest a synergistic effect of the use of masks and social distancing rather than opposing effects of the two recommendations. Developing social communication approaches that clarify the need to combine different strategies is a challenge for public health authorities.

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