The effect of BCG vaccination on infection and antibody levels against SARS-CoV-2-The results of ProBCG: a multicenter randomized clinical trial in Brazil.

OBJECTIVES: Evatuate if Bacillus Calmette-Guérin (BCG) vaccine could be used as a tool against SARS-CoV-2 based on the concept of trained immunity. METHODS: A multicenter, double-blinded, randomized clinical trial recruited health care workers (HCWs) in Brazil. The incidence rates of COVID-19, clinical manifestations, absenteeism, and adverse events among HCWs receiving BCG vaccine (Moreau or Moscow strains) or placebo were compared. BCG vaccine-mediated immune response before and after implementing specific vaccines for COVID-19 (CoronaVac or COVISHIELD) was analyzed. Cox proportional hazard and linear mixed effect modeling were used. RESULTS: A total of 264 volunteers were included for analysis (BCG = 134 and placebo = 130). The placebo group presented a COVID-19 cumulative incidence of 0.75% vs 0.52% of BCG. The Moreau strain also presented a higher incidence rate (1.60% × 0.22%). BCG did not show a protective hazard ratio against COVID-19. In addition, the log (immunoglobulin G) level against SARS-CoV-2 presented a higher increase in the BCG group, whether or not participants had COVID-19, but also without statistical significance. CONCLUSION: Our results suggest that BCG has a tendency of protection against SARS-CoV-2 and higher immunoglobulin G levels than placebo. The clinical trial was registered at https://clinicaltrials.gov/ (NCT04659941).

Spatial-temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities.

The COVID-19 pandemic caused impact on public health worldwide. Brazil gained prominence during the pandemic due to the magnitude of disease. This study aimed to evaluate the spatial-temporal dynamics of incidence, mortality, and case fatality of COVID-19 and its associations with social determinants in Brazilian municipalities and epidemiological week. We modeled incidence, mortality, and case fatality rates using spatial-temporal Bayesian model. "Bolsa Família Programme" (BOLSAFAM) and "proportional mortality ratio" (PMR) were inversely associated with the standardized incidence ratio (SIR), while "health insurance coverage" (HEALTHINSUR) and "Gini index" were directly associated with the SIR. BOLSAFAM and PMR were inversely associated with the standardized mortality ratio (SMR) and standardized case fatality ratio (SCFR). The highest proportion of excess risk for SIR and the SMR started in the North, expanding to the Midwest, Southeast, and South regions. The highest proportion of excess risk for the SCFR outcome was observed in some municipalities in the North region and in the other Brazilian regions. The COVID-19 incidence and mortality in municipalities that most benefited from the cash transfer programme and with better social development decreased. The municipalities with a higher proportion of non-whites had a higher risk of becoming ill and dying from the disease.

Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study.

In Brazil, vaccination has always cut across party political and ideological lines, which has delayed its start and brought the whole process into disrepute. Such divergences put the immunization of the population in the background and create additional hurdles beyond the pandemic, mistrust and scepticism over vaccines. We conduct a mathematical modelling study to analyse the impacts of late vaccination along with slowly increasing coverage, as well as how harmful it would be if part of the population refused to get vaccinated or missed the second dose. We analyse data from confirmed cases, deaths and vaccination in the state of Rio de Janeiro in the period between 10 March 2020 and 27 October 2021. We estimate that if the start of vaccination had been 30 days earlier, combined with efforts to drive vaccination rates up, about 31 657 deaths could have been avoided. In addition, the slow pace of vaccination and the low demand for the second dose could cause a resurgence of cases as early as 2022. Even when reaching the expected vaccination coverage for the first dose, it is still challenging to increase adherence to the second dose and maintain a high vaccination rate to avoid new outbreaks.

Fatores associados ao óbito em casos confirmados de COVID-19 no estado do Rio de Janeiro.

BACKGROUND: COVID-19 can occur asymptomatically, as influenza-like illness, or as more severe forms, which characterize severe acute respiratory syndrome (SARS). Its mortality rate is higher in individuals over 80 years of age and in people with comorbidities, so these constitute the risk group for severe forms of the disease. We analyzed the factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro. This cross-sectional study evaluated the association between individual demographic, clinical, and epidemiological variables and the outcome (death) using data from the Unified Health System information systems. METHODS: We used the extreme boosting gradient (XGBoost) model to analyze the data, which uses decision trees weighted by the estimation difficulty. To evaluate the relevance of each independent variable, we used the SHapley Additive exPlanations (SHAP) metric. From the probabilities generated by the XGBoost model, we transformed the data to the logarithm of odds to estimate the odds ratio for each independent variable. RESULTS: This study showed that older individuals of black race/skin color with heart disease or diabetes who had dyspnea or fever were more likely to die. CONCLUSIONS: The early identification of patients who may progress to a more severe form of the disease can help improve the clinical management of patients with COVID-19 and is thus essential to reduce the lethality of the disease.