The impact of COVID-19 vaccination on case fatality rates in a city in Southern Brazil

Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19

Objective

Our aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19).

Methods

Clinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort.

Result

In total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027-2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253-2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042-1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707-3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003-1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001-1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440-0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641-0.923) and 0.814 (95 % CI: 0.702-0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765-0.962) and 0.875 (95 % CI: 0.741-0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value.

Conclusion

In-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.

COVID-19 in Brazil: a 3-year update

Three years into the coronavirus disease (COVID)-19 pandemic and the world is still struggling with the aftermath of this global health crisis. In Brazil, we are witnessing serious economic, health, social, and political problems. The rapid spread of the virus in our country was the result of a shortage of vaccines and the lack of an effective national campaign to identify and report cases. This health crisis also intensified social inequalities, hitting Indigenous peoples hard due to the lack of access to health services. In addition, rising unemployment and overcrowding of the health system made contagion possible, especially among the most vulnerable, increasing the number of serious cases of the disease. It is important to highlight that emotional problems worsened, the educational system was severely affected, and domestic violence increased during the confinement period, in addition to the fact that the pandemic exposed the great disparities of regional inequalities that exist across the country, mainly concerning health management.

COVID-19 transmission dynamics and the impact of vaccination: modelling, analysis and simulations

Despite the lifting of COVID-19 restrictions, the COVID-19 pandemic and its effects remain a global challenge including the sub-Saharan Africa (SSA) region. Knowledge of the COVID-19 dynamics and its potential trends amidst variations in COVID-19 vaccine coverage is therefore crucial for policy makers in the SSA region where vaccine uptake is generally lower than in high-income countries. Using a compartmental epidemiological model, this study aims to forecast the potential COVID-19 trends and determine how long a wave could be, taking into consideration the current vaccination rates. The model is calibrated using South African reported data for the first four waves of COVID-19, and the data for the fifth wave are used to test the validity of the model forecast. The model is qualitatively analysed by determining equilibria and their stability, calculating the basic reproduction number R0 and investigating the local and global sensitivity analysis with respect to R0. The impact of vaccination and control interventions are investigated via a series of numerical simulations. Based on the fitted data and simulations, we observed that massive vaccination would only be beneficial (deaths averting) if a highly effective vaccine is used, particularly in combination with non-pharmaceutical interventions. Furthermore, our forecasts demonstrate that increased vaccination coverage in SSA increases population immunity leading to low daily infection numbers in potential future waves. Our findings could be helpful in guiding policy makers and governments in designing vaccination strategies and the implementation of other COVID-19 mitigation strategies.

Probabilistic survival modeling in health research: an assessment using cohort data from hospitalized patients with COVID-19 in a Latin American city

Probabilistic survival methods have been used in health research to analyze risk factors and adverse health outcomes associated with COVID-19. The aim of this study was to employ a probabilistic model selected among three distributions (exponential, Weibull, and lognormal) to investigate the time from hospitalization to death and determine the mortality risks among hospitalized patients with COVID-19. A retrospective cohort study was conducted for patients hospitalized due to COVID-19 within 30 days in Londrina, Brazil, between January 2021 and February 2022, registered in the database for severe acute respiratory infections (SIVEP-Gripe). Graphical and Akaike Information Criterion (AIC) methods were used to compare the efficiency of the three probabilistic models. The results from the final model were presented as hazard and event time ratios. Our study comprised of 7,684 individuals, with an overall case fatality rate of 32.78%. Data suggested that older age, male sex, severe comorbidity score, intensive care unit admission, and invasive ventilation significantly increased risks for in-hospital mortality. Our study highlights the conditions that confer higher risks for adverse clinical outcomes attributed to COVID-19. The step-by-step process for selecting appropriate probabilistic models may be extended to other investigations in health research to provide more reliable evidence on this topic.

COVID-19 vaccination and case fatality rates: a case report in a Brazilian municipality

Vaccination campaigns played a crucial role in reducing the incidence of COVID-19. However, a scant number of studies evaluated the impact of vaccination on case fatality rates (CFRs), including in Brazil. Our study aimed to compare CFRs according to vaccination status among subjects living in Arapongas (Paraná State, Brazil), considering the age composition of the population. Several strategies adopted by the Arapongas City Hall to minimize the spread of the virus were also elaborated upon. We accessed the 2021 database of the Arapongas Municipal Health Department, in which a total of 16,437 confirmed cases and 425 deaths were reported. The CFR was calculated as the ratio between COVID-19 deaths and the number of confirmed cases. Differences in age composition between unvaccinated and fully vaccinated individuals were observed in our study. Considering that CFR is a crude indicator and is highly sensitive to the age composition of the population, we adopted the average age distribution of confirmed cases among the three vaccination statuses (unvaccinated, partially, and fully) as a standard age distribution. The age-standardized CFR for unvaccinated and fully vaccinated groups were 4.55% and 2.42%, respectively. Fully vaccinated individuals showed lower age-specific CFRs in all age groups above 60 years than unvaccinated populations. Our findings strengthen the role of vaccination as a critical measure for preventing deaths among infected people and is particularly important to the ongoing reassessment of public health interventions and policies.

The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation.

Dermatological Manifestations in COVID-19: A Case Study of SARS-CoV-2 Infection in a Genetic Thrombophilic Patient with Mthfr Mutation

The present case study describes the dermatological manifestations of COVID-19 in a patient with genetic thrombophilia (MTHFR–C677T mutation) and the identification of a SARS-CoV-2 variant of interest (VOI). A female patient, 47 years old, unvaccinated, with thrombophilia, was diagnosed with COVID-19. She presented with urticarial and maculopapular eruptions from the seventh day of symptoms, which progressed to multiple lesions with dark centers (D-dimer value > 1450 ng/mL). The dermatological manifestations disappeared after 30 days, corroborating the reduction in D-dimer levels. Viral genome sequencing revealed infection by the VOI Zeta (P.2). Antibody testing, performed 30 days after the onset of symptoms, detected only IgG. The virus neutralization test showed the highest neutralizing titer for a P.2 strain, validating the genotypic identification. Lesions were suggested to be due to infection in skin cells causing a direct cytopathic effect or release of pro-inflammatory cytokines triggering erythematous and urticarial eruptions. In addition, vascular complications are also proposed to be due to the MTHFR mutation and increased D-dimer values. This case report is an alert about COVID-19 in patients with pre-existing vascular diseases, especially in unvaccinated patients, by VOI.

Evaluation of clinical outcomes of vaccinated and unvaccinated patients with hospitalization for COVID-19

We aimed to compare vaccinated and unvaccinated patients hospitalized with COVID-19 in terms of disease severity, need for intensive care unit (ICU) admission, and death. In addition, we determined the factors affecting the COVID-19 severity in vaccinated patients. Patients aged 18-65 years who were hospitalized for COVID-19 between September and December 2021 were retrospectively analyzed in three groups: unvaccinated, partially vaccinated, and fully vaccinated.A total of 854 patients were included. Mean age was 47.9 ± 10.6 years, 474 patients (55.5%) were male. Of these, 230 patients (26.9%) were fully vaccinated, 97 (11.3%) were partially vaccinated, and 527 (61.7%) were unvaccinated. Of the fully vaccinated patients, 67% (n = 153) were vaccinated with CoronaVac and 33% (n = 77) were vaccinated with Pfizer-BioNTech. All patients (n = 97) with a single dose were vaccinated with Pfizer-BioNTech. One hundred thirteen (13.2%) patients were transferred to ICU. A hundred (11.7%) patients were intubated and 77 (9.0%) patients died. Advanced age (P = 0.028, 95% CI = 1.00-1.07, OR = 1.038) and higher Charlson Comorbidity Index (CCI) (P < 0.001, 95% CI = 1.20-1.69, OR = 1.425) were associated with increased mortality, while being fully vaccinated (P = 0.008, 95% CI = 0.23-0.80, OR = 0.435) was associated with survival in multivariate analysis. Full dose vaccination reduced the need for ICU admission by 49.7% (95% CI = 17-70) and mortality by 56.5% (95% CI = 20-77). When the fully vaccinated group was evaluated, we found that death was observed more frequent in patients with CCI>3 (19.1 vs 5.8%, P < 0.01, OR = 3.7). Therefore, the booster vaccine especially in individuals with comorbidities should not be delayed, since the survival expectation is low in patients with a high comorbidity index.

Knowledge and Acceptance of the COVID-19 Vaccine for COVID-19 Disease Prevention among the Indian Population: A Mixed-Method Study

Aim: To assess the Knowledge and Acceptance of the COVID vaccine among the Indian population. Materials and methods: The present mixed-method study was conducted in two phases. The first phase: quantitative assessment of knowledge and acceptance for the COVID-19 vaccine using an E survey (N = 606). The second phase: qualitative assessment using semi-structured face-to-face interviews with the study participants (N = 30) and assessment was done using a thematic approach. Study participants were selected using the convenience sampling method. Results: It was found that a large proportion of subjects in the 16−25 year of age group knew the cause of disease. But knowledge about its transmission process was found to be more in >60 years of age gap and almost all the participants in all the age group preferred Covishield. The vaccine acceptance rate was found to be low as compared to the knowledge. Conclusion: Most study participants were found to have satisfactory knowledge, but acceptance rate was comparatively lesser. Hence, more information and awareness campaigns must be launched reassuring the population about vaccine safety.

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.