Severe Acute Respiratory Infection Surveillance in Brazil: the Role of Public, Private, and Philanthropic Health Care Units

Respiratory syncytial virus: challenges in diagnosis and impact on the elderly: Recommendations from a multidisciplinary panel

Respiratory syncytial virus (RSV) is an important cause of respiratory illness. While most attention is paid to childhood infection, the RSV burden in adults ≥60 y should also be considered. In Brazil, this is generally underrecognized, where greater focus is toward other respiratory pathogens. This article presents insights from a multidisciplinary panel gathered to review epidemiologic data and current diagnostic approaches to RSV in Brazil (and their limitations) and develop communication strategies to improve knowledge and awareness. National surveillance data indicate a steady increase in cases of RSV-related severe acute respiratory illness (RSV-SARI) in those aged ≥60 y in recent years, with high fatality rates (>30%). Routine RSV testing in older individuals with respiratory symptoms is relatively low. Educational activities targeted toward health-care professionals and the general public are critical to raising awareness of the importance of RSV in older individuals, particularly as protective vaccines are now available.

Viral co-detection of influenza virus and other respiratory viruses in hospitalized Brazilian patients during the first three years of the coronavirus disease (COVID)-19 pandemic: an epidemiological profile

Introduction

In Brazil, few studies were performed regarding the co-detection of respiratory viruses in hospitalized patients. In this way, the study aimed to describe the epidemiological profile of hospitalized patients due to influenza virus infection that presented co-detection with another respiratory virus.

Methods

The epidemiological analysis was made by collecting data from Open-Data-SUS. The study comprised patients infected by the influenza A or B virus with positive co-detection of another respiratory virus, such as adenovirus, bocavirus, metapneumovirus, parainfluenza virus (types 1, 2, 3, and 4), rhinovirus, and respiratory syncytial virus (RSV). The markers [gender, age, clinical signs and symptoms, comorbidities, need for intensive care unit (ICU) treatment, and need for ventilatory support] were associated with the chance of death. The data was collected during the first three years of the coronavirus disease (COVID)-19 pandemic-from December 19, 2019, to April 06, 2023.

Results

A total of 477 patients were included, among them, the influenza A virus was detected in 400 (83.9%) cases. The co-detection occurred, respectively, for RSV (53.0%), rhinovirus (14.0%), adenovirus (13.4%), parainfluenza virus type 1 (10.7%), parainfluenza virus type 3 (5.2%), metapneumovirus (3.8%), parainfluenza virus type 2 (3.6%), bocavirus (3.4%), and parainfluenza virus type 4 (1.5%). The co-detection rate was higher in the male sex (50.7%), age between 0-12 years of age (65.8%), and white individuals (61.8%). The most common clinical symptoms were cough (90.6%), dyspnea (78.8%), and fever (78.6%). A total of 167 (35.0%) people had at least one comorbidity, mainly cardiopathy (14.3%), asthma (8.4%), and diabetes mellitus (7.3%). The need for ICU treatment occurred in 147 (30.8%) cases, with most of them needing ventilatory support (66.8%), mainly non-invasive ones (57.2%). A total of 33 (6.9%) patients died and the main predictors of death were bocavirus infection (OR = 14.78 [95%CI = 2.84-76.98]), metapneumovirus infection (OR = 8.50 [95%CI = 1.86-38.78]), race (other races vs. white people) (OR = 3.67 [95%CI = 1.39-9.74]), cardiopathy (OR = 3.48 [95%CI = 1.13-10.71]), and need for ICU treatment (OR = 7.64 [95%CI = 2.44-23.92]).

Conclusion

Co-detection between the influenza virus and other respiratory viruses occurred, mainly with RSV, rhinovirus, and adenovirus being more common in men, white people, and in the juvenile phase. Co-detection of influenza virus with bocavirus and metapneumovirus was associated with an increased chance of death. Other factors such as race, cardiopathy, and the need for an ICU were also associated with a higher chance of death.

Machine learning in predicting severe acute respiratory infection outbreaks

Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Hospitalizations and deaths of children and adolescents with Severe Acute Respiratory Infection due to COVID-19 during the epidemiological year of 2020

This study aimed to analyze the profile of hospitalizations and factors associated with the deaths of children and adolescents with severe acute respiratory infection (SARI) caused by SARS-CoV-2 nationwide. The study comprised 6,843 children and adolescents hospitalized in 2020 who tested positive for COVID-19, based on data from the Influenza Epidemiological Surveillance Information System. Sociodemographic and clinical profiles, hospitalization frequency, lethality and recovery rates were analyzed. The outcome was recovery or death. The 6,843 children and adolescents comprised 1.9% of SARI hospitalized cases (n = 563,051). Of these, 57.7% developed critical SARI and 90% survived. Comorbidities were present in 40.8%, especially asthma, immunodepression, and neurological and cardiovascular diseases. The main symptoms were fever, cough, dyspnea, respiratory distress, and low oxygen saturation. Among those with critical SARI, 91.4% died. There was a higher frequency of children, especially those under five years of age and of mixed ethnicity. The highest hospitalization frequency occurred in the Southeastern and Northeastern regions, the highest recovery rates in the Southeastern and Southern regions, and the highest lethality rates in the Northern and Northeastern regions. Deaths were associated with ages ranging from 12 to 19 and being under one year of age, living in the Northern and Northeastern regions, progression to critical SARI, and having immunosuppression and cardiovascular disease. In contrast, asthma was associated with lower death rates. The frequency of complications and mortality rates caused by SARS-Cov-2 in the pediatric population are relevant, as well as the severity of the epidemic in the social inequality context and the health services' frailty.

The health burden of natural and technological disasters in Brazil from 2013 to 2021

Disasters deeply impact the health of the affected population and the economy of a country. The health burden of disasters in Brazil is underestimated and more studies are needed to underpin policies and actions for disaster risk reduction. This study analyzes and describes disasters that occurred in Brazil from 2013 to 2021. The Integrated Disaster Information System (S2iD) was accessed to obtain demographic data, disaster data according to Brazilian Classification and Codification of Disasters (COBRADE), and health outcome data (number of dead, injured, sick, unsheltered, displaced, and missing individuals and other outcomes). Database preparation and analysis were performed in Tableau. In total, 98.62% (50,481) of the disasters registered in Brazil from 2013 to 2021 are natural, with a significant increase in 2020 and 2021 due to the COVID-19 pandemic, a biological disaster. This disaster group also caused the highest number of deaths (321,111), as well as injured (208,720) and sick (7,041,099) people. By analyzing data for each geographic region, we observed differences regarding disasters frequency and their health outcomes. In Brazil, climatological disasters are the most frequent (23,452 events) and occur mainly in the Northeast region. Geological disasters have the highest lethality, which are more common in the Southeast; however, the most common disasters in the South and Southeast are those of the meteorological and hydrological groups. Therefore, since the greatest health outcomes are associated with disasters predicted in time and space, public policies for the prevention and management of disasters can reduce the impacts of these events.