Hospital-based Severe Acute Respiratory Syndrome surveillance from 2015 to 2020: etiology and clinical outcome

• Grant number: 22/02563-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2022
• End date: April 30, 2023
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Mariângela Ribeiro Resende
• Grantee: Carolina Marano Cunha
• Host Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Syndromic surveillance is a relevant tool for detection of emerging diseases. Concerning to respiratory transmissible infections since 2009, during influenza A H1N1 pandemic emerging, many countries implemented special detection of acute admitted cases of respiratory diseases. In 2019, with the Covid19 pandemic this surveillance system was enhanced in order to monitor a new pathogen SARS-Cov2. Our study has the following objectives: i. to analyse the incidence rate of severe acute respiratory syndrome (SARS) in pre-pandemic and pandemic period of Covid-19; II) to evaluate the final etiologic diagnosis and III) epidemiological and clinical outcome. Population and Methods: a retrospective study will evaluate cases of SARS reported at a Brazilian university hospital, reference for an area of 6,000,000 inhabitants. Inclusion criteria: patients with age e 18 y with SARS reported by Epidemiological Surveillance Section from January 2015 to December 2020. Exclusion criteria: patients without definition criteria of SARS according Brazilian Ministry of Health; cases with insufficient information and pregnant women. Stattistical analysis will be done in a descriptive central measures, media, median and dispersion measures for continuous variables and proportion for categorical variables. Data will be analysed with Epi Info version 7.2 (CDC, 2019) and software R. The temporal tendency will be evaluate by 'Seasonal Autoregressive Integrated Moving Average' (SARIMA). Survival analysis by Kaplan Meyer curve. Comparative analysis will be done by chi-square or Fisher exact test, t-student or ANOVA, according to data. 95% Confidence interval will be calculated and the difference will statistically significant if the p d 0.01.(AU)