ABSTRACT
Background: There is a great deal of uncertainty concerning which contexts would be safe for returning to school and about individual criteria that would reduce contact between the infected and susceptible people in the school setting. Therefore, the purpose of this study was to estimate the prevalence of infection by SARS-CoV-2 in students and school staff; and to identify predictors of infection, including both municipal epidemiological indicators and individual variables reported by the participants. Methods: This was a virological survey carried out among students (over 14 years old) and school staff in Sao Paulo state, between epidemiological weeks 43 to 49 of the year 2020. A self-administrated questionnaire including sociodemographic and clinical information was applied. Moreover, a nasopharynx swab was performed for virological testing (RT-PCR). We evaluate the relationship of COVID-19 epidemiological indicators of the residence municipality with the odds of SARS-CoV-2 infection. For this, a composite index relating recent mortality and previous incidence (RM/PI) was proposed based on the ratio of deaths recorded in the second and third week counted back to the sum of cases during the previous seven weeks (weeks 4 to 10 counted back). We obtained a multiple model using random-effects logit regression integrating epidemiological indicators and individual variables. Results: In total, 3436 participants were included, residents of 72 municipalities. The overall prevalence of infection was 1.7% (95%CI: 1.3%-2.2%). SARS-CoV-2 infection was independently associated with loss of smell, a history of pulmonary disease, and a recent trip outside the municipality. Moreover, the RM/PI index consistently predicted the SARS-CoV-2 infection (adjusted OR: 1.45; 95%CI 1.02-2.04). Based on these associations, we proposed a classification in four groups with different SARS-Cov-2 infection prevalence (0.54%, 1.27%, 3.8%, and 4.13%). Conclusion: Epidemiological and individual variables allowed classifying groups according to the infection probability in a school population of the state of Sao Paulo. This classification could help guide the return to classes in situations in which epidemiological control is evident, maintaining basic protection measures and increasing vaccination coverage.
Subject(s)
COVID-19 , Severe Acute Respiratory Syndrome , Lung DiseasesABSTRACT
Background: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system. Methods: We analyzed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who traveled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. Results: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset, including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41% - 96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51% - 97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). Conclusion: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modeling epidemiological trends.