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Integrating epidemiological and clinical predictors of SARS-CoV2 infection in students and school staff in the state of Sao Paulo

Diaz-Quijano, F. A.; da Silva, J. M. N.; D'Agostini, T. L.; de Camargo, J. P.; de Moraes, N. C. S. F. L.; Haddad, R.; Pereira, M. C. G.; Covas, D. T.; de Paula, R. A. C.

2021-06-25 epidemiology
10.1101/2021.06.21.21259213 medRxiv
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BackgroundThere 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. MethodsThis 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. ResultsIn 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%). ConclusionEpidemiological 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.

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