Genomic Surveillance of SARS-CoV-2 in Healthcare Workers: A Critical Sentinel Group for Monitoring the SARS-CoV-2 Variant Shift.

SARS-CoV-2 genome surveillance is important for monitoring risk groups and health workers as well as data on new cases and mortality rate due to COVID-19. We characterized the circulation of SARS-CoV-2 variants from May 2021 to April 2022 in the state of Santa Catarina, southern Brazil, and evaluated the similarity between variants present in the population and healthcare workers (HCW). A total of 5291 sequenced genomes demonstrated the circulation of 55 strains and four variants of concern (Alpha, Delta, Gamma and Omicron-sublineages BA.1 and BA.2). The number of cases was relatively low in May 2021, but the number of deaths was higher with the Gamma variant. There was a significant increase in both numbers between December 2021 and February 2022, peaking in mid-January 2022, when the Omicron variant dominated. After May 2021, two distinct variant groups (Delta and Omicron) were observed, equally distributed among the five Santa Catarina mesoregions. Moreover, from November 2021 to February 2022, similar variant profiles between HCW and the general population were observed, and a quicker shift from Delta to Omicron in HCW than in the general population. This demonstrates the importance of HCW as a sentinel group for monitoring disease trends in the general population.

Mortality due to COVID-19 in public and private hospitals in Florianópolis / SC / Mortalidade por COVID-19 em hospitais públicos e privados de Florianópolis/SC

Introduction: The access to COVID-19 hospital treatment is important to mitigate the impact caused by socioeconomic issues in the treatment of the disease. Objective: To analyze the difference between public and private hospital care in mortality due to COVID-19 in Florianópolis/SC, Brazil. Methods: Historical cohort with confirmed patient data with notification made between February 22nd 2020 and November 9th 2020 in hospitals in the city. Data were provided by the Municipal Health Secretariat. In order to control the socioeconomic factors that could simultaneously influence the search for the type of hospital and mortality, a double-robustness approach was used. In the first stage, pairing of notified individuals in public and private hospitals was carried out by genetic algorithm, using sample replacement. In the second stage, the probability of death was estimated, conditioned by the type of hospital (public or private), symptoms, comorbidities, socioeconomic factors, age, age squared, sex, race / skin color and month of symptom onset using a logistic regression. Finally, the difference between the densities of probability of death of the two hospital types was analyzed. Results: Data from 2,497 people, 1,244 from public hospitals and 1,253 from private hospitals were analyzed. The conditional probability of death assuming that all patients were notified in public hospitals was 0.0010 (95% CI 0.0001; 0.0046) and if all were notified in private hospitals it was 0.0009 (95% CI 0.0001; 0.0039). The difference between the two probabilities was -0,0002 (95% CI -0.0013; 0.0005). Conclusion: The probability of death from COVID-19 was similar among patients seen in public and private hospitals during the period studied.

Introdução: O acesso ao tratamento hospitalar da COVID-19 é importante para amenizar o impacto causado pelas questões socioeconômicas no tratamento da doença. Objetivo: Analisar diferença da atenção hospitalar pública ou privada na mortalidade por COVID-19 em Florianópolis/SC. Métodos: Coorte histórica com dados de pacientes confirmados com notificação realizada entre 22 de fevereiro de 2020 e 09 de novembro de 2020 em hospitais da cidade. Os dados foram fornecidos pela Secretaria Municipal de Saúde. Para controlar os fatores socioeconômicos que poderiam influenciar simultaneamente a busca do tipo de estabelecimento hospitalar e a mortalidade, utilizou-se abordagem de dupla-robustez. Na primeira etapa realizou-se pareamento de indivíduos notificados em hospitais públicos e privados por algoritmo genético. Na segunda etapa, estimou-se a probabilidade de óbito condicionada ao tipo de hospital (público ou privado), aos sintomas, comorbidades, fatores socioeconômicos, idade, idade ao quadrado, sexo, raça/cor da pele e mês do início dos sintomas por meio de regressão logística. Analisou-se, por fim, a diferença entre as densidades de probabilidade de óbito dos dois tipos hospitalares. Resultados: Foram analisados dados de 2.497 pessoas, 1.244 de hospitais públicos e 1.253 de privados. A probabilidade condicional de óbito assumindo que todos os pacientes fossem notificados em hospitais públicos foi de 0,0010 (IC 95% 0,0001; 0,0046) e se todos fossem notificados em hospitais privados foi de 0,0009 (IC 95% 0,0001; 0,0039). A diferença entre as duas probabilidades foi de -0,0002 (IC 95% -0,0013; 0,0005). Conclusão: A probabilidade de óbito por COVID-19 mostrou-se semelhante entre pacientes atendidos em hospitais públicos e privados no período estudado.