RESUMO OBJETIVOS: Estimar a prevalência e fatores associados à hesitação ao uso da vacina contra o vírus SARS-CoV-2 no Maranhão, Brasil. MÉTODOS: Estudo transversal de base populacional realizado de 19 a 30 de outubro de 2020. As estimativas consideraram agrupamento, estratificação e não resposta. A seleção da amostra foi realizada em três estágios (estrato, setores censitários e domicílio). Após análise sistemática, em cada estrato foram selecionados trinta setores, totalizando 150 setores, sendo o número de domicílios em cada setor fixado em 34, totalizando 5.100 domicílios e um indivíduo por domicílio (residente pelo menos há seis meses e com um ano de idade ou mais) selecionado por amostra aleatória simples. A intenção de ser vacinado foi questionada aos participantes. Foi realizada análise descritiva (frequências ponderadas) e teste do qui-quadrado de Pearson para verificar associação univariada entre as variáveis independentes e o desfecho (p < 0,05). Realizou-se análise multivariada robusta utilizando-se modelagem hierarquizada em três níveis. RESULTADOS: Foram entrevistados 4.630 indivíduos. A prevalência de hesitação vacinal foi de 17,5% (IC95% 16,1–19,1%). Após ajuste final do modelo, a hesitação vacinal foi estatisticamente maior entre moradores das cidades de Imperatriz (24,0%; RP = 1,48; IC95% 1,09–2,02) e de munícipios da Grande Ilha de São Luís (20,7%; RP = 1,34; IC95% 1,02–1,76), pessoas do sexo feminino (19,8%; RP = 1,44; IC95% 1,20–1,75), idosos (22,8%; RP = 1,79; IC95% 1,30–2,46), pertencentes às religiões de denominação evangélica (24,1%; RP = 1,49; IC95% 1,24–1,79) e entre aqueles sem relato de sintomas (18,6%; RP = 1,24; IC95% 1,02–1,51). Outras características socioeconômicas e demográficas, assim como variáveis relacionadas ao mercado de trabalho, comportamentos e condições de saúde dos entrevistados, não tiveram diferença estatística. CONCLUSÃO: A prevalência de hesitação vacinal no Maranhão e sua associação com fatores individuais, contextuais e clínicos revelam os grupos e contextos mais resistentes e que devem merecer atenção especial das estratégias públicas para garantir a ampla vacinação.

ABSTRACT OBJECTIVES: To estimate the prevalence and factors associated with hesitancy in getting the vaccine against SARS-CoV-2 in Maranhão, Brazil. METHODS: This is a cross-sectional population-based study conducted from October 19 to 30, 2020. The estimates were calculated based on clustering, stratification, and non-response. A three-stage sampling was adopted, considering stratum, census tracts, and domicile. After systematic analysis, thirty sectors were selected in each stratum, totaling 150 sectors. Each sector contained a fixed number of 34 households, thus totaling 5,100 households. One individual within each household (resident for at least six months and aged one year or more) was selected by a simple random sampling. We questioned participants about their vaccination intention. Univariate association between independent variables and the outcome were verified using descriptive analysis (weighted frequencies) and Pearson's chi-square test (p < 0.05). Robust multivariate analysis was performed using a three-level hierarchical model. RESULTS: We found 17.5% (95%CI 16.1–19.1%) of the 4,630 individuals interviewed to report hesitancy to be vaccinated against covid-19. After final model adjustment, vaccination hesitancy was statistically higher among residents of the cities of Imperatriz (24.0%; RP = 1.48; IC95% 1.09–2.02) and municipalities of the Grande Ilha de São Luís (20.7%; RP = 1.34; 95%CI 1.02–1.76), female individuals (19.8%; RP = 1.44; 95%CI 1.20–1.75), older adults (22.8%; RP = 1.79; IC95% 1.30–2.46), evangelicals (24.1%; RP = 1.49; 95%CI 1.24–1.79), and those without reported symptoms (18.6%; RP = 1.24; 95%CI 1.02–1.51). We found no statistical differences for other socioeconomic and demographic characteristics, as well as variables related to the labor market, behaviors, and health conditions of the interviewees. CONCLUSION: The prevalence of vaccine hesitancy in Maranhão and its association with individual, contextual, and clinical factors enable us to identify the groups and contexts of greatest resistance, requiring special attention from public strategies to ensure wide vaccination.

ABSTRACT OBJECTIVE To estimate the 2020 all-cause and COVID-19 excess mortality according to sex, age, race/color, and state, and to compare mortality rates by selected causes with that of the five previous years in Brazil. METHODS Data from the Mortality Information System were used. Expected deaths for 2020 were estimated from 2015 to 2019 data using a negative binomial log-linear model. RESULTS Excess deaths in Brazil in 2020 amounted to 13.7%, and the ratio of excess deaths to COVID-19 deaths was 0.90. Reductions in deaths from cardiovascular diseases (CVD), respiratory diseases, and external causes, and an increase in ill-defined causes were all noted. Excess deaths were also found to be heterogeneous, being higher in the Northern, Center-Western, and Northeastern states. In some states, the number of COVID-19 deaths was lower than that of excess deaths, whereas the opposite occurred in others. Moreover, excess deaths were higher in men aged 20 to 59, and in black, yellow, or indigenous individuals. Meanwhile, excess mortality was lower in women, in individuals aged 80 years or older, and in whites. Additionally, deaths among those aged 0 to 19 were 7.2% lower than expected, with reduction in mortality from respiratory diseases and external causes. There was also a drop in mortality due to external causes in men and in those aged 20 to 39 years. Moreover, reductions in deaths from CVD and neoplasms were noted in some states and groups. CONCLUSION There is evidence of underreporting of COVID-19 deaths and of the possible impact of restrictive measures in the reduction of deaths from external causes and respiratory diseases. The impacts of COVID-19 on mortality were heterogeneous among the states and groups, revealing that regional, demographic, socioeconomic, and racial differences expose individuals in distinct ways to the risk of death from both COVID-19 and other causes.

ABSTRACT OBJECTIVE To estimate the 2020 all-cause and COVID-19 excess mortality according to sex, age, race/color, and state, and to compare mortality rates by selected causes with that of the five previous years in Brazil. METHODS Data from the Mortality Information System were used. Expected deaths for 2020 were estimated from 2015 to 2019 data using a negative binomial log-linear model. RESULTS Excess deaths in Brazil in 2020 amounted to 13.7%, and the ratio of excess deaths to COVID-19 deaths was 0.90. Reductions in deaths from cardiovascular diseases (CVD), respiratory diseases, and external causes, and an increase in ill-defined causes were all noted. Excess deaths were also found to be heterogeneous, being higher in the Northern, Center-Western, and Northeastern states. In some states, the number of COVID-19 deaths was lower than that of excess deaths, whereas the opposite occurred in others. Moreover, excess deaths were higher in men aged 20 to 59, and in black, yellow, or indigenous individuals. Meanwhile, excess mortality was lower in women, in individuals aged 80 years or older, and in whites. Additionally, deaths among those aged 0 to 19 were 7.2% lower than expected, with reduction in mortality from respiratory diseases and external causes. There was also a drop in mortality due to external causes in men and in those aged 20 to 39 years. Moreover, reductions in deaths from CVD and neoplasms were noted in some states and groups. CONCLUSION There is evidence of underreporting of COVID-19 deaths and of the possible impact of restrictive measures in the reduction of deaths from external causes and respiratory diseases. The impacts of COVID-19 on mortality were heterogeneous among the states and groups, revealing that regional, demographic, socioeconomic, and racial differences expose individuals in distinct ways to the risk of death from both COVID-19 and other causes.

O estudo teve como objetivos, descrever o atraso na notificação de óbitos por COVID-19 na cidade de São Luís, Maranhão, Brasil, e demonstrar o impacto sobre o monitoramento oportuno e modelagem da pandemia. O estudo teve como objetivo secundário determinar a medida em que a nowcasting é capaz de diminuir a defasagem na notificação de óbitos. Analisamos os dados de mortalidade por COVID-19 registrados diariamente no Boletim Epidemiológico da Secretaria de Estado da Saúde do Maranhão e calculamos o atraso na notificação entre 23 de março e 29 de agosto de 2020. Para ilustrar o impacto do atraso na notificação de óbitos e testar a efetividade de uma nowcasting bayesiana para melhorar a qualidade dos dados, ajustamos um modelo hierárquico bayesiano semi-mecanístico. Apenas 17,8% dos óbitos foram notificados sem atraso ou no dia seguinte, enquanto 40,5% foram atrasados em mais de 30 dias. Devido ao atraso na notificação, houve uma subestimação inicial nos óbitos. Entre 7 de junho e 29 de agosto, 644 óbitos foram notificados, mas apenas 116 mortes ocorreram nesse período. O uso da técnica de nowcasting bayesiana melhorou parcialmente a qualidade dos dados de mortalidade no pico da epidemia, apresentando estimativas mais ajustadas ao cenário observado na cidade, mas não se mostrou útil quase dois meses depois do pico. O atraso na notificação de óbitos pode interferir diretamente nas decisões assertivas e oportunas sobre o combate à pandemia da COVID-19. Portanto, o sistema brasileiro de vigilância epidemiológica deve ser revisto urgentemente, e o registro da data do óbito deve ser obrigatório. A técnica de nowcasting mostrou ser parcialmente eficaz na melhoria dos dados de mortalidade no auge da pandemia, mas não depois.

This study describes the COVID-19 death reporting delay in the city of São Luís, Maranhão State, Brazil, and shows its impact on timely monitoring and modeling of the COVID-19 pandemic, while seeking to ascertain how nowcasting can improve death reporting delay. We analyzed COVID-19 death data reported daily in the Epidemiological Bulletin of the State Health Secretariat of Maranhão and calculated the reporting delay from March 23 to August 29, 2020. A semi-mechanistic Bayesian hierarchical model was fitted to illustrate the impact of death reporting delay and test the effectiveness of a Bayesian Nowcasting in improving data quality. Only 17.8% of deaths were reported without delay or the day after, while 40.5% were reported more than 30 days late. Following an initial underestimation due to reporting delay, 644 deaths were reported from June 7 to August 29, although only 116 deaths occurred during this period. Using the Bayesian nowcasting technique partially improved the quality of mortality data during the peak of the pandemic, providing estimates that better matched the observed scenario in the city, becoming unusable nearly two months after the peak. As delay in death reporting can directly interfere with assertive and timely decision-making regarding the COVID-19 pandemic, the Brazilian epidemiological surveillance system must be urgently revised and notifying the date of death must be mandatory. Nowcasting has proven somewhat effective in improving the quality of mortality data, but only at the peak of the pandemic.

La propuesta de este estudio es describir la demora en la notificación de muertes por COVID-19, en la ciudad São Luís, Maranhão, Brasil, y demostrar su impacto en el seguimiento puntual, así como en el modelaje de la pandemia de COVID-19. Un objetivo secundario fue confirmar el alcance, donde la previsión inmediata es capaz de mejorar el retraso en la notificación de las muertes. Analizamos los datos de muertes por COVID-19 diariamente en el Boletín Epidemiológico de la Secretaría de Estado de la Salud de Maranhão y calculamos los atrasos notificados desde el 23 de marzo al 29 de agosto, 2020. Con el fin de ilustrar el impacto del retraso en la notificación de muertes, y para probar la efectividad de la predicción inmediata bayesiana en la mejora de los datos de calidad, ajustamos un modelo jerárquico bayesiano semi-mecanicista. Solo un 17.8% de las muertes se notificaron sin atrasos o el día después, mientras que un 40.5% se vieron retrasadas durante más de 30 días. Debido a la demora informada, se produjo una subestimación inicial de muertes. No obstante, desde el 7 de junio al 29 de agosto, se informó de 644 muertes, pero solamente 116 muertes se produjeron durante este periodo. El uso de la técnica de predicción inmediata bayesiana mejoró parcialmente la calidad de la información de mortalidad durante el pico de la epidemia, presentando estimaciones que se ajustan mejor al escenario observado en la ciudad, pero no fue útil casi 2 meses después del pico. El retraso en la notificación de muertes podría interferir directamente en la toma de decisiones asertivas y puntuales, respecto a la pandemia de COVID-19. Por consiguiente, se debe revisar urgentemente el sistema brasileño de vigilancia epidemiológica y la notificación de la fecha de muerte debería ser obligatoria. La técnica de predicción inmediata ha demostrado ser bastante efectiva para mejorar la calidad de los datos de mortalidad solamente en el pico pandémico, pero no después.

RESUMO OBJETIVOS: Estimar a prevalência e fatores associados à hesitação ao uso da vacina contra o vírus SARS-CoV-2 no Maranhão, Brasil. MÉTODOS: Estudo transversal de base populacional realizado de 19 a 30 de outubro de 2020. As estimativas consideraram agrupamento, estratificação e não resposta. A seleção da amostra foi realizada em três estágios (estrato, setores censitários e domicílio). Após análise sistemática, em cada estrato foram selecionados trinta setores, totalizando 150 setores, sendo o número de domicílios em cada setor fixado em 34, totalizando 5.100 domicílios e um indivíduo por domicílio (residente pelo menos há seis meses e com um ano de idade ou mais) selecionado por amostra aleatória simples. A intenção de ser vacinado foi questionada aos participantes. Foi realizada análise descritiva (frequências ponderadas) e teste do qui-quadrado de Pearson para verificar associação univariada entre as variáveis independentes e o desfecho (p < 0,05). Realizou-se análise multivariada robusta utilizando-se modelagem hierarquizada em três níveis. RESULTADOS: Foram entrevistados 4.630 indivíduos. A prevalência de hesitação vacinal foi de 17,5% (IC95% 16,1–19,1%). Após ajuste final do modelo, a hesitação vacinal foi estatisticamente maior entre moradores das cidades de Imperatriz (24,0%; RP = 1,48; IC95% 1,09–2,02) e de munícipios da Grande Ilha de São Luís (20,7%; RP = 1,34; IC95% 1,02–1,76), pessoas do sexo feminino (19,8%; RP = 1,44; IC95% 1,20–1,75), idosos (22,8%; RP = 1,79; IC95% 1,30–2,46), pertencentes às religiões de denominação evangélica (24,1%; RP = 1,49; IC95% 1,24–1,79) e entre aqueles sem relato de sintomas (18,6%; RP = 1,24; IC95% 1,02–1,51). Outras características socioeconômicas e demográficas, assim como variáveis relacionadas ao mercado de trabalho, comportamentos e condições de saúde dos entrevistados, não tiveram diferença estatística. CONCLUSÃO: A prevalência de hesitação vacinal no Maranhão e sua associação com fatores individuais, contextuais e clínicos revelam os grupos e contextos mais resistentes e que devem merecer atenção especial das estratégias públicas para garantir a ampla vacinação.

ABSTRACT OBJECTIVES: To estimate the prevalence and factors associated with hesitancy in getting the vaccine against SARS-CoV-2 in Maranhão, Brazil. METHODS: This is a cross-sectional population-based study conducted from October 19 to 30, 2020. The estimates were calculated based on clustering, stratification, and non-response. A three-stage sampling was adopted, considering stratum, census tracts, and domicile. After systematic analysis, thirty sectors were selected in each stratum, totaling 150 sectors. Each sector contained a fixed number of 34 households, thus totaling 5,100 households. One individual within each household (resident for at least six months and aged one year or more) was selected by a simple random sampling. We questioned participants about their vaccination intention. Univariate association between independent variables and the outcome were verified using descriptive analysis (weighted frequencies) and Pearson's chi-square test (p < 0.05). Robust multivariate analysis was performed using a three-level hierarchical model. RESULTS: We found 17.5% (95%CI 16.1–19.1%) of the 4,630 individuals interviewed to report hesitancy to be vaccinated against covid-19. After final model adjustment, vaccination hesitancy was statistically higher among residents of the cities of Imperatriz (24.0%; RP = 1.48; IC95% 1.09–2.02) and municipalities of the Grande Ilha de São Luís (20.7%; RP = 1.34; 95%CI 1.02–1.76), female individuals (19.8%; RP = 1.44; 95%CI 1.20–1.75), older adults (22.8%; RP = 1.79; IC95% 1.30–2.46), evangelicals (24.1%; RP = 1.49; 95%CI 1.24–1.79), and those without reported symptoms (18.6%; RP = 1.24; 95%CI 1.02–1.51). We found no statistical differences for other socioeconomic and demographic characteristics, as well as variables related to the labor market, behaviors, and health conditions of the interviewees. CONCLUSION: The prevalence of vaccine hesitancy in Maranhão and its association with individual, contextual, and clinical factors enable us to identify the groups and contexts of greatest resistance, requiring special attention from public strategies to ensure wide vaccination.

O estudo teve como objetivos, descrever o atraso na notificação de óbitos por COVID-19 na cidade de São Luís, Maranhão, Brasil, e demonstrar o impacto sobre o monitoramento oportuno e modelagem da pandemia. O estudo teve como objetivo secundário determinar a medida em que a nowcasting é capaz de diminuir a defasagem na notificação de óbitos. Analisamos os dados de mortalidade por COVID-19 registrados diariamente no Boletim Epidemiológico da Secretaria de Estado da Saúde do Maranhão e calculamos o atraso na notificação entre 23 de março e 29 de agosto de 2020. Para ilustrar o impacto do atraso na notificação de óbitos e testar a efetividade de uma nowcasting bayesiana para melhorar a qualidade dos dados, ajustamos um modelo hierárquico bayesiano semi-mecanístico. Apenas 17,8% dos óbitos foram notificados sem atraso ou no dia seguinte, enquanto 40,5% foram atrasados em mais de 30 dias. Devido ao atraso na notificação, houve uma subestimação inicial nos óbitos. Entre 7 de junho e 29 de agosto, 644 óbitos foram notificados, mas apenas 116 mortes ocorreram nesse período. O uso da técnica de nowcasting bayesiana melhorou parcialmente a qualidade dos dados de mortalidade no pico da epidemia, apresentando estimativas mais ajustadas ao cenário observado na cidade, mas não se mostrou útil quase dois meses depois do pico. O atraso na notificação de óbitos pode interferir diretamente nas decisões assertivas e oportunas sobre o combate à pandemia da COVID-19. Portanto, o sistema brasileiro de vigilância epidemiológica deve ser revisto urgentemente, e o registro da data do óbito deve ser obrigatório. A técnica de nowcasting mostrou ser parcialmente eficaz na melhoria dos dados de mortalidade no auge da pandemia, mas não depois.

This study describes the COVID-19 death reporting delay in the city of São Luís, Maranhão State, Brazil, and shows its impact on timely monitoring and modeling of the COVID-19 pandemic, while seeking to ascertain how nowcasting can improve death reporting delay. We analyzed COVID-19 death data reported daily in the Epidemiological Bulletin of the State Health Secretariat of Maranhão and calculated the reporting delay from March 23 to August 29, 2020. A semi-mechanistic Bayesian hierarchical model was fitted to illustrate the impact of death reporting delay and test the effectiveness of a Bayesian Nowcasting in improving data quality. Only 17.8% of deaths were reported without delay or the day after, while 40.5% were reported more than 30 days late. Following an initial underestimation due to reporting delay, 644 deaths were reported from June 7 to August 29, although only 116 deaths occurred during this period. Using the Bayesian nowcasting technique partially improved the quality of mortality data during the peak of the pandemic, providing estimates that better matched the observed scenario in the city, becoming unusable nearly two months after the peak. As delay in death reporting can directly interfere with assertive and timely decision-making regarding the COVID-19 pandemic, the Brazilian epidemiological surveillance system must be urgently revised and notifying the date of death must be mandatory. Nowcasting has proven somewhat effective in improving the quality of mortality data, but only at the peak of the pandemic.

La propuesta de este estudio es describir la demora en la notificación de muertes por COVID-19, en la ciudad São Luís, Maranhão, Brasil, y demostrar su impacto en el seguimiento puntual, así como en el modelaje de la pandemia de COVID-19. Un objetivo secundario fue confirmar el alcance, donde la previsión inmediata es capaz de mejorar el retraso en la notificación de las muertes. Analizamos los datos de muertes por COVID-19 diariamente en el Boletín Epidemiológico de la Secretaría de Estado de la Salud de Maranhão y calculamos los atrasos notificados desde el 23 de marzo al 29 de agosto, 2020. Con el fin de ilustrar el impacto del retraso en la notificación de muertes, y para probar la efectividad de la predicción inmediata bayesiana en la mejora de los datos de calidad, ajustamos un modelo jerárquico bayesiano semi-mecanicista. Solo un 17.8% de las muertes se notificaron sin atrasos o el día después, mientras que un 40.5% se vieron retrasadas durante más de 30 días. Debido a la demora informada, se produjo una subestimación inicial de muertes. No obstante, desde el 7 de junio al 29 de agosto, se informó de 644 muertes, pero solamente 116 muertes se produjeron durante este periodo. El uso de la técnica de predicción inmediata bayesiana mejoró parcialmente la calidad de la información de mortalidad durante el pico de la epidemia, presentando estimaciones que se ajustan mejor al escenario observado en la ciudad, pero no fue útil casi 2 meses después del pico. El retraso en la notificación de muertes podría interferir directamente en la toma de decisiones asertivas y puntuales, respecto a la pandemia de COVID-19. Por consiguiente, se debe revisar urgentemente el sistema brasileño de vigilancia epidemiológica y la notificación de la fecha de muerte debería ser obligatoria. La técnica de predicción inmediata ha demostrado ser bastante efectiva para mejorar la calidad de los datos de mortalidad solamente en el pico pandémico, pero no después.

ABSTRACT OBJECTIVE: To estimate the seroprevalence of SARS-CoV-2 in the state of Maranhão, Brazil. METHODS: A population-based household survey was performed, from July 27, 2020 to August 8, 2020. The estimates considered clustering, stratification and non-response. Qualitative detection of IgM and IgG antibodies was performed in a fully-automated Elecsys® Anti-SARS-CoV-2 electrochemiluminescence immunoassay on the Cobas® e601 analyzer (Roche Diagnostics). RESULTS: In total, 3,156 individuals were interviewed. Seroprevalence of total antibodies against SARS-CoV-2 was 40.4% (95%CI 35.6-45.3). Population adherence to non-pharmaceutical interventions was higher at the beginning of the pandemic than in the last month. SARS-CoV-2 infection rates were significantly lower among mask wearers and among those who maintained social and physical distancing in the last month compared to their counterparts. Among the infected, 26.0% were asymptomatic. The infection fatality rate (IFR) was 0.14%, higher for men and older adults. The IFR based on excess deaths was 0.28%. The ratio of estimated infections to reported cases was 22.2. CONCLUSIONS: To the best of our knowledge, the seroprevalence of SARS-CoV-2 estimated in this population-based survey is one of the highest reported. The local herd immunity threshold may have been reached or might be reached soon.

ABSTRACT OBJECTIVE: To estimate the seroprevalence of SARS-CoV-2 in the state of Maranhão, Brazil. METHODS: A population-based household survey was performed, from July 27, 2020 to August 8, 2020. The estimates considered clustering, stratification and non-response. Qualitative detection of IgM and IgG antibodies was performed in a fully-automated Elecsys® Anti-SARS-CoV-2 electrochemiluminescence immunoassay on the Cobas® e601 analyzer (Roche Diagnostics). RESULTS: In total, 3,156 individuals were interviewed. Seroprevalence of total antibodies against SARS-CoV-2 was 40.4% (95%CI 35.6-45.3). Population adherence to non-pharmaceutical interventions was higher at the beginning of the pandemic than in the last month. SARS-CoV-2 infection rates were significantly lower among mask wearers and among those who maintained social and physical distancing in the last month compared to their counterparts. Among the infected, 26.0% were asymptomatic. The infection fatality rate (IFR) was 0.14%, higher for men and older adults. The IFR based on excess deaths was 0.28%. The ratio of estimated infections to reported cases was 22.2. CONCLUSIONS: To the best of our knowledge, the seroprevalence of SARS-CoV-2 estimated in this population-based survey is one of the highest reported. The local herd immunity threshold may have been reached or might be reached soon.