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.

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.

Resumo Objetivos: avaliar a associação entre o fumo na gestação e a ocorrência de excesso de peso e déficit de crescimento no primeiro semestre de vida. Métodos: estudo de coorte com amostra de 460 crianças no baseline. As crianças foram avaliadas em quatro momentos, sendo aferidos em todas as avaliações peso e comprimento para serem convertidos nos índices comprimento por idade (C/I) e Índice de massa corporal por idade (IMC/I) em escore-z. O tempo até a ocorrência de déficit de crescimento e excesso de peso foi calculado em dias e comparado ao fumo materno durante a gestação. Para avaliar a associação entre tabagismo na gestação eos desfechos, foi calculado o Hazard Ratio por meio da regressão de Cox, ajustando-se por variáveis de confusão selecionadas a partir de gráficos acíclicos direcionados. Resultados: o tempo até a ocorrência de déficit de crescimento e excesso de peso foi menor em crianças cujas mães fumaram durante a gestação. O tabagismo na gestação foi um fator de risco para o déficit de comprimento (HR=2,84; IC95%=1,42-5,70) e para o excesso de peso (HR=1,96; IC95%=1,09-3,53), mesmo após o ajuste. Conclusão: o tabagismo materno foi um fator modificável associado com desfechos antropométricos independente de fatores de confusão, demonstrando a necessidade de ações de combate ao tabagismo na gestação, afim de prevenir precocemente desvios nutricionais.

Abstract Objectives: to evaluate the association between smoking during pregnancy and nutritional status. Methods: cohort study with a sample of 460 children in the baseline. The children were assessed four times, being measured for weight and length to be converted in indexes length forage (L/A) and body mass index forage (BMI/A) in Z-score. The time until occurrence of growth deficit and overweight was calculated in days and compared to maternal smoking during pregnancy. To assess the association between smoking during pregnancy and the outcomes, a Hazard Ratio by Cox regression was obtained, adjusting by confounding variables selected from Directed Acyclic Graphs (DAG). Results: the time until occurrence of growth deficit and overweight was lower in children whose mothers smoked during pregnancy. Smoking during pregnancy was a risk factor for length deficit (HR = 2.84; CI95% = 1.42 to 5.70) and for overweight (HR = 1.96; CI95% = 1, 09 to 3.53), even after the adjustment. Conclusions: maternal smoking was a changeable factor associated with anthropometric outcomes, which demonstrates the need for actions to combat smoking during pregnancy in order to prevent early nutritional deviations.