Evaluation of the design of the influenza-like illness sentinel surveillance system in Brazil.

The influenza-like illness (ILI) sentinel surveillance operates in Brazil to identify respiratory viruses of public health relevance circulating in the country and was first implemented in 2000. Recently, the COVID-19 pandemic reinforced the importance of early detection of the circulation of new viruses in Brazil. Therefore, an analysis of the design of the ILI sentinel surveillance is timely. To this end, we simulated a sentinel surveillance network, identifying the municipalities that would be part of the network according to the criteria defined in the design of the ILI sentinel surveillance and, based on data from tested cases of severe acute respiratory illness (SARI) from 2014 to 2019, we drew samples for each sentinel municipality per epidemiological week. The draw was performed 1,000 times, obtaining the median and 95% quantile interval (95%QI) of virus positivity by Federative Unit and epidemiological week. According to the ILI sentinel surveillance design criteria, sentinel units would be in 64 municipalities, distributed mainly in capitals and their metropolitan areas, recommending 690 weekly samples. The design showed good sensitivity (91.65% considering the 95%QI) for qualitatively detecting respiratory viruses, even those with low circulation. However, there was important uncertainty in the quantitative estimate of positivity, reaching at least 20% in 11.34% of estimates. The results presented here aim to assist in evaluating and updating the ILI sentinel surveillance design. Strategies to reduce uncertainty in positivity estimates need to be evaluated, as does the need for greater spatial coverage.

Resurgence of human respiratory syncytial virus during COVID-19 pandemic in Southern Brazil.

Respiratory Syncytial Virus (RSV) is an important cause of respiratory infection in humans. Severe cases are common in children ≤2 years old, immunocompromised individuals, and the elderly. In 2020, RSV infection reduced in Rio Grande do Sul (RS), southern Brazil; however, in 2021 resurgence of RSV was observed. This study analyzed epidemiological and genetic features of RSV infection cases reported in 2021 in RS. Nasopharyngeal samples collected from individuals with respiratory infection negative for SARS-CoV-2, Influenza A and B viruses were assessed for the presence of RSV by real time RT-qPCR. RSV-A and RSV-B genomic sequencing and phylogenetic reconstructions were performed for genotyping and clade characterization. Among 21,035 respiratory samples analyzed, 2,947 were positive for RSV, 947 of which were hospitalized patients. Positive cases were detected year-round, with the highest number in June-July (winter). Children <1 year comprised 56.28% (n = 533) of the hospitalized patients infected with RSV, whereas 14.46% (n = 137) were individuals >60 years. Of a total of 361 deaths, 14.68% (n = 53) were RSV positive, mostly patients >60 years old (73.58%, n = 39). Chronic kidney disease, cardiopathy, Down syndrome and neurological diseases were associated with RSV infection. RSV-A was identified in 58.5% (n = 117/200) of the patients, and RSV-B in 41.5% (n = 83/200). Of 95 RSV genomes recovered from SARI cases, 66 were RSV-A GA.2.3.5 genotype, while 29 were RSV-B GB.5.0.5a genotype. This study provides epidemiological and molecular data on RSV cases in RS during the COVID-19 pandemic and highlights that investigation of different respiratory viruses is essential for decision-making and disease prevention and control measures.

Machine learning in predicting severe acute respiratory infection outbreaks.

Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Machine learning in predicting severe acute respiratory infection outbreaks / Aprendizado de máquina na previsão de surtos de síndrome respiratória aguda grave / Aprendizaje automático en la predicción de brotes de síndrome respiratorio agudo grave

Abstract: Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Resumo: Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.

Resumen: Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.

Evaluation of the design of the influenza-like illness sentinel surveillance system in Brazil / Evaluación del diseño de la vigilancia centinela de la enfermedad tipo influenza en Brasil / Avaliação do desenho da vigilância sentinela de síndrome gripal no Brasil

Abstract: The influenza-like illness (ILI) sentinel surveillance operates in Brazil to identify respiratory viruses of public health relevance circulating in the country and was first implemented in 2000. Recently, the COVID-19 pandemic reinforced the importance of early detection of the circulation of new viruses in Brazil. Therefore, an analysis of the design of the ILI sentinel surveillance is timely. To this end, we simulated a sentinel surveillance network, identifying the municipalities that would be part of the network according to the criteria defined in the design of the ILI sentinel surveillance and, based on data from tested cases of severe acute respiratory illness (SARI) from 2014 to 2019, we drew samples for each sentinel municipality per epidemiological week. The draw was performed 1,000 times, obtaining the median and 95% quantile interval (95%QI) of virus positivity by Federative Unit and epidemiological week. According to the ILI sentinel surveillance design criteria, sentinel units would be in 64 municipalities, distributed mainly in capitals and their metropolitan areas, recommending 690 weekly samples. The design showed good sensitivity (91.65% considering the 95%QI) for qualitatively detecting respiratory viruses, even those with low circulation. However, there was important uncertainty in the quantitative estimate of positivity, reaching at least 20% in 11.34% of estimates. The results presented here aim to assist in evaluating and updating the ILI sentinel surveillance design. Strategies to reduce uncertainty in positivity estimates need to be evaluated, as does the need for greater spatial coverage.

Resumen: La vigilancia centinela de la enfermedad tipo infuenza (ETI) funciona en Brasil para identificar los virus respiratorios de importancia para la salud pública que circulan en el país y comenzó a ser implementada en 2000. Recientemente, la pandemia de COVID-19 ha reforzado la importancia de la detección temprana de la circulación de nuevos virus en el territorio brasileño. Así, se hace oportuno un análisis del diseño de la vigilancia centinela de la ETI. Para ello, simulamos una red centinela identificando los municipios que formarían parte de la red según los criterios definidos en el diseño de la vigilancia centinela de la ETI y, a partir de los datos de casos testados de infección respiratoria aguda grave (IRAG) de 2014 a 2019, se extrajeron muestras para cada municipio centinela por semana epidemiológica. El sorteo se repitió 1.000 veces y se obtuvo la mediana y el intervalo cuantílico del 95% (IC95%) de la positividad por virus, por Unidad Federativa y semana epidemiológica. Según los criterios del diseño de la vigilancia centinela de la ETI, unidades centinelas estarían en 64 municipios, distribuidas principalmente en capitales y zonas metropolitanas de las capitales, preconizando 690 muestras semanales. El diseño presentó una buena sensibilidad (total de 91,65% considerando el IC95%) para la detección cualitativa de los virus respiratorios, incluso los de baja circulación. Sin embargo, hubo una importante incertidumbre en la estimación cuantitativa de la positividad, alcanzando al menos el 20% en el 11,34% de las estimaciones. Los resultados presentados aquí tienen como objetivo ayudar en la evaluación y actualización del diseño de la red centinela. Es necesario evaluar las estrategias para reducir la incertidumbre en las estimaciones de positividad, al igual que la necesidad de una mayor cobertura espacial.

Resumo: A vigilância sentinela de síndrome gripal atua no Brasil identificando os vírus respiratórios de importância para a saúde pública circulantes no país, e começou a ser implementada em 2000. Recentemente, a pandemia de COVID-19 reforçou a importância da detecção precoce de novos vírus em circulação no território brasileiro. Assim, se faz oportuna uma análise do desenho da vigilância sentinela de síndrome gripal. Para tal, simulamos uma rede sentinela, identificando os municípios que fariam parte da rede segundo os critérios definidos no desenho da vigilância sentinela de síndrome gripal, e, a partir dos dados de casos testados de síndrome respiratória aguda grave (SRAG) de 2014 a 2019, sorteamos amostras para cada município sentinela por semana epidemiológica. O sorteio foi repetido mil vezes, obtendo-se a mediana e intervalo quantílico de 95% (IQ95%) da positividade para cada vírus por Unidade Federativa e semana epidemiológica. Segundo os critérios do desenho da vigilância sentinela de síndrome gripal, unidades sentinelas estariam em 64 municípios, distribuídas principalmente em capitais e suas zonas metropolitanas, o que preconizou 690 amostras semanais. O desenho apresentou boa sensibilidade (total de 91,65%, considerando o IQ95%) para a detecção qualitativa dos vírus respiratórios, mesmo os de baixa circulação. Porém, houve importante incerteza na estimativa quantitativa de positividade, chegando a, pelo menos, 20% em 11,34% das estimativas. Os resultados aqui apresentados visam auxiliar a avaliação e a atualização do desenho da rede sentinela. Estratégias para reduzir a incerteza nas estimativas de positividade precisam ser avaliadas, assim como a necessidade de maior cobertura espacial.

The effectiveness of COVID-19 vaccines against severe cases and deaths in Brazil from 2021 to 2022: a registry-based study.

Background: Brazil started the COVID-19 mass vaccination in January 2021 with CoronaVac and ChAdOx1, followed by BNT162b2 and Ad26.COV2.S vaccines. By the end of 2021, more than 317 million vaccine doses were administered in the adult population. This study aimed at estimating the effectiveness of the primary series of COVID-19 vaccination and booster shots in protecting against severe cases and deaths in Brazil during the first year of vaccination. Methods: A cohort dataset of over 158 million vaccination and severe cases records linked from official national registries was analyzed via a mixed-effects Poisson model, adjusted for age, state of residence, time after immunization, and calendar time to estimate the absolute vaccine effectiveness of the primary series of vaccination and the relative effectiveness of the booster. The method permitted analysis of effectiveness against hospitalizations and deaths, including in the periods of variant dominance. Findings: Vaccine effectiveness against severe cases and deaths remained over 25% and 50%, respectively, after 19 weeks from primary vaccination of BNT162b2, ChAdOx1, or CoronaVac vaccines. The boosters conferred greater protection than the primary series of vaccination, with heterologous boosters providing marginally greater protection than homologous. The effectiveness against hospitalization during the Omicron dominance in the 60+ years old population started at 61.7% (95% CI, 26.1-86.2) for ChAdOx1, 95.6% (95% CI, 82.4-99.9) for CoronaVac, and 72.3% (95% CI, 51.4-87.4) for the BNT162b2 vaccine. Interpretation: This study provides real-world evidence of the effectiveness of COVID-19 vaccination in Brazil, including during the Omicron wave, demonstrating protection even after waning effectiveness. Comparisons of the effectiveness among different vaccines require caution due to potential bias effects related to age groups, periods in the pandemic, and eventual behavioural changes. Funding: Fundação Oswaldo Cruz (FIOCRUZ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), Pan American Health Organization (PAHO), Departamento de Ciência e Tecnologia da Secretaria de Ciência, Tecnologia, Inovação e Insumos Estratégicos em Saúde do Ministério da Saúde do Brasil (DECIT/SCTIE/MS).

Severe acute respiratory infection surveillance in Brazil: the role of public, private and philanthropic healthcare units.

Epidemiological surveillance and notification of respiratory infections are important for management and control of epidemics and pandemics. Fact-based decisions, like social distancing policies and preparation of hospital beds, are taken based on several factors, including case numbers; hence, health authorities need quick access to reliable and well-analysed data. We aimed to analyse the role of the Brazilian public health system in the notification and hospitalization of patients with severe acute respiratory infection (SARI). Data of SARI cases in Brazil (2013-20) were obtained from SIVEP-Gripe platform, and legal status of each healthcare unit (HCU) responsible for case notification and hospitalization was obtained from the National Registry of Health Facilities (CNES) database. HCUs that are part of the hospital network were classified as 'Public Administration', 'Business Entities', 'Philanthropic Entities' or 'Individuals'. SARI notification data from Brazilian macro-regions (North, Northeast, Midwest, Southeast and South) were analysed and compared between administrative spheres. This study reveals that hospitalizations due to SARI increased significantly in Brazil during the coronavirus disease 2019 (COVID-19) pandemic, especially in HCUs of Public Administration. In the Southeast and South, where incidence of SARI is high, philanthropic HCUs also contribute to hospitalization of SARI cases and attend up to 7.4% of the cases notified by the Public Administration. The number of cases is usually lower in other regions, but in 2020 the Northeast showed more hospitalizations than the South. In the South, SARI season occurs later; however, in 2020, an early peak was observed because of COVID-19. Notably, the contribution of each administrative sphere that manages hospital networks in Brazil in the control and management of SARI varies between regions. Our approach will allow managers to assess the use of public resources, given that there are different profiles of healthcare in each region of Brazil and that the public health system has a major role in notifying and attending SARI cases.

Faster indicators of chikungunya incidence using Google searches.

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.

Identification of priority groups for COVID-19 vaccination in Brazil. / Identificação de grupos prioritários para a vacinação contra COVID-19 no Brasil.

In a context of community transmission and shortage of vaccines, COVID-19 vaccination should focus on directly reducing the morbidity and mortality caused by the disease. It was thus essential to define priority groups for vaccination by the Brazilian National Immunization Program (PNI in Portuguese), based on the risk of hospitalization and death from the disease. We calculated overrisk according to sex, age group, and comorbidities using hospitalization and death records from severe acute respiratory illness with confirmation of COVID-19 (SARI-COVID) in all of Brazil in the first 6 months of the epidemic. Higher overrisk was associated with male sex (hospitalization = 1.1 and death = 1.2), age over 45 years for hospitalization (OvRag ranging from 1.1 to 8.5), and age over 55 year for death (OvRag ranging from 1.5 to 18.3). In the groups with comorbidities, chronic kidney disease, diabetes mellitus, cardiovascular disease, and chronic lung disease were associated with overrisk, while there was no such evidence for asthma. Chronic kidney disease or diabetes and age over 60 showed an even stronger association, reaching overrisk of death 14 and 10 times greater than in the general population, respectively. For all the comorbidities, there was higher overrisk at older ages, with a downward gradient in the oldest age groups. This pattern was reversed when examining overrisk in the general population, for both hospitalization and death. The current study provided evidence of overrisk of hospitalization and death from SARI-COVID, assisting the definition of priority groups for COVID-19 vaccination.

Em um contexto de transmissão comunitária e escassez de vacinas, a vacinação contra a COVID-19 deve focar na redução direta da morbidade e da mortalidade causadas pela doença. Portanto, é fundamental a definição de grupos prioritários para a vacinação pelo Programa Nacional de Imunizações (PNI), baseada no risco de hospitalização e óbito pela doença. Para tal, calculamos o sobrerrisco por sexo, faixa etária e comorbidades por meio dos registros de hospitalização e óbito por síndrome respiratória aguda grave com confirmação de COVID-19 (SRAG-COVID) em todo o Brasil nos primeiros seis meses de epidemia. Apresentaram maior sobrerrisco pessoas do sexo masculino (hospitalização = 1,1 e óbito = 1,2), pessoas acima de 45 anos para hospitalização (SRfe variando de 1,1 a 8,5) e pessoas acima de 55 anos para óbitos (SRfe variando de 1,5 a 18,3). Nos grupos de comorbidades, doença renal crônica, diabetes mellitus, doença cardiovascular e pneumopatia crônica conferiram sobrerrisco, enquanto para asma não houve evidência. Ter doença renal crônica ou diabetes mellitus e 60 anos ou mais mostrou-se um fator ainda mais forte, alcançando sobrerrisco de óbito 14 e 10 vezes maior do que na população geral, respectivamente. Para todas as comorbidades, houve um sobrerrisco mais alto em idades maiores, com um gradiente de diminuição em faixas mais altas. Esse padrão se inverteu quando consideramos o sobrerrisco em relação à população geral, tanto para hospitalização quanto para óbito. O presente estudo forneceu evidências a respeito do sobrerrisco de hospitalização e óbito por SRAG-COVID, auxiliando na definição de grupos prioritários para a vacinação contra a COVID-19.

En un contexto de transmisión comunitaria y escasez de vacunas, la vacunación contra la COVID-19 debe enfocarse en la reducción directa de la morbilidad y de la mortalidad causadas por la enfermedad. Por lo tanto, es fundamental la definición de grupos prioritarios para la vacunación por el Programa Nacional de Inmunizaciones (PNI), basada en el riesgo de hospitalización y óbito por la enfermedad. Para tal fin, calculamos el sobrerriesgo por sexo, franja de edad y comorbilidades mediante los registros de hospitalización y óbito por síndrome respiratorio agudo grave con confirmación de COVID-19 (SRAG-COVID) en todo Brasil, durante los primeros seis meses de epidemia. Presentaron mayor sobrerriesgo personas del sexo masculino (hospitalización = 1,1 y óbito = 1,2), personas por encima de 45 años para hospitalización (SRfe variando de 1,1 a 8,5) y personas por encima de 55 años para óbitos (SRfe variando de 1,5 a 18,3). En los grupos de comorbilidades, enfermedad renal crónica, diabetes mellitus, enfermedad cardiovascular y neumopatía crónica ofrecieron sobrerriesgo, mientras que para el asma no hubo evidencia. Sufrir una enfermedad renal crónica o diabetes mellitus y tener 60 años o más mostró un factor todavía más fuerte, alcanzando sobrerriesgo de enfermedad 14 y 10 veces mayor que en la población general, respectivamente. Para todas las comorbilidades, hubo un sobrerriesgo más alto en edades mayores, con un gradiente de disminución en franjas más altas. Este patrón se invirtió cuando consideramos el sobrerriesgo en relación con la población general, tanto para hospitalización como para óbito. El presente estudio proporcionó evidencias respecto al sobrerriesgo de hospitalización y óbito por SRAG-COVID, ayudando en la definición de grupos prioritarios para la vacunación contra la COVID-19.

Risk assessment for HIV infection in men who have sex with men and the contribution of sexual partner networks. / Avaliação de risco para infecção HIV em homens que fazem sexo com homens e a contribuição das redes de parceiros sexuais.

This study aimed to evaluate the risk of HIV infection in men who have sex with men (MSM) by developing an index that considers sex partner networks. The index variables were age, ethnicity/skin color, schooling, relationship type, condom use in receptive and insertive relationships, self-perception of the possibility of HIV infection, sexually transmitted infections, and rapid HIV testing results. We used data from a cross-sectional MSM egocentric network survey conducted in Rio de Janeiro between 2014 and 2015. The initial research volunteer is called ego, each partner is called alter, and each pair of people in a relationship is called the dyad. Multiple logistic regression was used to define the coefficients of the equations for the elaboration of the indices. The index ranged from 0 to 1; the closer to 1, the higher the risk of HIV infection. HIV prevalence was 13.9% among egos. The mean egos index with an HIV-reactive test was 57% higher than non-reactive, and the same profile was observed in the index values of dyads. The index allowed the incorporation of network data through the dyads and contributed to the identification of individuals with a higher likelihood of acquiring HIV.

O objetivo do estudo foi avaliar o risco de infecção por HIV em homens que fazem sexo com homens (HSH) a partir do desenvolvimento de um índice que considere as redes de parceiros sexuais. As variáveis do índice foram faixa etária, raça/cor, escolaridade, tipo de relacionamento, uso de preservativo em relações receptivas e insertivas, autopercepção da chance de se infectar pelo HIV, história de infecções sexualmente transmissíveis, além dos resultados dos testes rápidos para HIV. Foram utilizados dados de uma pesquisa de rede egocêntricas HSH, com desenho transversal, realizada no Rio de Janeiro entre 2014 e 2015. O voluntário inicial da pesquisa é denominado ego, cada parceiro é alter, e cada par de pessoas em um relacionamento é a díade. Utilizou-se regressão logística múltipla para definição dos coeficientes das equações para elaboração dos índices. O índice variou de 0 a 1, quanto mais próximo de 1, maior o risco de infecção por HIV. A prevalência de HIV dos egos foi de 13,9%. A média do índice dos egos com teste HIV reagente foi 57% maior do que aqueles não reagentes, o mesmo perfil foi observado nos valores dos índices das díades. O índice permitiu incorporar os dados das redes por meio das díades e contribuiu para a identificação de indivíduos com maior chance de aquisição do HIV.

Identificação de grupos prioritários para a vacinação contra COVID-19 no Brasil / Identificación de grupos prioritarios para la vacunación contra la COVID-19 en Brasil / Identification of priority groups for COVID-19 vaccination in Brazil

Em um contexto de transmissão comunitária e escassez de vacinas, a vacinação contra a COVID-19 deve focar na redução direta da morbidade e da mortalidade causadas pela doença. Portanto, é fundamental a definição de grupos prioritários para a vacinação pelo Programa Nacional de Imunizações (PNI), baseada no risco de hospitalização e óbito pela doença. Para tal, calculamos o sobrerrisco por sexo, faixa etária e comorbidades por meio dos registros de hospitalização e óbito por síndrome respiratória aguda grave com confirmação de COVID-19 (SRAG-COVID) em todo o Brasil nos primeiros seis meses de epidemia. Apresentaram maior sobrerrisco pessoas do sexo masculino (hospitalização = 1,1 e óbito = 1,2), pessoas acima de 45 anos para hospitalização (SRfe variando de 1,1 a 8,5) e pessoas acima de 55 anos para óbitos (SRfe variando de 1,5 a 18,3). Nos grupos de comorbidades, doença renal crônica, diabetes mellitus, doença cardiovascular e pneumopatia crônica conferiram sobrerrisco, enquanto para asma não houve evidência. Ter doença renal crônica ou diabetes mellitus e 60 anos ou mais mostrou-se um fator ainda mais forte, alcançando sobrerrisco de óbito 14 e 10 vezes maior do que na população geral, respectivamente. Para todas as comorbidades, houve um sobrerrisco mais alto em idades maiores, com um gradiente de diminuição em faixas mais altas. Esse padrão se inverteu quando consideramos o sobrerrisco em relação à população geral, tanto para hospitalização quanto para óbito. O presente estudo forneceu evidências a respeito do sobrerrisco de hospitalização e óbito por SRAG-COVID, auxiliando na definição de grupos prioritários para a vacinação contra a COVID-19.

En un contexto de transmisión comunitaria y escasez de vacunas, la vacunación contra la COVID-19 debe enfocarse en la reducción directa de la morbilidad y de la mortalidad causadas por la enfermedad. Por lo tanto, es fundamental la definición de grupos prioritarios para la vacunación por el Programa Nacional de Inmunizaciones (PNI), basada en el riesgo de hospitalización y óbito por la enfermedad. Para tal fin, calculamos el sobrerriesgo por sexo, franja de edad y comorbilidades mediante los registros de hospitalización y óbito por síndrome respiratorio agudo grave con confirmación de COVID-19 (SRAG-COVID) en todo Brasil, durante los primeros seis meses de epidemia. Presentaron mayor sobrerriesgo personas del sexo masculino (hospitalización = 1,1 y óbito = 1,2), personas por encima de 45 años para hospitalización (SRfe variando de 1,1 a 8,5) y personas por encima de 55 años para óbitos (SRfe variando de 1,5 a 18,3). En los grupos de comorbilidades, enfermedad renal crónica, diabetes mellitus, enfermedad cardiovascular y neumopatía crónica ofrecieron sobrerriesgo, mientras que para el asma no hubo evidencia. Sufrir una enfermedad renal crónica o diabetes mellitus y tener 60 años o más mostró un factor todavía más fuerte, alcanzando sobrerriesgo de enfermedad 14 y 10 veces mayor que en la población general, respectivamente. Para todas las comorbilidades, hubo un sobrerriesgo más alto en edades mayores, con un gradiente de disminución en franjas más altas. Este patrón se invirtió cuando consideramos el sobrerriesgo en relación con la población general, tanto para hospitalización como para óbito. El presente estudio proporcionó evidencias respecto al sobrerriesgo de hospitalización y óbito por SRAG-COVID, ayudando en la definición de grupos prioritarios para la vacunación contra la COVID-19.

In a context of community transmission and shortage of vaccines, COVID-19 vaccination should focus on directly reducing the morbidity and mortality caused by the disease. It was thus essential to define priority groups for vaccination by the Brazilian National Immunization Program (PNI in Portuguese), based on the risk of hospitalization and death from the disease. We calculated overrisk according to sex, age group, and comorbidities using hospitalization and death records from severe acute respiratory illness with confirmation of COVID-19 (SARI-COVID) in all of Brazil in the first 6 months of the epidemic. Higher overrisk was associated with male sex (hospitalization = 1.1 and death = 1.2), age over 45 years for hospitalization (OvRag ranging from 1.1 to 8.5), and age over 55 year for death (OvRag ranging from 1.5 to 18.3). In the groups with comorbidities, chronic kidney disease, diabetes mellitus, cardiovascular disease, and chronic lung disease were associated with overrisk, while there was no such evidence for asthma. Chronic kidney disease or diabetes and age over 60 showed an even stronger association, reaching overrisk of death 14 and 10 times greater than in the general population, respectively. For all the comorbidities, there was higher overrisk at older ages, with a downward gradient in the oldest age groups. This pattern was reversed when examining overrisk in the general population, for both hospitalization and death. The current study provided evidence of overrisk of hospitalization and death from SARI-COVID, assisting the definition of priority groups for COVID-19 vaccination.

Description and comparison of demographic characteristics and comorbidities in SARI from COVID-19, SARI from influenza, and the Brazilian general population. / SRAG por COVID-19 no Brasil: descrição e comparação de características demográficas e comorbidades com SRAG por influenza e com a população geral.

The study aims to describe patients hospitalized for severe acute respiratory illness (SARI) due to COVID-19 (SARI-COVID) in Brazil according to demographic characteristics and comorbidities up to the 21st Epidemiological Week of 2020. The study aimed to compare these characteristics with those of patients hospitalized for SARI due to influenza in 2019/2020 (SARI-FLU) and with the Brazilian general population. The proportions of demographic characteristics, comorbidities, and pregnant and postpartum women among patients hospitalized for SARI-COVID and SARI-FLU were obtained from the SIVEP-Gripe database, and the estimates for the Brazilian population were obtained from the population projections performed by Brazilian Institute of Geography and Statistics, Information System on Live Birth data, and nationwide surveys. Compared to the Brazilian population, patients hospitalized for SARI-COVID showed a higher proportion of males, elderly individuals and those aged 40 to 59 years, comorbidities (diabetes mellitus, cardiovascular disease, chronic kidney disease, and chronic lung diseases), and pregnant/postpartum women. Compared to the general population, Brazilians hospitalized for SARI-FLU showed higher prevalence rates of ages 0 to 4 years or over 60 years, white race/color, comorbidities (diabetes, chronic kidney disease, asthma, and other chronic lung diseases), and pregnant/postpartum women. The data suggest that these groups are evolving to more serious forms of the disease, so that longitudinal studies are extremely relevant for investigating this hypothesis and supporting appropriate public health policies.

SRAG por COVID-19 no Brasil: descrição e comparação de características demográficas e comorbidades com SRAG por influenza e com a população geral / IRAG por COVID-19 en Brasil: descripción y comparación de características demográficas y comorbilidades con el IRAG por influenza y con la población general / Description and comparison of demographic characteristics and comorbidities in SARI from COVID-19, SARI from influenza, and the Brazilian general population

O presente estudo tem o objetivo de descrever os pacientes hospitalizados por síndrome respiratória aguda grave (SRAG) em decorrência da COVID-19 (SRAG-COVID), no Brasil, quanto às suas características demográficas e comorbidades até a 21ª Semana Epidemiológica de 2020. Buscou-se comparar essas características com as dos hospitalizados por SRAG em decorrência da influenza em 2019/2020 (SRAG-FLU) e com a população geral brasileira. As frequências relativas das características demográficas, comorbidades e de gestantes/puérperas entre os pacientes hospitalizados por SRAG-COVID e SRAG-FLU foram obtidas por meio do Sistema de Informação de Vigilância Epidemiológica da Gripe (SIVEP-Gripe), e as estimativas para a população geral brasileira foram obtidas por meio de projeções populacionais realizadas pelo Instituto Brasileiro de Geografia e Estatística, dados do Sistema de Informações sobre Nascidos Vivos e de pesquisas de âmbito nacional. Entre os hospitalizados por SRAG-COVID, observou-se uma elevada proporção, em relação ao perfil da população geral brasileira, de indivíduos do sexo masculino, idosos ou com 40 a 59 anos, com comorbidades (diabetes mellitus, doença cardiovascular, doença renal crônica e pneumopatias crônicas) e de gestantes/puérperas. Já entre os hospitalizados por SRAG-FLU, observou-se prevalências superiores às populacionais de indivíduos de 0 a 4 anos de idade ou idosos, de raça ou cor branca, com comorbidades (diabetes mellitus, doença renal crônica, asma e outras pneumopatias crônicas) e de gestantes/puérperas. Esses grupos podem estar evoluindo para casos mais graves da doença, de forma que estudos longitudinais na área são de extrema relevância para investigar esta hipótese e melhor subsidiar políticas públicas de saúde.

El objetivo del presente estudio es describir a los pacientes hospitalizados por infección respiratoria aguda grave (IRAG) a consecuencia de la COVID-19 (IRAG-COVID), en Brasil, respecto a sus características demográficas y comorbilidades hasta la 21ª Semana Epidemiológica de 2020. Se buscó comparar estas características con las de los hospitalizados por SRAS, a consecuencia de la influenza en 2019/2020 (IRAG-FLU) y con la población general brasileña. Las frecuencias relativas de las características demográficas, comorbilidades y de embarazadas/puérperas entre los pacientes hospitalizados por IRAG-COVID y IRAG-FLU se obtuvieron mediante el Sistema de Información de la Vigilancia Epidemiológica de la Gripe (SIVEP-Gripe), y las estimaciones para la población general brasileña se consiguieron mediante proyecciones poblacionales realizadas por el Instituto Brasileño de Geografía e Estadística, datos del Sistema de Informaciones sobre Nascidos Vivos y de investigaciones de ámbito nacional. Entre los hospitalizados por IRAG-COVID, se observó una elevada proporción, respecto al perfil de la población general brasileña, de individuos del sexo masculino, ancianos o con 40 a 59 años, con comorbilidades (diabetes mellitus, enfermedad cardiovascular, enfermedad renal crónica y neumopatías crónicas) y de embarazadas/puérperas. Ya entre los hospitalizados por IRAG-FLU, se observaron prevalencias superiores a las poblacionales de individuos de 0 a 4 años de edad o ancianos, de raza o color blanco, con comorbilidades (diabetes mellitus, enfermedad renal crónica, asma y otras neumopatías crónicas) y de embarazadas/puérperas. Estos grupos pueden estar evolucionando hacia casos más graves de la enfermedad, por ello, los estudios longitudinales en esta área son de extrema relevancia para investigar esta hipótesis y apoyar mejor las políticas públicas de salud.

The study aims to describe patients hospitalized for severe acute respiratory illness (SARI) due to COVID-19 (SARI-COVID) in Brazil according to demographic characteristics and comorbidities up to the 21st Epidemiological Week of 2020. The study aimed to compare these characteristics with those of patients hospitalized for SARI due to influenza in 2019/2020 (SARI-FLU) and with the Brazilian general population. The proportions of demographic characteristics, comorbidities, and pregnant and postpartum women among patients hospitalized for SARI-COVID and SARI-FLU were obtained from the SIVEP-Gripe database, and the estimates for the Brazilian population were obtained from the population projections performed by Brazilian Institute of Geography and Statistics, Information System on Live Birth data, and nationwide surveys. Compared to the Brazilian population, patients hospitalized for SARI-COVID showed a higher proportion of males, elderly individuals and those aged 40 to 59 years, comorbidities (diabetes mellitus, cardiovascular disease, chronic kidney disease, and chronic lung diseases), and pregnant/postpartum women. Compared to the general population, Brazilians hospitalized for SARI-FLU showed higher prevalence rates of ages 0 to 4 years or over 60 years, white race/color, comorbidities (diabetes, chronic kidney disease, asthma, and other chronic lung diseases), and pregnant/postpartum women. The data suggest that these groups are evolving to more serious forms of the disease, so that longitudinal studies are extremely relevant for investigating this hypothesis and supporting appropriate public health policies.

Raltegravir-Induced Adaptations of the HIV-1 Integrase: Analysis of Structure, Variability, and Mutation Co-occurrence.

The human immunodeficiency virus type 1 (HIV-1) has several proteins of therapeutic importance, many of which are currently used as drug targets in antiretroviral therapy. Among these proteins is the integrase, which is responsible for the integration of the viral DNA into the host genome - a crucial step for HIV-1 replication. Given the importance of this protein in the replication process, three integrase inhibitors are currently used as an option for antiretroviral therapy: Raltegravir, Elvitegravir, and Dolutegravir. However, the crescent emergence of mutations that cause resistance to these drugs has become a worldwide health problem. In this study, we compared the variability of each position of the HIV-1 integrase sequence in clinical isolates of Raltegravir-treated and drug-naïve patients by calculating their Shannon entropies. A co-occurrence network was created to explore how mutations co-occur in patients treated with Raltegravir. Then, by building tridimensional models of the HIV-1 integrase intasomes, we investigated the relationship between variability, architecture, and co-occurrence. We observed that positions bearing some of the major resistance pathways are highly conserved among non-treated patients and variable among the treated ones. The residues involved in the three main resistance-related mutations could be identified in the same group when the positions were clustered according to their entropies. Analysis of the integrase architecture showed that the high-entropy residues S119, T124, and T125, are in contact with the host DNA, and their variations may have impacts in the protein-DNA recognition. The co-occurrence network revealed that the major resistance pathways N155H and Q148HR share more mutations with each other than with the Y143R pathway, this observation corroborates the fact that the N155H pathway is most commonly converted into Q148HRK than into Y143RCH pathway in patients' isolates. The network and the structure analysis also support the hypothesis that the resistance-related E138K mutation may be a mechanism to compensate for mutations in neighbor lysine residues to maintain DNA binding. The present study reveals patterns by which the HIV-1 integrase adapts during Raltegravir therapy. This information can be useful to comprehend the impacts of the drug in the enzyme, as well as help planning new therapeutic approaches.

Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors.

Influenza constitutes a major challenge to world health authorities due to high transmissibility and the capacity to generate large epidemics. This study aimed to characterize the diffusion process of influenza A (H1N1) by identifying the starting point of the epidemic as well as climatic and sociodemographic factors associated with the occurrence and intensity of transmission of the disease. The study was carried out in the Brazilian state of Paraná, where H1N1 caused the largest impact. The units of spatial and temporal analysis were the municipality of residence of the cases and the epidemiological weeks of the year 2009, respectively. Under the Bayesian paradigm, parametric inference was performed through a two-part spatiotemporal model and the integrated nested Laplace approximation (INLA) algorithm. We identified the most likely starting points through the effective distance measure based on mobility networks. The proposed estimation methodology allowed for rapid and efficient implementation of the spatiotemporal model, and provided evidence of different patterns for chance of occurrence and risk of influenza throughout the epidemiological weeks. The results indicate the capital city of Curitiba as the probable starting point, and showed that the interventions that focus on municipalities with greater migration and density of people, especially those with higher Human Development Indexes (HDIs) and the presence of municipal air and road transport, could play an important role in mitigation of effects of future influenza pandemics on public health. These results provide important information on the process of introduction and spread of influenza, and could contribute to the identification of priority areas for surveillance as well as establishment of strategic measures for disease prevention and control. The proposed model also allows identification of epidemiological weeks with high chance of influenza occurrence, which can be used as a reference criterion for creating an immunization campaign schedule.

The introduction of dengue follows transportation infrastructure changes in the state of Acre, Brazil: A network-based analysis.

Human mobility, presence and passive transportation of Aedes aegypti mosquito, and environmental characteristics are a group of factors which contribute to the success of dengue spread and establishment. To understand this process, we assess data from dengue national and municipal basins regarding population and demographics, transportation network, human mobility, and Ae. aegypti monitoring for the Brazilian state of Acre since the first recorded dengue case in the year 2000 to the year 2015. During this period, several changes in Acre's transport infrastructure and urbanization have been started. To reconstruct the process of dengue introduction in Acre, we propose an analytic framework based on concepts used in malaria literature, namely vulnerability and receptivity, to inform risk assessments in dengue-free regions as well as network theory concepts for disease invasion and propagation. We calculate the probability of dengue importation to Acre from other Brazilian states, the evolution of dengue spread between Acrean municipalities and dengue establishment in the state. Our findings suggest that the landscape changes associated with human mobility have created favorable conditions for the establishment of dengue virus transmission in Acre. The revitalization of its major roads, as well as the increased accessibility by air to and within the state, have increased dengue vulnerability. Unplanned urbanization and population growth, as observed in Acre during the period of study, contribute to ideal conditions for Ae. aegypti mosquito establishment, increase the difficulty in mosquito control and consequently its local receptivity.