O objetivo deste trabalho foi analisar as causas múltiplas de morte de uma coorte de pacientes notificados com tuberculose (TB) e apresentar uma proposta de investigação de causas presumíveis. Realizou-se linkage probabilístico entre o Sistema de Informação de Agravos de Notificação (SINAN) 2006 e o Sistema de Informação sobre Mortalidade (SIM), 2006-2008. Ocorreram 825 mortes, das quais 23% por TB, 16% com TB e 61% sem menção da TB. Duzentos e quinze (42,7%) óbitos ocorreram antes do término do esquema básico de tratamento e não tinham menção da TB, cujo perfil foi distinto do padrão quando a TB era uma das causas associadas. A elevada frequência de doenças do aparelho respiratório, AIDS e causas mal definidas sugerem falha na qualidade da informação. Elaborou-se proposta de correção das causas associadas no SIM e de investigação de óbito com base na relação de causas presumíveis. De acordo com a proposta, 26 óbitos poderiam ter a causa básica modificada. Este estudo destaca a gravidade do quadro da TB e a importância do linkage para a vigilância da TB e melhoria das informações do SIM e do SINAN.

The Notifiable Diseases Information System (SINAN) enables knowledge of the profile of people with active tuberculosis (TB) in a country of continental dimensions such as Brazil. Available in all Brazilian municipalities and states, the system enables continuous consolidation of data, evaluation and monitoring of actions related to TB control in the country. The purpose of this paper is to present the specificities of SINAN-Net related to TB, including the follow-up screen, the record linkage and the follow-up report. Additionally, we describe the main variables and indicators and the challenges and limitations of the system.

To analyze the trends of COVID-19 in Brazil in 2020 by Federal Units (FU). Method: Ecological time-series based on cumulative confirmed cases of COVID-19 from March 11 to May 12. Joinpoint regression models were applied to identify points of inflection in COVID-19 trends, considering the days since the 50 th confirmed case as time unit. Results: Brazil reached its 50 th confirmed case of COVID-19 in 11 March 2020 and, 63 days after that, on May 12, 177,589 cases had been confirmed. The trends for all regions and FU are upward. In the last segment, from the 31 st to the 63 rd day, Brazil presented a daily percentage change (DPC) of 7.3% (95%CI= 7.2;7.5). For the country the average daily percentage change (ADPC) was 14.2% (95%CI: 13.8;14.5). The highest ADPC values were found in the North, Northeast and Southeast regions. Conclusions: In summary, our results show that all FUs in Brazil present upward trends of COVID-19. In some FUs, the slowdown in DPC in the last segment must be considered with caution. Each FU is at a different stage of the pandemic and, therefore, non-pharmacological measures should be adopted accordingly.

ObjectivesTo identify scenarios based on socioeconomic, epidemiological and operational healthcare factors associated with tuberculosis incidence in Brazil.DesignEcological study.SettingsThe study was based on new patients with tuberculosis and epidemiological/operational variables of the disease from the Brazilian National Information System for Notifiable Diseases and the Mortality Information System. We also analysed socioeconomic and demographic variables.ParticipantsThe units of analysis were the Brazilian municipalities, which in 2015 numbered 5570 but 5 were excluded due to the absence of socioeconomic information.Primary outcomeTuberculosis incidence rate in 2015.Data analysisWe evaluated as independent variables the socioeconomic (2010), epidemiological and operational healthcare indicators of tuberculosis (2014 or 2015) using negative binomial regression. Municipalities were clustered by the k-means method considering the variables identified in multiple regression models.ResultsWe identified two clusters according to socioeconomic variables associated with the tuberculosis incidence rate (unemployment rate and household crowding): a higher socioeconomic scenario (n=3482 municipalities) with a mean tuberculosis incidence rate of 16.3/100 000 population and a lower socioeconomic scenario (2083 municipalities) with a mean tuberculosis incidence rate of 22.1/100 000 population. In a second stage of clusterisation, we defined four subgroups in each of the socioeconomic scenarios using epidemiological and operational variables such as tuberculosis mortality rate, AIDS case detection rate and proportion of vulnerable population among patients with tuberculosis. Some of the subscenarios identified were characterised by fragility in their information systems, while others were characterised by the concentration of tuberculosis cases in key populations.ConclusionClustering municipalities in scenarios allowed us to classify them according to the socioeconomic, epidemiological and operational variables associated with tuberculosis risk. This classification can support targeted evidence-based decisions such as monitoring data quality for improving the information system or establishing integrative social protective policies for key populations.

ResumoA qualidade da informação é fundamental no monitoramento e na avaliação das ações de controle dos agravos, como a tuberculose (TB). O objetivo deste trabalho foi analisar a concordância entre o encerramento do Sistema de Informação de Agravos de Notificação (SINAN) e as causas de morte no Sistema de Informação sobre Mortalidade (SIM). Realizou-se um linkage probabilístico entre o SINAN de 2006 e o SIM de 2006 a 2008. A confiabilidade do encerramento foi analisada por meio do índice kappa. Dos 417 casos encerrados por óbito no SINAN, 88,7% foram encontrados no SIM. Dos 82 casos encerrados como óbito por outra causa, 42,7% apresentaram a TB como causa básica ou associada no SIM, enquanto 41,5% não tinham menção à TB. O coeficiente PABAK (Prevalance and Bias Adjusted Kappa) revelou concordância excelente entre o desfecho óbito no campo encerramento do SINAN e a presença ou não do óbito de TB no SIM. Uma recomendação para os Estados e municípios que utilizam o relacionamento entre o SINAN e o SIM para aumentar a completude e a consistência do SINAN-TB é a investigação no SIM não apenas dos casos notificados sem encerramento, mas também dos casos encerrados por abandono e por transferência.