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NIHR Global Health Research Program. EMLA has a CNPq Research Productivity Scholarship. Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil. Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil. Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil. Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil. Universidade Federal da Bahia. Faculdade de Economia. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. Rede CoVida – Ciência, Informação e Solidariedade. Salvador, BA, Brasil. The COVID-19 pandemic has challenged researchers and policy makers to identify public safety measures for preventing the collapse of healthcare systems and reducing deaths. This narrative review summarizes the available evidence on the impact of social distancing measures on the epidemic and discusses the implementation of these measures in Brazil. Articles on the effect of social distancing on COVID-19 were selected from the PubMed, medRXiv and bioRvix databases. Federal and state legislation was analyzed to summarize the strategies implemented in Brazil. Social distancing measures adopted by the population appear effective, particularly when implemented in conjunction with the isolation of cases and quarantining of contacts. Therefore, social distancing measures, and social protection policies to guarantee the sustainability of these measures, should be implemented. To control COVID-19 in Brazil, it is also crucial that epidemiological monitoring is strengthened at all three levels of the Brazilian National Health System (SUS). This includes evaluating and using supplementary indicators to monitor the progression of the pandemic and the effect of the control measures, increasing testing capacity, and making disaggregated notifications and testing results transparent and broadly available.

Background Due to the increasing availability of individual-level information across different electronic datasets, record linkage has become an efficient and important research tool. High quality linkage is essential for producing robust results. The objective of this study was to describe the process of preparing and linking national Brazilian datasets, and to compare the accuracy of different linkage methods for assessing the risk of stillbirth due to dengue in pregnancy. Methods We linked mothers and stillbirths in two routinely collected datasets from Brazil for 2009–2010: for dengue in pregnancy, notifications of infectious diseases (SINAN); for stillbirths, mortality (SIM). Since there was no unique identifier, we used probabilistic linkage based on maternal name, age and municipality. We compared two probabilistic approaches, each with two thresholds: 1) a bespoke linkage algorithm; 2) a standard linkage software widely used in Brazil (ReclinkIII), and used manual review to identify further links. Sensitivity and positive predictive value (PPV) were estimated using a subset of gold-standard data created through manual review. We examined the characteristics of false-matches and missed-matches to identify any sources of bias. Results From records of 678,999 dengue cases and 62,373 stillbirths, the gold-standard linkage identified 191 cases. The bespoke linkage algorithm with a conservative threshold produced 131 links, with sensitivity = 64.4% (68 missed-matches) and PPV = 92.5% (8 false-matches). Manual review of uncertain links identified an additional 37 links, increasing sensitivity to 83.7%. The bespoke algorithm with a relaxed threshold identified 132 true matches (sensitivity = 69.1%), but introduced 61 false-matches (PPV = 68.4%). ReclinkIII produced lower sensitivity and PPV than the bespoke linkage algorithm. Linkage error was not associated with any recorded study variables. Conclusion Despite a lack of unique identifiers for linking mothers and stillbirths, we demonstrate a high standard of linkage of large routine databases from a middle income country. Probabilistic linkage and manual review were essential for accurately identifying cases for a case-control study, but this approach may not be feasible for larger databases or for linkage of more common outcomes.

Submitted by Maria Creuza Silva (mariakreuza@yahoo.com.br) on 2017-05-30T12:34:19Z No. of bitstreams: 1 Gloria Teixeira. 2016 Revista-ENSAIOS-DIÁLOGOS.pdf: 252768 bytes, checksum: 4134dc247e47f6b8ad912c16f2a15d91 (MD5) Made available in DSpace on 2017-05-30T12:34:19Z (GMT). No. of bitstreams: 1 Gloria Teixeira. 2016 Revista-ENSAIOS-DIÁLOGOS.pdf: 252768 bytes, checksum: 4134dc247e47f6b8ad912c16f2a15d91 (MD5) Rio de Janeiro

Submitted by Maria Creuza Silva (mariakreuza@yahoo.com.br) on 2017-05-02T20:06:45Z No. of bitstreams: 1 artigo 23. 2016.pdf: 1694295 bytes, checksum: cd45fdbac6c4b3b8aab4eaf7e5730c31 (MD5) Made available in DSpace on 2017-05-02T20:06:45Z (GMT). No. of bitstreams: 1 artigo 23. 2016.pdf: 1694295 bytes, checksum: cd45fdbac6c4b3b8aab4eaf7e5730c31 (MD5) This original study describes the intra-urban distribution of cases of leprosy in residents under 15 years old in Salvador, Bahia, Brazil; the study also identifies the environment in which Mycobacterium leprae is being transmitted. The cases were distributed by operational classification, clinical forms, type of contact and the addresses were geo-referenced by neighborhood. Between 2007 and 2011, were reported 145 cases of leprosy in target population living in Salvador, corresponding to detection rates of 6.21, 6.14, 5.58, 5.41 and 6.88/100,000 inhabitants, respectively. The spatial distribution of the disease was focal. Of the 157 neighborhoods of Salvador, 44 (28.6%) notified cases of leprosy and in 22 (50%) of these were detected more than 10 cases per 100,000 inhabitants. The infectious forms were found in 40% of cases. Over 90% of cases had been living in Salvador for more than five years. Overall, 52.6% reported having had contact with another infected individual inside the household and 25% in their social circle. In Salvador, M. leprae transmission is established. The situation is a major concern, since transmission is intense at an early age, indicating that this endemic disease is expanding and contacts extend beyond individual households. Rio de Janeiro