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Background: The Zika virus (ZIKV) epidemic hit Brazil in 2015 and resulted in a generation of children at risk of congenital Zika syndrome (CZS). The social vulnerability of certain segments of the population contributed to the disproportional occurrence of CZS in the Brazilian Northeast, the poorest region in the country. Living conditions are essential factors in understanding the social determination of CZS, which is embedded in a complex interaction between biological, environmental, and social factors. Salvador, the biggest city in the region, played a central role in the context of the epidemic and was a pioneer in reporting the ZIKV infection and registering a high number of cases of CZS. The aim of the study was identifying the incidence and spatial distribution pattern of children with CZS in the municipality of Salvador, according to living conditions.Methods: This is an ecological study that uses the reported cases of ZIKV and CZS registered in the epidemiological surveillance database of the Municipal Secretariat of Health of the city of Salvador between August of 2015 and July of 2016. The neighborhoods formed the analysis units and the thematic maps were built based on the reported cases. Associations between CZS and living conditions were assessed using the Kernel ratio and a spatial autoregressive linear regression model.Results: Seven hundred twenty-six live births were reported, of which 236 (32.5%) were confirmed for CZS. Despite the reports of ZIKV infection being widely distributed, the cases of CZS were concentrated in poor areas of the city. A positive spatial association was observed between living in places with poorer living conditions and births of children with CZS.Conclusions: This study shows the role of living conditions in the occurrence of births of children with CZS and indicates the need for approaches that recognize the part played by social inequalities in determining CZS and in caring for the children affected. [ABSTRACT FROM AUTHOR]

Background: Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods: The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results: It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion: The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources. [ABSTRACT FROM AUTHOR]