Introduction Synthetic anabolic-androgenic steroids (AAS) were developed with the purpose of obtaining drugs capable of increasing protein synthesis associated with a lower degree of virilization. Its use is common among bodybuilders who aim to increase physical strength and muscle mass in the short term. However, AAS cause side effects, which restrict their therapeutic use. Objective To identify factors associated with AAS use by exercise enthusiasts at gyms in São Luís, MA. Methods A cross-sectional study was carried out at 17 gyms. The sample totaled 723 exercise enthusiasts, who answered a structured questionnaire with multiple choice questions related to their lifestyle and the consumption of nutritional supplements and AAS use. Logistic regression analysis was used to verify the association of socioeconomic, demographic and behavioral factors with AAS use. Results Of the 723 exercise enthusiasts, 10.65% reported having used AAS. Of these, 97.4% declared their awareness of some side effect caused by AAS use. Factors associated with AAS use were: being male, age between 20 and 29 years, consumption of food supplements and participation in exercise for over a year. Conclusion The prevalence of AAS use by exercise enthusiasts at gyms in São Luís is high, and the risk factors identified enable us to target specific populations with preventive actions. Level of Evidence IIC; Cross-sectional study.

A malária é um problema de saúde pública global, com 216 milhões de casos em 2016, sendo que no Brasil houve 191 mil em 2017. É transmitida especialmente em áreas endêmicas, na região da Amazônia brasileira, embora com registros nas áreas não endêmicas com elevada letalidade. Nesse sentido, buscou-se caracterizar o perfil epidemiológico dos portadores de malária na região extra-amazônica brasileira nos anos de 2012 a 2017 e para isso, foi realizado um estudo descritivo utilizando-se de dados secundários dos casos confirmados e notificados de malária na região extra-Amazônica brasileira, obtidos dos arquivos no Sistema de Informação de Agravos de Notificação. No período em questão, foram notificados 3.797 casos, a maioria homens (79,0%), de 20–39 anos de idade (48%) e 40–59 anos de idade (35,5%), raça não branca (46,6%), na região sudeste do país e em decorrência P. vivax (66,7%), e cujos primeiros sintomas surgiram nos meses de dezembro a março. Observou-se perfil importante de características associadas aos casos na área não endêmica do país e que ações de controle e vigilância permanentes precisam ser realizadas contra o mosquito transmissor da doença.

Background/Objectives: The prevalence of metabolic syndrome (MetS) is increasing worldwide, and an increasing number of cases are diagnosed in younger age groups. This study aimed to propose predictive models based on demographic, anthropometric, and non-invasive clinical variables to predict MetS in adolescents. Methods: A total of 2064 adolescents aged 18–19 from São Luís-Maranhão, Brazil were enrolled. Demographic, anthropometric, and clinical variables were considered, and three criteria for diagnosing MetS were employed: Cook et al., De Ferranti et al. and the International Diabetes Federation (IDF). A feed-forward artificial neural network (ANN) was trained to predict MetS. Accuracy, sensitivity, and specificity were calculated to assess the ANN’s performance. The ROC curve was constructed, and the area under the curve was analyzed to assess the discriminatory power of the networks. Results: The prevalence of MetS in adolescents ranged from 5.7% to 12.3%. The ANN that used the Cook et al. criterion performed best in predicting MetS. ANN 5, which included age, sex, waist circumference, weight, and systolic and diastolic blood pressure, showed the best performance and discriminatory power (sensitivity, 89.8%; accuracy, 86.8%). ANN 3 considered the same variables, except for weight, and exhibited good sensitivity (89.0%) and accuracy (87.0%). Conclusions: Using non-invasive measures allows for predicting MetS in adolescents, thereby guiding the flow of care in primary healthcare and optimizing the management of public resources.

Objetivo: descrever o cenário de mortalidade por causas evitáveis de crianças menores de cinco anos, residentes nas regiões brasileiras, entre os anos de 2010 e 2019. Metodologia: trata-se de um estudo ecológico que utilizou a Lista de Causas de Mortes Evitáveis por Intervenções do Sistema Único de Saúde (SUS) e dados oficiais do Ministério da Saúde. Foram calculados a Taxa de Mortalidade na Infância (TMIN) e a TMIN por causas evitáveis, mortalidade evitável proporcional por regiões e por grupos de causas, além de identificadas as características sociodemográficas da gestante e do recém-nascido das regiões do Brasil, comparando o primeiro com o segundo quinquênio da década em estudo. Resultados: no período estudado, ocorreram 439.204 óbitos de crianças na infância, dos quais 65,3% seriam evitáveis. Houve uma queda na TMIN e na TMIN por causas evitáveis em todas as regiões, apesar dessas causas se mostrarem heterogêneas. Predominaram os óbitos reduzíveis por adequada ‘atenção à mulher na gestação’ e mais de 80% deles ocorreram em menores de um ano. Dentre as crianças que morreram com até um ano de idade, a maioria apresentou baixo peso ao nascer e eram filhos de mães jovens e com ensino médio incompleto. Conclusão: as características e predominância da mortalidade evitável indica as fragilidades na assistência à saúde do binômio mãe-filho, tornando imperioso combater as disparidades regionais que nela interferem.

Background/Objectives: the prevalence of metabolic syndrome (MetS) is increasing worldwide, and an increasing number of cases are diagnosed in younger age groups. This study aimed to propose predictive models based on demographic, anthropometric and clinical non-invasive variables to predict MetS in adolescent. Methods: a total of 2,064 adolescents aged 18-19 from São Luís-Maranhão-Brazil were enrolled. Demographic, anthropometric, and clinical variables were considered, and three criteria for diagnosing MetS were employed: Cook et al. (2003), De Ferranti et al. (2004), and the International Diabetes Federation (IDF, 2007). A feed-forward artificial neural network (ANN) was trained to predict MetS. Accurate, sensitivity, and specificity were calculated to assess the ANN performance. The ROC curve was constructed, and the area under the curve was analyzed to assess the discriminatory power of the networks. Results: the prevalence of MetS in adolescents ranged from 5.7% to 12.3%. The ANN that used the Cook et al. criterion performed best in predicting MetS. ANN 5, which included age, sex, waist circumference, weight, and systolic and diastolic blood pressure, showed the best performance and discriminatory power (sensitivity, 89.8%; accuracy, 86.8%). ANN 3 considered the same variables, except for weight, and exhibited good sensitivity (89.0%) and accuracy (87.0%). Conclusions: using non-invasive measures allows for predicting MetS in adolescents, thereby guiding the flow of care in primary healthcare and optimizing the management of public resources.