OBJETIVOS: Verificar a diversidade de criadouros e tipos de imóveis freqüentados por fêmeas de Aedes albopictus e Aedes aegypti. MÉTODOS: O estudo foi realizado nos anos de 2002 e 2003 no bairro de CampoGrande, Rio de Janeiro, RJ. Realizou-se pesquisa larvária em diferentes tipos de imóveis. As larvas encontradas foram identificadas em laboratório. A freqüência de larvas dessas duas espécies foi computada nos diversos criadouros disponíveis. Foram calculados os índices de infestação predial e de Breteau, as diferenças foram testadas pelo qui-quadrado. RESULTADOS:Os tipos de imóveis positivos para os aedinos foram: residências (83,9% do total); igrejas, escolas, clubes (6,8%); terrenos baldios (6,4%); e comércios (2,8%). Das 9.153 larvas, 12,0% eram de Aedes albopictus e 88,0% de Aedes aegypti. Para aquela espécie, os recipientes onde foram mais encontradas foram ralos (25,4%), latas, garrafas, vasilhames (23,9%) e vasos com plantas (16,2%). Aedes aegypti mostrou-se mais freqüente nos criadouros que Aedes albopictus (χ 2 =145,067, p<0,001). Também ocorreu diferença significante na freqüência dessas espécies em criadouros artificiais do que em naturais (χ CONCLUSÕES:Verificou-se a freqüência das fêmeas de Aedes albopictus e Ae. aegypti em variados tipos de criadouros e tipos de imóveis para postura. A oferta abundante de recipientes artificiais inservíveis nas residências, associada à capacidade de Ae. albopictus de freqüentar também os criadouros naturais, contribui sobremaneira para sua adaptação gradativa ao meio antrópico. ABSTRACT OBJECTIVE: To assess the diversity of oviposition containers and buildings where females of Aedes albopictus and Aedes aegypti can be found.

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Background Machine learning investigates how computers can automatically learn. The present study aimed to predict dietary patterns and compare algorithm performance in making predictions of dietary patterns. Methods We analysed the data of public employees (n = 12,667) participating in the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil). The K‐means clustering algorithm and six other classifiers (support vector machines, naïve Bayes, K‐nearest neighbours, decision tree, random forest and xgboost) were used to predict the dietary patterns. Results K‐means clustering identified two dietary patterns. Cluster 1, labelled the Western pattern, was characterised by a higher energy intake and consumption of refined cereals, beans and other legumes, tubers, pasta, processed and red meats, high‐fat milk and dairy products, and sugary beverages; Cluster 2, labelled the Prudent pattern, was characterised by higher intakes of fruit, vegetables, whole cereals, white meats, and milk and reduced‐fat milk derivatives. The most important predictors were age, sex, per capita income, education level and physical activity. The accuracy of the models varied from moderate to good (69%–72%). Conclusions The performance of the algorithms in dietary pattern prediction was similar, and the models presented may provide support in screener tasks and guide health professionals in the analysis of dietary data.

Trauma-related injuries in traffic-accident victims can be quite serious. Evaluating the factors contributing to traffic accidents is critical for the effective design of programs aimed at reducing traffic accidents. Therefore, this study identified which factors related to traffic accidents are associated with injury severity in hospitalized victims. Factors related to traffic accidents, injury severity, disability and data collected from blood toxicology were evaluated, along with associated severity and disability indices with data collected from toxicology on victims of traffic accidents at the largest tertiary hospital in Latin America. One hundred and twenty-eight victims of traffic accidents were included, of whom the majority were young adult men, motorcyclists, and pedestrians. The most frequent injuries were traumatic brain injury and lower-limb fractures. Alcohol use, hit-and-run victims, and longer hospital stays were shown to lead to greater injury severity. Women, elderly individuals, and pedestrians tend to suffer greater disability post-injury. Therefore, traffic accidents occur more frequently among young male adults, motorcyclists, and those who are hit by a vehicle, with trauma to the head and lower limbs being the most common injury. Injury severity is greater in pedestrians, elderly individuals and inebriated individuals. Disability was higher in older individuals, in women, and in pedestrians.

The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time (p < 0.05). The random forest performed well (r = 0.98, R2 = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.