Objective: To assess the prevalence of obesity, overweight (including obesity) and thinness in children of the city of Florianopolis (southern Brazil). Design: Cross-sectional study. Subjects: Representative sample of 7-10-y-old schoolchildren of the first four grades of elementary schools (1432 girls, 1504 boys). Methods: Measurements of weight, height and triceps skinfold thickness (TSF) were taken following standard techniques. The body mass index (BMI) was computed as weight/height 2 . Nutritional status was defined using two references: (1) the Must et al reference for BMI and TSF to define thinness, overweight and obesity (5th, 85th and 95th percentiles, respectively); (2) the International Obesity Task Force (IOTF) BMI cutoffs to define overweight and obesity. Results: Using BMI, according to the Must et al, and IOTF references, the prevalence of obesity was 10.6 and 5.5%, respectively; overweight (including obesity) affected 26.2 and 22.1% of children, respectively. According to the Must et al reference, the prevalence of thinness was 3.2%. Using TSF rather than BMI, according to the Must et al references, fewer children were classified as obese (8.0%) or overweight (20.2%) and more children were classified as thin (4.9%). Conclusion: This study supports the previously reported high frequencies of childhood overweight and obesity in developing countries. The data allow comparisons with other studies carried out in Brazil and other parts of the world.

RESUMOObjetivos: Avaliar o programa de rastreamento neonatal da Secretaria de Estado da Saúde de Santa Catarina, em relação ao hipotireoidismo congênito (HC), e estimar sua prevalência nas crianças rastreadas. Método: Foram rastreadas 390.759 crianças no período de 01/94 a 12/98, sendo avaliada: a cobertura do programa, as idades na coleta da 1ª amostra de sangue para dosagem de TSH, os tempos para envio da amostra ao laboratório central (LACEN), resultado da dosagem do TSH e localização das crianças com exames alterados e as idades na 1 a consulta e no início do tratamento e a dose de L-T4 prescrita. Resultados: A cobertura do programa foi de 81%, tendo sido detectadas 123 crianças com HC, com prevalência de 1:3.177. A idade média das crianças na coleta da 1 a amostra foi de 17,6 dias. As médias de tempo foram: 7,4 dias para a chegada da amostra ao LACEN, 2,4 dias para o resultado da dosagem do TSH e 7,6 dias para a localização da criança e a 1 a consulta. Todas as crianças detectadas foram atendidas no Hospital Infantil Joana de Gusmão e tinham, em média, idade de 40,2 dias na 1 a consulta e no início do tratamento. A dose média de L-T4 prescrita foi 12,5µg/kg/dia. Conclusão: O tempo para a dosagem do TSH e a dose de L-T4 prescrita são adequados. As demais variáveis estão fora do tempo preconizado, acarretando atraso no início do tratamento. A prevalência de HC é de 1 caso para cada 3.177 crianças rastreadas. Methods: 390,759 newborns were screened for CH between 01/94 and 12/98 to evaluate: the program coverage, children's age at the time of the 1 st blood sample for TSH measurement, time to transport blood samples to the core laboratory (LACEN), time to obtain TSH results, time to locate children with abnormal exams, maternal age at the time of the 1 st clinical appointment and beginning of treatment, and dose of L-T4 prescribed. Results: Program coverage was 81% and 123 children were diagnosed with CH and the estimated prevalence was 1:3,177. The mean age at the time of the 1 st blood sample was 17.6 days. The mean intervals between blood sampling and the various outcomes were: 7.4 days to transport samples to LACEN, 2.4 days to obtain laboratory results, 7.6 days to locate positive-screened children and bring them to their 1 st clinical appointment. All positive-screened children were evaluated at Joana de Gusmão Sick Children Hospital. The mean age at the time of the 1 st clinical appointment and beginning of treatment was 40.2 days. The mean dose of L-T4 prescribed was 12.5µg/kg/day. Conclusion: Time between TSH dosage and prescription of L-T4 is appropriate. All other variables are outside the recommended time frame, resulting in delays artigo original

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Abstract-Prevention of childhood obesity is a global public health priority. Obesity is associated with dyslipidemia, type 2 diabetes and long-term vascular complications. Therefore, obesity prevalence remains high making it imperative to continue surveillance and alternatives or plans to pre-vent it. We developed an innovative population strategy to support initiatives that work towards reducing the obesity epidemic in Brazil. The educational game developed -Space Adventures -is aimed to promote the consumption of healthier food by children. The theme resembles a galaxy and different idealized planets corresponding to the daily meals. To evaluate the educational game developed, test were conducted with children from the ages of 5 to 10; the food choices of each volunteer were analyzed during the execution of the game. Two institutions tested the educational game developed and it was observed that children of a primary school (Group 1) showed significantly better learning-acquisition scores than children of a children's hospital (Group 2). Group 1 performed better in the educational game than Group 2, based in healthy eating choices. The results of this study provide evidence to support the importance of innovative strategies in health.

Background Studies in Europe show immigrant children to be more vulnerable to health problems. Portugal has a gap in knowledge about the health of immigrant children. The objective of this paper is to present results on a cohort study for health trajectories of immigrant children in the Amadora Municipality which has one of the highest numbers of immigrants in Portugal. We will analyse health profile characteristics of immigrant and native children and their utilisation of health services. Methods Prospective cohort study in Amadora health units (questionnaires and patient registers data). Participants: 420 native and immigrant children born in 2015 registered in Amadora Primary Health Care Centers (PHCC); recruitment from June 2019 to March 2020. Main outcomes: psychomotor development (Mary Sheridan); emotional and behavioural problems (SDQ); BMI; vaccination; Results From the 420 children recruited, 48,3% were immigrant mostly from Brazil and Portuguese speaking African countries, 41 children were born outside the EU (1st generation). From the 126 children who had no routine medical examination at the age of 4, 59% were immigrant. Almost all immigrant children had vaccinations up to date (90%). Around 70% of native and 60% of immigrant children achieved all parameters in Mary Sheridan's test (p = 0.09). Overweight was found in 28% of native and 22% of immigrant children (p = 0.2). Median SDQ score for externalizing behaviours is different for 1st generation immigrants suggesting higher behaviour problems for this group of children (p = 0,003). Conclusions Most differences in overall outcomes occur between 1st generation immigrant and other children; immigrants showed significantly higher emotional and behaviour problems. Early identification of above difficulties and higher utilisation of routine health examinations should be a priority. Key messages 1st generation immigrant children appear more at risk for emotional and behaviour difficulties. Early identification of above difficulties and higher utilisation of routine health examinations should be a priority.

Background: Bayesian classifiers have the advantage of determining the class to which a given record belongs compared to traditional classifiers, taking as base the probability of an element belonging to a class. Thus, the diagnosis of diseases such as osteoporosis and osteopenia can become faster and precise. This paper presents an assessment of the accuracy of the Bayesian classifiers Bayes Net, Naive Bayes and Averaged One-Dependence Estimators to support diagnoses of osteopenia and osteoporosis. Method: The methodology that guided the development of this research relied on the choice of database, the study of the Bayes Net, Naive Bayes and Averaged One-Dependence Estimators algorithms, and the description of the experiments. Results: The algorithm with the highest specificity was Bayes Net, (53.0±0.27). The highest accuracy was obtained using the AODE classifier (83.0±0.17). Our results showed higher mean instances correctly classified using the Naive Bayes algorithm (82.84±14.42), and the average of incorrectly classified instances was higher for Bayes Net (17.46±14.76). Conclusion: Based on the statistical measures analyzed in the experiments (instances classified correctly and incorrectly, the kappa coefficient, mean absolute error, sensitivity, specificity, accuracy, recall, F-measure, and Area Under Curve (AUC)), all classifiers showed good results, thus, given these data, it is possible to produce a reasonably accurate estimate of the diagnosis.