Predictors of gaming disorder in children and adolescents : a school-based study

Objective: To determine whether psychiatric and gaming pattern variables are associated with gaming disorder in a school-based sample. Methods: We analyzed data from the Brazilian High-Risk Cohort for Psychiatric Disorders, a community sample aged 10 to 18, using questionnaires on gaming use patterns. We applied the Gaming Addiction Scale to diagnose gaming disorder and the Development and Well-Being Behavior Assessment for other diagnoses. Results: Out of 407 subjects, 83 (20.4%) fulfilled the criteria for gaming disorder. More role-playing game players were diagnosed with gaming disorder that any other genre. Gaming disorder rates increased proportionally to the number of genres played. Playing online, being diagnosed with a mental disorder, and more hours of non-stop gaming were associated with higher rates of gaming disorder. When all variables (including age and gender) were considered in a logistic regression model, the number of genres played, the number of non-stop hours, the proportion of online games, and having a diagnosed mental disorder emerged as significant predictors of gaming disorder. Conclusion: Each variable seems to add further risk of gaming disorder among children and adolescents. Monitoring the length of gaming sessions, the number and type of genres played, time spent gaming online, and behavior changes may help parents or guardians identify unhealthy patterns of gaming behavior

Concentrados de ácidos graxos insaturados obtidos a partir de óleo de Carpa (Cyprinus carpio) utilizando o método da complexação com uréia

O trabalho teve como objetivo estabelecer as melhores condições para obter concentrados de ácidos graxos mono e poliinsaturados a partir do óleo branqueado de carpa (Cyprinus carpio), utilizando o método de complexação com uréia. Os fatores de estudo foram: relação de uréia-ácido graxo (2:1 e 6:1), temperatura de cristalização (4ºC e -12ºC) e tempo de cristalização (14 e 24 h). As respostas para análise estatística foram o somatório de ácidos graxos saturados (Σ(AGS)); somatório de ácidos graxos monoinsaturados e poliinsaturados (Σ(AGMI+AGPI)); somatório de ácido graxo eicosapentaenoico e docosahexaenóico (Σ(EPA+DHA)). A relação uréia/AG se mostrou muito significativa, de forma diretamente proporcional na obtenção dos concentrados. A relação entre temperatura e rendimento dos concentrados foi de forma inversamente proporcional, e o tempo foi significativo, porém com menor influência. As melhores condições para a obtenção de concentrados foram: maior relação de uréia/AG (6:1), menor temperatura (-12ºC) e menor tempo (14 h). Sendo que nestas condições, os AGMI+AGPI constituíram 85,2% do concentrado, e entre estes os EPA+DHA foram de 9,4%. As frações líquidas apresentaram rendimento em massa de até 65,4%, e seu percentual de ácidos graxos livres ficou em média 35,8 g/100g

Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions.

BackgroundPneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates.MethodsWe assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile.ResultsOur method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age.ConclusionsOur approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses

Bayesian Model Averaging With Change Points to Assess the Impact of Vaccination and Public Health Interventions.

Background: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. Methods: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile. Results: Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to \u3c1 years of age. Conclusions: Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses