Predicting the prevalence of type 2 diabetes in Brazil: a modeling study

AimsWe adopted a modeling approach to predict the likely future prevalence of type 2 diabetes, taking into account demographic changes and trends in obesity and smoking in Brazil. We then used the model to estimate the likely future impact of different policy scenarios, such as policies to reduce obesity.MethodsThe IMPACT TYPE 2 DIABETES model uses a Markov approach to integrate population, obesity, and smoking trends to estimate future type 2 diabetes prevalence. We developed a model for the Brazilian population from 2006 to 2036. Data on the Brazilian population in relation to sex and age were collected from the Brazilian Institute of Geography and Statistics, and data on the prevalence of type 2 diabetes, obesity, and smoking were collected from the Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone Survey (VIGITEL).ResultsThe observed prevalence of type 2 diabetes among Brazilians aged over 25 years was 10.8% (5.2–14.3%) in 2006, increasing to 13.7% (6.9–18.4%) in 2020. Between 2006 and 2020, the observed prevalence in men increased from 11.0 to 19.1% and women from 10.6 to 21.3%. The model forecasts a dramatic rise in prevalence by 2036 (27.0% overall, 17.1% in men and 35.9% in women). However, if obesity prevalence declines by 1% per year from 2020 to 2036 (Scenario 1), the prevalence of diabetes decreases from 26.3 to 23.7, which represents approximately a 10.0% drop in 16 years. If obesity declined by 5% per year in 16 years as an optimistic target (Scenario 2), the prevalence of diabetes decreased from 26.3 to 21.2, representing a 19.4% drop in diabetes prevalence.ConclusionThe model predicts an increase in the prevalence of type 2 diabetes in Brazil. Even with ambitious targets to reduce obesity prevalence, type 2 diabetes in Brazil will continue to have a large impact on Brazilian public health

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Reabilitação com mantenedor de espaço estético-funcional na perda precoce de dente decíduo – Relato de caso clínico

Reabilitação com mantenedor de espaço estético-funcional na perda precoce de dente decíduo – Relato de caso clínic

Atividades de educação em saúde dos estudantes de nutrição nos cenários de prática no município de Cabedelo (Paraíba) – Relato de experiência

Trata-se de um relato de experiência das práticas dos estudantes de Nutrição na Estratégia de Saúde da Família. O objetivo deste trabalho foi descrever atividades de educação em saúde nas práticas dos estudantes de Nutrição, na Área de Saúde Coletiva, através dos estágios desenvolvidos junto às Unidades Básicas de Saúde, entendendo a necessidade de modificação do perfil de formação dos profissionais de saúde. Os estudantes realizam visitas domiciliares para aqueles usuários que necessitam de atendimento nutricional e que possuem alguma dificuldade de locomoção, aconselhamento dietético junto à nutricionista durante o atendimento às gestantes e puericultura. Planejam a realização de atividades educativas e oficinas com a própria comunidade. Através de práticas desenvolvidas por esses alunos na Estratégia Saúde da Família, a troca de saberes entre profissionais e a comunidade vem enriquecendo e modificando o perfil dos nossos profissionais de saúde

Prevalência e preditores antropométricos de pressão arterial elevada em escolares de João Pessoa - PB

FUNDAMENTO: A hipertensão arterial pertence ao grupo de doenças cardiovasculares de maior mortalidade no mundo e pode se iniciar na infância. OBJETIVO: Avaliar a prevalência de níveis pressóricos elevados em crianças da rede pública de ensino e sua associação com indicadores antropométricos. MÉTODOS: Trata-se de um estudo transversal, composto por uma amostra aleatória de 750 escolares, entre 6-9 anos de idade, da rede de ensino municipal de João Pessoa, Paraíba. Os dados foram coletados por uma equipe previamente treinada. A pressão foi aferida com técnica auscultatória com auxílio de estetoscópio e esfigmomanômetro aneroide. Os dados antropométricos coletados foram as medidas do peso, da estatura e da circunferência abdominal. Foram feitos testes de associação qui-quadrado e t de Student para comparações de médias, ambos com nível de significância de 5,0%. Foram construídos três modelos de regressão logística, relacionando nível pressórico elevado com as variáveis antropométricas para encontrar um melhor modelo de predição. RESULTADOS: A prevalência de níveis pressóricos elevados foi de 13,6%. A variável índice de massa corporal (IMC) apresentou associação significativa com o aumento da pressão arterial (p < 0,0001) e a maior razão de chances (OR = 1,17). A elevação dos níveis pressóricos também ocorreu com o aumento do peso (p < 0,0001) e da circunferência abdominal (p < 0,0001). CONCLUSÃO: A associação do excesso de peso com a elevação da pressão arterial identificada destaca a necessidade de intervenção e medidas de controle do estado nutricional, como educação alimentar, para prevenção e tratamento da obesidade como fator de risco das doenças cardiovasculares na faixa etária pediátrica e futura. (Arq Bras Cardiol 2010; 95(5): 629-634