CONTROLE E PROFILAXIA: ANÁLISE DOCUMENTAL DA INCIDÊNCIA DE CASOS DE HIPERTENSÃO ARTERIAL SISTÊMICA UNIDADES BÁSICAS DE SÁUDE EM ARACOIABA-CE

Introdução: a hipertensão arterial sistêmica (HAS) é uma condição clínica multifatorial descrita por níveis elevados de pressão arterial. É uma doença crônica e atualmente constitui um problema de saúde pública do Brasil. Visando a busca constante de meios que possibilitem uma melhoria na qualidade de vida dos acometidos com a doença, toma-se como base a implementação de políticas públicas de intervenções quanto ao quadro de manifestação da doença. Objetivo: analisar os dados referentes ao acompanhamento e profilaxia realizados em uma UBS de uma cidade no interior do Estado do Ceará. Método: trata-se de um estudo bibliográfico, do tipo epidemiológico. O levantamento de dados ocorreu entre os meses de fevereiro e maio de 2018, com os dados de uma Unidade Básica de Saúde (UBS) no município de Aracoiaba/CE. Houve uma análise documental no que diz respeito ao acompanhamento dos profissionais enfermeiros de 3 principais UBS do município, as intervenções são de cunho profilático e de controle. A obtenção de dados deu-se com a análise de dados disponibilizados através do portal da secretaria de saúde do município. Resultados: os cuidados implementados foram: acompanhar o tratamento farmacológico dos pacientes diagnosticados; informar quanto aos cuidados nos hábitos alimentares cotidianos, panfletagem na triagem da recepção da UBS. Conclusão: o acompanhamento que está sendo realizado é de essencial importância no manejo de pacientes portadores de HAS, avaliando a evolução do paciente, determinando se há ou não eficácia nas técnicas de acompanhamento, para que o quadro clínico da doença seja regredido até um limiar próximo de cura

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