AVALIAÇÃO DA SAÚDE SEXUAL E REPRODUTIVA DA MULHER: IMPORTÂNCIA DO ACOMPANHAMENTO GINECOLOGICO REGULAR

Introduction: The consultation with the gynecologist is complicated by several factors, such as health problems, workload and cultural issues. Objective: The objective of this study is to identify factors that influence gynecological care based on the understanding of health class issues. Methods: This is a descriptive, exploratory, multi-method study, with participants aged 18 or over from health courses at the University Center of Patos de Minas (UNIPAM). An efficient questionnaire made on Google . The ethics committee approved the study with number 2,512,677 in 2018. The observed data were analyzed with the Statistical Program for Statistical Analysis (SPSS - 14.0). Results: 332 professionals participated in the study, the majority of whom were Caucasian, single and with children, with an average age of 22 years. It was found that 41 percent of the students did not undergo the examination. The following factors hinder gynecological treatment: lack of appointment times (24.21%); lack of money to pay for the appointment (17.64%); lack of gynecological signs and symptoms (17.12%); difficulty in accessing public health services (12.63%), feeling deprived (14%), lack of sexual life (9.56%), lack of normal communication skills (4.84%). Conclusion: The data obtained contribute to the knowledge and understanding of aspects of gynecological care. This information should be taken into account when implementing measures to reduce the effects of routine gynecological care.Introdução: A consulta ao ginecologista é complicada por diversos fatores, como problemas de saúde, carga horária e problemas culturais. Objetivo: O objetivo deste estudo é identificar fatores que influenciam a assistência ginecológica a partir da compreensão das questões da classe de saúde. Métodos: Trata-se de um estudo descritivo, exploratório, multimétodos, com participantes de 18 anos ou mais de cursos da área da saúde do Centro Universitário de Patos de Minas (UNIPAM).  Um questionário eficiente feito no Google . O comitê de ética aprovou o estudo com número 2.512.677 em 2018. Os dados observados foram analisados ​​com o Programa Estatístico para Análise Estatística (SPSS - 14.0). Resultados: participaram do estudo 332 profissionais, a maioria caucasianos, solteiros e com filhos, idade média de 22 anos. Verificou-se que 41 por cento dos alunos não realizaram o exame. Elencam-se os fatores que dificultam o tratamento ginecológico: falta de horário de consulta (24,21%); falta de dinheiro para pagar a consulta (17,64%); sem sinais e sintomas ginecológicos (17,12%); dificuldade de acesso aos serviços públicos de saúde (12,63%), sentir-se privado (14%), sem vida sexual (9,56%), não ter habilidades normais de comunicação (4,84%). Conclusão: Os dados obtidos contribuem para o conhecimento e compreensão de aspectos da assistência ginecológica. Esta informação deve ser levada em consideração na implementação de medidas para reduzir os efeitos dos cuidados ginecológicos de rotina

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

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Geolocalização de internações cadastradas no Sistema de Informações Hospitalares do Sistema Único de Saúde: uma solução baseada no programa estatístico R

Resumo Objetivo: apresentar uma solução para permitir a geolocalização de internações processadas junto ao Sistema de Informações Hospitalares do Sistema Único de Saúde do Brasil. Métodos: para espacializar as AIHs, estruturou-se um script em linguagem R, com base nos pacotes microdatasus e CepR; o script foi aplicado para levantamento das AIHs em Goiás, referentes ao ano de 2015; após o download e pré-processamento dos dados, foi detalhado o procedimento para espacialização das AIHs. Resultados: das 361.213 AIHs processadas, foi possível extrair 24.220 códigos de endereçamento postal (CEPs) diferentes; deste conjunto de CEPs, foram geolocalizados 23.910 (98,7%); estes CEPs geolocalizados permitiram a espacialização de 97,7% das AIHs processadas para o estado de Goiás. Conclusão: é possível espacializar as AIHs com alto índice de sucesso; o método detalhado neste trabalho abre um novo leque de possibilidades para o desenho de estudos avaliativos, formulação de políticas e planejamento de ações de cuidado em saúde

Proposta de metodologia para estimar a área de cobertura potencial por equipes de atenção primária

Objetivo. Apresentar metodologia para avaliação empírica da atenção primária à saúde (APS) por meio da construção de representações digitais das áreas de cobertura potencial das equipes da APS. Métodos. Estudo de natureza metodológica. As áreas potenciais foram construídas por análise combinatória entre setores censitários e localização das unidades básicas de saúde (UBS) que apresentavam equipes de APS no Brasil. Foram utilizadas seis regras para parametrizar o algoritmo de construção das áreas potenciais. Assim, foram estipuladas seis restrições que viabilizaram o modelo utilizado: seleção de setores censitários próximos à UBS; setores contíguos; setores mutuamente excludentes; setores localizados no mesmo município da UBS; somatório de 4 500 usuários por equipe de saúde em cada UBS; e volume de população adscrita proporcional ao número de equipes de APS alocadas na UBS. A partir de 316 574 setores censitários e 39 758 UBS, foi desenvolvida uma matriz de vizinhança sobre a qual iterou um algoritmo de grafo que testava combinações de setores que atendessem simultaneamente as regras estipuladas. Resultados. Foram definidos 1 901 114 arcos ligando 30 351 setores censitários, permitindo a construção de 26 907 áreas potenciais. A partir desse resultado é possível fazer análises inframunicipais no que tange ao monitoramento de indicadores da APS. Os parâmetros customizáveis do algoritmo podem ser ajustados para contemplar diferentes conjuntos de regras e adaptados para diferentes países. Conclusões. O uso de abordagens amparadas em geoprocessamento pode criar condições para avaliação do impacto da APS, levando-se em conta bases de dados secundárias e com nível de análise inframunicipal, de UBS e até mesmo de equipes