Fatores de risco relacionados à prematuridade ao nascer: um estudo caso-controle

Introdução: Vários fatores têm sido identificados quanto à possível relação com a prematuridade ao nascer. Objetivos: Avaliar os fatores de risco relacionados à prematuridade ao nascer. Metodolgia: Foi realizado um estudo caso-controle, num Hospital de Caruaru-PE, com 259 puérperas. Após o parto, as mulheres foram entrevistadas sobre possíveis fatores de risco durante a gravidez e, em seguida, foram submetidas a um exame periodontal, utilizando o Registro Periodontal Simplificado (PSR). Foram utilizados os testes estatísticos: Qui-quadrado de Pearson ou Exato de Fisher e t-Student. A hipótese de homogeneidade de variâncias foi verificada pelo teste F de Levene. Resultados: Observou-se a associação da prematuridade e baixo peso ao nascercom: tabagismo, etilismo, não realização de pré-natal completo, violência física e psicológica. Quanto à avaliação das alterações periodontais, os escores 1 (37,5%) e 2 (25,0%) estiveram mais prevalentes no grupo caso, enquanto que no controle foram mais prevalentes os escores 0, 3 e 4, não havendo associação com a variável estudada.Conclusões: Vários fatores de risco foram identificados para prematuridade ao nascer. Apesar da Doença Periodontal não ter sido associada a este desfecho, em face das discussões científicas sobre o tema, sugere-se uma maior exploração do assunto

Fatores de risco relacionados à prematuridade ao nascer: um estudo caso-controle

Introdução: Vários fatores têm sido identificados quanto à possível relação com a prematuridade ao nascer. Objetivos: Avaliar os fatores de risco relacionados à prematuridade ao nascer. Metodolgia: Foi realizado um estudo caso-controle, num Hospital de Caruaru-PE, com 259 puérperas. Após o parto, as mulheres foram entrevistadas sobre possíveis fatores de risco durante a gravidez e, em seguida, foram submetidas a um exame periodontal, utilizando o Registro Periodontal Simplificado (PSR). Foram utilizados os testes estatísticos: Qui-quadrado de Pearson ou Exato de Fisher e t-Student. A hipótese de homogeneidade de variâncias foi verificada pelo teste F de Levene. Resultados: Observou-se a associação da prematuridade e baixo peso ao nascercom: tabagismo, etilismo, não realização de pré-natal completo, violência física e psicológica. Quanto à avaliação das alterações periodontais, os escores 1 (37,5%) e 2 (25,0%) estiveram mais prevalentes no grupo caso, enquanto que no controle foram mais prevalentes os escores 0, 3 e 4, não havendo associação com a variável estudada.Conclusões: Vários fatores de risco foram identificados para prematuridade ao nascer. Apesar da Doença Periodontal não ter sido associada a este desfecho, em face das discussões científicas sobre o tema, sugere-se uma maior exploração do assunto

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

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq