Prevalência e fatores associados ao aleitamento materno em crianças menores de 2 anos de idade

Introdução: o Aleitamento Materno (AM) é um ato importante para a saúde da criança, prevenindo doenças da infância e doenças crônicas futuras, sendo necessário Aleitamento Materno Exclusivo (AME) nas primeiras horas de vida e nos primeiros 6 meses da criança. Na Bahia e no Brasil, observa-se a baixa adesão ao aleitamento materno, fazendo-se necessário um estudo do tema. Objetivo: estimar a prevalência e os fatores associados ao AM em crianças menores de 2 anos de idade. Metodologia: estudo de corte transversal analítico com abordagem quantitativa, desenvolvido a partir da aplicação de questionário para mães de crianças com até dois anos de idade, amostra calculada de IC95% e erro amostral de 5%, com base em observação prévia do número de crianças atendidas no período da pesquisa (n=290), totalizando 134. Resultados: a prevalência do AM encontrada foi de 68,4%, enquanto que a do AME foi de 33,8%. Houve associação da prática de AM com os seguintes fatores: mãe amamentada na infância, idade gestacional > 37 semanas, presença de seis ou mais consultas de pré-natal, AM iniciado na 1ª hora de vida, AM iniciado no hospital, crianças de até 6 meses e ausência do uso de chupeta. Conclusões: o AM apresentou prevalência maior que o esperado e o AME, o contrário. Foram encontradas relações estatisticamente significantes entre algumas variáveis e a prevalência do AM. Diantedos resultados, políticas públicas de saúde voltadas para crianças menores de 2 anos e mães podem ser realizadas no município visando uma maior adesão à amamentação

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