Evolução dos cadastros individuais no SISAB a partir do novo financiamento da Atenção Básica: Um estudo descritivo

It is understood that the new financing of Primary Care through Previne Brasil instituted in November 2019, implied the need for individual registrations as a premise for transferring resources to the municipalities. In the meantime, it is noteworthy that this study aims to highlight the evolution of the number of individual registrations of e-SUS AB among the Northeastern states in the period between the third quarter of 2019 (2019Q3) and the third quarter of 2020 (2020Q3). This is a descriptive cross-sectional study of a quantitative nature, based on secondary data collected from the Primary Care Information System between February and March 2021. When analyzing the results, the state that showed the greatest evolution in the number of registrations was Bahia with a percentage equivalent to 7.12%, while only Ceará showed no growth in the number of registrations. The other states in the Northeast region did not show an increase of more than 4%. After one year of implementation of the Program, it was observed that the evolution of the registrations is still incipient, and new investigations are needed with the municipalities in order to identify strategies to increase individual registrations and therefore maintain the cost of Primary Health Care.Se entiende que la nueva financiación de la Atención Primaria a través de Previne Brasil, instituida en noviembre de 2019, implicó la necesidad de registros individuales como premisa para la transferencia de recursos a los municipios. Mientras tanto, cabe destacar que este estudio tiene como objetivo mostrar la evolución del número de registros individuales de e-SUS AB entre los estados del noreste en el período comprendido entre el tercer trimestre de 2019 (2019Q3) y el tercer trimestre de 2020 (2020Q3). Se trata de un estudio descriptivo transversal de carácter cuantitativo, basado en datos secundarios recogidos del Sistema de Información de Atención Primaria entre febrero y marzo de 2021. Al analizar los resultados, el estado que mostró la mayor evolución en el número de registros fue Bahía con un porcentaje equivalente al 7,12%, mientras que sólo Ceará no mostró crecimiento en el número de registros. Los demás estados que componen la región noreste no registraron un crecimiento superior al 4%. Después de un año de implementación del programa, se observó que la evolución de las inscripciones es aún incipiente, lo que exige nuevas investigaciones con los municipios para identificar estrategias que permitan aumentar las inscripciones individuales y, por lo tanto, mantener el costo de la Atención Primaria de Salud. Traducción realizada con la versión gratuita del traductor www.DeepL.com/TranslatorCompreende-se que o novo financiamento da Atenção Básica através do Previne Brasil instituído em novembro de 2019, implicou na necessidade de cadastros individuais como premissa para repasse de recursos aos municípios. Neste ínterim, ressalta-se que este estudo pretende evidenciar a evolução do número de cadastros individuais do e-SUS AB entre os estados do Nordeste no período entre o terceiro quadrimestre de 2019 (2019Q3) e o terceiro quadrimestre de 2020 (2020Q3). Trata-se de um estudo transversal descritivo de natureza quantitativa, pautado em dados secundários coletados do Sistema de Informação da Atenção Básica, entre fevereiro e março de 2021. Ao analisar os resultados, aquele estado que apresentou maior evolução no número de cadastros foi a Bahia com percentual equivalente a 7,12%, enquanto que apenas o Ceará não apresentou crescimento no número de cadastros. Os outros estados que compõem a região Nordeste, não apresentaram crescimento superior a 4%. Em um ano de implantação do Programa, observou-se que a evolução dos cadastros ainda é incipiente, sendo necessárias novas investigações junto aos municípios a fim de serem levantadas estratégias para incremento dos cadastros individuais e por conseguinte manutenção do custeio da Atenção Primária à Saúde

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