Assistência hospitalar como indicador da desigualdade social

OBJECTIVE: To test a model for the study of inequalities in hospitalizations in the city of Ribeirão Preto (SP), understanding them to be due both to the social position of inpatients and also to health care policies in Brazil. MATERIAL AND METHOD: Using a hospital information system in existence for more than 25 years in the city of Ribeirão Preto - SP, 56.293 hospitalizations of municipal inhabitants occurring in some of the 12 general hospitals in 1993, were studied. Using the Brazilian occupancy classification for mortality, these inpatients were grouped on 6 occupational levels, as in the British classification: professional, intermediate, qualified non manual, qualified manual, partially qualified and unqualified. RESULTS AND CONCLUSION: Two-thirds of the inpatients had no place in the i.e. did not belong to the economically active population - and consisted of housewives, pensioners, children and students - and one third had some economic activity and thus belonged to the economically the active population. A close association was found between social strata and the classification of the hospital financing system into private, private group clinic and public health system patients. There were differences in hospital parameters as well as in morbidity patterns between these groups. The inequalities relating to average age, average age of hospital deaths, mean lengths of stay, hospital mortality, re-internment and frequency of diseases are discussed.This model allows the social position of the inpatient to be estimated using the hospital financing system, including also those patients with no economic activity, which covers the majority of the population. Social mechanisms created to compensate for inequalities in the welfare state do not cancel out the social differences.OBJETIVO: Testar um modelo para o estudo das desigualdades nas hospitalizações no Município de Ribeirão Preto (SP), entendidas como decorrentes da posição social dos pacientes e das políticas de assistência médico-hospitalar no Brasil. MATERIAL E MÉTODO: Foram estudadas 56.293 internações, ocorridas no ano de 1993, de pessoas residentes em Ribeirão Preto (SP) hospitalizadas nos 12 hospitais da cidade. Foram estabelecidos 6 níveis ocupacionais segundo a classificação brasileira de ocupações, a saber: profissionais, intermédios, qualificados não manuais, qualificados manuais, semiqualificados e não qualificados. RESULTADOS E CONCLUSÕES: Dois terços dos pacientes internados não tinham inserção econômica (fora da População Economicamente Ativa (PEA) - constituídos por donas-de-casa, aposentados, menores, estudantes - e um terço deles possuía uma ocupação definida na PEA. Foi encontrada forte associação entre os estratos sociais e o sistema de financiamento da hospitalização, classificado em particulares, medicina de grupo e sistema único de saúde. Houve diferenças em parâmetros das hospitalizações bem como no perfil de morbidade desses grupos. Foram discutidas as desigualdades na idade na hospitalização, idade ao morrer na internação, na duração média das internações, no coeficiente de mortalidade hospitalar, nas reinternações e na freqüência das doenças à internação. Este modelo permitiu inferir a posição social dos pacientes pelo sistema médico que utilizam nas hospitalizações, mesmo naqueles sem inserção econômica e que constituem a maioria. Os mecanismos sociais compensatórios do estado de bem-estar não conseguiram anular as diferenças

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Assistência hospitalar como indicador da desigualdade social Hospital assistance as an indicator of social inequality

OBJETIVO: Testar um modelo para o estudo das desigualdades nas hospitalizações no Município de Ribeirão Preto (SP), entendidas como decorrentes da posição social dos pacientes e das políticas de assistência médico-hospitalar no Brasil. MATERIAL E MÉTODO: Foram estudadas 56.293 internações, ocorridas no ano de 1993, de pessoas residentes em Ribeirão Preto (SP) hospitalizadas nos 12 hospitais da cidade. Foram estabelecidos 6 níveis ocupacionais segundo a classificação brasileira de ocupações, a saber: profissionais, intermédios, qualificados não manuais, qualificados manuais, semiqualificados e não qualificados. RESULTADOS E CONCLUSÕES: Dois terços dos pacientes internados não tinham inserção econômica (fora da População Economicamente Ativa (PEA) - constituídos por donas-de-casa, aposentados, menores, estudantes - e um terço deles possuía uma ocupação definida na PEA. Foi encontrada forte associação entre os estratos sociais e o sistema de financiamento da hospitalização, classificado em particulares, medicina de grupo e sistema único de saúde. Houve diferenças em parâmetros das hospitalizações bem como no perfil de morbidade desses grupos. Foram discutidas as desigualdades na idade na hospitalização, idade ao morrer na internação, na duração média das internações, no coeficiente de mortalidade hospitalar, nas reinternações e na freqüência das doenças à internação. Este modelo permitiu inferir a posição social dos pacientes pelo sistema médico que utilizam nas hospitalizações, mesmo naqueles sem inserção econômica e que constituem a maioria. Os mecanismos sociais compensatórios do estado de bem-estar não conseguiram anular as diferenças.OBJECTIVE: To test a model for the study of inequalities in hospitalizations in the city of Ribeirão Preto (SP), understanding them to be due both to the social position of inpatients and also to health care policies in Brazil. MATERIAL AND METHOD: Using a hospital information system in existence for more than 25 years in the city of Ribeirão Preto - SP, 56.293 hospitalizations of municipal inhabitants occurring in some of the 12 general hospitals in 1993, were studied. Using the Brazilian occupancy classification for mortality, these inpatients were grouped on 6 occupational levels, as in the British classification: professional, intermediate, qualified non manual, qualified manual, partially qualified and unqualified. RESULTS AND CONCLUSION: Two-thirds of the inpatients had no place in the i.e. did not belong to the economically active population - and consisted of housewives, pensioners, children and students - and one third had some economic activity and thus belonged to the economically the active population. A close association was found between social strata and the classification of the hospital financing system into private, private group clinic and public health system patients. There were differences in hospital parameters as well as in morbidity patterns between these groups. The inequalities relating to average age, average age of hospital deaths, mean lengths of stay, hospital mortality, re-internment and frequency of diseases are discussed.This model allows the social position of the inpatient to be estimated using the hospital financing system, including also those patients with no economic activity, which covers the majority of the population. Social mechanisms created to compensate for inequalities in the welfare state do not cancel out the social differences

Assistência hospitalar como indicador da desigualdade social

OBJETIVO: Testar um modelo para o estudo das desigualdades nas hospitalizações no Município de Ribeirão Preto (SP), entendidas como decorrentes da posição social dos pacientes e das políticas de assistência médico-hospitalar no Brasil. MATERIAL E MÉTODO: Foram estudadas 56.293 internações, ocorridas no ano de 1993, de pessoas residentes em Ribeirão Preto (SP) hospitalizadas nos 12 hospitais da cidade. Foram estabelecidos 6 níveis ocupacionais segundo a classificação brasileira de ocupações, a saber: profissionais, intermédios, qualificados não manuais, qualificados manuais, semiqualificados e não qualificados. RESULTADOS E CONCLUSÕES: Dois terços dos pacientes internados não tinham inserção econômica (fora da População Economicamente Ativa (PEA) - constituídos por donas-de-casa, aposentados, menores, estudantes - e um terço deles possuía uma ocupação definida na PEA. Foi encontrada forte associação entre os estratos sociais e o sistema de financiamento da hospitalização, classificado em particulares, medicina de grupo e sistema único de saúde. Houve diferenças em parâmetros das hospitalizações bem como no perfil de morbidade desses grupos. Foram discutidas as desigualdades na idade na hospitalização, idade ao morrer na internação, na duração média das internações, no coeficiente de mortalidade hospitalar, nas reinternações e na freqüência das doenças à internação. Este modelo permitiu inferir a posição social dos pacientes pelo sistema médico que utilizam nas hospitalizações, mesmo naqueles sem inserção econômica e que constituem a maioria. Os mecanismos sociais compensatórios do estado de bem-estar não conseguiram anular as diferenças