Efetividade do serviço móvel de urgência (Samu): uso de séries temporais interrompidas

OBJETIVO: Avaliar o desempenho do serviço de atendimento móvel de urgência (Samu) na região do Grande ABC, utilizando como condição traçadora o infarto agudo do miocárdio. MÉTODOS: A análise de séries temporais interrompidas foi a abordagem de escolha para testar efeitos imediatos e graduais da intervenção na população de estudo. A pesquisa compreendeu séries temporais mensais ajustadas da taxa de mortalidade hospitalar por infarto agudo do miocárdio no período entre 2000 e 2011. Os dados foram extraídos do Sistema de Informações sobre Mortalidade, usando a análise de regressão segmentada para avaliar o nível e tendência da intervenção antes e após sua implementação. Para fortalecer a validade interna do estudo, foi incluída uma região controle. RESULTADOS: A análise de séries temporais interrompidas mostrou redução de 0,04 mortes por 100.000 habitantes na taxa de mortalidade em relação à tendência subjacente desde a implantação do serviço de atendimento médico de urgência (p = 0,0040; IC95% -0,0816 – -0,0162) e uma redução no nível de 2,89 mortes por 100.000 habitantes (p = 0,0001; IC95% -4,3293 – -1,4623), ambos com significância estatística. Em relação à região controle, a Baixada Santista, a diferença da tendência do resultado entre desfecho de intervenção e controle pós-intervenção de -0,0639 mortes por 100.000 habitantes mostrou-se estatisticamente significativa (p = 0,0031; IC95% -0,1060 – -0,0219). Não podemos excluir confundimentos, mas limitamos sua presença no estudo incluindo séries de região controle. CONCLUSÕES: Embora a análise de séries temporais interrompidas tenha limitações, essa modelagem pode ser útil para a análise de desempenho de políticas e programas. Apesar de a intervenção estudada não ser uma condição que por si só implica na efetividade, a efetividade não estaria presente sem essa intervenção, que, integrada a outras condições, gera um resultado positivo. O Samu é uma estratégia cuja expansão precisa ser levada em consideração ao formular e consolidar políticas com foco nas urgências e emergências.OBJECTIVE: To evaluate the performance of the Mobile Emergency Medical Services (SAMU) in the ABC Region, using myocardial infarction as tracer condition. METHODS: The analysis of interrupted time series was the approach chosen to test immediate and gradual effects of the intervention on the study population. The research comprised adjusted monthly time series of the hospital mortality rate by myocardial infarction in the period between 2000 and 2011. Data were extracted from the Mortality Information System (SIM), using segmented regression analysis to evaluate the level and trend of the intervention before and after its implementation. To strengthen the internal validity of the study, a control region was included. RESULTS: The analysis of interrupted time series showed a reduction of 0.04 deaths per 100,000 inhabitants in the mortality rate compared to the underlying trend since the implementation of the Emergency Medical Services (p = 0.0040; 95%CI: −0.0816 – −0.0162) and a reduction in the level of 2.89 deaths per 100,000 inhabitants (p = 0.0001; 95%CI: −4.3293 – −1.4623), both with statistical significance. Regarding the control region, Baixada Santista, the difference in the result trend between intervention outcome and post-intervention control of −0.0639 deaths per 100,000 inhabitants was statistically significant (p = 0.0031; 95%CI: −0.1060 – −0.0219). We cannot exclude confounders, but we limited their presence in the study by including control region series. CONCLUSIONS: Although the analysis of interrupted time series has limitations, this modeling can be useful for analyzing the performance of policies and programs. Even though the intervention studied is not a condition that in itself implies effectiveness, the latter would not be present without the former, which, integrated with other conditions, generates a positive result. SAMU is a strategy that must be expanded when formulating and consolidating policies focusing on emergency care

HCV Genotypes, Characterization of Mutations Conferring Drug Resistance to Protease Inhibitors, and Risk Factors among Blood Donors in São Paulo, Brazil

Background: Hepatitis C virus (HCV) infection is a global health problem estimated to affect almost 200 million people worldwide. the aim of this study is to analyze the subtypes and existence of variants resistant to protease inhibitors and their association with potential HCV risk factors among blood donors in Brazil.Methods: Repeat anti-HCV reactive blood donors are systematically asked to return for retest, notification, and counseling in which they are interviewed for risk factors for transfusion-transmitted diseases. We analyzed 202 donors who returned for counseling from 2007 to 2010 and presented enzyme immunoassay-and immunoblot-reactive results. the HCV genotypes and resistance mutation analyses were determined by the direct sequencing of the NS5b and NS3 regions, respectively. the HCV viral load was determined using an in-house real-time PCR assay targeting the 5'-NCR.Results: HCV subtypes 1b, 1a, and 3a were found in 45.5%, 32.0%, and 18.0% of the donors, respectively. the mean viral load of genotype 1 was significantly higher than that of the genotype 3 isolates. Subtype 1a was more frequent among young donors and 3a was more frequent among older donors. Protease inhibitor-resistant variants were detected in 12.8% of the sequenced samples belonging to genotype 1, and a higher frequency was observed among subtype 1a (20%) in comparison to 1b (8%). There was no difference in the prevalence of HCV risk factors among the genotypes or drug-resistant variants.Conclusions: We found a predominance of subtype 1b, with an increase in the frequency of subtype 1a, in young subjects. Mutations conferring resistance to NS3 inhibitors were frequent in treatment-naive blood donors, particularly those infected with subtype 1a. These variants were detected in the major viral population of HCV quasispecies, have replicative capacities comparable to nonresistant strains, and could be important for predicting the response to antiviral triple therapy.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundacao Pro-Sangue/Hemocentro de São PauloFundacao Prosangue Hemoctr São Paulo, São Paulo, BrazilUniversidade Federal de São Paulo, Infect Dis Div DIPA, São Paulo, BrazilUniv São Paulo, Fac Med, Discipline Med Sci, São Paulo, BrazilHCFMUSP, Dept Pathol, LIM Lab Medice Lab 03, São Paulo, BrazilUniv Sao Joao del Rei, Divinopolis, MG, BrazilUniv São Paulo, Fac Med, Dept Infect Dis, São Paulo, BrazilUniversidade Federal de São Paulo, Infect Dis Div DIPA, São Paulo, BrazilWeb of Scienc

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

Evaluation of Primary Resistance to HIV Entry Inhibitors Among Brazilian Patients Failing Reverse Transcriptase/Protease Inhibitors Treatment Reveal High Prevalence of Maraviroc Resistance-Related Mutations

Entry inhibitor is a new class of drugs that target the viral envelope protein. This region is variable; hence resistance to these drugs may be present before treatment. The aim of this study was to analyze the frequency of patients failing treatment with transcriptase reverse and protease inhibitors that would respond to the entry inhibitors Enfuvirtide, Maraviroc, and BMS-806. The study included 100 HIV-1 positive patients from one outpatient clinic in the city of Sao Paulo, for whom a genotype test was requested due to treatment failure. Proviral DNA was amplified and sequenced for regions of gp120 and gp41. A total of 80 could be sequenced and from those, 73 (91.3%), 5 (6.3%) and 2 (2.5%) were classified as subtype B, F, and recombinants (B/F and B/C), respectively. CXCR4 co-receptor use was predicted in 30% of the strains. Primary resistance to Enfuvirtide was found in 1.3%, following the AIDS Society consensus list, and 10% would be considered resistant if a broader criterion was used. Resistance to BMS-806 was higher; 6 (7.5%), and was associated to non-B strains. Strikingly, 27.5% of samples harbored one or more mutation among A316T, I323V, and S405A, which have been related to decreased susceptibility of Maraviroc; 15% of them among viruses predictive to be R5. A more common mutation was A316T, which was associated to the Brazilian B strain harboring the GWGR motif at the tip of V3 loop and their derivative sequences. These results may be impact guidelines for genotype testing and treatment in Brazil.Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)[04/15856-9]Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)[09/52381-2