Plasmodium falciparum malaria is a threat to public health, but Plasmodium vivax malaria is most prevalent in Latin America, where the incidence rate has been increasing since 2016, particularly in Venezuela and Brazil. The Brazilian Amazon reported 193,000 cases in 2017, which were mostly confirmed as P. vivax (~ 90%). Herein, the relationships among malaria incidence rates and the proportion of accumulated deforestation were contrasted using data from the states of Acre and Rondônia in the south-western Brazilian Amazon. The main purpose is to test the hypothesis that the observed difference in incidence rates is associated with the proportion of accumulated deforestation. An ecological study using spatial and temporal models for mapping and modelling malaria risk was performed. The municipalities of Acre and Rondônia were the spatial units of analysis, whereas month and year were the temporal units. The number of reported malaria cases from 2009 until 2015 were used to calculate the incidence rate per 1000 people at risk. Accumulated deforestation was calculated using publicly available satellite images. Geographically weighted regression was applied to provide a local model of the spatial heterogeneity of incidence rates. Time-series dynamic regression was applied to test the correlation of incidence rates and accumulated deforestation, adjusted by climate and socioeconomic factors. The malaria incidence rate declined in Rondônia but remained stable in Acre. There was a high and positive correlation between the decline in malaria and higher proportions of accumulated deforestation in Rondônia. Geographically weighted regression showed a complex relationship. As deforestation increased, malaria incidence also increased in Acre, while as deforestation increased, malaria incidence decreased in Rondônia. Time-series dynamic regression showed a positive association between malaria incidence and precipitation and accumulated deforestation, whereas the association was negative with the human development index in the westernmost areas of Acre. Landscape modification caused by accumulated deforestation is an important driver of malaria incidence in the Brazilian Amazon. However, this relationship is not linearly correlated because it depends on the overall proportion of the land covered by forest. For regions that are partially degraded, forest cover becomes a less representative component in the landscape, causing the abovementioned non-linear relationship. In such a scenario, accumulated deforestation can lead to a decline in malaria incidence.

Resumo Objetivo: Analisar fatores de risco para óbito em indivíduos com síndrome respiratória aguda grave por COVID-19. Métodos: Coorte retrospectiva, constituída de indivíduos adultos com COVID-19, de março a setembro de 2020, notificados pelo sistema de vigilância epidemiológica do estado do Acre, Brasil. Empregou-se regressão de Cox. Resultados: Entre 57.700 indivíduos analisados, a incidência foi de 2.765,4/100 mil habitantes, e a mortalidade, de 61,8/100 mil hab. Os fatores de risco para o óbito foram ser do sexo masculino (HR=1,48 - IC95% 1,25;1,76), ter idade ≥60 anos (HR=10,64 - IC95% 8,84;12,81), sintoma de dispneia (HR=4,20 - IC95% 3,44;5,12) e apresentar multimorbidade (HR=2,23 - IC95% 1,77;2,81), com destaque para cardiopatas e diabetes mellitus. Os sintomas ‘dor de garganta’ e ‘cefaleia’ estavam presentes nos casos leves da doença. Conclusão: Ser homem, idoso, apresentar cardiopatia, diabetes mellitus e dispneia foram características associadas ao óbito pela COVID-19.

The Brazilian Amazon, a vital tropical region, faces escalating threats from human activities, agriculture, and climate change. This study aims to assess the relationship between forest fire occurrences, meteorological factors, and hospitalizations due to respiratory diseases in the Legal Amazon region from 2009 to 2019. Employing simultaneous equation models with official data, we examined the association between deforestation-induced fires and respiratory health issues. Over the studied period, the Legal Amazon region recorded a staggering 1,438,322 wildfires, with 1,218,606 (85%) occurring during August–December, known as the forest fire season. During the forest fire season, a substantial portion (566,707) of the total 1,532,228 hospital admissions for respiratory diseases were recorded in individuals aged 0–14 years and 60 years and above. A model consisting of two sets of simultaneous equations was constructed. This model illustrates the seasonal fluctuations in meteorological conditions driving human activities associated with increased forest fires. It also represents how air quality variations impact the occurrence of respiratory diseases during forest fires. This modeling approach unveiled that drier conditions, elevated temperatures, and reduced precipitation exacerbate fire incidents, impacting hospital admissions for respiratory diseases at a rate as high as 22 hospital admissions per 1000 forest fire events during the forest fire season in the Legal Amazon, 2009–2019. This research highlights the urgent need for environmental and health policies to mitigate the effects of Amazon rainforest wildfires, stressing the interplay of deforestation, climate change, and human-induced fires on respiratory health.

We aimed to evaluate the accuracy of deterministic and stochastic statistical models by means of a protocol developed in a free programming environment for monthly time-series analysis of the incidence of confirmed dengue cases in the states and federal district of Brazil from January 2000 to December 2017. This was an ecological time-series study conducted to evaluate and validate the accuracy of 10 statistical models for predicting the new cases of dengue. Official data on the monthly cases of dengue from January 2000 to December 2016 were used to train the statistical models, while those for the period January–December 2017 were used to test the predictive capacity of the models by considering three forecasting horizons (12, 6, and 3 months). Deterministic models proved to be reliable for predicting dengue in a 12-month forecasting horizon, while stochastic models were reliable for predicting the disease in a 3-month forecasting horizon. We were able to reliably employ models for predicting dengue in the states and federal district of Brazil. Hence, we strongly recommend incorporating these models in state health services for predicting dengue and for decision-making with regard to the advanced planning of interventions before the emergence of epidemics.

Introdução: A cultura de segurança do paciente é fator contribuinte para a manutenção do bem-estar do usuário no sistema de saúde, pois por meio dela obtém-se uma sistematização organizada e de qualidade do cuidado ao paciente, prevenindo possíveis intercorrências que possam trazer danos. Objetivo: Analisar a Cultura de Segurança do Paciente (CSP) na perspectiva dos profissionais de saúde no Hospital de Referência do Alto Rio Juruá, na Amazônia Ocidental Brasileira. Método: Trata-se de um estudo transversal desenvolvido em um hospital público de médio porte em um município da Amazônia Ocidental. O questionário Hospital Survey on Patient Safety Culture, da Agency for Healthcare Research and Quality foi aplicado em 280 profissionais, no período dezembro de 2016 a fevereiro de 2017. Foi realizada a análise descritiva dos dados e a consistência interna do instrumento. Resultados: Os resultados apontam as melhores avaliações nas dimensões de Trabalho em equipe nos âmbitos das unidades (60%) e Aprendizado organizacional (60%). Os aspectos com os piores resultados foram as dimensões de Respostas não punitivas aos erros (18%) e Frequência de eventos relatados (32%). A análise de confiabilidade interna (Alpha de Cronbach) das dimensões variou entre 0,35 a 0,90. Conclusão: A cultura do medo parece predominar nesse hospital, contudo o estudo demonstrou que há possibilidades de melhoria em todas as dimensões da CSP. Os valores do Alpha de Cronbach apresentaram semelhança com os resultados obtidos pelo processo de validação.

Resumo Objetivo Avaliar a capacidade preditiva de diferentes modelos de série temporal de casos de malária no estado do Amapá, Brasil, no período 1997-2016. Métodos Estudo ecológico de séries temporais com casos de malária registrados no Amapá. Foram utilizados dez modelos estatísticos determinísticos ou estocásticos para simulação e teste em horizontes de previsão de 3, 6 e 12 meses. Resultados O teste inicial mostrou que a série é estacionária. Os modelos determinísticos apresentaram melhor desempenho do que os modelos estocásticos. O modelo ARIMA apresentou erros absolutos menores do que 2% na escala logarítmica e erros relativos 3,4-5,8 vezes menores em relação ao modelo nulo. A predição de casos futuros de malária nos horizontes de 6 e 12 meses de antecedência foi possível. Conclusão Recomenda-se o uso de modelo ARIMA para a previsão de cenários futuros e para a antecipação do planejamento nos serviços de saúde dos estados da Região Amazônica.