MODELAGEM GEOESPACIAL APLICADA À ANÁLISE MULTITEMPORAL DA OCORRÊNCIA DA ESQUISTOSSOMOSE NO ESTADO DE SERGIPE 2010-2014

Schistosomiasis is an ancient disease and to date constitutes a worldwide public health problem. In Brazil, about 25 million people live in areas at risk of contracting the endemic (Brasil, 2014). The state of Sergipe in northeastern Brazil, has the highest prevalence of Chagas disease in the Federation (Brasil, 2013). Given this, the study aimed to identify vulnerable areas and different schistosomiasis occurrence of situations in the state of Sergipe. Initially, from the database of the Schistosomiasis Control Program (PCE), from 2010 to 2014, the prevalence spatialized. Then, based on the average municipal prevalence in this period, we applied the model geoestatíscos by interpolation, Inverse Distance Weighted – IDW, to identify the areas of greatest vulnerability to occurrence of the disease in the state. In the temporal analysis, from 2010 to 2014, it was hardly evident change in the epidemiological profile of the state. The study identified that municipalities are most vulnerable in six of the eight state territory, which highlights the high incidence of the disease. Concluding that the Sergipe population is extremely vulnerable to the occurrence of schistosomiasis and requires the attention of the government to reverse this situation.A esquistossomose é uma doença milenar e, até hoje se constitui em um problema mundial de Saúde Pública. No Brasil, cerca de 25 milhões de pessoas vivem em áreas sob o risco de contrair a endemia (Brasil, 2014). O estado de Sergipe, no nordeste do Brasil, apresenta uma das maiores prevalências da endemia na Federação (Brasil, 2013). Diante disto, o estudo objetivou identificar áreas vulneráveis e diferentes situações de ocorrência da esquistossomose no estado de Sergipe. Inicialmente, a partir da base de dados do Programa de Controle da Esquistossomose (PCE), 2010 a 2014, espacializou-se a prevalência. Em seguida, com base nas médias das prevalências municipais neste período, aplicou-se o modelo geoestatísticos por interpolação, Inverso da Distância Ponderada – IDW, para identificação das áreas de maior vulnerabilidade a ocorrência da doença no estado. Na análise temporal, 2010 a 2014, quase não foi evidenciada mudança no perfil epidemiológico do estado. O estudo identificou que os municípios de maior vulnerabilidade estão em seis dos oitos territórios estaduais, o que evidencia a alta incidência da doença. Concluindo que a população sergipana está, extremamente, vulnerável a ocorrência da esquistossomose e necessita da atenção do poder público para reverter esse quadro

Epidemiological characteristics and geographical distribution of schistosomiasis and geohelminths, in the State of Sergipe, according to data from the Schistosomiasis Control Program in Sergipe

INTRODUÇÃO: A esquistossomose é endêmica no Brasil, com elevada prevalência no Estado de Sergipe, apesar da existência do Programa de Controle da Esquistossomose (PCE). MÉTODOS: Foi realizado levantamento de dados do PCE-Sergipe de 2005 a 2008. A partir da matriz bruta formulou-se planilha de dados no software Access e analisou-se frequência e distribuição geográfica das infecções por Schistosoma mansoni e outros enteroparasitos. Estes dados foram exportados para o software Spring 5.0.5 para georreferenciamento e confecção de mapas temáticos de distribuição espacial e temporal por ano de avaliação. RESULTADOS: Foram positivos para S. mansoni 13,6% (14471/106287) de exames nos anos de 2005, 11,2% (16196/145069) em 2006, 11,8% (10220/86824) em 2007 e 10,6% (8329/78859) em 2008. A análise de mapas mostrou elevada prevalência da doença em Sergipe, em particular nos municípios Ilha das Flores, Santa Rosa de Lima, Santa Luzia do Itanhi e São Cristóvão. Além disso, avaliamos a associação entre as frequências dessas doenças parasitárias com indicadores sociais e de desenvolvimento dos diferentes municípios, de acordo com os dados do Instituto Brasileiro de Geografia e Estatística (IBGE) e da Superintendência de Recursos Hídricos (SRH). Observamos que os municípios com prevalência da esquistossomose maior do que 15% têm menor concentração de rede de esgotos (índice de higiene); p = 0,05. Adicionalmente, os municípios com prevalência de infecção por ancilostomídeos maior do que 10% apresentam um menor IDH educacional; p = 0,04. CONCLUSÕES: Ressalta-se a importância de maior controle dos fatores de risco ambientais e educacionais, na tentativa de reduzir prevalências dessas doenças parasitárias. ________________________________________________________________________________________ ABSTRACT: INTRODUCTION: Schistosomiasis is endemic in Brazil, with high prevalence in the State of Sergipe, despite the existence of the Schistosomiasis Control Program (PCE). METHODS: The data from Sergipe's PCE between 2005 and 2008 were surveyed. From the raw information, a database was created on a spreadsheet using the Access software. The frequency and geographic distribution of infections due to Schistosoma mansoni and other intestinal parasites were analyzed. These data were exported to the Spring 5.0.5 software for georeferencing and preparation of thematic maps of the spatial and temporal distribution according to year of evaluation. RESULTS: In 2005, 13.6% (14,471/106,287) of the tests were positive for S. mansoni, 11.2% (16,196/145,069) in 2006, 11.8% (10,220/86,824) in 2007 and 10.6% (8,329/78,859) in 2008. Analysis on the maps showed that there was high prevalence of the disease in Sergipe, and particularly in the municipalities of Ilha das Flores, Santa Rosa de Lima, Santa Luzia do Itanhi and São Cristóvão. Furthermore, we evaluated the association between the frequencies of these parasitic diseases and social and developmental indicators in the different municipalities, according to data from the Brazilian Institute for Geography and Statistics (IBGE) and the Department of Water Resources (SRH). We found that municipalities with schistosomiasis prevalence higher than 15% had lower coverage of sewage systems (hygiene index) (p = 0.05). Additionally, municipalities with hookworm prevalence higher than 10% had lower educational HDI (p = 0.04). CONCLUSIONS: The importance of greater control over environmental risk and educational factors needs to be emphasized in attempts to reduce the prevalence of these parasitic diseases

Biomphalaria species distribution and its effect on human Schistosoma mansoni infection in an irrigated area used for rice cultivation in northeast Brazil

The role of irrigated areas for the spread of schistosomiasis is of worldwide concern. The aim of the present study was to investigate the spatial distribution of the intermediate snail host Biomphalaria in an area highly endemic for schistosomiasis due to Schistosoma mansoni, evaluating the relationship between irrigation and types of natural water sources on one hand, and the influence of place and time of water exposure on the intensity of human infection on the other. A geographical information system (GIS) was used to map the distribution of the intermediate snail hosts in Ilha das Flores, Sergipe, Brazil, combined with a clinical/epidemiological survey. We observed a direct correlation between the intensity of human infection with S. mansoni and irrigation projects. Malacological studies to identify snail species and infection rates showed that B. glabrata is the main species responsible for human schistosomiasis in the municipality, but that B. straminea also plays a role. Our results provide evidence for a competitive selection between the two snail species in rice fields with a predominance of B. glabrata in irrigation systems and B. straminea in natural water sources

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