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[利用空间分析在巴西巴伊亚州劳罗·德弗雷塔斯市识别血吸虫病风险区域]

[Identification of schistosomiasis risk areas using spatial analysis in Lauro de Freitas, Bahia State, Brazil].

作者信息

Cardim Luciana Lobato, Ferraudo Antonio Sergio, Pacheco Selma Turrioni Azevedo, Reis Renato Barbosa, Silva Marta Mariana Nascimento, Carneiro Deborah Daniela M Trabuco, Bavia Maria Emilia

机构信息

Escola de Medicina Veterinária, Universidade Federal da Bahia, Salvador, Brasil.

出版信息

Cad Saude Publica. 2011 May;27(5):899-908. doi: 10.1590/s0102-311x2011000500008.

Abstract

The spread of schistosomiasis mansoni defies efforts by Brazil's Unified National Health System, thus demonstrating the need to reassess endemic control programs in the country. The aim of this study was to demarcate geographic areas at risk of schistosomiasis in Lauro de Freitas, Bahia State, Brazil, and to establish the epidemiological and socioeconomic profile of the disease in this municipality (county). Kernel density estimator exploratory analysis was used for visual identification of areas at risk. Kulldorff & Nagarwalla's spatial analysis was used to obtain statistically significant clusters and to measure risk. These technologies identified four risk areas for schistosomiasis. Clusters identified within the risk areas were characterized by lower socioeconomic conditions. Multiple correspondence analyses showed a distinct profile for positive patients in the primary cluster. The techniques employed here represent an important methodological acquisition for tracking and controlling schistosomiasis in Lauro de Freitas.

摘要

曼氏血吸虫病的传播使巴西统一国家卫生系统的努力受挫,从而表明有必要重新评估该国的地方病控制项目。本研究的目的是划定巴西巴伊亚州劳罗·德·弗雷塔斯市曼氏血吸虫病的危险地理区域,并确定该市(县)该疾病的流行病学和社会经济概况。核密度估计探索性分析用于直观识别危险区域。库尔道夫和纳加尔瓦拉的空间分析用于获得具有统计学意义的聚集区并衡量风险。这些技术识别出了四个曼氏血吸虫病风险区域。在风险区域内识别出的聚集区的特点是社会经济条件较差。多重对应分析显示了主要聚集区内阳性患者的独特特征。这里采用的技术是劳罗·德·弗雷塔斯市追踪和控制曼氏血吸虫病的一项重要方法学成果。

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