Exploring spatial and temporal patterns of visceral leishmaniasis in endemic areas of Bangladesh.
Exploring spatial and temporal patterns of visceral leishmaniasis in endemic areas of Bangladesh.
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DOI：
10.1186/s41182-017-0069-2
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发表时间：
2017
影响因子：
4.5
通讯作者：
Rahman MM
中科院分区：
文献类型：
--
作者：
Dewan A;Abdullah AYM;Shogib MRI;Karim R;Rahman MM
内脏利什曼病是印度次大陆一个相当大的公共卫生负担。该病在孟加拉国中北部地区高度流行，影响到最贫穷和最边缘化的社区。尽管内脏利什曼病（VL）在该区域造成死亡率、严重发病率和社会经济压力，但该疾病的时空动态在很大程度上仍未得到探索，特别是在孟加拉国。本研究对2010 - 2014年每月从街道医院获得的VL病例进行了研究。使用全局和局部空间自相关技术来确定疾病的空间异质性。此外，空间扫描测试用于识别孟加拉国流行地点的统计显著时空集群。整体和局部空间自相关表明VL的分布具有空间自相关特征，表现为连续型和迁移型扩散；而前者是研究区VL传播的主要类型。空间扫描测试显示，该疾病在集群内的发病率比非集群区域高10倍。尽管两种测试的算法和聚类检测方法存在差异，但它们都在相同的地理区域识别出了聚类。在这项工作中生成的群集图可被公共卫生官员用来确定干预领域的优先次序。此外，当已知疾病高风险地区时，可以更有效地处理控制VL的举措。由于预计全球环境变化将把目前的病媒分布转移到新的地点，这项工作的结果可以帮助确定潜在的暴露人群，以便制定适应策略。
Visceral leishmaniasis is a considerable public health burden on the Indian subcontinent. The disease is highly endemic in the north-central part of Bangladesh, affecting the poorest and most marginalized communities. Despite the fact that visceral leishmaniasis (VL) results in mortality, severe morbidity, and socioeconomic stress in the region, the spatiotemporal dynamics of the disease have largely remained unexplored, especially in Bangladesh. Monthly VL cases between 2010 and 2014, obtained from subdistrict hospitals, were studied in this work. Both global and local spatial autocorrelation techniques were used to identify spatial heterogeneity of the disease. In addition, a spatial scan test was used to identify statistically significant space-time clusters in endemic locations of Bangladesh. Global and local spatial autocorrelation indicated that the distribution of VL was spatially autocorrelated, exhibiting both contiguous and relocation-type of diffusion; however, the former was the main type of VL spread in the study area. The spatial scan test revealed that the disease had ten times higher incidence rate within the clusters than in non-cluster zones. Both tests identified clusters in the same geographic areas, despite the differences in their algorithm and cluster detection approach. The cluster maps, generated in this work, can be used by public health officials to prioritize areas for intervention. Additionally, initiatives to control VL can be handled more efficiently when areas of high risk of the disease are known. Because global environmental change is expected to shift the current distribution of vectors to new locations, the results of this work can help to identify potentially exposed populations so that adaptation strategies can be formulated.