Incidence of visceral leishmaniasis in the Vaishali district of Bihar, India: spatial patterns and role of inland water bodies
AbstractThe role of the distribution of inland water bodies with respect to the transmission of visceral leishmaniasis (VL) and its dominant vector, Phlebotomous argentipes, has been studied at the regional scale in Bihar, eastern India. The Landsat TM sensor multispectral scanning radiometer, with a spatial resolution of 30 m in the visible, reflective-infrared and shortwave- infrared (SWIR) bands, was used to identify water bodies using the normalized differential pond index (NDPI) calculated as follows: (Green – SWIR I)/(Green + SWIR I). Nearest neighbour and grid square statistics were used to delineate spatial patterns and distribution of the sandfly vector and the disease it transmits. The female P. argentipes sandfly was found to be associated with the distance from open water and particularly abundant near non-perennial river banks (68.4%; P <0.001), while its association with rivers was focused further away from the water source (X2 = 26.3; P <0.001). The results also reveal that the distribution of VL is clustered around non-perennial riverbanks, while the pattern is slightly random around the perennial river banks. The grid square technique illustrate that the spatial distribution of the disease has a much stronger correlation with lower density of open waters surfaces as well as with sandfly densities (X2 = 26.0; P <0.001). The results of our study suggest that inland water presence poses a risk for VL by offering suitable breeding sites for P. argentipes, a fact that should be taken into account when attempting to control disease transmission.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2011 Gouri Sankar Bhunia, Shreekant Kesari, Nandini Chatterjee, Dilip Kumar Pal, Vijay Kumar, Alok Ranjan, Pradeep Das
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.