A spatial analysis of human Schistosoma japonicum infections in Hubei, China, during 2009-2014.

ABSTRACT

BACKGROUND: The province of Hubei is located in the middle of China, near the middle and lower reaches of the River Yangtze, and is an area where schistosomiasis is endemic. It is challenging to control this disease in this environment, and it would be useful to identify clusters of infection and transmission, as well as their distributions during recent years. Therefore, this study aimed to analyze the spatial distribution of schistosomiasis in Hubei, in order to facilitate the effective control and elimination of this disease. METHODS: We collected schistosomiasis surveillance data from all endemic counties in Hubei during 2009-2014. A geographical information system (ArcGIS, version 10.1) was used to link the counties' geographical data with the epidemiological data, and the spatial scanning method (FleXScan v3.1.2) was used to identify spatial clusters of human infections with Schistosoma japonicum. RESULTS: In Hubei, patients who exhibited stool test results that were positive for S. japonicum accounted for > 50 % of all cases in China during 2009-2014. However, each endemic county in Hubei exhibited a declining trend in the number of human S. japonicum infections during the study period. The ArcGIS analyses revealed that the middle reaches of the River Yangtze were highly endemic for S. japonicum infections. Spatial scan analyses revealed the following infection clusters two clusters in ten counties during 2009, two clusters in nine counties during 2010, three clusters in 12 counties during 2011, two clusters in 12 counties during both 2012 and 2013 and two clusters in ten counties during 2014. Most of the cluster regions were located in the lake and marshland regions along the basins of the River Yangtze. CONCLUSION: We successfully identified schistosomiasis clusters at the county level in Hubei during 2009-2014, and our results revealed that the clusters were typically located in lake and marshland regions. These data may be useful for controlling and eliminating schistosomiasis in other high-risk areas.