Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics
Abstract Background Marching towards the elimination of schistosomiasis in China, both the incidence and prevalence have witnessed profound decline over the past decades, with the strategy shifting from morbidity control to transmission control. The current challenge is to find out hotspots of trans...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2014-12-01
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| Series: | Parasites & Vectors |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13071-014-0578-3 |
| _version_ | 1797811715526623232 |
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| author | Feng-hua Gao Eniola Michael Abe Shi-zhu Li Li-juan Zhang Jia-Chang He Shi-qing Zhang Tian-ping Wang Xiao-nong Zhou Jing Gao |
| author_facet | Feng-hua Gao Eniola Michael Abe Shi-zhu Li Li-juan Zhang Jia-Chang He Shi-qing Zhang Tian-ping Wang Xiao-nong Zhou Jing Gao |
| author_sort | Feng-hua Gao |
| collection | DOAJ |
| description | Abstract Background Marching towards the elimination of schistosomiasis in China, both the incidence and prevalence have witnessed profound decline over the past decades, with the strategy shifting from morbidity control to transmission control. The current challenge is to find out hotspots of transmission risk for precise targeted control in low-prevalence areas. This study assessed the risk at the village level, using the spatial and temporal characteristics of Schistosomiasis japonica in Anhui province from 2006 to 2012. Method The comprehensive database was generated from annual surveillance data at village level in Anhui province between 2006 and 2012, comprising schistosomiasis prevalence among humans and cattle, occurrence rate of infected environments and incidence rate of acute schistosomiasis. The database parameters were matched with geographic data of the study area and fine scale spatial-temporal cluster analysis based on retrospective space-time scan statistics was used to assess the clustering pattern of schistosomiasis. The analysis was conducted by using SaTScan 9.1.1 and ArcGIS 10.0. A spatial statistical modelling was carried out to determine the spatial dependency of prevalence of human infection by using Geoda 1.4.3. Result A pronounced decline was found in the prevalence of human infection, cattle infection, occurrence rate of environment with infected vector snails and incidence rate of acute schistosomiasis from 2006 to 2012 by 48.6%, 71.5%, 91.9% and 96.4%, respectively. Meanwhile, all 4 indicators showed a statistically significant clustering pattern both in time and space, with a total of 16, 6, 8 and 4 corresponding clustering foci found respectively. However, the number of clustering foci declined with time, and none was found after year 2010. All clustering foci were mainly distributed along the Yangtze River and its connecting branches. The result shows that there is a direct spatial relationship between prevalence of human infection and the other indicators. Conclusion A decreasing trend in space-time clustering of schistosomiasis endemic status was observed between 2006 and 2012 in Anhui province. Nevertheless, giving the complexity in schistosomiasis control, areas within the upper-stream of Yangtze River in Anhui section and its connecting branches should be targeted for effective implementation of control strategies in the future. |
| first_indexed | 2024-03-13T07:27:50Z |
| format | Article |
| id | doaj.art-d5f404ab49054fda8576e4a52df2e2de |
| institution | Directory Open Access Journal |
| issn | 1756-3305 |
| language | English |
| last_indexed | 2024-03-13T07:27:50Z |
| publishDate | 2014-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Parasites & Vectors |
| spelling | doaj.art-d5f404ab49054fda8576e4a52df2e2de2023-06-04T11:17:49ZengBMCParasites & Vectors1756-33052014-12-017111110.1186/s13071-014-0578-3Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statisticsFeng-hua Gao0Eniola Michael Abe1Shi-zhu Li2Li-juan Zhang3Jia-Chang He4Shi-qing Zhang5Tian-ping Wang6Xiao-nong Zhou7Jing Gao8Anhui Provincial Institute of Schistosomiasis ControlDepartment of Zoology, Federal University LafiaNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Key Laboratory of Parasite and Vector Biology, Ministry of HealthNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Key Laboratory of Parasite and Vector Biology, Ministry of HealthAnhui Provincial Institute of Schistosomiasis ControlAnhui Provincial Institute of Schistosomiasis ControlAnhui Provincial Institute of Schistosomiasis ControlNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Key Laboratory of Parasite and Vector Biology, Ministry of HealthNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Key Laboratory of Parasite and Vector Biology, Ministry of HealthAbstract Background Marching towards the elimination of schistosomiasis in China, both the incidence and prevalence have witnessed profound decline over the past decades, with the strategy shifting from morbidity control to transmission control. The current challenge is to find out hotspots of transmission risk for precise targeted control in low-prevalence areas. This study assessed the risk at the village level, using the spatial and temporal characteristics of Schistosomiasis japonica in Anhui province from 2006 to 2012. Method The comprehensive database was generated from annual surveillance data at village level in Anhui province between 2006 and 2012, comprising schistosomiasis prevalence among humans and cattle, occurrence rate of infected environments and incidence rate of acute schistosomiasis. The database parameters were matched with geographic data of the study area and fine scale spatial-temporal cluster analysis based on retrospective space-time scan statistics was used to assess the clustering pattern of schistosomiasis. The analysis was conducted by using SaTScan 9.1.1 and ArcGIS 10.0. A spatial statistical modelling was carried out to determine the spatial dependency of prevalence of human infection by using Geoda 1.4.3. Result A pronounced decline was found in the prevalence of human infection, cattle infection, occurrence rate of environment with infected vector snails and incidence rate of acute schistosomiasis from 2006 to 2012 by 48.6%, 71.5%, 91.9% and 96.4%, respectively. Meanwhile, all 4 indicators showed a statistically significant clustering pattern both in time and space, with a total of 16, 6, 8 and 4 corresponding clustering foci found respectively. However, the number of clustering foci declined with time, and none was found after year 2010. All clustering foci were mainly distributed along the Yangtze River and its connecting branches. The result shows that there is a direct spatial relationship between prevalence of human infection and the other indicators. Conclusion A decreasing trend in space-time clustering of schistosomiasis endemic status was observed between 2006 and 2012 in Anhui province. Nevertheless, giving the complexity in schistosomiasis control, areas within the upper-stream of Yangtze River in Anhui section and its connecting branches should be targeted for effective implementation of control strategies in the future.https://doi.org/10.1186/s13071-014-0578-3Fine scale spatial-temporal scan statisticsSchistosomiasis japonicaInfection risk analysisAnhui province |
| spellingShingle | Feng-hua Gao Eniola Michael Abe Shi-zhu Li Li-juan Zhang Jia-Chang He Shi-qing Zhang Tian-ping Wang Xiao-nong Zhou Jing Gao Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics Parasites & Vectors Fine scale spatial-temporal scan statistics Schistosomiasis japonica Infection risk analysis Anhui province |
| title | Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics |
| title_full | Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics |
| title_fullStr | Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics |
| title_full_unstemmed | Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics |
| title_short | Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics |
| title_sort | fine scale spatial temporal cluster analysis for the infection risk of schistosomiasis japonica using space time scan statistics |
| topic | Fine scale spatial-temporal scan statistics Schistosomiasis japonica Infection risk analysis Anhui province |
| url | https://doi.org/10.1186/s13071-014-0578-3 |
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