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      Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China

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          Abstract

          Background

          The Poyang Lake Region, one of the major epidemic sites of schistosomiasis in China, remains a severe challenge. To improve our understanding of the current endemic status of schistosomiasis and to better control the transmission of the disease in the Poyang Lake Region, it is important to analyse the clustering pattern of schistosomiasis and detect the hotspots of transmission risk.

          Results

          Based on annual surveillance data, at the village level in this region from 2009 to 2014, spatial and temporal cluster analyses were conducted to assess the pattern of schistosomiasis infection risk among humans through purely spatial (Local Moran’s I, Kulldorff and Flexible scan statistic) and space-time scan statistics (Kulldorff). A dramatic decline was found in the infection rate during the study period, which was shown to be maintained at a low level. The number of spatial clusters declined over time and were concentrated in counties around Poyang Lake, including Yugan, Yongxiu, Nanchang, Xingzi, Xinjian, De’an as well as Pengze, situated along the Yangtze River and the most serious area found in this study. Space-time analysis revealed that the clustering time frame appeared between 2009 and 2011 and the most likely cluster with the widest range was particularly concentrated in Pengze County.

          Conclusions

          This study detected areas at high risk for schistosomiasis both in space and time at the village level from 2009 to 2014 in Poyang Lake Region. The high-risk areas are now more concentrated and mainly distributed at the river inflows Poyang Lake and along Yangtze River in Pengze County. It was assumed that the water projects including reservoirs and a recently breached dyke in this area were partly to blame. This study points out that attempts to reduce the negative effects of water projects in China should focus on the Poyang Lake Region.

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          Most cited references32

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          Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk.

          An estimated 779 million people are at risk of schistosomiasis, of whom 106 million (13.6%) live in irrigation schemes or in close proximity to large dam reservoirs. We identified 58 studies that examined the relation between water resources development projects and schistosomiasis, primarily in African settings. We present a systematic literature review and meta-analysis with the following objectives: (1) to update at-risk populations of schistosomiasis and number of people infected in endemic countries, and (2) to quantify the risk of water resources development and management on schistosomiasis. Using 35 datasets from 24 African studies, our meta-analysis showed pooled random risk ratios of 2.4 and 2.6 for urinary and intestinal schistosomiasis, respectively, among people living adjacent to dam reservoirs. The risk ratio estimate for studies evaluating the effect of irrigation on urinary schistosomiasis was in the range 0.02-7.3 (summary estimate 1.1) and that on intestinal schistosomiasis in the range 0.49-23.0 (summary estimate 4.7). Geographic stratification showed important spatial differences, idiosyncratic to the type of water resources development. We conclude that the development and management of water resources is an important risk factor for schistosomiasis, and hence strategies to mitigate negative effects should become integral parts in the planning, implementation, and operation of future water projects.
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            A flexibly shaped spatial scan statistic for detecting clusters

            Background The spatial scan statistic proposed by Kulldorff has been applied to a wide variety of epidemiological studies for cluster detection. This scan statistic, however, uses a circular window to define the potential cluster areas and thus has difficulty in correctly detecting actual noncircular clusters. A recent proposal by Duczmal and Assunção for detecting noncircular clusters is shown to detect a cluster of very irregular shape that is much larger than the true cluster in our experiences. Methods We propose a flexibly shaped spatial scan statistic that can detect irregular shaped clusters within relatively small neighborhoods of each region. The performance of the proposed spatial scan statistic is compared to that of Kulldorff's circular spatial scan statistic with Monte Carlo simulation by considering several circular and noncircular hot-spot cluster models. For comparison, we also propose a new bivariate power distribution classified by the number of regions detected as the most likely cluster and the number of hot-spot regions included in the most likely cluster. Results The circular spatial scan statistics shows a high level of accuracy in detecting circular clusters exactly. The proposed spatial scan statistic is shown to have good usual powers plus the ability to detect the noncircular hot-spot clusters more accurately than the circular one. Conclusion The proposed spatial scan statistic is shown to work well for small to moderate cluster size, up to say 30. For larger cluster sizes, the method is not practically feasible and a more efficient algorithm is needed.
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              Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland.

              Pollution hotspots in urban soils need to be identified for better environmental management. It is important to know if there are hotspots and if the hotspots are statistically significant. In this study identification of pollution hotspots was investigated using Pb concentrations in urban soils of Galway City in Ireland as an example, and the influencing factors on results of hotspot identification were investigated. The index of local Moran's I is a useful tool for identifying pollution hotspots of Pb pollution in urban soils, and for classifying them into spatial clusters and spatial outliers. The results were affected by the definition of weight function, data transformation and existence of extreme values. Compared with the results for the positively skewed raw data, the transformed data and data with extreme values excluded revealed a larger area for the high value spatial clusters in the city centre. While it is hard to decide the best way of using this index, it is suggested that all these influencing factors should be considered until reasonable and reliable results are obtained. GIS mapping can be applied to help evaluate the results via visualization of the spatial patterns. Meanwhile, selected pollution hotspots (extreme values) in this study were confirmed by re-analyses and re-sampling.
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                Author and article information

                Contributors
                14211020071@fudan.edu.cn
                robert.bergquist@outlook.com
                hslynn@shmu.edu.cn
                hufei.nc@gmail.com
                jxlindandan@163.com
                yuwan_0922@163.com
                Lisz@chinacdc.cn
                huy@lreis.ac.cn
                epistat@gmail.com
                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central (London )
                1756-3305
                8 March 2017
                8 March 2017
                2017
                : 10
                : 136
                Affiliations
                [1 ]ISNI 0000 0001 0125 2443, GRID grid.8547.e, Department of Epidemiology and Biostatistics, School of Public Health, , Fudan University, ; Shanghai, 200032 China
                [2 ]Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032 China
                [3 ]ISNI 0000 0001 0125 2443, GRID grid.8547.e, Laboratory for Spatial Analysis and Modeling, School of Public Health, , Fudan University, ; Shanghai, 200032 China
                [4 ]ISNI 0000 0001 0125 2443, GRID grid.8547.e, Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, , Fudan University, ; Shanghai, 200032 China
                [5 ]Ingerod, Brastad, Sweden
                [6 ]Jiangxi Institute of Schistosomiasis Prevention and Control, Nanchang, 330000 China
                [7 ]National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, 200032 China
                Article
                2059
                10.1186/s13071-017-2059-y
                5341164
                28270197
                5f14a030-ee03-4a00-ab6c-ebc77c48c374
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 November 2016
                : 23 February 2017
                Funding
                Funded by: National Natural Science Foundation of China (CN)
                Award ID: 81673239
                Award Recipient :
                Funded by: National Science Fund for Distinguished Young Scholars
                Award ID: No. 81325017
                Award Recipient :
                Funded by: Chang Jiang Scholars Program
                Award ID: T2014089
                Funded by: Fourth Round of Three-Year Public Health Action Plan of Shanghai, China
                Award ID: 15GWZK0101
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Parasitology
                schistosomiasis,spatio-temporal,poyang lake region, china
                Parasitology
                schistosomiasis, spatio-temporal, poyang lake region, china

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