Spatial pattern of schistosomiasis in Xingzi, Jiangxi Province, China: the effects of environmental factors

Particle and Fibre Toxicology

Qingwu Jiang³  Qiulin Jiang⁴  Bo Tao⁴  Jie Gao³  Zengliang Wang³  Yue Chen²  Zhijie Zhang¹  Yi Hu³ 

[1] Biomedical Statistical Center, Fudan University, Shanghai 200032, China;Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario, Canada;Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China;Xingzi Station for Schitosomiasis Prevention and Control, Xingzi, Jiangxi Province 332800, China

关键词: Geographic information systems;    Environmental factors;    Geostatistical prediction;    Oncomelania hupensis;    Schistosoma japonicum;   

Others  :  1225330
DOI  :  10.1186/1756-3305-6-214

 received in 2013-04-06, accepted in 2013-07-18,  发布年份 2013

【 摘 要 】

Background

The recent rebounds of schistosomiasis in the middle and lower reaches of the Yangtze River pose a challenge to the current control strategies. In this study, identification of potential high risk snail habitats was proposed, as an alternative sustainable control strategy, in Xingzi County, China. Parasitological data from standardized surveys were available for 36,208 locals (aged between 6–65 years) from 42 sample villages across the county and used in combination with environmental data to investigate the spatial pattern of schistosomiasis risks.

Methods

Environmental factors measured at village level were examined as possible risk factors by fitting a logistic regression model to schsitosomiasis risk. The approach of ordinary kriging was then used to predict the prevalence of schistosomiasis over the whole county.

Results

Risk analysis indicated that distance to snail habitat and wetland, rainfall, land surface temperature, hours of daylight, and vegetation are significantly associated with infection and the residual spatial pattern of infection showed no spatial correlation. The predictive map illustrated that high risk regions were located close to Beng Lake, Liaohuachi Lake, and Shixia Lake.

Conclusions

Those significant environmental factors can perfectly explain spatial variation in infection and the high risk snail habitats delineated by the predicted map of schistosomiasis risks will help local decision-makers to develop a more sustainable control strategy.

【 授权许可】

   
2013 Hu et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Zhou XN, Wang LY, Chen MG, Wu XH, Jiang QW, Chen XY, Zheng J, Utzinger J: The public health significance and control of schistosomiasis in China–then and now. Acta Trop 2005, 96:97-105.
• [2]Zhou XN, Guo JG, Wu XH, Jiang QW, Zheng J, Dang H, Wang XH, Xu J, Zhu HQ, Wu GL, et al.: Epidemiology of schistosomiasis in the People’s Republic of China, 2004. Emerg Infect Dis 2007, 13:1470-1476.
• [3]McManus DP, Gray DJ, Li Y, Feng Z, Williams GM, Stewart D, Rey-Ladino J, Ross AG: Schistosomiasis in the People’s Republic of China: the Era of the Three Gorges Dam. Clin Microbiol Rev 2010, 23:442-466.
• [4]Liang S, Yang C, Zhong B, Qiu D: Re-emerging schistosomiasis in hilly and mountainous areas of Sichuan, China. Bull World Health Organ 2006, 84:139-144.
• [5]Utzinger J, Bergquist R, Shu-Hua X, Singer BH, Tanner M: Sustainable schistosomiasis control–the way forward. Lancet 2003, 362:1932-1934.
• [6]Zhou YB, Yang MX, Wang QZ, Zhao GM, Wei JG, Peng WX, Jiang QW: Field comparison of immunodiagnostic and parasitological techniques for the detection of Schistosomiasis japonica in the People’s Republic of China. AmJTrop Med Hyg 2007, 76:1138-1143.
• [7]Chen XY: Schistosomiasis control strategy, administrative pattern, financial support and technological processes under different background in China. Shanghai: Fudan University, Department of Epidemiology; 2005. [PhD thesis]
• [8]Zhang ZJ, Carpenter TE, Lynn HS, Chen Y, Bivand R, Clark AB, Hui FM, Peng WX, Zhou YB, Zhao GM, et al.: Location of active transmission sites of Schistosoma japonicum in lake and marshland regions in China. Parasitology 2009, 136:737-746.
• [9]Utzinger J, Mayombana C, Mez K, Tanner M: Evaluation of chemical and physical-morphological factors as potential determinants of Biomphalaria pfeifferi (Krauss, 1848) distribution. Mem Inst Oswaldo Cruz 1997, 92:323-328.
• [10]Cressie N: Statistics for spatial data. New York: John Wiley & Sons, Inc; 1993.
• [11]Isaaks EH, Srivastava RM: An Introduction to Applied Geostatistics. New York: Oxford University Press; 1989.
• [12]Papaspiliopoulus O, Roberts GO, Skold M: Non-centered parameterizations for hierarchical models and data augmentation. In Bayesian statistics 7. Edited by Bernardo M, Bayarri S, Berger JO, Dawid AP, Heckerman D, Smith AFM, West M. USA: Oxford University Press; 2003:307-326.
• [13]Zhao QP, Jiang MS, Littlewood DT, Nie P: Distinct genetic diversity of Oncomelania hupensis, intermediate host of Schistosoma japonicum in mainland China as revealed by ITS sequences. PLoS Negl Trop Dis 2010, 4:e611.
• [14]Garcia RG: Tolerance of Oncomelania hupensis quadrasi to varying concentrations of dissolved oxygen and organic pollution. Bull World Health Organ 1972, 47:59-70.
• [15]Clements AC, Moyeed R, Brooker S: Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology 2006, 133:711-719.
• [16]Appleton C: Review of literature on abiotic factors influencing the distribution and life-cycles of Bilharziasis intermediate host snails. Malacol Rev 1978, 11:1-25.
• [17]Sturrock RF: The intermediate hosts and host- parasite relationships. In Human schistosomiasis. Edited by Jordan P, Webbe G, Sturrock R. Wallingford: CAB International; 1993:33-85.
• [18]Zheng Y, Qang Q, Zhao G, Zhong J, Zhang S: The function of the overlaying climate data in analysis of Oncomelania snail distribution. Chin. Public Health 1998, 14:724-725.
• [19]Guo JG, Vounatsou P, Cao CL, Utzinger J, Zhu HQ, Anderegg D, Zhu R, He ZY, Li D, Hu F, et al.: A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China. Acta Trop 2005, 96:213-222.
• [20]Singer BH, de Castro MC: Bridges to sustainable tropical health. Proc Natl Acad Sci 2007, 104:16038-16043.
• [21]Zhang Z, Carpenter TE, Chen Y, Clark AB, Lynn HS, Peng W, Zhou Y, Zhao G, Jiang Q: Identifying high-risk regions for schistosomiasis in Guichi, China: a spatial analysis. Acta Trop 2008, 107:217-223.
• [22]Clements AC, Firth S, Dembele R, Garba A: Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in West Africa. B World Health Organ 2009, 87:921-929.
• [23]Gosoniu L, Vounatsou P, Sogoba N, Smith T: Bayesian modeling of geostatistical malaria risk data. Geospat Health 2006, 1:127-139.
• [24]Sampson PD, Gottorp P: Nonparametric estimation of nonstationary spatial covariance structure. J Am Stat Assoc 1992, 87:108-119.
• [25]Damian D, Sampson PD, Guttorp P: Bayesian estimation of semi-parametric nonstationary spatial covariance structures. Environmetrics 2001, 12:161-178.
• [26]Zhou YB, Zheng HM, Jiang QW: A diagnostic challenge for Schistosomiasis japonica in China: consequences on praziquantel-based morbidity control. Parasit Vectors 2011, 4:194. BioMed Central Full Text
• [27]Wang XH, Zhou XN, Vounatsou P, Chen Z, Utzinger J, Yang K, Steinmann P, Wu XH: Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic ‘gold’ standard. PLoS Negl Trop Dis 2008, 2:e250.
• [28]Gelman A: Objections to Bayesian statistics. Bayesian Anal 2008, 3:445-450.