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

BackgroundThe 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.MethodsEnvironmental 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.ResultsRisk 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.ConclusionsThose 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.

[1]  Genming Zhao,et al.  Field comparison of immunodiagnostic and parasitological techniques for the detection of Schistosomiasis japonica in the People's Republic of China. , 2007, The American journal of tropical medicine and hygiene.

[2]  Jürg Utzinger,et al.  The public health significance and control of schistosomiasis in China--then and now. , 2005, Acta tropica.

[3]  R. Sturrock,et al.  The intermediate hosts and host-parasite relationships. , 1993 .

[4]  L Gosoniu,et al.  Bayesian modelling of geostatistical malaria risk data. , 2006, Geospatial health.

[5]  Penelope Vounatsou,et al.  Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard , 2008, PLoS neglected tropical diseases.

[6]  A. Gelman.  Objections to Bayesian statistics , 2008 .

[7]  M. Tanner,et al.  Evaluation of chemical and physical-morphological factors as potential determinants of Biomphalaria pfeifferi (Krauss, 1848) distribution. , 1997, Memorias do Instituto Oswaldo Cruz.

[8]  Bruijning Cf.  Epidemiology of schistosomiasis. , 1982 .

[9]  Gail M. Williams,et al.  Schistosomiasis in the People's Republic of China: the Era of the Three Gorges Dam , 2010, Clinical Microbiology Reviews.

[10]  Jing Xu,et al.  Epidemiology of Schistosomiasis in the People’s Republic of China, 2004 , 2007, Emerging infectious diseases.

[11]  M. Tanner,et al.  A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China. , 2005, Acta tropica.

[12]  T E Carpenter,et al.  Location of active transmission sites of Schistosoma japonicum in lake and marshland regions in China , 2009, Parasitology.

[13]  M. Tanner,et al.  Sustainable schistosomiasis control—the way forward , 2003, The Lancet.

[14]  Yue Chen,et al.  Identifying high-risk regions for schistosomiasis in Guichi, China: a spatial analysis. , 2008, Acta tropica.

[15]  Archie C A Clements,et al.  Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in western Africa. , 2009, Bulletin of the World Health Organization.

[16]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[17]  R. Garcia.  Tolerance of Oncomelania hupensis quadrasi to varying concentrations of dissolved oxygen and organic pollution. , 1972, Bulletin of the World Health Organization.

[18]  J. Q. Smith,et al.  1. Bayesian Statistics 4 , 1993 .

[19]  R Moyeed,et al.  Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa , 2006, Parasitology.

[20]  S. Liang,et al.  Re-emerging schistosomiasis in hilly and mountainous areas of Sichuan, China. , 2006, Bulletin of the World Health Organization.

[21]  P. Guttorp,et al.  Bayesian estimation of semi‐parametric non‐stationary spatial covariance structures , 2001 .

[22]  C. Bruijning.  Epidemiology of schistosomiasis. , 1982, Acta Leidensia.

[23]  D. Littlewood,et al.  Distinct Genetic Diversity of Oncomelania hupensis, Intermediate Host of Schistosoma japonicum in Mainland China as Revealed by ITS Sequences , 2010, PLoS neglected tropical diseases.

[24]  M. J. Bayarri,et al.  Non-Centered Parameterisations for Hierarchical Models and Data Augmentation , 2003 .

[25]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[26]  P. Guttorp,et al.  Nonparametric Estimation of Nonstationary Spatial Covariance Structure , 1992 .

[27]  Q. Jiang,et al.  A diagnostic challenge for schistosomiasis japonica in China: consequences on praziquantel-based morbidity control , 2011, Parasites & Vectors.

[28]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[29]  B. Singer,et al.  Bridges to sustainable tropical health , 2007, Proceedings of the National Academy of Sciences.