1921
Volume 81, Issue 3
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Detailed knowledge of how local landscape patterns influence the distribution of , the intermediate host snail of , might facilitate more effective schistosomiasis control. We selected 12 villages in a mountainous area of Eryuan County, Yunnan Province, People’s Republic of China, and developed Bayesian geostatistical models to explore heterogeneities of landscape composition in relation to distribution of . The best-fitting spatio-temporal model indicated that the snail density was significantly correlated with environmental factors. Specifically, snail density was positively correlated with wetness and inversely correlated with the normalized difference vegetation index and mollusciciding, and snail density decreased as landscape patterns became more uniform. However, the distribution of infected snails was not significantly correlated with any of the investigated environmental factors and landscape metrics. Our enhanced understanding of ecology is important for spatial targeting of schistosomiasis control interventions.

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References

  1. Steinmann P, Keiser J, Bos R, Tanner M, Utzinger J, 2006. Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk. Lancet Infect Dis 6 : 411–425. [Google Scholar]
  2. Chen MG, Feng Z, 1999. Schistosomiasis control in China. Parasitol Int 48 : 11–19. [Google Scholar]
  3. Utzinger J, Zhou XN, Chen MG, Bergquist R, 2005. Conquering schistosomiasis in China: the long march. Acta Trop 96 : 69–96. [Google Scholar]
  4. Zhou XN, Guo JG, Wu XH, Jiang QW, Zheng J, Dang H, Wang XH, Xu J, Zhu HQ, Wu GL, Li YS, Xu XJ, Chen HG, Wang TP, Zhu YC, Qiu DC, Dong XQ, Zhao NQ, Xia G, Wang LY, Zhang SQ, Lin DD, Chen MG, Hao Y, 2007. Epidemiology of schistosomiasis in the People’s Republic of China, 2004. Emerg Infect Dis 13 : 1470–1476. [Google Scholar]
  5. Liang S, Seto EY, Remais JV, Zhong B, Yang C, Hubbard A, Davis GM, Gu X, Qiu D, Spear RC, 2007. Environmental effects on parasitic disease transmission exemplified by schistosomiasis in western China. Proc Natl Acad Sci USA 104 : 7110–7115. [Google Scholar]
  6. Xu B, Gong P, Biging G, Liang S, Seto E, Spear R, 2004. Snail density prediction for schistosomiasis control using IKONOS and ASTER images. Photogramm Eng Rem S 70 : 1285–1294. [Google Scholar]
  7. Steinmann P, Zhou XN, Matthys B, Li YL, Li HJ, Chen SR, Yang Z, Fan W, Jia TW, Li LH, Vounatsou P, Utzinger J, 2007. Spatial risk profiling of Schistosoma japonicum in Eryuan County, Yunnan Province, China. Geospatial Health 2 : 59–73. [Google Scholar]
  8. Seto E, Xu B, Liang S, Gong P, Wu W, Davis G, Qiu D, Gu X, Spear R, 2002. The use of remote sensing for predictive modeling of schistosomiasis in China. Photogramm Eng Rem S 68 : 167–174. [Google Scholar]
  9. Wang LD, Chen HG, Guo JG, Zeng XJ, Hong XL, Xiong JJ, Wu XH, Wang XH, Wang LY, Xia G, Hao Y, Chin DP, Zhou XN, 2009. A strategy to control transmission of Schistosoma japonicum in China. N Engl J Med 360 : 121–128. [Google Scholar]
  10. Liang S, Yang C, Zhong B, Qiu D, 2006. Re-emerging schistosomiasis in hilly and mountainous areas of Sichuan, China. Bull World Health Organ 84 : 139–144. [Google Scholar]
  11. Wang RB, Wang TP, Wang LY, Guo JG, Yu Q, Xu J, Gao FH, Yin ZC, Zhou XN, 2004. Study on the re-emerging situation of schistosomiasis epidemics in areas already under control and interruption. Chin J Epidemiol 25 : 564–567. [Google Scholar]
  12. Wang L, Utzinger J, Zhou XN, 2008. Schistosomiasis control: experiences and lessons from China. Lancet 372 : 1793–1795. [Google Scholar]
  13. Gong P, Xu B, Liang S, 2006. Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases. Sci China C Life Sci 49 : 573–582. [Google Scholar]
  14. Zhou XN, Malone JB, Kristensen TK, Bergquist NR, 2001. Application of geographic information systems and remote sensing to schistosomiasis control in China. Acta Trop 79 : 97–106. [Google Scholar]
  15. Yang GJ, Vounatsou P, Zhou XN, Utzinger J, Tanner M, 2005. A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. Acta Trop 96 : 117–129. [Google Scholar]
  16. Brooker S, Hay SI, Bundy DAP, 2002. Tools from ecology: useful for evaluating infection risk models? Trends Parasitol 18 : 70–74. [Google Scholar]
  17. Yuan Y, Xu XJ, Dong HF, Jiang MS, Zhu HG, 2005. Transmission control of schistosomiasis japonica: implementation and evaluation of different snail control interventions. Acta Trop 96 : 191–197. [Google Scholar]
  18. Guo JG, Vounatsou P, Cao CL, Utzinger J, Zhu HQ, Anderegg D, Zhu R, He ZY, Li D, Hu F, Chen MG, Tanner M, 2005. A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China. Acta Trop 96 : 213–222. [Google Scholar]
  19. Gardner RH, Turner MG, O’Neill RV, 2001. Landscape Ecology in Theory and Practice: Pattern and Process. Bremen, Germany: Springer.
  20. Kitron U, 1998. Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35 : 435–445. [Google Scholar]
  21. Martens WJM, McMichael AJ, 2002. Environmental Change, Climate and Health: Issues and Research Methods. Cambridge, United Kingdom: Cambridge University Press.
  22. Van Benthem BH, Vanwambeke SO, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, Lambin EF, Somboon P, 2005. Spatial patterns of and risk factors for seropositivity for dengue infection. Am J Trop Med Hyg 72 : 201–208. [Google Scholar]
  23. Linard C, Lamarque P, Heyman P, Ducoffre G, Luyasu V, Tersago K, Vanwambeke SO, Lambin EF, 2007. Determinants of the geographic distribution of Puumala virus and Lyme borreliosis infections in Belgium. Int J Health Geogr 6 : 15. [Google Scholar]
  24. Burel F, Baudry J, 2003. Landscape Ecology: Concepts, Methods and Applications. St. Albans, United Kingdom: Science Publishers.
  25. Gelman AB, 2004. Bayesian Data Analysis. Boca Raton, FL: CRC Press.
  26. Lawson AB, Rodeiro V, Carmen L, Browne WJ, 2003. Disease Mapping with WinBUGS and MLwiN. Chichester, United Kingdom: Wiley.
  27. Gemperli A, Vounatsou P, Kleinschmidt I, Bagayoko M, Lengeler C, Smith T, 2004. Spatial patterns of infant mortality in Mali: the effect of malaria endemicity. Am J Epidemiol 159 : 64–72. [Google Scholar]
  28. Koukounari A, Sacko M, Keita AD, Gabrielli AF, Landouré A, Dembelé R, Clements AC, Whawell S, Donnelly CA, Fenwick A, Traoré M, Webster JP, 2006. Assessment of ultrasound morbidity indicators of schistosomiasis in the context of large-scale programs illustrated with experiences from Malian children. Am J Trop Med Hyg 75 : 1042–1052. [Google Scholar]
  29. Wang XH, Wu XH, Zhou XN, 2006. Bayesian estimation of community prevalences of Schistosoma japonicum infection in China. Int J Parasitol 36 : 895–902. [Google Scholar]
  30. Basáñez MG, Marshall C, Carabin H, Gyorkos T, Joseph L, 2004. Bayesian statistics for parasitologists. Trends Parasitol 20 : 85–91. [Google Scholar]
  31. Raso G, Vounatsou P, Gosoniu L, Tanner M, N’Goran EK, Utzinger J, 2006. Risk factors and spatial patterns of hookworm infection among schoolchildren in a rural area of western Côte d’Ivoire. Int J Parasitol 36 : 201–210. [Google Scholar]
  32. Yang GJ, Vounatsou P, Zhou XN, Tanner M, Utzinger J, 2005. A Bayesian-based approach for spatio-temporal modeling of county level prevalence of Schistosoma japonicum infection in Jiangsu province, China. Int J Parasitol 35 : 155–162. [Google Scholar]
  33. Yang K, Wang XH, Yang GJ, Wu XH, Qi YL, Li HJ, Zhou XN, 2008. An integrated approach to identify distribution of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, in a mountainous region in China. Int J Parasitol 38 : 1007–1016. [Google Scholar]
  34. Steinmann P, Zhou XN, Li YL, Li HJ, Chen SR, Yang Z, Fan W, Jia TW, Li LH, Vounatsou P, 2007. Helminth infections and risk factor analysis among residents in Eryuan County, Yunnan Province, China. Acta Trop 104 : 38–51. [Google Scholar]
  35. Li XB, 2006. Analysis for schistosomiasis status of Eryuan County from 2000 to 2004. Parasit lnfect Dis 4 : 148–149. [Google Scholar]
  36. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A, 2002. Bayesian measures of model complexity and fit. J R Stat Soc B 64 : 583–639. [Google Scholar]
  37. Box GE, Jenkins GM, Reinsel GC, 1994. Time Series Analysis: Forecasting and Control. Third edition. San Francisco, CA: Prentice Hall.
  38. Gelman A, Rubin DB, 1992. Inference from iterative simulations using multiple sequences. Stat Sci 7 : 457–472. [Google Scholar]
  39. Gurarie D, King CH, 2005. Heterogeneous model of schistosomiasis transmission and long-term control: the combined influence of spatial variation and age-dependent factors on optimal allocation of drug therapy. Parasitology 130 : 49–65. [Google Scholar]
  40. Jiang Z, Zheng QS, Wang XF, Guan LZ, Hua HZ, 1997. Analysis of social factors and human behavior attributed to family distribution of schistosomiasis japonica cases. Southeast Asian J Trop Med Public Health 28 : 285–290. [Google Scholar]
  41. Zhou XN, Wang LY, Chen MG, Wu XH, Jiang QW, Chen XY, Zheng J, Utzinger J, 2005. The public health significance and control of schistosomiasis in China: then and now. Acta Trop 96 : 97–105. [Google Scholar]
  42. Zhou XN, Li DD, Yang HM, Chen MG, Sun LP, Yang GJ, Hong QB, Malone JB, 2002. Use of Landsat TM satellite surveillance data to measure the impact of the 1998 flood on snail intermediate host dispersal in the lower Yangtze River Basin. Acta Trop 82 : 199–205. [Google Scholar]
  43. Kristensen TK, Malone JB, McCarroll JC, 2001. Use of satellite remote sensing and geographic information systems to model the distribution and abundance of snail intermediate hosts in Africa: a preliminary model for Biomphalaria pfeifferi in Ethiopia. Acta Trop 79 : 73–78. [Google Scholar]
  44. Zhang ZY, Xu DZ, Zhou XN, Zhou Y, Liu SJ, 2005. Remote sensing and spatial statistical analysis to predict the distribution of Oncomelania hupensis in the marshlands of China. Acta Trop 96 : 205–212. [Google Scholar]
  45. Li F, Xia DG, Ma CH, Jia XM, Zhang XZ, 1999. Reappearance of infected snails in positive snail spots in a mountainous region of Yunnan province. J Appl Parasit Dis 7 : 61–64. [Google Scholar]
  46. Li YS, Raso G, Zhao ZY, He YK, Ellis MK, McManus DP, 2007. Large water management projects and schistosomiasis control, Dongting Lake region, China. Emerg Infect Dis 13 : 973–979. [Google Scholar]
  47. Herzog F, 2001. Landscape metrics for assessment of landscape destruction and rehabilitation. Environ Manage 27 : 91–107. [Google Scholar]
  48. Yang GJ, Zhou XN, Wang TP, Li DD, Hong QB, Sun LP, 2002. Spatial autocorrelation analysis on schistosomiasis cases and Oncomelania snails in three provinces of the lower reach of Yangtze River. Chin J Parasitol Parasit Dis 20 : 6–9. [Google Scholar]
  49. Davis GM, Wilke T, Zhang Y, Xu XJ, Qiu CP, Spolsky C, Qiu DC, Li Y, Xia MY, Feng Z, 1999. Snail-Schistosoma, Paragonimus interactions in China: population ecology, genetic diversity, coevolution and emerging diseases. Malacologia 41 : 355–377. [Google Scholar]
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Appendix

  • Received : 09 Feb 2008
  • Accepted : 10 Mar 2009

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