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Acta Trop. 2014 Apr 18. pii: S0001-706X(14)00136-3. doi: 10.1016/j.actatropica.2014.04.013. [Epub ahead of print]

Eco-social determinants of Schistosoma japonicum infection supported by multi-level modelling in Eryuan County, People's Republic of China.

Author information

  • 1Jiangsu Institute of Parasitic Diseases, Key Laboratory of Parasitic Disease Control and Prevention (Ministry of Health), Jiangsu Provincial Key Laboratory of Parasite Molecular Biology, Wuxi 214064, People's Republic of China.
  • 2National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai 200025, People's Republic of China. Electronic address: ipdzhouxn@sh163.net.
  • 3National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai 200025, People's Republic of China.
  • 4Eryuan Institute for Schistosomiasis Control, Eryuan 671200, People's Republic of China.
  • 5Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, PO Box CH-4002, Basel, Switzerland; University of Basel, PO Box CH-4003, Basel, Switzerland.
  • 6Ingerod 407, Brastad, Sweden.

Abstract

Schistosomiasis remains of considerable public health concern in many tropical and subtropical regions of the world, including the People's Republic of China. The effectiveness of schistosomiasis control interventions are, among other factors, governed by the social-ecological context. However, eco-social determinants of schistosomiasis are poorly understood, particularly at the household or village levels. In the current study, residents in 26 villages of Eryuan county, Yunnan Province, People's Republic of China, were screened for Schistosoma japonicum infection with a serological assay that was followed by stool examination for sero-positive individuals. Bayesian multilevel models with spatial random effects were employed to profile the S. japonicum infection risk based on known transmission sites of S. japonicum that are scattered across individual land parcels in this part of the country. The key risk factors identified with this approach were the absence of a sanitary stall house for livestock and presence of living and infected intermediate host snails in close proximity. We conclude that a spatially explicit Bayesian multilevel approach can deepen our understanding of eco-social determinants that govern schistosomiasis transmission at a small geographical scale.

Copyright © 2014. Published by Elsevier B.V.

KEYWORDS:

Bayesian multilevel model; Eco-social determinants; People's Republic of China; Schistosomiasis

PMID:
24751418
[PubMed - as supplied by publisher]
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