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Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard

Author

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  • Xian-Hong Wang
  • Xiao-Nong Zhou
  • Penelope Vounatsou
  • Zhao Chen
  • Jürg Utzinger
  • Kun Yang
  • Peter Steinmann
  • Xiao-Hua Wu

Abstract

Background: Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. Methodology/Principal Findings: We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the ‘true’ prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. Conclusion/Significance: Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales. Author Summary: Schistosomiasis is a serious public health problem in the People's Republic of China and elsewhere, and mapping of risk areas is important for guiding control interventions. Here, a 10-year surveillance database from Dangtu County in the southeastern part of the People's Republic of China was utilized for modeling the spatial and temporal distribution of infections in relation to environmental features and socioeconomic factors. Disease surveillance was done on the basis of a serological test, and we explicitly considered the imperfect sensitivity and specificity of the test when modeling the ‘true’ infection prevalence of Schistosoma japonicum. We then produced a risk map for S. japonicum transmission, which can assist decision making for local control interventions. Our work emphasizes the importance of accounting for the uncertainty in the diagnosis of schistosomiasis, and the potential of predicting the spatial and temporal distribution of the disease when using a Bayesian modeling framework. Our study can therefore serve as a template for future risk profiling of neglected tropical diseases studies, particularly when exploring spatial and temporal disease patterns in relation to environmental and socioeconomic factors, and how to account for the influence of diagnostic uncertainty.

Suggested Citation

  • Xian-Hong Wang & Xiao-Nong Zhou & Penelope Vounatsou & Zhao Chen & Jürg Utzinger & Kun Yang & Peter Steinmann & Xiao-Hua Wu, 2008. "Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 2(6), pages 1-9, June.
  • Handle: RePEc:plo:pntd00:0000250
    DOI: 10.1371/journal.pntd.0000250
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    Cited by:

    1. Nicholas A S Hamm & Ricardo J Soares Magalhães & Archie C A Clements, 2015. "Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(12), pages 1-24, December.
    2. Jun-Fang Xu & Jing Xu & Shi-Zhu Li & Tia-Wu Jia & Xi-Bao Huang & Hua-Ming Zhang & Mei Chen & Guo-Jing Yang & Shu-Jing Gao & Qing-Yun Wang & Xiao-Nong Zhou, 2013. "Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 7(3), pages 1-11, March.

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