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

Date Published: June 11, 2008

Publisher: Public Library of Science

Author(s): Xian-Hong Wang, Xiao-Nong Zhou, Penelope Vounatsou, Zhao Chen, Jürg Utzinger, Kun Yang, Peter Steinmann, Xiao-Hua Wu, Simon Brooker

Abstract: BackgroundSpatial 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 FindingsWe 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/SignificanceBayesian 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.

Partial Text: Schistosomiasis japonica is a zoonotic disease caused by the digenetic trematode Schistosoma japonicum. Historically, the disease was endemic in 12 provinces of the People’s Republic of China, with more than 10 million individuals infected [1]–[3]. Sustained control efforts implemented over the past 50 years have confined S. japonicum to seven provinces and brought down the number of infected people to less than 1 million [1]–[3]. The mean infection intensity has also decreased significantly [2]. However, surveillance and interventions are still warranted in 435 counties according to the 2005 annual report on the epidemiologic status of schistosomiasis in the People’s Republic of China [4].

In this study, we estimated the ‘true’ S. japonicum prevalence in a schistosome-endemic county of the People’s Republic of China by explicitly taking into consideration the diagnostic error of a widely used serological test, i.e. IHA. Additionally, we explored the spatial distribution over time, and produced a predictive risk map for the year 2005. Since antibody-based immunological tests, such as IHA and ELISA, cannot distinguish between an active and a recently cleared infection, these techniques result in low specificity in areas where chemotherapy is provided on a regular basis [31]. Thus, the analysis of uncorrected seroprevalence data would only be suggestive of the overall infection pressure [38]. In order to better understand the epidemiologic characteristics of schistosomiasis japonica, we accounted for the lack of sensitivity and specificity of the standard serological test employed in our study setting by using a Bayesian approach, and compared the outcome with that of the uncorrected model that assumed 100% sensitivity and specificity.



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