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Spatial analysis of schistosomiasis in Hunan and Jiangxi provinces in the People's Republic of China

Authors :
Kefyalew Addis Alene
Catherine A. Gordon
Archie C. A. Clements
Gail M. Williams
Darren J. Gray
Xiao-Nong Zhou
Yuesheng Li
Jürg Utzinger
Johanna Kurscheid
Simon Forsyth
Jie Zhou
Zhaojun Li
Guangpin Li
Dandan Lin
Zhihong Lou
Shengming Li
Jun Ge
Jing Xu
Xinling Yu
Fei Hu
Shuying Xie
Donald P. McManus
Source :
Diseases; Volume 10; Issue 4; Pages: 93
Publication Year :
2022
Publisher :
University of Basel, 2022.

Abstract

Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People’s Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, β = −1.158; 95% credible interval [CrI]: −2.104, −0.116) in Hunan and temperature (°C, β = −4.359; 95% CrI: −9.641, −0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.

Details

Database :
OpenAIRE
Journal :
Diseases; Volume 10; Issue 4; Pages: 93
Accession number :
edsair.doi.dedup.....d48528401b5a77d718f2d0ba605e7990
Full Text :
https://doi.org/10.5451/unibas-ep90501