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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.6 P.921-925


Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998~2001

Author(s):  CHI Wen-xue, WANG Jin-feng, LI Xin-hu, ZHENG Xiao-ying, LIAO Yi-lan

Affiliation(s):  China University of Geosciences, Beijing 100083, China; more

Corresponding email(s):   chiwx@126.com, wangjf@lreis.ac.cn

Key Words:  Birth defects, Neural tube defects (NTDs), Disease map, Spatial analysis, Bayesian smoothing, China

CHI Wen-xue, WANG Jin-feng, LI Xin-hu, ZHENG Xiao-ying, LIAO Yi-lan. Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998~2001[J]. Journal of Zhejiang University Science A, 2007, 8(6): 921-925.

@article{title="Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998~2001",
author="CHI Wen-xue, WANG Jin-feng, LI Xin-hu, ZHENG Xiao-ying, LIAO Yi-lan",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998~2001
%A CHI Wen-xue
%A WANG Jin-feng
%A LI Xin-hu
%A ZHENG Xiao-ying
%A LIAO Yi-lan
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 6
%P 921-925
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0921

T1 - Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998~2001
A1 - CHI Wen-xue
A1 - WANG Jin-feng
A1 - LI Xin-hu
A1 - ZHENG Xiao-ying
A1 - LIAO Yi-lan
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 6
SP - 921
EP - 925
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A0921

Objective: To estimate the prevalence rates of neural tube defects (NTDs) in Heshun County, Shanxi Province, china by bayesian smoothing technique. Methods: A total of 80 infants in the study area who were diagnosed with NTDs were analyzed. Two mapping techniques were then used. Firstly, the GIS software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. Results: The classical statistical approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable. The maps produced by the Bayesian technique indicate the tendency of villages in the southeastern region to produce higher prevalence or risk values. Conclusions: The bayesian smoothing technique addresses the issue of heterogeneity in the population at risk and it is therefore recommended for use in explorative mapping of birth defects. This approach provides procedures to identify spatial health risk levels and assists in generating hypothesis that will be investigated in further detail.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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