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

http://doi.org/10.1631/jzus.2007.A0921


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",
volume="8",
number="6",
pages="921~925",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0921"
}

%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

TY - JOUR
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


Abstract: 
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

Reference

[1] Antó, J.M., Cullinan, P., 2001. Clusters, classification and epidemiology of interstitial lung diseases concepts, methods and critical reflections. Eur. Resp. J., 18(Suppl. 32):101-106.

[2] Bergamaschi, R., Montomoli, C., Candeloro, E., Monti, M., Cioccale, R., Bernardinelli, L., Fratino, P., Cosi, V., 2006. Bayesian mapping of multiple sclerosis prevalence in the province of Pavia, northern Italy. J. Neurol. Sci., 244:127-131.

[3] Berke, O., 2004. Exploratory disease mapping: kriging the spatial risk function from regional count data. Int. J. Health Geogr., 3:18.

[4] Berke, O., 2005. Exploratory spatial relative risk mapping. Prevent. Veter. Med., 71:173-182.

[5] Dolk, H., 2004. Epidemiologic approaches to identifying environmental causes of birth defects. Am. J. Med. Genet., 125C:4-11.

[6] Finkelman, R.B., 2004. Potential health impacts of burning coal beds and waste banks. Int. J. Coal Geol., 59:19-24.

[7] Frey, L., Hauser, W.A., 2003. Epidemiology of neural tube defects. Epilepsia, 44(Suppl. 3):4-13.

[8] Goujard, J., 1999. Clusters of birth defects: emergency and management. A review of some publications. Eur. J. Epidemiol., 15:853-862.

[9] Jarup, L., Best, N., Toledano, M.B., Wakefield, J., Elliott, P., 2002. Geographical epidemiology of prostate cancer in Great Britain. Int. J. Cancer, 97:695-699.

[10] Lawson, A.B., Böhning, D., Biggeri, A., Lesaffre, E., Viel, J.F., John, W., Sons, L., 1999. Disease Mapping and its Uses. In: Lawson, A.B. (Ed.), Disease Mapping and Risk Assessment for Public Health. John Wiley & Sons, New York, p.3-13.

[11] MacNab, Y.C., 2004. Bayesian spatial and ecological models for small-area accident and injury analysis. Accident Anal. & Prevent., 36:1019-1028.

[12] Maiti, T., 1998. Hierarchical Bayes estimation of mortality rates for disease mapping. J. Statist. Planning and Inference, 69:339-348.

[13] Mather, F.J., Chen, V.W., Morgan, L.H., Correa, C.N., Shaffer, J.G., Srivastav, S.K., Rice, J.C., George, B., Swalm, C.M., Wu, X.C., et al., 2006. Hierarchical modeling and other spatial analyses in prostate cancer incidence data. Am. J. Prevent. Med., 30(2S):88-100.

[14] Meza, J.L., 2003. Empirical bayes estimation smoothing of relative risks in disease mapping. J. Statist. Planning and Inference, 112:43-62.

[15] Sankoh, O.A., Berke, O., Simboro, S., Becher, H., 2002. Bayesian and GIS Mapping of Childhood Mortality in Rural Burkina Faso. Control of Tropical Infectious Diseases. Uni-Heidelberg Discussion Paper.

[16] Shen, X.M., Wu, S.H., Yan, C.H., 2001. Impacts of low-level lead exposure on development of children recent studies in China. Clin. Chim. Acta, 313:217-220.

[17] Staubach, C., Schmid, V., Knorr-Held, L., Ziller, M., 2002. A Bayesian model for spatial wildlife disease prevalence data. Prevent. Veter. Med., 56:75-87.

[18] Sun, G.F., 2004. Arsenic contamination and arsenicosis in China. Toxicol. Appl. Pharm., 198:268-271.

[19] Verkasalo, P.K., Kokki, E., Pukkala, E., Vartiainen, T., Kiviranta, H., Penttinen, A., Pekkanen, J., 2004. Cancer risk near a polluted river in Finland. Environ. Health Perspect., 112(9):1026-1031.

[20] Wall, P.A., Devine, O.J., 2000. Interactive analysis of the spatial distribution of disease using a geographic information system. J. Geogr. Syst., 2:243-256.

[21] Wu, J.L., Wang, J.F., Meng, B., Chen, G., Pang, L.H., Song, X.M., Zhang, K.L., Zhang, T., Zheng, X.Y., 2004. Exploratory spatial data analysis for the identification of risk factors to birth defects. BMC Public Health, 4:23.

[22] Zheng, B.S., Ding, Z.H., Huang, R.G., Zhu, J.M., Yu, X.Y., Wang, A.M., Zhou, D.X., Mao, D.J., Su, H.C., 1999. Issues of health and disease relating to coal use in southwestern China. Int. J. Coal Geol., 40:119-132.

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