Skip to main content
Log in

Risk assessment of human neural tube defects using a Bayesian belief network

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Neural tube defects (NTDs) constitute the most common type of birth defects. How much risk of NTDs could an area take? The answer to this question will help people understand the geographical distribution of NTDs and explore its environmental causes. Most existing methods usually take the spatial correlation of cases into account and rarely consider the effect of environmental factors. However, especially in rural areas, the NTDs cases have a little effect on each other across space, whereas the role of environmental factors is significant. To demonstrate these points, Heshun, a county with the highest rate of NTDs in China, was selected as the region of interest in the study. Bayesian belief network was used to quantify the probability of NTDs occurred at villages with no births. The study indicated that the proposed method was easy to apply and high accuracy was achieved at a 95% confidence level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aronsky D, Haug PJ (2000) Automatic identification of patients eligible for a pneumonia guideline. Proc/AMIA Annu Symp 12–16

  • Burnside E, Rubin D, Shachter R (2000) A Bayesian network for mammography. Proc/AMIA Annu Symp 106–110

  • Canales RA, Leckie JO (2007) Application of a stochastic model to estimate children’s short-term residential exposure to lead. Stoch Environ Res Risk Assess 21:737–745

    Article  Google Scholar 

  • Carmichael SL, Nelson V, Shaw GM, Wasserman CR, Croen LA (2003) Socio-economic status and risk of conotruncal heart defects and orofacial clefts. Paediatr Perinat Epidemiol 17:264–271

    Article  Google Scholar 

  • Carmona RH (2005) The global challenges of birth defects and disabilities. Lancet 366:1142–1144

    Article  Google Scholar 

  • Chi WX, Wang JF, Li XH, Zheng XY, Liao YL (2007) Application of GIS-based spatial filtering method for neural tube defects disease mapping. J Wuhan Univ (Nat Sci Ed) 12(6):1125–1130

    Article  Google Scholar 

  • Christakos G (2002) On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. Adv Water Resour 25:1257–1274

    Article  Google Scholar 

  • Frey L, Hauser WA (2003) Epidemiology of neural tube defects. Epilepsia 44:4–13

    Article  Google Scholar 

  • Hamilton PW, Montironi R, Abmayr W, Bibbo M, Anderson N, Thompson D, Bartels PH (1995) Clinical applications of Bayesian belief networks in pathology. Pathologica 87(3):237–245

    CAS  Google Scholar 

  • Heckerman D (1997) Bayesian networks for data mining. Data Min Knowl Disc 1(1):79–119

    Article  Google Scholar 

  • Heern GA, Tyler J, Mandeya A (2003) Agricultural chemical exposures and birth defects in the Eastern Cape Province, South Africa A case–control study. Environ Health 2(1):11–18

    Article  Google Scholar 

  • Hemmi I (2008) Bayesian estimation of the incidence rate in birth defects monitoring. Congenit Anom 28(2):103–109

    Article  Google Scholar 

  • Hu HT (2003) Methods to prevent Shanxi birth defect. China.org.cn. Available viable by http://www.china.org.cn/english/2003/sep/74927.htm

  • Ismaila AS, Canty A, Thabane L (2007) Comparison of Bayesian and frequentist approaches in modeling risk of preterm birth near the Sydney Tar Ponds, Nova Scotia, Canada. BMC Med Res Methodol 7:39–52

    Article  Google Scholar 

  • Lammer EJ (1998) Gene-environment analyses in human birth defects research. Neurotoxicol Teratol 25(3):351

    Article  Google Scholar 

  • Lee SM, Abbott PA (2003) Bayesian networks for knowledge discovery in large databasets: basics for nurse researchers. J Biomed Inform 36:389–399

    Article  Google Scholar 

  • Lee E, Park Y, Shin JG (2008) Large engineering project risk management using a Bayesian belief network. Expert Syst Appl doi:10.1016/j.eswa.2008.07.057

  • Li XH, Wang JF, Liao YL, Meng B, Zheng XY (2006) A geo-analysis for the environmental cause of human birth defects. Toxicol Environ Chem 88(3):551–559

    Article  CAS  Google Scholar 

  • Lin JH, Haug PJ (2008) Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. J Biomed Inform 41:1–14

    Article  Google Scholar 

  • Maglogiannis I, Zafiropoulos E, Platis A, Lambrinoudakis C (2006) Risk analysis of a patient monitoring system using Bayesian network modeling. J Biomed Inform 39:637–647

    Article  CAS  Google Scholar 

  • Maskery SM, Hu H, Hooke J, Shriver CD, Liebman MN (2008) A Bayesian derived network of breast pathology co-occurrence. J Biomed Inform 41:242–250

    Article  Google Scholar 

  • McCabe B, AbouRizk SM, Goebel R (1998) Belief networks for construction performance diagnostics. J Comput Civil Eng ASCE 12(2):93–100

    Article  Google Scholar 

  • Ritz B, Yu F, Fruin S, Chapa G, Shaw GM, Harris JA (2002) Ambient air pollution and risk of birth defects in southern California. Am J Epidemiol 155(1):17–25

    Article  Google Scholar 

  • Rushton G, Lolonis P (1996) Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 15:717–726

    Article  CAS  Google Scholar 

  • Sankoh OA, 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

    Google Scholar 

  • Sebastiani P, Nolan VG, Baldwin CT, Abad-Grau MM, Wang L, Adewoye AH, McMahon LC, Farrer LA, Taylor JG, Kato GJ, Gladwin MT, Steinberg MH (2008) A network model to predict the risk of death in sickle cell disease. Blood 41(3):432–441

    Google Scholar 

  • UIman C, Taneli F, Oksel F, Hakerlerler H (2005) Zinc-deficient sprouting blight potatoes and their possible relation with neural tube defects. Cell Biochem Funct 23:69–72

    Article  Google Scholar 

  • Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modeling. Ecol Model 203:312–318

    Article  Google Scholar 

  • Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2008) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Int J Geogr Inf Sci (in press)

  • Wiwanitkit V (2008) Estimating cancer risk due to benzene exposure in some urban areas in Bangkok. Stoch Environ Res Risk Assess 22:135–137

    Article  Google Scholar 

  • Wu JL, Wang JF, Meng B, Chen G, Pang LH, Song XM, Zhang KL, Zhang T, Zheng XY (2004) Exploratory spatial data analysis for the identification of risk factors to birth defects. BMC Public Health 4:23–33

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Project of the National Natural Science Foundation of China (70571076&40471111), the Hi-Tech Research and Development Program of China (2006AA12Z15), the National Basic Research Priorities Program (2001CB5103) of the Ministry of Science and Technology of the People’s Republic of China,and Knowledge Innovation Program of the CAS (KZCX2-YW-3-8).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jinfeng Wang or Xiaoying Zheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liao, Y., Wang, J., Guo, Y. et al. Risk assessment of human neural tube defects using a Bayesian belief network. Stoch Environ Res Risk Assess 24, 93–100 (2010). https://doi.org/10.1007/s00477-009-0303-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-009-0303-5

Keywords

Navigation