A comparison of methods for spatial relative risk mapping of human neural tube defects

Original Paper


Birth defects are a major cause of infant mortality and disability in many parts of the world. Yet the etiology of neural tube defects (NTDs), the most common types of birth defects, is still unknown. The construction and analysis of maps of disease incidence data can help explain the geographical distribution of NTDs and can point to possible environmental causes of these birth defects. We compared two methods of mapping spatial relative risk of NTDs: (1) hierarchical Bayesian model, and (2) Spatial filtering method. Heshun county, which has the highest rate of NTDs in China, was selected as the region of interest. Both methods were used to produce a risk map of NTDs for rural Heshun for 1998–2001. Hierarchical Bayesian model estimated the relative risk for any given village in Heshun by “borrowing” strength from other villages in the study region. It did not remove all the random spatial noise in the rude disease rate. There were several areas of high incidence scattered around its risk map with no readily apparent pattern. The spatial filtering method calculated the relative risk for all villages based on a series of circulars. The risk map from the spatial filtering method revealed some spatial clusters of NTDs in Heshun. These two methods differed in their ability to map the spatial relative risk of NTDs. Distributional assumption of relative risk and the target of the risk assessment should be taken into consideration when choosing which method to use.


Neural-tube birth defects Hierarchical Bayesian model Spatial filtering method Risk mapping 


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Institute of Population ResearchPeking UniversityBeijingPeople’s Republic of China
  2. 2.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Nature Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.College of Geoscience and Surveying EngineeringChina University of Mining and TechnologyBeijingPeople’s Republic of China

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